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Path Planning and following for Autonomous Vehicles and Its Application to Intersection with Moving Obstacles

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Title:
Path Planning and following for Autonomous Vehicles and Its Application to Intersection with Moving Obstacles
Creator:
Kim, Mincheul
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[Gainesville, Fla.]
Florida
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University of Florida
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english
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Master's ( M.S.)
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University of Florida
Degree Disciplines:
Mechanical Engineering
Mechanical and Aerospace Engineering
Committee Chair:
CRANE,CARL D,III
Committee Co-Chair:
WIENS,GLORIA JEAN

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autonomous-vehicle -- pathplanning
Mechanical and Aerospace Engineering -- Dissertations, Academic -- UF
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government publication (state, provincial, terriorial, dependent) ( marcgt )
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Mechanical Engineering thesis, M.S.

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Abstract:
The algorithm to find the optimal route to the destination is the most necessary part for an autonomous vehicle. Much research has been conducted for the optimal path search problem. These researches can be divided into offline path planning and online path planning. However, each path planning algorithm has conflicting prerequisites, advantages, and disadvantages. Although offline path planning guarantees optimal path to the destination, it cannot run without a prior information about the world before the path search. Online path planning is suitable for dynamic environments, but cannot guarantee an optimal result. Therefore, there is a need for an algorithm that allows the autonomous vehicle to reach its destination successfully in a dynamically changing traffic environment, taking advantage of two different algorithms above. First, existing algorithms for path finding will be reviewed. Among them is the A* algorithm which is widely used for optimized path search, and its related search will be discussed. Second, mapping algorithms for applying a path search algorithm will be discussed. Finally, a review of the studies designed to avoid moving obstacles will be presented. Based on the established studies, this research will introduce the dynamic local search area and utilize the offline path planning efficiency and the dynamic environment correspondence function of online path planning. This local search area is defined as a limited search range centered on the autonomous vehicle and guarantees the optimal route to the destination through the route search to the replaced temporary target within this range. In addition, we will introduce an algorithm to track the optimized path, which is designed to perform the action to detect the obstacles while following the path and to respond appropriately to evade the obstacles. The developed algorithms have been tested through simulations. The results of the tests and future improvements to this research are presented. ( en )
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In the series University of Florida Digital Collections.
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Includes vita.
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Includes bibliographical references.
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Description based on online resource; title from PDF title page.
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This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (M.S.)--University of Florida, 2017.
Local:
Adviser: CRANE,CARL D,III.
Local:
Co-adviser: WIENS,GLORIA JEAN.
Statement of Responsibility:
by Mincheul Kim.

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PATH PLANNING AND FOLLOWING FOR AUTONOMOUS VEHICLES AND ITS APPLICATION TO INTERSECTION WITH MOVING OBSTACLES By MINCHEUL KIM A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2017

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2017 Mincheul Kim

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To my wife, Minji Kim

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4 ACKNOWLEDGMENTS I thank my advisor Dr. Carl Crane for his kind help and advice during my graduate education. I also thank Dr. Gloria Wiens, my committee member, for her help and advice. I also thank my colleagues and friends a t CIMAR member, Ne a l Patrick and John Esposito. As working on the FDOT project together, they gave their first steps and guidance on my research journey. We went very well and had a good time. I also thank Minji Kim, my wife. Thanks to her dedicated help f or two years, my research ended well.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 ABSTRACT ................................ ................................ ................................ ................... 10 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 12 Motiva tion ................................ ................................ ................................ ............... 12 Path Planning Algorithm ................................ ................................ ......................... 13 Research Goal ................................ ................................ ................................ ........ 14 Problem Statement ................................ ................................ ........................... 15 Developmen t ................................ ................................ ................................ .... 15 2 REVIEW OF LITERATURE ................................ ................................ .................... 17 Path Finding Algorithm ................................ ................................ ............................ 17 Dijkstra Algorithm ................................ ................................ ............................. 17 A* Algorithm ................................ ................................ ................................ ..... 18 Other Path Plann ing Algorithms ................................ ................................ ....... 19 Mapping Algorithm ................................ ................................ ................................ .. 20 Occupancy Grid ................................ ................................ ................................ 20 Traversability Grid ................................ ................................ ............................ 22 Avoid Ob stacles ................................ ................................ ................................ ...... 22 Potential Field ................................ ................................ ................................ ... 23 Avoidability Measure ................................ ................................ ........................ 25 3 DYNAMIC LOCAL WINDOW PATH PLANNING ALGORITHM .............................. 27 World Modeling ................................ ................................ ................................ ....... 28 Two dimension Grid Modeling ................................ ................................ .......... 28 Variables of Vehicle in Coordinates ................................ ................................ .. 29 Kinematic Equation and Types of Vehicles ................................ ...................... 31 Pre process ................................ ................................ ................................ ............ 32 Goal Transition in Local Search Area ................................ ................................ ..... 33 Temporary Local Search Area Building ................................ ............................ 33 Goal Transition ................................ ................................ ................................ 34 Cost Evaluation ................................ ................................ ................................ ....... 36 Environmental Cost ................................ ................................ .......................... 37 Path Plann ing in Local Search Area ................................ ................................ ....... 38

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6 Initialize Planning ................................ ................................ ............................. 38 Step Finding ................................ ................................ ................................ ..... 39 Check Arrival State ................................ ................................ ........................... 39 Set Path ................................ ................................ ................................ .................. 39 Iterative Running ................................ ................................ ................................ ..... 41 4 PATH TRACKING ALGORITHM ................................ ................................ ............ 42 Target Waypoint Selection ................................ ................................ ...................... 42 Following Target Waypoint ................................ ................................ ..................... 44 Co llision Detection and Response ................................ ................................ .......... 47 Probabilistic Moving Obstacle Grid ................................ ................................ ... 47 5 SIMULATION RESULTS ................................ ................................ ........................ 53 Software ................................ ................................ ................................ .................. 53 Configuration ................................ ................................ ................................ .......... 53 Path Planning without Moving Obstacle ................................ ................................ .. 56 Scenario I: Path Planning without Moving Obstacle with Initial Search Area .... 56 Scenario II: Path Planning without Moving Obstacle with Off line Path Planning ................................ ................................ ................................ ........ 57 Path Planning with Moving Obstacles ................................ ................................ ..... 59 Scenario III: Path Planning behind a Moving Object ................................ ........ 59 Scenario IV: Path Planning with Three Crossing Moving Objects .................... 64 Scenario V: Path Planning Coming in Front ................................ ..................... 67 6 CONCLUSION AND FUTURE WORK ................................ ................................ .... 72 Conclusion ................................ ................................ ................................ .............. 72 Future Work ................................ ................................ ................................ ............ 73 APPENDIX A STRUCTURE OF CODE ................................ ................................ ........................ 75 B SOURCE CODE ................................ ................................ ................................ ..... 77 LIST OF REFERE NCES ................................ ................................ ............................... 89 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 91

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7 LIST OF TABLES Table page 3 1 Characteristic of grid cells. ................................ ................................ ................. 36 3 2 Cost value to environmental type. ................................ ................................ ...... 38 4 1 Risk cost to moving obstacle. ................................ ................................ ............. 49 5 1 Results of Scenario I. ................................ ................................ ......................... 56 5 2 Results of Scenario II. ................................ ................................ ........................ 59 5 3 Results of Scenario III. ................................ ................................ ....................... 62 5 4 Results of Scenario IV. ................................ ................................ ....................... 66 5 5 Results of Scenario V. ................................ ................................ ........................ 70

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8 LIST OF FIGURES Figure page 3 1 Flowchart of the dynamic local window path planner ................................ .......... 27 3 2 Relation between global and local coordinate system ................................ ........ 30 3 3 The process of goal transition ................................ ................................ ............. 35 4 1 The process of target waypoint selection ................................ ........................... 43 4 2 Flowchart of target waypoint selection. ................................ ............................... 44 4 3 Geometry of the following target waypoint. ................................ ......................... 46 4 4 The process of cost evaluation ................................ ................................ ........... 50 4 5 The search range line and its discretized search points. ................................ .... 52 5 1 Visualized map. ................................ ................................ ................................ .. 55 5 2 Cost map. ................................ ................................ ................................ ........... 55 5 3 Results for scenario I ................................ ................................ .......................... 57 5 4 Results for scenario II ................................ ................................ ......................... 58 5 5 Initial state for scenario III ................................ ................................ ................... 60 5 6 Before detecting the obstacle ................................ ................................ ............. 61 5 7 After detecting the obstacle ................................ ................................ ................ 62 5 8 Following the obstacle ................................ ................................ ........................ 62 5 9 Results for scenario III ................................ ................................ ........................ 63 5 10 Initial state for scenario IV ................................ ................................ .................. 65 5 11 Before entering the intersection ................................ ................................ .......... 66 5 12 After exiting the intersection ................................ ................................ ............... 66 5 13 Results for scenario IV ................................ ................................ ....................... 67 5 14 Initial state for scenario V ................................ ................................ ................... 68 5 15 Before detecting the obstacle ................................ ................................ ............. 69

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9 5 16 After detecting the obstacle ................................ ................................ ................ 70 5 17 Fol lowing the path behind obstacle ................................ ................................ .... 70 5 18 Results for scenario V ................................ ................................ ........................ 71

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10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science PATH PLANNING AND FOLLOWING FOR AUTONOMOUS VEHICLES AND ITS APPLICATION TO INTERSECTION WITH MOVI NG OBSTACLES By Mincheul Kim May 2017 Chair: Carl D. Crane III Major: Mechanical Engineering The algorithm to find the optimal route to the destination is the most necessary part for an autonomous vehicle Much research has been conducted for the optimal path search problem T hese researches can be divided into offline path planning and online path planning. However, each path planning algorithm ha s conflicting prerequisites, advantage s, and disadvantages. Although offline path planning guarantees optimal path to the destination, it cannot run without a prior information about the world before the path search. Online path planning is suitable for dynamic environment s but cannot guarantee an optimal result. Therefore, there is a need for an algorithm that allows the autonomous vehicle to reach its destination successfully in a dynamically changing traffic environment, taking advantage of two different algorithms above. First, existing algo rithms for path finding will be reviewed Among them is the A* algorithm which is widely used for optimized path searc h, and its related search will be discussed. Second, mapping algorithm s for applying a path search algorithm will be discussed. Finally, a review of the studies designed to avoid moving obstacles will be presented

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11 Based on the established studies, this research will introduce the dynamic local search area and utilize the offline path planning efficiency and the dynamic environment correspon dence function of online path planning. This local search area is defined as a limited search range centered on the autonomous vehicle and guarantees the optimal route to the destination through the route search to the replaced temporary target within this range. In addition, we will introduce an algorithm to track the optimized path, which is designed to perform the action to detect the obstacles while following the path and to respond appropriately to evade the obstacles The developed algorithms have bee n tested through simulations T he result s of the test s and future improvement s to this research are presented

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12 CHAPTER 1 INTRODUCTION Motivation Since the produ ction of the first gasoline car developed in Germany in 1885, the car made our life more enriching thanks to the remarkable speed of development. With faster speeds and increased loads, one can send more loads faster and further through it According to a survey in 2014, there are 7.9 m illion vehicles in the United States and the rate of growth is gradually increasing. The advancement of technology has further advanced to the point that it allows the car to move without human command We call this an autonomous vehicle Autonomous vehicles are also called unmanned vehicle driverless cars, self driving cars, or robotics cars, which means an automobile that can operate automatically without human control Autonomous vehicle s travel only by designating its own position and surroundin g environment with sensors, such as radar, Light Detection and Ranging( LIDAR ) Global Positioning System( GPS ) Inertial Navigation System( INS ) and vision camera s Indeed, there is already a growing number of autonomous vehicles in the military, as well as Google cars, which are now leading the automotive technology, or Tesla's cars, now in commercial production. However, irrespective of the technical development of these vehicles, traffic accidents caused by collisions between cars are not easily reduced. According to the data for 2015, the total number of drivers who died in the United States is 35,092 This is a phenomenon that can not be solved even in a conventional vehicle operated by a human driver, and an autonomous vehicle that wi ll appear in the fut ure also has the same problem that must be solved.

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13 As we have already seen, there is always the risk of unexpected movement of moving obstacles like conventional cars that people are driving. In most cases, an operation to avoid such moving obstacles is performed at the discretion of the driver who operates the vehicle. Such a driver 's judgment may be accumulated based on experience, or it may cause a momentary reflex to take action to move in the opposite direction to the current operation. However, it is difficult to safely avoid moving obstacles in this way. Even now, when the era of autonomous vehicles is about to arrive, computers cannot behave like human beings or experience immediate reflexes, or hunches. The computer also cannot assure that th e identified obstacles will anticipate the risk of the collision and make the appropriate evasive maneuver. Now, to develop effective algorithms for autonomous vehicles that are adaptive to dynamically changing traffic conditions, it is important to know h ow the various objects are expected to be positioned and how they behave and how efficient paths are performed within the optimized time and search space To do this, a brief review of the existing path planning algorithms is now presented Path Planning A lgorithm The path planning algorithm can be divided into an offline path planning algorithm and an online path planning algorithm. The offline path planning algorithm is, in other words, the global path planning algorithm. Offline path planning needs a pr iori information about the whole world and after calculating the most optimal path, it communicates the path to the robot and the robot follows the path with the help of the path tracking algorithm. Therefore, the offline pass algorithm is suitable for creating the most optimal algorithm for reaching the goal. However, this offline path algorithm needs to be give n p rior information completely for

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14 path planning and the larger the search ra nge, the greater the computational speed and space that is required Also it cannot appropriately respond to a dynamically changing environment. The online path planning algorithm is, in other words, a local path planning algorithm. This is done in real time. This algorithm contrasts with the above case where the robot has a prior map information and is used when an unexpected obstacle appears or when the surrounding environment cha nges. For this path planning, the robot needs to detect the environment using onboard sensors in real time. Thus, the online path planning algorithm is an effective algorithm in a changing dynamic environment. However, due to limited computational capabili ty (memory size, time efficiency, sensor range limitation), the algorithm uses a simplified algorithm or heuristic algori thm for path finding. Therefore, the online path planning algorithm is unlikely to produce an optimized algorithm. I t is necessary to combine the two algorithms properly to search the path to reach the goal in the dynamic environment including the moving obstacles. In the next sections the problem will be defined and an approach will be devise d to solve the problem. Research G oal It is important to find a generic and reliable path planning algorithm that can be applied to any type of autonomous vehicle to reach the goal in a rapidly changing traffic environment. The purpose of this study is to simulate various situat ions that are difficult to realize and to apply the appropriate algorithm

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15 Problem Statement The goal of this research is to devise an algorithm that produces an optimal path in time to reach the goal in a dynamic environment with moving obstacles. The fol lowing elements are given or assumed to achieve th is goal : The autonomous vehicle must autonomously calculate the route after receiving only the information of the destination without the input of any external computation The position and orientation of the vehicle shall be measured in the global coordinate system using GPS and INS. These global coordinate s are used to convert local information into global information The sensor for recognizing the environment of the car i s the LIDAR or vision camera mounted on the car. In this case, since each sensor has a limited range of sensor detection and the vehicle can obtain necessary information only within the range Autonomous vehicles must be driven by realistic inputs. It must be manipulated only with the steering input to manipulate the head ing of the car, and with the throttle and brakes responsible for the acceleration/ deceleration of the car. Sudden side movements or jumps are prohibited An autonomous vehicle must select t he best route to reach its destination using the information obtained Development In the above problem statement the solution to arrive at the destination is as follows. First, autonomous vehicle modeling will be applied to the algorithm and position and speed information of the vehicle will be obtained. T he onboard sensor s observe the surrounding terrain information and the behavior of other vehi cles moving around. Based on this information, the world environment is modeled by a grid and the cost of e ach grid cell is calculated The cost map is search ed for the optimal route to the goal The path planning algorithm creates an optimal path to the goal based on the cost map and hands it to the autonomous vehicle. T he path following algorithm is applied from the

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16 starting point to the goal point based on the path The following presents a novel idea to solve this problem : A new path planning algorithm has been devised to allow the autonomous vehicle to reach its destination in a dynamic environment. This algorithm searches the optimized route within a limited range based on the limited local range of each sensor It then repeats the same route search according to the movement of the vehicle to arriv e at the destination. To find optimal path in the range an improved A algorithm is used A new cost calculation algorithm is introduced to reflect the information of the observed dynamically moving vehicle to the above improved path finding algorithm. T his cost is determined by the current location and velocity of the obstacle, and this cost is fused with the cost map, affecting the path tracking behavior of the autonomous vehicle A simulation environment is created that can test the above algorithm T his allows for visualization of the input and output in a GUI environment. A keyboard or mouse may be used for the operation of the autonomous vehicle, and a mouse may be used for the destination input

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17 CHAPTER 2 REVIEW OF LITERATURE In this section, a review of path planning algorithm s that ha ve been studied for autonomous vehicles will be presented First, the offline algorithm that plans from the given starting point to the destination point will be discussed the most common of which is the A algo rithm. Next, algorithms which act to avoid dynamic obstacles will be reviewed Path F inding A lgorithm Dijkstra A lgorithm The Dijkstra algorithm is an algorithm for finding the shortest path between nodes in a graph [Dijkstra 1959]. A graph is a type of data structure that consists of vertices and edges connecting vertices This graph data structure is widely used in path finding algorithms because it can be applied to cities and roads problem s This Dijkstra algorithm is applied to negative weightless graphs and is used for most problems finding the fastest possible path w ith the least possible cost. The Dijkstra algorithm is a method of determining the shortest distance by calculating the distance by visiting the neighboring node by looping However, the disadvantages of the Dijkstra algorithm are as follows. The Dijkstra algorithm nodes all distances that can travel between paths. So, all directions of the origin node must be searched in the network. Therefore, to apply this, the space that needs to be searched will become larger and the time to execute the algorithm will become longer In addition, making the network can be slo w due to various variables such as motor vehicle congestion and work attendance. Therefore, extended version of the Dijkstra algorithm, such as the A algorithm were developed

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18 A* A lgorithm The A algorithm is the search a lgorithm proposed in 1968 by P eter H art et al The A* algorithm finds the shortest path from the given starting node to the target node in a graphical data structure. The A algorithm uses a heuristic estimation function to estimat e the best path when passing thr ough each node. Although this A algorithm does not always guarantee an optimal path, the optimal path can be detected by appropriately selecting the above heuristic estimator function. The following is a cost evaluation fun ction of the A* algorithm. (2 1) : cost evaluation function for th node : cost of the current state : cost when moving from the current state to the goal state When finder moves from the current state to the next state, it first searches for the point where is minimized. Therefore, the performance is clearly depending on the heuristic function and in the case of it is the same as the Dijkstra algorithm. Hereinafter, the execution process of the A* algorithm is briefly as follows : 1. Put adjacent nodes found in the starting rectangle into the open list 2. Find the lowest cost in the open list and select it as the current node 3. Take it out of an open list and put it into a closed list 4. For the adjacent node to the current node if it is inaccessible or is on a closed list, ignore it, otherwise continue If it is not in the open list, add it to the open list and make the parent of this node the current node Record costs for this node If it is already in the open list, use cost to find out if this node is better, and if its cost is smaller, it means that it is a better length, so change the parent node to the node Then recalc ulate the and costs of the node 5. A dd a target node to an open list If o pen list became empty (in this case, it fails to find the target node so there is no route to reach ) we should stop.

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19 6. Save the path (going from the target node to the parent node until we get to the starting node this is the path we are looking for) Other P ath P lanning A lgorithm s A. Stentz c ommented about the D algorithm in his book [Stentz 1994]. This algorithm was introduced to improv e the problem of the existing A* algorit hm. The A algorithm has a problem that it takes a long time because it stops the process when the path search is blocked and calculates the pa th again from that point. The D algorithm introduces the concept of backpointer to optimize the search speed and space by recalculating the path back to the previous backpointer state when the path becomes clogged P.Shamsinejadsm saw that an exhaustive, complete, comprehensive search technique cannot navigate the optimal path within a given time [Shamsinejadsm 1959] He distinguishes between global and local path navigation. The global method is suitable for obstacles to be static and the search target to find the optimal path. On the contrary, the local method is suitable for dynamic dynamical e nvironments requiring only information of local obstacles. However, the path created by the local method is only valid for the local optimal path. P.Shamsinejadsm devised a local path search based on a genetic algorithm to obtain high accuracy such as glob al route search, and at the same time to achieve proper execution speed such as local path search. A g enetic a lgorithm is an algorithm based on the biological genetic theory of nature. It is a method to obtain an optimal solution through repetition of sele ction/crossover/mutation/ substitution process es He expected the speed of navigation to be very important, and found genetic structures to be appropriate for complex pathways. He also found that the length of the

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20 chromosome fixed in the path search through the genetic algorithm is simpler and enables faster genetic function, but it does not show the complicated path as a disadvantage. However, he assumed that the chromosomes can be stretched freely By applying this genetic algorithm, E. Zawodny devised an algorithm that ensures that multiple vehicle can be positioned with each other maintaining the line of sight without obstacles interfering [Zawodny 2003] This algorithm was compared with the exhaustive search algorithm, and it produced the optimum path fo r the shorter execution time and thus it is very helpful for the location selection for direct communication among multiple vehicle. Mapping A lgorithm An autonomous vehicle must design a map to apply the path planning algorithm with given information of the surrounding environment or measured information with its onboard sensors. Sometimes there is a lack of the priori information or limitation of measured sensor data. P ast research es on how to map the real world so that the path planning algorithm can be applied is now reviewed Occupancy Grid The Occupancy Grid is an algorithm that creates a map by utilizing the position of a robot together with indeterminate sensor data information. The concept of Occupancy grids was developed in 1989 by Carnegie Mell on University to provide accurate path mapping and navigation for autonomous robots. This concept incorporates various environmental variables as a background to the occupancy grid [Elfes 1989]. In other words, the robot can fuse each piece of information to one large w orld m ap, depending on the sensor data it collected without the precompiled designated path. The occupancy grid map represents 2D or 3D spatial

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21 information as a set of individual cells called a grid This grid contain s a specific state variable that indicates whether an obstacle is present in the grid which is either occupied or empty, and the two states combined to yield a probability of 1. The j udgment of these characteristics is obtained by applying the Bayes Law th rough continuous observation. Therefore, the robot can perform the obstacle avoidance using the A algorithm with probability that each cell has an obstacle. The Occupancy grids discussed above are models in a static environment. Therefore, Occupancy grids are not suitable for applying to the dynamic of the real world. The concept of a temporal occupancy grid was further developed following the traditional o ccupancy g rid to solve the issue The Temporal Occupancy Grid (TOG ) proposed by Daniel Arbuckle and o thers in 2002 is designed to classify the characteristics of objects that temporarily occupy space in the environment [Arbuckle 2002] TOG is method that can classify the occupancy of grid cells according to time, including time data in the existing spatia l coordinate information. In other words, traffic patterns, including the space occupied by fast moving objects and spaces occupied by slower object s, can be obtained differently through TOG As TOG is different from Occupancy Grid, the way to distinguish between static obstacles and dynamic obstacles is as follows. Static obstacles are assigned a high occupancy probability over all time scales. Conversely, a cell with a high expectation of a dynamic ob stacle would have a high occupancy probability for a short time scale, or a low occupancy probability for a long time scale

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22 Traversability Grid There are cases in which such o ccupancy g rid and t emporal o ccupancy g rid are applied as actual cases. In the Urban Navigator of the U niversity of F lorida it created a grid based map through a s mart a rbiter which fuses sensor data [Crane 2005 ] This concept the so called Traversability Grid focuses on describing the condition of the terrain and the class of obstacles according to the fluctuating cycle in real time [Crane 2006] The map consists of a 121x121 set of horizontal and vertical cells, each of which represents 0.5m of the real world. The centra l cell (60,60) showed the measured cen ter position of the car, and the north was always aligned with the grid. Therefore, the map could track information 30m by 30m, and all smart sensor components needed to be synchronized with latitude /longitude, heading information received from the Global Position Sensor( GPOS ) component. Costs are given from 2 to 12 ; 2 is a road that cannot be traverse d 12 represents the optimal path, and 7 represents the normal state which may or may not be good for the sensor. T hese values are also colored and compreh ensively described in a GUI These grids are then sent to smart arbiter which combine s this information into a single cost map Avoid Obstacles So far, methods for world modeling for appl ication to the optimal path algorithm have been discussed The next thing is to see how moving objects can be avoided in the world when they are encounter ed Avoiding moving obstacles is more difficult than avoiding static obstacles. Because the position and velocity information on moving obstacles is constantly changing, it is difficult to devise an algorithm that calculates the path in real time

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23 keeping pace with the dynamically changing variables T hus, most past studies are focused on estimating or measuring the path of moving obstacles. Previous studies ha ve introduced probabilistic models to explain the dynamic behavior of obstacles to solve this problem. Another approach was the space time concept Space time theory was created by adding additional time dimensions to existing spatial dimensions. In space time theory, obstacles have been replaced by static obstacles, and the dynamic obstacle avoidance process has been simplified to avoid static obstacles in space time. This theory could be applied to real time avoidance [Fujimura 1989, Erdmann 1986, Shin 19 90] ; or to V irtual F orce F ield (VFF) and V ector F ield H istogram (VFH) theories have been developed to avoid real time obstacles [Borenstein 1989, Borenstein 1991]. Yung and Ye introduced Collision Zones which would never go through autonomous vehicles in transit, to ensure that there is no collision [Yung 1998] Unlike the Collision Zone s the potential field theory introduces the force field theory rather than defining the area where the robot should not go Potential Field Among the vario us algorithms to avoid and respond to obstacles is the potential field theory. The potential field fills the environment space of a robot with a virtual force field. In this virtual force field, the goal creates the force to pull the robot, and the obstac le creates the force to push the robot Because of its simplicity and mathematical beauty, the potential field theory is widely used in the path planning of mobile robo ts. However, Ge and C ui have raised the question that most studies related to potential fields focus only on motion planning in a static environment consisting of static goal and obstacles [G e 2002]. They have found

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24 that the problem of the traditional attractive potential field was only consider ing the relative distance of the robot to a fixed spatial goal They presented a new attractive potential field that is different from the traditional theory ; t his new theory is determined by the relative position and velocity between the target and the robot when determining the force acting on robot T he result of the virtual forces coming from this new potential field allows the robot to take appropriate action in response to the goal or obstacle s The advantage of this theory is that it does not require pr eemptive knowledge of the path of obstacles I nstead, the path is only planned by real time measurement of the information of the obstacles. Ge calculated the potential field according to the following equation : (2 2) Where and is the position of the robot and target at time and is the velocity of the robot and target at time Therefore, is a Euclidean distance between robot and target at time is the relative velocity between robot and target at time And are scalar positive parameters, and are positive numbers Therefore, the force acting on the robo t is the gradient value of the potential field, and this potential field can be altered in real time depending on the relative distance between the robot and the target an d the relative speed. Therefore, by calcul ating the virtual forces in the potential fields for all obstacles the sum of these virtual forces influences the robot to judge and adjust its movement s accordingly T his theory is worthy as it shows the proper relationship between the position and speed between the robot and the obstacle

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25 Avoidability M easure Based on potential field, Ko and Lee proposed a technique for avoiding moving obstacles and applying them to a robot movement [K o 1996]. They proposed a concept called A voidablility M easure (AVM), which defines the conditions associate d with the collision of a robot and an obstacle pair. Ko introduced the concept of AVM to avoid real time obstacles. AVM is the inverse of the probability that a vehicle will strike an obstacle ; i t therefore represents how easily the robot can avoid obstac les. In AVM, the distance between the robot and obstacles can be used to detect collisions between them ; t he probability of a collision can be determined also by the relative velocity between the robot and the obstacle that is also approaching (or moving away) from that distance. Therefore, they set the distance and the traveling speed to state variables to account for the possibility of avoiding collisions. (2 3) (2 4) rep resent the position of the robot and obstacle at time represents the radius of an obstacle. So AVM increases as the distance or increases. While there are many functions that can satisfy AVM, they propose a function called Virt ual Distance Function (VDF) to express the pulling power of robots VDF which is a function of the relative distance and velocity between the robot and the obstacle produc ing the virtual distance that the robots needs to maintain as the threshold value so that it can avoid obstacles (2 5)

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26 where (2 6) VDF created a virtual potential field that emits a force that pulls the robot from the target At each sampling time this potential field is updated and the force acting on the robot is determined by the gradient of thi s virtual potential field This algorithm is suitable for avoiding moving obstacles because it considers its motion as well as distance from the obstacle.

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27 CHAPTER 3 DYNAMIC LOCAL WINDOW PATH PLANNING ALGORITHM Figure 3 1 shows the progress of the algorithm for searching the optimal path in a dynamic environment with moving obstacles. This algorithm is based on a graph structure. All coordinates of a map are defined as nodes with individual characteristics Fi g ure 3 1. Flowchart of the dynamic local wind ow path planner When a goal point is assigned to the autonomous vehicle, it fuses terrain data with visual camera data LIDAR GPS and INS. In addition, the vehicle transmits the velocity and position information of the individual moving objects to an algorithm for searching the final path together with the terrain data This information is converted into

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28 cells with individual costs, and the set of these cells is transformed into a single cost map and used to create the input values of t he path tracking algorithm described below This path finding algorithm first creates a temporary goal point within a certain space within the local coordinate system of the autonomous vehicle to reach from the starting point to the goal point ; t hen an optimal path is set up to the temporary goal point. While the vehicle is moving along that path, the algorithm recalculates the optimal path by setting a new temporary goal point. Through such a process, the car ultimately reaches the initial goal p oint Before applying this algorithm, the process of modeling the world to apply it to the algorithm will be presented Then each step of the algorithm will be discussed in turn. World M odeling World modeling d efines the simplified real time world, kinema tics and dynamics of autonomous vehicle in that world before applying path search algorithm The following steps are performed before the algorithm executes Two dimension G rid M odeling This step simplifies the world to a set of grids in a two dimensional coordinate system, and displays all the objects in the world including cars and obstacles as a grid with coordinates. Each object has a center of mass at its position, and it is assumed that all physical elements s uch as mass, force, and inertia are applied only to that point. T his grid has information such as the environment variable, obstacle recognition, autonomous vehicle recognition, etc., and has a cost value related to the information ; t his information can al so be changed in real time.

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29 Variables of V ehicle in C oordinates There are many variables associated with modeling the vehicle They have been simplified to a series of moving points in a two dimensional coordinate system. Therefore, this work will look at the modeling of a mobile robot that exhibits the most similar forms and behaviors to a conventional car like vehicle in the algorithm application. The first is circular modeling. Circular modeling can be best explained by thinking of a robot as a c ircle ; i n other words, a single circle that draws a certain radius around a center of mass is composed of one robot Since this circular modeling does not need to consider the rotation according to the heading, it has the advantage of simplify ing a calcula tion However, the simulation to be implement ed is an automobile, and rotation is a key characteristic that cannot be excluded, so excluding it is not an effective wa y to produce a model Pasha mentioned k inematic limitations of car like robots in his research [Pasha 2003]. He saw that a robot moving in a plane had a total of three degrees of freedom ; an x axis and y axis movement, and an angle about an axis perpendicular to the plane. Therefore, the robot has three environmental variables O n the other hand, the input to this robot is velocity; which is again divided into linear velocity and angular velocity, and the input variable is The number of this input variable is smaller than the number of environment variable, so the robot c an be called a s nonholonomic robot The characteristic equation model that can be applied to this nonholonomic robot is the automobile modeling defined in the coordinate system proposed by Ahmad Abu Hatab [Ahmad Abu Hatab 2013]. He declared two coordinate systems, the first being a

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30 fixed global coordinate system in which the robot will travel. The other coordinate system is the local coordinate system that moves with the robot Fi gure 3 2 Relation between global and local coordinate system The coordinate system defined above is shown in figure 3 2 above. This global coordinate system and the local coordinate system can be replaced by the following rotation matrix, which receives the input value as direction of the rob ot (3 1) (3 2)

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31 Kinematic E quation and T ypes of V ehicle s Automobile behavior refers to the basic behavior that is achieved through the manipulation of a vehicle The first thing steering is a circular inp ut about the steering axis, which changes the heading of the vehicle The second thing throttle and brakes are responsible for accelerating and decelerating the vehicle. At this time, the maximum value of the acceleration rate is fixed ; a nd it is assume d that the deceleration rate is a bit larger than the maximum acceleration because of a safety concern The following is a matrix equation that shows how the three coordinate values that make up the vehicle change according to the linear velocity and angular velocity (3 3 ) The first and second elements of the vector indicate the amount of change in the linear velocit y of the vehicle and are the derivative val ues of the position information respectively. The t hird element is the change in angular velocity of the vehicle T he position and heading of the vehicle are changed according to the linear velocity and the angular velocity which are input values of the vehicle (3 4 ) (3 5 ) (3 6 ) where is the time the input was applied.

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32 An entity is an object that has de fault values for the above physical variables and behavior. This entity can be inherited and define two details shapes. The first is a player to apply this path search algorithm, and the other is a moving obstacle Player The player is the model of the autonomous vehicle that is to be implement ed This player can receive prior environmental data. In addition, environmental data can be received through a local sensor, and information (position, speed) of moving obstacles aro und the object can be obtained with the same sensor T he player can also fuse the obtained information into a single large world map. The player can calculate the optimal route from the start point to the goal point with this fused world map and has a prim itive driver and tracking algorithm that can follow the optimal route to reach the goal Moving Obstacle A m oving obstacl e is any object that moves at a speed and direction in the world This movement may follow a given goal with a scenario or roam freel y However, in a typical intersection environment most of the moving obstacles are assumed to be conventional vehicles and the actions that these vehicles can take are limited ; so, they cannot move around freely. Therefore when applying this algorithm moving obstacles are designed to have the following environment variables position, velocity and dependent variable start direction and arrival direction Pre p rocess Once the model has been implemented to apply the algorithm, the algorithm can be started The algorithm goes through a pre process step before execution. In this step, it verifie s whether the input value to be executed by the algorithm is valid, and the n calculat es various pre computable variables necessary for executing the algorithm.

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33 The input value of this algorithm is fused map data This fus ed map is a single grid map in which two different maps are combined into one. One map is a cost map, consisting of the environmental cost values There costs are considered as static values. An other map is a moving obstacle map, which is a dynamic map based on the predicted path generated by the observed moving obstacle. A nd the output value of this algorithm is a set of points that make up the optimized path Goal T ransition in L ocal S earch A rea Once the model has been implemented for appl ication to the algorithm above, the algorithm can be started This step includes setting a limited search range and temporary destination conversion in the limited search range of the vehicle before the route search for the optimal path planning Temporary Local S earch A rea B uilding Arras proposed two modeling and planning steps for robot local path planning and obstacle avoidance [Arras 2002]. In the model ling stage, the shape and the dynamic of the robot a re considered. In the planning stage, the path of the goal point branches from the point composed of coordinates. Further, Arras performed a path search in the robot local frame rather than performing the calculation in th e global frame. This is because in applying the same search algorithm doing it in a global frame makes the computation more complicated as well as increases the difficult y of immediately respond ing to dynamically changing variables. In this research, the path planning algor ithm is influenced by the resea rch of centered on its current position to perform the estimation within a limited range. This search scope is typically set in a grid like shape which confines the range so that it is detectable for the sensor

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34 Goal Transition This path planning algorithm is utilized to identify the optimal path within a limited search range as presented above Therefore, there is a need to shift the initial goal to a more limited temporary, search range, which is further revised in each pass planning phase The initial goal is transformed into a temporary changed goal for each pass planning phase. To set this temporary goal we must check whether there is an initial goal point within the limi ted search range set above. If the initial goal point is outside the search range, the temporary goal point is set on the boundary of the search range, where M anhattan distance between the initial goal and boundary of the search area is the smallest. The Manhattan distance is a concept developed by Hermann Minkowski in the 19 th century, a method for estimating the distance between two points in Cartesian coordinates [Krause 1987]. The Manhattan distance is the sum of the lengths of the vectors and projected onto points on a coordinate axis in the Cartesian coordinate system. Let are vectors, (3 7 ) For example, if the position of two points on the 2D plane are and the Manhattan distance is Conversely, if the initial goal is above the search range boundary or within the search range, the temporary goal point is set as the initial goal point and no more goal transition s are made

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35 The following figures depict how a goal transition occurs A B C D Figure 3 3 The process of goal transition A) Initial State B) Define l ocal s earch a rea C) Transition from initial goal to temporary goal D) Next step Figure 3 3 A is the first step of the algorithm. S is the starting point the current location of the player and E is the given initial goal In the case of an offline global path algorithm, there will be increased time and space required to find the optimal path from S to E. Figure 3 3 B shows how to limit the search space. This limited search space is called a local search area and it is extended to a certain area ar ound the current position of the player In general, this search area can be set at most detectable sensor area of the player

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36 Figure 3 3 C is the transition of the given initial goal E into the local search area defined above. Given the confinement to 2D space, a point on the boundary of the local area which has nearest distance to the given initial goal was chosen Figure 3 3 D depicts the process of the search algorithm after the player moves ; th e algorithm is re called again by the iterative process to be described later. Thus, an updated search area will be set around the new location of the player The local search area of a player may be a polygon or a curvature shape including a call or a circle, as the local sensor ( such as a LIDAR ) has a radially extending sense area However, the algorithm depicted limited the local position to a square of certain size, for simplification of operation. The goal transition is a step that is performed every time an algorithm is executed until a n initial goal exists outside the local search area. If the initial goal is reached within this local search area there is no need to perform a goal transition again and the step is skipped Cost E valuation If the initial goal has transitioned to the local search area the cost for all environmental variables within the local search range will be calculated and fused a map This map depicts a set of two dimensional, individual grid cells declared in the previous world modeling. The grid cell has the following cha racteristics : Table 3 1. Characteristic of grid cells Type Description Int[][] P osition The position of this grid in global frame Int cost The cost of this grid Boolean isStartCell Whether this grid is the starting point Boolean isFinishCell Whether this grid is the goal point

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37 Cost will be used as the input value of the heuristic function when determining the route to the player using the A* algorithm. Th is algorithm designs the path toward the lowest cost through computations involving he uristic function values. Environmental C ost The environmental cost is the value for the environment al variable used in the existing offline path planning. In other words, this value is a statistically considered variable in the path search, i e the cost associated with the terrain elements This cost may be pre entered the vehicle in advance, or it may be computed through visually and spatially analyzed information via a terrain detection s ensor mounted on the vehicle Importantly, this value is a static value meaning it does not change between path searches. The following is an example of this kind of environment variable in intersection : Travel lane : The area in which the vehicle can move. Generally, it refers to well cleaned asphalt or concrete roads and is the preferred route for vehicles to move. A grid with this property has a low cost, which causes the vehicle to move toward the grid Verge : This area occup ies most of the simulated intersection model and is restrict ed for entry, due to high cost value s In the real world, this area would be full of static objects such as buildings. The re ason for giving a high cost to this area is that the collision between the structures and vehicles may be catastrophic to progress Shoulder : This ar ea is an edge or boundary of a travel lane which has higher cost than the travel lane but less cost than the verge. Therefore, movement to a shoulder is not impossible

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38 Center line : This area controls the direction of movement of the vehicle at the road and has a role of dividing the lane. A vehicle moving in a lane cannot cross the opposite lane beyond this center line. The following table shows static cost valu e s given for the above mentioned environmental variables as designed to apply the discussed algorithm to this simulation Table 3 2 Cost value to environmental type Type Cost Description Travel lane 1 Movable area Verge 99 Unmovable area Shoulder 4 Not preferred to travel, but can Center line 99 Cannot across Not real data. Path P lanning in L ocal S earch A rea This step is the process of deriving the optimal path from the above defined local search range to the replaced temporary goal Here, the fused map is a cost grid map including : the fusing grid cell, the starting point, and the temporary goal These conver ses the terrain information collected from the sensor Initialize P lanning This step receive s the fusing map and initialize s variables and is the first function declared when the path planning algorithm is executed. Through check ing the start point, converted goal, local search area and fused cost map values are confirmed as correctly declared and maximum step is specified This limitation is necessary to stop the route search algorithm immediately after determining that the number exceeds the allowable number of search units. It then declares an array that can temporarily store the path found through path search. If the validity of the above input values is

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39 confirmed and the output variables are created, the follow ing step by step search is performed Step Finding It is an essential function of path search through A* algorithm ; most of the various A* algorithms are this part. For each step, a neighbor node accessible from the current node is put into the open lis t based on the A* algorithm. Then, the heuristic function selects a grid having the lowest cost value a s a target node to be next, returning the type of the target node The current node is then included in the closed list and excluded from the search The return value of this step is the grid type, which will go to the check arrival state step to determine whether to continue or abort the algorithm Check A rrival S tate This step determines whether to continue the route search according to the above ste p by step search results The result of iterative execution is shown as the type of the corresponding grid. If the result is a temporary goal destination it is judged as arrived, and the search function is terminated If not, this trigger is increased by one and the neighbor grid is searched again. If this trigger exceeds the maximum search range, the algorithm determines that there is no way to reach that temporary goal and the algorithm terminates (returns no route ) Set P ath In this step, the optimal path from the start point to the temporary goal point is found through the above algorithm and placed in an array so that the autonomous vehicle can follow the path Having received the grid at the current location and the grid at the goal point confirm ing that the path search is complete, the result is stored in an array This result then goes backwards from the temporary goal to the start point

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40 because the path is based on the A* algorithm result So, when put in an array, the path reverse s to go into the time of the array. The final data then becomes a set of waypoints of the optimal path from the start point to the temporal goal point. The key point of this step is to extract only the required waypoints out of the optimal pat h When the path is searched through the first A algorithm, the result is generated by the step of each visited cell. Therefore, there is a potential for several problems, such as: programming efficiency, storage space and the behavior which must be follow ed whilst following a path to create a grid pass es through all points. In addition, unexpected problems can occur due to sudden speed increase/decrease frequent route changes and rotation switching. Therefore it is efficient to give only the wayp oint that the vehicle must pass The process of specifying key waypoints is as follows : 1. Put the first data at the beginning of the array 2. Put the data of the next step into the next array. Comparing this with we calculate whether the position at moved horizontally or vertically 3. Now, from if the movement of the corresponding step and the movement of the previous step differs from the movement of all the steps and the movement of the previous step, the coordinates of the current step are stored. Every time a direction change (vertical horizontal, horizontal vertical) occurs, the coordinates are stored 4. Since the last coordinate is the result value, it must be inserted at the end of the array 5. Consequ ently, the array is the set of the coordinates of the start point + the coordinates of the next step point + the coordinates of breaking point + goal point Examples : If the number of grids in optimal path between the start point and the goal point is 120, then the traditional A* algorithm has a total of 120 path points because each cell is 1 in size. To check all 120 points, the speed of the car between

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41 point checks exceeded the check time, so it may not be possible to check properly as there may be trouble circling previous points. Therefore, extracting only a fraction of the waypoints that have a decisive influence on the optimal path and giving it to the vehicle helps to use more efficient path space Iterative R unning After each successful sea rch of the best path, the temporary destination is compared with the initial destination whenever the algorithm is re executed At this point, i terative execution continues if the temporary goal is different from the initial destination. This initial ite ration is done after the first delay has elapsed since the original algorithm was fully executed and the local search area was created Now depending on the current speed of the car, the interval at which the next iteration is executed differs. At slow speeds, it takes a long time to move the path, so update time may be slow. At faster speeds, the update time should be shorter because it tak es a shorter time to move the path The calling cycle of the repeated function is given by : (3 8 ) (3 9 ) The result of t his iterative execution optimal path and are given to the player The player follows the path using the path tracking algorithm. The player then readjusts local search area and temporarily goal after (ms) With these information, the path finding algorithm is repeated, until the player reach es the initial goal

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42 CHAPTER 4 PATH TRACKING ALGORITHM This algorithm defines how the autonomous vehicle executes the input value, when a certain optimal path is created for the above pathfinding algorithm. Th e optimal path contains information of the route given to the autonomous vehicle with a set of the coordinates: the coordinates of waypoints that must be passed, the coordinates of the start point and the coordinates of the goal point. The following steps are taken by the robot, to the goal along the route o btained through the path finding algorithm. Target W aypoint S election The a utonomous vehicle compare s its position with a set of waypoints that they have entered and determine its target point. First, the vehicle sets to 1 to obtain the first one of the waypoints of the input route. After the vehicle calculates the distance from its current position to the goal destination it also calculates the distance between the point of the waypoint and the goal destin ation (4 1) (4 2) is the Euclidean distance from the th waypoint to goal destination, and is the Euclidean distance from the current vehicle position to the goal destination.

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43 A B C D Figure 4 1 The process of target waypoint selection A) Initial State (calculating distances) B) P assed first waypoint C) R ecalculating distances D) P assed second waypoint Figure 4 1 A is the state when the autonomous vehicle received the first mission way point. Here, S means the autonomous vehicle and E means the (temporal) goal point. W1 means first mission waypoint, and W2 means second mission point. T he blue solid line represents the distance from the current vehicle posit ion to the goal , and the solid red line represents the distance from the current mission way point to the goal The mission way point that the vehicle must follow is the first way point because the goal point d istance from the mission point is shorter than the distance from the current point to the goal point, Figure 4 1 B is the state after passing the first mission way point. From here, the distance from the current vehicle to the goal is shorter than the distance from the first way point to the goal Therefore, is incremented by 1, then the car selects the next level of mission point. Figure 4 1 C likewise checks the next mission way point. It

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44 recalculates again and compare two of them. Because is longer than the vehicle goes towards the second waypoint Figure 4 1 D is the fi nal appearance. Figure 4 2 shows whole process of target waypoint selection. Figure 4 2 Flowchart of target waypoint selection Following T arget W aypoint Once a target waypoint is selected, a kinematic method is needed that allows the robot to move to the waypoint. Coulter at Carnegie Mellon university devised a kinematic model algorithm for robot to destination [Coulter 1992]. The robot computes the arc from the present position to the destination position as much as it should go, and calculate the loo k ahead distance to pursue the destination. Based on the method, the tracking algorithm for this autonomous vehicle is as follow s :

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45 1. Determine current position and heading of the vehicle 2. Determine the position of the waypoint 3. Covert the position of the waypo int into the coordinate system of the vehicle 4. Calculate the angle and look ahead distance between the vehicle and waypoint. 5. Compare the heading of the vehicle and angle 6. Calculate steering and throttle input T he vehicle calculate s the angle between i ts position coordinates and the waypoint This angle is obtained on a global coordinate basis as (4 3) is the heading that the vehic le should aim for to reach the waypoint is compared to the heading which the vehicle is currently facing and if there is a difference, the vehicle will begin to rotate within the maximum rotation angle to the target waypoint This rotation is satisfied if it falls within the allowable offset an d the alignment of the heading and the acceleration of the vehicle are made simultaneously. The look ahead distance between the position of the current vehicle and the position of the waypoint is then calculated. If the waypoint is not the goal point, t he robot accelerates to the maximum speed (4 4) After calculating the angle to rotate and the look ahead distance to go, the vehicle then generates the steering and throttle inputs based on these values and moves to the waypoint.

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46 Figure 4 3 Geometry of the following target waypoint Since the robot is moving in real time, it is possible that it has already passed its mission waypoint. Therefore, there is a circular offset around each waypoint, wherein if the robot enters the range, it is considered to have arrived at the waypoint, prompting the robot to go towards the following waypoint. Finally, if the waypoint to which the robot should go is the goal point, the robot approaches a relatively smaller range than the offset above and stops when it arrives within the offset. All execution sequences are shown in Figure 4 2. As the robot follows the path of environmental variables, dynam ic obstacle detection and avoidance movements should be performed. The following is an algorithm that avoids collision and detection while the robot follows the path

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47 Collision D etection and R esponse Collision detection and avoidance are essential to ensur e safe car behavior at an intersection. Unrecognized static obstacles or moving obstacles can pose a major threat for catastrophic disaster to vehicle operation in real time situations. The detection of the autonomous vehicle depends on the moving obstac le expected cost value of the local search range computed in real time with the above algorithm. The Probabilistic Moving Obstacle Grid (PGM O ) method is used to determine the range of risk generated by moving obstacles. Probabilistic Moving Obstacle Grid There is a need to assign a cost to the obstacles as if there are only stationary obstacle s (such as the environmental variables above) one can simply give them a high cost, to defer entry to that area However, in the case of moving obstacles, it is impossible to assign a cost simply because they change their position s in real time. Therefore, the following cost allocation method was devised Probability Grid of Moving Object (PGMO) is a method to calculate the cost for moving obstacles, as measured o n the local sensor of the vehicle. The PGMO places a high cost on the grid corresponding to the location of the measured obstacle. Further the speed and the longer th e distance traveled by the obstacle, the larger the area occupied by the obstacle's future path become s Therefore, the cost range is broadened by the speed of the obstacle (the magnitude of velocity), and widened toward the heading of the obstacle (direct ion of the velocity).

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48 The following is a method to calculate the risk area of dynamic obstacles where the position the magnitude of velocity and direction of the velocity are measured First, there are three risk a reas for the obstacle. The risk area is a set of cells with the values of the lowest risk cost, the medium risk cost, and the highest risk cost, respectively. The risk area of the moving obstacles are as follows: The next step sets the size of the individual risk areas that this moving obstacles is expected to occupy. ( 4 5 ) is the breadth of the risk area and (4 6) is the vertical part of the risk area. is the minimum safe distance to avoid collision and has the same value for all obstacles in this simulation is the cur rent velocity of the observed obstacle, and is the maximum velocity that this moving obstacle can have. Finally, is a constant according to the risk level, ranging from the lowest risk to the highest risk: ( 4 7 ) In other words, at the lowest risk, the risk area is equal to the because At the highest risk, the v ertical length of risk area is much longer than the horizontal length of it because we will pace the heading axis of the moving obstacle equally on the axis.

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49 Through the obtained values and the risk area around the position of the moving obstacle is represented as a set of cells having the following coordinates. ( 4 8 ) ( 4 9 ) is a set of points that are uniformly extended on both sides around the position of the obstacles, and is a set of points whose forward coordinate are mo re deflected than the backward coordinates around the position of the obstacle. Because the obstacle is expected to move in the direction of velocity, sudden back warding is impossible after the time has passed. Now based on the heading of the observed mov ing obstacles, we rotate the upper risk area. is a set of x coordinates of the risk after the rotation. ( 4 1 0 ) And is a set of y coordinates of the risk after the rotation. ( 4 1 1 ) The area of risk thus rotated is cost adjusted for each risk level. Table 4 1 Risk cost to moving obstacle Risk level Risk cost Lowest risk 16 0 0 Medium risk 13 0.5 1 Highest risk 10 1 2 Not real data. The following is a series of steps on how a cost is awarded by the PGMO method 1. The onboard sensors of the vehicle determine the position and velocity at the time of detection of moving obstacles in the sensor range

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50 2. The current position of the obstacle and the minimum safety distance are given highest risk cost to the adjacent cell The ratio of the current speed to the maximum speed of the obstacle is calculated according to the direction of the obstacle, to obtain the danger area corresponding to each risk 3. Considering the heading of the obstacle adjust the risk ar ea for each of the above risks to more heading The risk level is consisted of following: Lowest risk cost, Medium risk cost, and Highest risk cost. 4. From the lowest risk to highest risk the cost is given to the risk area that has been modified to suit th e heading above Figure 4 4 displays the above procedure A B C D Figure 4 4 The process of cost evaluation A) Initial state (measuring moving obstacle) B) Set risk areas to risk levels C) Rotate risk areas D) Set cost to each risk areas

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51 The path of each moving obstacle is predicted according to the position and velocity observation which defines the risk area of the moving obstacle. The information about this risk area will be included in the moving obstacle m ap used to account for the behavior of the autonomous vehicle between path tracking. There are many theories for detect ing the potential collision of objects : Bounded Box theory, A xis A cross T riangle C entroid S egments and so on. The method used here in, is designed as follows. The vehicle leaves its current position and the acceleration goes towards the heading ; a vehicle moving in real time will soon be in a certain range at the end of the heading direction. A straight line is drawn from the center of m ass of the vehicle, to the direction of the heading. This straight line can be assumed to be the future positions of the vehicle Then a set of coordinates is specified from the start point of this straight line (car center of gravity) to the end In thi s research, twenty discr e tized detection points with initial position of the vehicle and direction of the vehicle were specified: ( 4 12 ) (4 13 ) (4 14 ) where A discretized search point was selected, b ecause the search was within the cell of the grid. Figure 4 5 shows details

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52 Figure 4 5 The search range line and its discretized search points At every reference time, the car initia lizes a series of maximum risk value s and compare s 20 detection points to the maximum value declared above, therefore redefin ing the largest value as the maximum risk value. W hen the vehicle moves along the path it takes from above, it compares the maximum value (4 15 ) The behavior of the vehicle based on leve l follows : If the value does not change it means that there is no dynamic obstacle at least to a certain distance in the heading direction. The car runs normally If the value indicates lowest risk level, there is no dynamic obstacle up to a certain distance in the car heading direction, but the dynamic obstacl e is less likely in the future. The car does not acce lerate and remain current velocity If the value indicates medium risk level there is a higher probability that there will be more moving obstacles in this search line. The car decelerates If the value indicates highest risk level, there will be dynamic obstacles in this search line. The car stops immediately

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53 CHAPTER 5 SIMULATION RESULTS Software In this research the simulation was made to verify the pathfinding algorithm. It would be better if it was applie d to the real environment, that is, to the autonomous vehicle, but first it is tested in simulation to examine the suitability, feasibility, and constraints of these algorithms for the simulator. This simulation implement s a simple crossing of a cross shaped two lane crossing and allows for the implementation of adjustable moving obstacles A n autonomous vehicle is also implemented to apply the corresponding path planning algorithm. Additional functions, algorithms, and GU Is were also written using Java's awt and Swing native packages. This simulation is written in Java under the OSX environment. There are many programming languages such as C / C ++, FORTRAN, which guarantees high speed, and MATLAB, which i s easy to express mathematical process However, Java is used here because Java is an object oriented programming language so it has many strengths. For example, autonomous vehicles and conventional cars in simulation ha ve common attributes of the same vehicle. In other words, it is advantageous to de clare a certain entity and to easily implement various transformations, cars or moving objects with the framework. This leads to improved performance through reusability of program sources and optimization of memor y space. Configuration T he simulation environment needs to be set before testing the algorithm

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54 First, the environment variables are set up to which the simulation will be applied. The world model in which the simulation will take place is an intersection where two lanes intersect each other. The top of the map points northward The absolute north of all simulators is degrees absolute. This value decreases in the counterclockwise direction, and increases in the clockwise direction. Thus, the north is degrees, the west is degrees, the south is degrees, and the east is degrees or degrees. To maintain a sequence of angles, the angle beyond degrees automatically adds I ts size is scaled to 600px by 600px. T he discretized unit time is declared to measure the execution time of the simulation. This Unit Time(UT) is added by 1 every time the simulation is updated in the main program, and follows the following equation : Results are shown below. Figure 5 1 is a visual ized map of the intersection where this experiment is performed Figure 5 1 consists of several environment variables. First, the gray space is the travel lane, which means that the car can travel. And the space marked in green is the verge which means that the car cannot go. At the bottom of the map, the current player object is shown as a white object. Figure 5 2 shows the map to be applied to the path search algorithm by converting each element of the above visualized map into cost. The travel lane has the lowest cost and is marked in green and the verge part has the highest cost and is marked in black. And the part that was not shown in the visualized map is the shoulder part, it has medium cost, and the cost map is yellow.

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55 Figure 5 1 V isual ized map Figure 5 2 C ost map

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56 Path P lanning without M oving O bstacle In the first simulation, the algorithm is tested to find the optimal path from the obstacle free state to the destination. P erformance improvements through comparison with traditional off line path planning results will also be discussed Scenario I: Path P lanning without M oving O bst acle with I nitial S earch A rea The following values are declared for simulation. Location of the car in the map : Maximum speed of car: Maximum acceleration of the car: Maximum angular velocity of the car: Position of destination in the map : Area of local search area : Figure 5 3 A shows that the path planning algorithm to the destination is executed in real time. Figure 5 3 B shows the result of the position shift change t o destination. From the start of the first search algorithm to the generation of the first route, the speed of 30 1 UT was taken, and the arrival time to the last destination through the new route search through the moving and iterative algorithm call was 1 6 9 6 UT A total of 94 search algorithms were called, and the search time of the average search algorithm was 4 1170 UT Figure 5 3 C and Figure 5 3 D are graphs showing changes in velocity and heading per UT Table 5 1. Results of Scenario I Type Description Final arrival time 1696 UT First path find completion time 30 1 UT Number of search algorithm calls 94 Average path finding time 4.1170 UT

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57 A B C D Figure 5 3 Results for scenario I A) Execution scene B) P osition changes C) V elocity changes D) H eading changes Scenario I I : Path P lanning without M oving O bstacle with O ff line P ath P lanning The following values are declared for the simulation. Location of the car in the map : Maximum speed of car: Maximum acceleration of the car: Maximum angular velocity of the car: Position of destination in the map :

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58 Figure 5 4 A shows the result of a series of processes that the route searches for the car to the destination using the conventional off line path planning algorithm and then the car arrives at the destination along the route when the route search is completed. Figure 5 4 B shows the result of the position shift change to de stination. It took 2776 UT from the start of the off line search algorithm to the path generation A B C D Figure 5 4 Results for scenario I I A) Execution scene B) P osition change C) V elocity change D) H eading change

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59 After receiving this optimal route, the car moved to its destination and its arrival time was 5750 UT Figure 5 4 C and Figure 5 4 D are graphs showing changes in speed and heading per unit time Table 5 2 Results of Scenario II Type Description Final arrival time 3971 UT First path find completion time 2776 UT Number of search algorithm calls 1 Average path finding time 2776 UT Comparing the two results of Scenario I, II, the algorithm developed in this research reached the destination at about 40% faster than the existing offline path planning algorithm. Also, the generation time of first optimal route of this algorithm was abou t 9.22 times faster than that of the conventional algorithm. Therefore, we can see that the algorithm of this research is more efficient than the off line pass planning algorithm to reach the destination under obstacle free environment. Path P lanning with M oving O bstacles In the second simulation, we will look at the implementation of our research based path search algorithm i n an environment with obstacles Scenario II I: Path P lanning behind a M oving O bject The following values are declared for the simulation. First, the attributes for autonomous vehicle are determined as follow: Location of the car in the map : Maximum speed of car: Maximum acceleration of the car: Maximum angular velocity of the car: Position of destination in the map : Next, the attributes for moving obstacle are determined as follow: Range of initial position in the map :

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60 Direction of heading: North Range of speed of velocity of the moving obstacle: Maximum acceleration of the moving obstacle: Maximum angular velocity of the moving obstacle: Figure 5 5 A is the first step in this scenario. A m oving obstacle moving from south to north and it located abov e an autonomous vehicle The autonomous vehicle leaves from the south of the intersection, enters the intersection, and moves towards the north lane. In Figure 5 5 B, the blue solid line represents the vehicle's obstacle search line, and area of risk gener ated by the moving obstacle can be identified A B Figure 5 5 Initial state for scenario II I A) World map B) Moving object map Figure 5 6 A shows the application of a dynamic window path finding algorithm to find the optimal path to the destination in local search area As Figure 5 6 B, since there is no currently detected obstacle in front of the vehicle, the autonomous vehicle begins to move along the searched path

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61 A B Figure 5 6 Be fore detect ing the obstacle A) World B) Moving object map In Figure 5 7 A and Figure 5 7 B, the autonomous vehicle detected an obstacle ahead when it moving to its destination through continuous route search. When examining the maximum value at the obstacle search stage, the value is the highest risk cost. Then the autonomous vehicle decelerates rapidly to avoid collision with the preceding obstacle. In Figure 5 8 A and Figure 5 8 B, the autonomous vehicle continues to move behind the o bstacles in front. Since this intersection is currently assumed to be single lane, autonomous vehicle cannot attempt to overtake the obstacle, because the risk area created by the obstacle occupies most of the lane. Therefor e the autonomous vehicle advance s to the speed of the preceding obstacle until it reaches the destination.

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62 A B Figure 5 7 After detecting the obstacle A) World map B) Moving object map A B Figure 5 8 Following the obstacle A) World map B) Moving object map Table 5 3 Results of Scenario II I Type Description Final arrival time 2405 UT First path find completion time 30 1 UT Number of search algorithm calls 40 Average path finding time 3 9 UT

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63 A B C Figure 5 9 Results for scenario II I A) Position change B) Velocity change C) Heading change According to Figure 5 9 B, the autonomous vehicle has detected an obstacle ahead of it at approximately 689UT. Since the real of the moving obstacle has the highest risk level, the behavior corresponding to the highest risk level according to the collision detection algorithm of the autonomous vehicle is immediate deceleration. When the vehicle is immediately decelerated, the distance between the preceding obstacle and the autonomous vehicle becomes farther away, so the autonomous vehicle starts to accelera te again. Because of this, the graph of velocity change shows

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64 that the speed of autonomous vehicle is constantly fluctuating up and down. In future research, the collision detection and response algorithm should be improved so that the autonomous vehicle c an keep up with the speed of the moving obstacle without any rapid rate of acceleration or deceleration Scenario I V: Path P lanning with T hree C rossing M oving O bjects The following values are declared for the simulation. First, attributes for autonomou s vehicle are determined as follow: Location of the car in the map : Maximum speed of car: Maximum acceleration of the car: Maximum angular velocity of the car: Position of destination in the map : Next, attributes for first moving obstacle are determined as follow: Initial position in the map : Direction of heading: East S peed of velocity of the moving obstacle: Maximum acceleration of the moving obstacle: Maximum angular velocity of the moving obstacle: Next, attributes for second moving obstacle are determined as follow: Initial position in the map : Direction of heading: West S peed of velocity of the moving obstacle: Maximum acceleration of the moving obstacle: Maximum angular velocity of the moving obstacle: Finally, attributes for third moving obstacle are determined as follow: Initial position in the map : Di rection of heading: East Speed of velocity of the moving obstacle: Maximum acceleration of the moving obstacle: Maximum angular velocity of the moving obstacle:

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65 Figure 5 10 A is the first step in this scenario. Moving obstacles are three in total, two vehicle s moving from west to east and one vehicle moving from east to west. The autonomous vehicle leaves from the south of the intersection, enters the intersection, and moves towards the west lane. In Figure 5 10 B, the blue solid line represents the vehicle's obstacle search line, and two different areas of risk generated by moving obstacles can be identified A B Figure 5 10 Initial state fo r scenario I V A) World map B) Moving object map Figure 5 11 A shows the application of a dynamic window path finding algorithm to find the optimal path to the destination in local search area This car is currently in suspension. This is because the obstacle detection line of the autonomous vehicle is included in the risk grid generated by the moving obstacle according to the moving obstacle map of Figure 5 11 B. Since the maximum value of thi s obstacle detection line indicates a high risk, the car will decelerate Figure 5 12 A and Figure 5 12 B show a n autonomous vehicle finally moving out of the intersection and moving toward its destination When examining the maximum

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66 value at the obstacle search stage, the autonomous vehicle starts accelerating along the original path because there is no moving obstacle A B Figure 5 11 Before entering the intersection A) World B) Moving object map A B Figure 5 12 After exiting the intersection A) World map B) Moving object map Table 5 4 Results of Scenario I V Type Description Final arrival time 13 41 UT First path find completion time 559 UT Number of search algorithm calls 34 Average path finding time 16 94 UT

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67 A B C Figure 5 1 3 Results for scenario I V A) Position change B) Velocity change C) Heading change According to Figure 5 13 B, the autonomous vehicle stopped at about 715 UT UT the autonomous vehicle continues to follow the path, and it started left turn from about 925 UT according to Figure 5 13 A and Figure 5 13 C. Scenario V: Path P lanning C o ming in F ront The following values are declared for the simulation. First, attributes for autonomous vehicle are determined as follow:

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68 Location of the car in the map : Maximum speed of car: Maximum acceleration of the car: Maximum angular velocity of the car: Position of destination in the map : Next, attributes for moving obstacle are determined as follow: Range of initial position in the map : Direction of heading: East Range of speed of velocity of the moving obstacle: Maximum acceleration of the moving obstacle: Maximum angular velocity of the moving obstacle: Figure 5 14 A is the initial state in this scenario. A moving o bstacle advancing from north and it will take left turn in the intersection to go east The autonomous vehicle leaves from the south of the intersection, enters the intersection, and moves towards the north lane. In Figure 5 14 B, the blue solid line repre sents the vehicle's obstacle search line, and area of risk generated by the moving obstacle can be identified A B Figure 5 14 Initial state for scenario V A) World map B) Moving object map Figure 5 15 A shows the application of a dynamic window path finding algorithm to find the optimal path to the destination in local search area As Figure 5 15 B, since

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69 there is no currently detected obstacle in front of the vehicle, the autonomous vehicle begins to move along the searched path A B Figure 5 15 Before detecting the obstacle A) World B) Moving object map In Figure 5 16 A and Figure 5 16 B, the autonomous vehicle detected an obstacle ahead when it moving to its destination through continuous route search. When examining the maximum value at the obstacle search stage, the value is start from low risk cost. The autonomous vehicle then slows down its velocity. Because the obstacle is turned left and move to eastbound, examined maximum risk values increasing. So, depends on the risk value, the autonomous vehicle decelerates rapidly to avoid collision with the preceding obstacle. In Figure 5 17 A and Figure 5 17 B, the autonomous vehicle continues to mo ve behind the obstacles Since there is no obstacle to observe in front, there is no risk value ahead the vehicle Therefore, the autonomous vehicle start to accelerate along the path until it rea ches the destination.

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70 A B Figure 5 16 After detecting the obstacle A) World map B) Moving object map A B Figure 5 17 Following the path behind obstacle A) World map B) Moving object map Table 5 5 Results of Scenario V Type Description Final arrival time 2405 UT First path find completion time 301 UT Number of search algorithm calls 40 Average path finding time 3.9 UT According to Figure 5 18 B, the autonomous vehicle stopped at about 769 UT, because the obstacle left from the north to the east in the intersection blocked the

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71 obstacles complet ely exiting the intersection, begins to move north towards the path again. A B C Figure 5 18 Results for scenario V A) Position change B) Velocity change C) Heading change

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72 CHAPTER 6 CONCLUSION AND FUTURE WORK Conclusion In this research, an algorithm was developed for an autonomous vehicle in an intersection with dynamic obstacle s to find an optimal path without obstacles to reach the destination. P revious research was examined that calculate d an optimal path t o reach the destination. Also, the methods of modeling the real world for applying the path search algorithm were analyzed. Finally, some methods for dynamic obstacle avoidance were also examined. To solve the previous problems, a path planning algorithm w as devised along with a path tracking algorithm for the autonomous vehicle. First, the path planning algorithm repeatedly searches the optimal path within a limited range to reach the destination. This limited range is based on sensors mounted on the vehic le, and the destination has been converted to a temporary destination so that a partial optimization path can be searched within this range. The optimal path to reach the converted destination is calculated by the A* algorithm based on the cost within the limited search range. The c ost for path planning could be divided into a static environmental factor and dynamic environmental factor. The static environmental factor was calculated based on the observed terrain environment around the vehicle, and the dyna mic environmental factor was calculated according to the cost based on different risk values within a risk area generated by the expected path of the observed dynamic obstacle. This area of risk varied with the velocity and position of the dynamic obstacle

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73 Each of the optimal routes calculated based on the cost map is then handed over to the autonomous vehicle, and the vehicle carries out various process to apply the path tracking algorithm along the route and to reach the destination. T he behavior of an a utonomous vehicle to reach the destination was analyzed starting with selecting the mission waypoint that the car will compare to the current location and given route to reach the goal. H ow autonomous vehicles can take optimal behaviors to avoid collision s was also examined depending on the observed moving obstacles and the range of risk they generate. Through simulation, the performance difference between the path planning algorithm designed in this study and the conventional offline path planning algori thm was analyzed As a result, it is confirmed that the path planning algorithm of this study guarantees the autonomous vehicle to move to the optimal route to the destination in a shorter time. It is also confirmed through various scenarios that this algo rithm moves along the optimal path to the destination in the direction of minimizing collision with obstacles, in accordance with the environment of various dynamically varying intersections. Future Work In this research, the focus was on an algorithm for the path search of an autonomous vehicle moving on a 2D plane. In addition to modeling the world in a 2D plane, one can consider a 3D model that considers the elevation of the terrain and obstacles. In fact, the height of the terrain can be measured throug h sensors. Using the data, the estimation of movement and behaviors of autonomous vehicle and obstacles will change. Then a more complex algorithm of optimal path search should be considered.

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74 Also, in this simulation, the behavior of an automobile or an o bstacle that responds to a signal of a traffic signal or an algorithm for adjusting a virtual traffic signal is not realized. In the future, when an autonomous vehicle becomes available, the role of the smart signal in the intersection to control the autonomous vehicle will also become important. Therefore, it will be necessary to study algorithms that can organ ically combine the control of this smart signal and the behavior of autonomous vehicles. The model of the intersection used in this study was a simple intersection of transverse and longitudinal two lanes crossing each other. In real life, however, there m ay be various roundabouts of three lane or four lane crossing intersections, as well as T shaped crossroads, as well as complex intersections found on highways or rotary type intersection. A more sophisticated path planning algorithm is needed for autonomo us vehicles in accordance with these various intersection type. Finally, a program called ROS has been spreading around the world for autonomous robots ROS is a cutting edge open source software robot control program of the current generation, and many co mpanies, organizations and schools are utilizing this project. There are many researches on autonomous mobile robots through ROS. Future work w ill simulate multiple obstacle modules through local world and each obstacle will create an autonomous vehicle, mechanical design, it is desirable that objects can implement motion predicted by implementing a virtual sensor or the like.

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75 APPENDIX A STRUCTURE OF CODE The Java 1.8 version was used to simulate this algorithm because the programming language is multi p latform capable, program coding is simple, and the GUI for input and output is easy to handle This simulation can be divided into four packages simulation (root), Aster entity and util are described in Table 1 below. Each package and its contents are recorded in the following table Table A 1 Packages contained in the implementation of the algorithm Package Description simulation Contains main file & GUI simulation.AStar Contains A* algorithm and fused map simulation.entity Contains entity, extended entity simulation.util Contains utility files Table A 2 Classes contained in package simulation Class Description Main Contains main program moMap Contains costs of moving obstacle in map WorldPanel Implementing GUI, input event (mouse, keyboard) Table A 3 Classes contained in package simulation.AStar Class Description GridCell Define grid cell HuristicAStar Define improved A* algorithm for path finding Map Fusing map Table A 4 Classes contained in package simulation.entity Class Description Entity Define original object Obstacle Inherit Entity, to define moving obstacle Player Inherit Entity, to define autonomous vehicle

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76 Table A 5 Classes contained in package simulation.util Class Description Clock Define unit time for simulation Vector2 Contains vector calculation of mathematic methods Task Define iterative running for path planning algorithm

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77 APPENDIX B SOURCE CODE Player. java /** Initializes a new Player instance. */ public Player() { this rotation = DEFAULT_ROTATION ; this thrustPressed = false ; this thrustBackPressed = false ; this rotateLeftPressed = false ; this rotateRightPressed = false ; this animationFrame = 0; this xCoordinationDestine = 0.0; // goal initialize this yCoordinationDestine = 0.0; // goal initialize this missonState = true ; // check mission completed this destRotation = DEFAULT_ROTATION ; tempX = new int [50000]; tempY = new int [50000]; check = new boolean [50000]; letstart = false ; missionX = new int [50000]; missionY = new int [50000]; missionStep = 0; this whois = 33; finderPlayer = new HuristicAStar(); try { out = new BufferedWriter( new FileWriter( "out.txt" )); } catch (IOException e ) { System. err .println( e ); e .printStackTrace(); } } public void update(Main main ) { /** 020617 Check collision */ int mouseX = ( int ) this .getPosition(). x ; int mouseY = ( int ) this .getPosition(). y ; double longX = Math.cos( rotation ); double longY = Math.sin( rotation ); max = 0; for ( int i =0; i <20; i ++) { realtoX [ i ]= mouseX +( int )( longX ( i +1)); if ( realtoX [ i ]<0){ realtoX [ i ]=0;} else if ( realtoX [ i ]>599){ realtoX [ i ]=599;}

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78 realtoY [ i ]= mouseY 1+( int )( longY ( i +1)); if ( realtoY [ i ]<0){ realtoY [ i ]=0;} else if ( realtoY [ i ]>599){ realtoY [ i ]=599;} main map gridCell [ realtoX [ i ]][ realtoY [ i ]]. isSensor = true ; } for ( int i = 0; i <20; i ++) { max = Math.max( max ( int ) main map gridCell [ realtoX [ i ]][ realtoY [ i ]]. type ); } /** Select behavior of vehicle */ if ( this missonState == false && this autonomousMode ==1) // Mission point Set + Autonomous Mode = 1 { this .stageOne( main ); } else if ( this missonState == false && this autonomousMode ==2 && this letstart ) { this .stageTwo( main ); // path following algorithm } else { } } } public void fnAcceleration(Vector2 velocity double rotation double rate double coefficient ) { velocity .add( new Vector2( rotation ).scale( rate coefficient )); if ( velocity .getLengthSquared() >= MAX_VELOCITY_MAGNITUDE MAX_VELOCITY_MAGNITUDE ) { velocity .normalize().scale( MAX_VELOCITY_MAGNITUDE ); } } public void fnDeceleration(Vector2 velocity double rotation double rate double coefficient double minVelocity ) { //velocity.add(new Vector2(rotation).scale(THRUST_MAGNITUDE)); if ( velocity .getLengthSquared() < minVelocity minVelocity ){} else { velocity .add( new Vector2( rotation ).scale( rate coefficient )); if ( velocity .getLengthSquared() == 0.0){ setVelocity( new Vector2( rotation ).scale(getNowVelocity())); } } } public void stageOne (Main game ){ // Calculate to go heading double dx = this .getPosition(). x this .getXCoordinationDestine(); double dy = this .getPosition(). y this .getYCoordinationDestine(); double rad = Math.abs(Math.toDegrees(Math.atan2( dx dy ) + 1.57079632)); double temp_rotation = Math.abs(Math.toDegrees( rotation )); for ( int i = 0; i < game entities .size(); i ++) { // 0, 1, 2, 3 Entity a = game entities .get( i ); for ( int j = i +1; j <= game staticentities .size() + 1; j ++) { Entity b = game entities .get( j ); if ( a == game player ) {

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79 double dxx = this .getPosition(). x b .getPosition(). x ; double dyy = this .getPosition(). y b .getPosition(). y ; double radd = Math.toDegrees(Math.atan2( dyy dxx )); if ( radd >0){ radd = radd 360;} if ( rad != temp_rotation ) { rotate(( rad temp_rotation )>0 ? ROTATION_SPEED : ROTATION_SPEED ); setVelocity( new Vector2( rotation ).scale(Math.sqrt( velocity .getLengthSquared ()))); } } } } // Calculate toGO destance this destLength = Math.sqrt( dx dx + dy dy ); // destLength : changing distance between car and destination // gotoDestLength : fixed goTO distance if ( gotoDestLength == 0.0){ gotoDestLength = destLength ; } else {} if ( destLength > 0 && ( destLength / gotoDestLength < 0.3) ){ if ( velocity .getLengthSquared() == 0.0){ setVelocity( new Vector2( rotation ).scale(getNowVelocity())); } } else if ( destLength > 0 ){ // accelerate this .fnAcceleration( velocity rotation THRUST_MAGNITUDE 1.0); if ( velocity .getLengthSquared() >= MAX_VELOCITY_MAGNITUDE MAX_VELOCITY_MAGNITUDE ) { velocity .normalize().scale( MAX_VELOCITY_MAGNITUDE ); } } } public void stageTwo(Main game ){ //checker = 1; // for ( int i = 0; i < this missionStep ; i ++){ // System.out.println(i+" th mission waypoint : "+this.missionX[i]+", y : "+this.missionY[i] + ", check : + this.check[i]); } //System.out.println(checker + "(=checker) !( x:"+missionX[checker]+", y : "+missionY[checker]+")" + ", checker = "+check[checker]); //System.out.println(checker + "(=checker) !( x:"+this.getPosition().x+", y : + this.getPosition().y+")"); // calculate distance between waypoint and curre nt position /** get mission waypoint data */ double dx = this missionX [ checker ] this .getPosition(). x ; double dy = this missionY [ checker ] this .getPosition(). y ; if ( checker ==1){} /** Process to select next mission waypoint */ this destLength = Math.sqrt( dx dx + dy dy ); // System.out.println("destLEngth = "+destLength); double rad = Math.toDegrees(Math.atan2( dy dx )) ;

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80 if ( rad >0){ rad = rad 360; } // Calculate toGO heading double current_rotation = Math.toDegrees( rotation ); /** compare distance between waypoint and current position to goal */ double CtoDx = this missionX [ missionStep 1] this .getPosition(). x ; double CtoDy = this missionY [ missionStep 1] this .getPosition(). y ; double ChtoDx = this missionX [ missionStep 1] this missionX [ checker ]; double ChtoDy = this missionY [ missionStep 1] this missionY [ checker ]; double CtoDLength = Math.sqrt(( CtoDx CtoDx )+ ( CtoDy CtoDy )); double ChtoDLength = Math.sqrt(( ChtoDx ChtoDx )+ ( ChtoDy ChtoDy )); //System.out.println("CtoDLength : "+CtoDLength+", "+"CHtoDLength : "+ChtoDLength); if (( CtoDLength < ChtoDLength ) && this check [ checker ] == false ) { checker ++; } else { if ( this check [ checker ] == true ) { if ( this currentStage == false ){ this currentStage = true ;System. out .println( currentStage );} if ( destLength > 3.0) { if ( rad != current_rotation ) { rotate(( rad current_rotation )<0 ? ROTATION_SPEED : ROTATION_SPEED ); setVelocity( new Vector2( rotation ).scale(Math.sqrt( velocity .getLengthSquared()))); } velocity .add( new Vector2( rotation ).scale( THRUST_MAGNITUDE )); if ( velocity .getLengthSquared() >= MAX_VELOCITY_MAGNITUDE MAX_VELOCITY_MAGNITUDE ) { velocity .normalize().scale( MAX_VELOCITY_MAGNITUDE ); } } else { setVelocity( new Vector2( rotation ).scale(0)); } if ( this max == 16){ velocity .add( new Vector2( rotation ).scale( THRUST_MAGNITUDE *5)); setVelocity( new Vector2( rotation ).scale(0)); } } else // checker is false { if ( this currentStage == true ){ this currentStage = false ; } if ( destLength < 5.0) { checker ++; } else { if ( rad != current_rotation ) { rotate(( rad current_rotation )<0 ? ROTATION_SPEED : ROTATION_SPEED ); setVelocity( new Vector2( rotation ).scale(Math.sqrt( velocity .getLengthSquared()))); } if (Math.abs( rad current_rotation )< ROTATION_SPEED *30) { if ( this max < 10) // no moving obstacle

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81 { velocity .add( new Vector2( rotation ).scale( THRUST_MAGNITUDE )); if ( velocity .getLengthSquared() >= MAX_VELOCITY_MAGNITUDE MAX_VELOCITY_MAGNITUDE ) { velocity .normalize().scale( MAX_VELOCITY_MAGNITUDE ); } } else if ( this max < 13) // low risk { velocity .add( new Vector2( rotation ).scale(0)); if ( velocity .getLengthSquared() >= MAX_VELOCITY_MAGNITUDE MAX_VELOCITY_MAGNITUDE /4) { velocity .normalize().scale( MAX_VELOCITY_MAGNITUDE /2); } } else if ( this max < 16) // medium risk { velocity .add( new Vector2( rotation ).scale( THRUST_MAGNITUDE )); if ( velocity .getLengthSquared() >= MAX_VELOCITY_MAGNITUDE MAX_VELOCITY_MAGNITUDE /9) { velocity .normalize().scale( MAX_VELOCITY_MAGNITUDE /3); } } else if ( this max == 16){ // high risk velocity .add( new Vector2( rotation ).scale( THRUST_MAGNITUDE *5)); setVelocity( new Vector2( rotation ).scale(0)); } } } } } } public double getCurrentVelocity(){ double currentVelocity ; double currentVX = this .getVelocity(). x ; double currentVY = this .getVelocity(). y ; currentVelocity = Math.sqrt( currentVX currentVX + currentVY currentVY ); return currentVelocity ; } Obstacle. java public Obstacle(Random random int positionX int positionY double velocity int direction int destination ) { // Position this position = new Vector2( positionX positionY ); this direction = direction ; this destination = destination ; // Velocity this rotation = Math. PI / 2.0 direction ; INIT_VELOCITY = velocity ; this velocity = new Vector2( rotation ).scale( INIT_VELOCITY ); // 121916 this whois = 56; passSignalN = passSignalW = passSignalE = passSignalS = false ; // Hasn't yet pass any signal } public void update(Main main ) { // Distance between signals and obstacle this distToSignalS = Math.sqrt(( this .getPosition(). x 325.0)*( this .getPosition(). x 325.0)+ ( this .getPosition(). y 350.0)*( this .getPosition(). y 350.0)); this distToSignalE = Math.sqrt(( this .getPosition(). x 350.0)*( this .getPosition(). x 350.0)+ ( this .getPosition(). y 275.0)*( this .getPosition(). y 275.0)); this distToSignalN = Math.sqrt(( this .getPosition(). x 275.0)*( this .getPosition(). x 275.0)+ ( this .getPosition(). y 250.0)*( this .getPosition(). y 250.0));

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82 this distToSignalW = Math.sqrt(( this .getPosition(). x 250.0)*( this .getPosition(). x 250.0)+ ( th is .getPosition(). y 325.0)*( this .getPosition(). y 325.0)); /** 192016 Check Obstacle passing signals */ checkPassSignalSremain(300,350,350,350, this position ); checkPassSignalEremain(350,350,250,300, this position ); checkPassSignalNremain(250,300,250,250, this position ); checkPassSignalWremain(250,250,300,350, this position ); checkPassSignalSout(250,300,350,350, this position ); checkPassSignalEout(350,350,300,350, this position ); checkPassSignalNout(300,350,250,250, this position ); checkPassSignalWout(250,250,250,300, this position ); /** 01.10.17 Tue set cost of moving obstacle to map */ Vector2 AsPosition = this .getPosition(); int intAsPositionX = ( int ) AsPosition x ; int intAsPositionY = ( int ) AsPosition y ; /* Set range of risk areas */ int fourtyRe = ( int )(80* normVelocity / MAX_VELOCITY ); int twentyRE = ( int )(30* normVelocity / MAX_VELOCITY ); int tenRe = ( int )(20* normVelocity / MAX_VELOCITY ); int xLRedge = intAsPositionX fourtyRe ; if ( xLRedge <1){ xLRedge =0;} int xRRedge = intAsPositionX + fourtyRe ; if ( xRRedge >598){ xRRedge =598;} int yURedge = intAsPositionY fourtyRe ; if ( yURedge <1){ yURedge =0;} int yDRedge = intAsPositionY + fourtyRe ; if ( yDRedge >598){ yDRedge =598;} int xLRedgeT = intAsPositionX twentyRE ; if ( xLRedgeT <1){ xLRedgeT =0;} int xRRedgeT = intAsPositionX + twentyRE ; if ( xRRedgeT >598){ xRRedgeT =598;} int yURedgeT = intAsPositionY twentyRE ; if ( yURedgeT <1){ yURedgeT =0;} int yDRedgeT = intAsPositionY + twentyRE ; if ( yDRedgeT >598){ yDRedgeT =598;} int xLRedgeTT = intAsPositionX tenRe ; if ( xLRedgeTT <1){ xLRedgeTT =0;} int xRRedgeTT = intAsPositionX + tenRe ;

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83 if ( xRRedgeTT >598){ xRRedgeTT =598;} int yURedgeTT = intAsPositionY tenRe ; if ( yURedgeTT <1){ yURedgeTT =0;} int yDRedgeTT = intAsPositionY + tenRe ; if ( yDRedgeTT >598){ yDRedgeTT =598;} double Nrotation = rotation (Math. PI /2); /* Low risk */ int velX2 = 20+( int )( this CURRENT_VELOCITY / MAX_VELOCITY 20); int velY2 = 20+( int )( this CURRENT_VELOCITY / MAX_VELOCITY 40); int [] testX2 = new int [ velX2 ]; int [] testY2 = new int [ velY2 ]; int [] TranTestX2 = new int [ velX2 ]; int [] TranTestY2 = new int [ velY2 ]; for ( int i =0; i < velX2 ; i ++){ for ( int j =0; j < velY2 ; j ++){ testX2 [ i ]= intAsPositionX ( velX2 /2)+ i ; testY2 [ j ]= intAsPositionY 10+ j ; } } for ( int i =0; i < velX2 ; i ++){ for ( int j =0; j < velY2 ; j ++){ TranTestX2 [ i ] = ( int )((( testX2 [ i ] intAsPositionX )*Math.cos( Nrotation )) (( testY2 [ j ] intAsPositionY )*Math.sin( Nrotation ))+ intAsPositionX ); TranTestY2 [ j ] = ( int )((( testX2 [ i ] intAsPositionX )*Math.sin( Nrotation ))+(( testY2 [ j ] intAsPositionY )*Math.cos( Nrotation ))+ intAsPositionY ); if ( TranTestX2 [ i ]<0){ TranTestX2 [ i ]=0;} else if ( TranTestX2 [ i ]>599){ TranTestX2 [ i ]=599;} if ( TranTestY2 [ j ]<0){ TranTestY2 [ j ]=0;} else if ( TranTestY2 [ j ]>599){ TranTestY2 [ j ]=599;} main map gridCell [ TranTestX2 [ i ]][ TranTestY2 [ j ]]. type = 10; // game.map.gridCell[TranTestX2[i]][TranTestY2[j]].cost = 10; if ( TranTestX2 [ i ]==( int ) main .getPlayer().getPosition(). x && TranTestY2 [ i ]==( int ) main .getPlayer().getPosition(). y ) { max = 16; } } } /* medium risk */ int velX1 = 20+( int )( this CURRENT_VELOCITY / MAX_VELOCITY 10); int velY1 = 20+( int )( this CURRENT_VELOCITY / MAX_VELOCITY 20); int [] testX1 = new int [ velX1 ]; int [] testY1 = new int [ velY1 ]; int [] TranTestX1 = new int [ velX1 ]; int [] TranTestY1 = new int [ velY1 ]; for ( int i =0; i < velX1 ; i ++){ for ( int j =0; j < velY1 ; j ++){ testX1 [ i ]= intAsPositionX ( velX1 /2)+ i ; testY1 [ j ]= intAsPositionY 10+ j ; }

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84 } for ( int i =0; i < velX1 ; i ++){ for ( int j =0; j < velY1 ; j ++){ TranTestX1 [ i ] = ( int )((( testX1 [ i ] intAsPositionX )*Math.cos( Nrotation )) (( testY1 [ j ] intAsPositionY )*Math.sin( Nrotation ))+ intAsPositionX ); TranTestY1 [ j ] = ( int )((( testX1 [ i ] intAsPositionX )*Math.sin( Nrotation ))+(( testY1 [ j ] intAsPositionY )*Math.cos( Nrotation ))+ intAsPositionY ); if ( TranTestX1 [ i ]<0){ TranTestX1 [ i ]=0;} else if ( TranTestX1 [ i ]>599){ TranTestX1 [ i ]=599;} if ( TranTestY1 [ j ]<0){ TranTestY1 [ j ]=0;} else if ( TranTestY1 [ j ]>599){ TranTestY1 [ j ]=599;} main map gridCell [ TranTestX1 [ i ]][ TranTestY1 [ j ]]. type = 13; // game.map.gridCell[TranTestX1[i]][TranTestY1[j]].cost = 13; } } /* high risk */ int [] testX = new int [20]; int [] testY = new int [20]; int [] TranTestX = new int [20]; int [] TranTestY = new int [20]; for ( int i =0; i <20; i ++){ for ( int j =0; j <20; j ++){ testX [ i ]= intAsPositionX 10+ i ; testY [ j ]= intAsPositionY 10+ j ; } } for ( int i =0; i <20; i ++){ for ( int j =0; j <20; j ++){ TranTestX [ i ] = ( int )((( testX [ i ] intAsPositionX )*Math.cos( Nrotation )) (( testY [ j ] intAsPositionY )*Math.sin( Nrotation ))+ intAsPositionX ); TranTestY [ j ] = ( int )((( testX [ i ] intAsPositionX )*Math.sin( Nrotation ))+(( testY [ j ] intAsPositionY )*Math.cos( Nrotation ))+ intAsPositionY ); if ( TranTestX [ i ]<0){ TranTestX [ i ]=0;} else if ( TranTestX [ i ]>599){ TranTestX [ i ]=599;} if ( TranTestY [ j ]<0){ TranTestY [ j ]=0;} else if ( TranTestY [ j ]>599){ TranTestY [ j ]=599;} main map gridCell [ TranTestX [ i ]][ TranTestY [ j ]]. type = 16; // game.map.gridCell[TranTestX[i]][TranTestY[j]].cost = 16; } } CURRENT_VELOCITY = Math.sqrt( this velocity .getLengthSquared()); /** Adjust heading to center of lane */ adjustLaneCenter( this direction this destination ( int ) this position x ( int ) this position y this rotation ); if ( max == 16) { setVelocity( new Vector2( rotation ).scale(0)); } }

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85 HuristicAStar. java public GridCell[] findPath(Map map ) { minCost = 10; map game getPlayer (). letstartTrigger = true ; for ( int i = map game .getPlayer(). SlidingWindowXL ; i <= map game .getPlayer(). SlidingWindowXR ; i ++){ for ( int j = map game .getPlayer(). SlidingWindowYU ; j <= map game .getPlayer(). SlidingWindowYD ; j ++){ minCost = Math.min( map gridCell [ i ][ j ].getCost(), minCost ); } } this map = map ; GridCell.reset(); pass = 0; count = 0; tempX = new int [150000]; tempY = new int [150000]; check = new boolean [150000]; missionStep = 0; missionDirection = 0; edge = new Vector(); done = new Vector(); state = NOT_FOUND ; if (GridCell.getStartCell() == null ){ return null ; } if (GridCell.getFinishCell() == null ){ return null ; } edge .addElement(GridCell.getStartCell()); while ( state == NOT_FOUND && pass < maxSteps ){ pass ++; state = step(); //step() : finding function } if ( state == FOUND ){ main .getPlayer(). passFound = true ; setPath( map ); } else if ( state == NO_PATH ){ } // pass(+1) exceed max_pass else { } return null ; } public void setPath(Map map ){ // Present completed path /** 012317 Initialzied variables */ main .getPlayer(). checker = 0; try { main .getPlayer(). out .write( "path found!!" ); main .getPlayer(). out .newLine(); } catch (IOException e ) { e .printStackTrace();

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86 } GridCell.setShowPath( true ); boolean finished = false ; GridCell next ; GridCell now = GridCell.getFinishCell(); GridCell stop = GridCell.getStartCell(); while (! finished ){ main world pass = this count ; main costMap pass = this count ; main .getPlayer(). pass = this count ; // 112916 main world trigger = 1; tempX [ count ] = now .getPosition(). x ; tempY [ count ] = now .getPosition(). y ; if ( count ==0){ check [ count ]= true ;} else { check [ count ] = false ;} next = map .getLowestAdjacent( now ); now = next ; now .setPartOfPath( true ); if ( now == stop ){ finished = true ; } count ++; } for ( int i = count ; i >0; i -){ /** AStar > WorldPanel */ main world tempX [ this count i ]= this tempX [ i ]; main world tempY [ this count i ]= this tempY [ i ]; main world check [ this count i ] = this check [ i ]; main costMap tempX [ this count i ]= this tempX [ i ]; main costMap tempY [ this count i ]= this tempY [ i ]; main costMap check [ this count i ] = this check [ i ]; } main .getPlayer(). letstart = true ; for ( int i = count ; i >=0; i -){ if ( i == count 1){ main .getPlayer(). missionX [ this missionStep ]= this tempX [ count 1]; main .getPlayer(). missionY [ this missionStep ]= this tempY [ count 1]; main .getPlayer(). check [ this missionStep ]= this check [ count 1]; this missionStep ++; main .getPlayer(). missionStep = this missionStep ; } else if ( i == count 2){ main .getPlayer(). missionX [ this missionStep ]= this tempX [ count 2]; main .getPlayer(). missionY [ this missionStep ]= this tempY [ count 2]; main .getPlayer(). check [ this missionStep ]= this check [ count 2]; this missionStep ++; main .getPlayer(). missionStep = this missionStep ;

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87 if ( this tempX [ count 1]== this tempX [ count 2]){ missionDirection = 0 else { missionDirection = 1 } else { int checkDirection ; if ( this tempX [ i ]== this tempX [ i +1]){ checkDirection = 0; else { checkDirection = 1; } if ( checkDirection == missionDirection ){} else { main .getPlayer(). missionX [ this missionStep ]= this tempX [ i ]; main .getPlayer(). missionY [ this missionStep ]= this tempY [ i ]; main .getPlayer(). check [ this missionStep ]= this check [ i ]; missionDirection = checkDirection ; this missionStep ++; main .getPlayer(). missionStep = this missionStep ; } if ( i ==0){ main .getPlayer(). missionX [ this missionStep ]= this tempX [0]; main .getPlayer(). missionY [ this missionStep ]= this tempY [0]; main .getPlayer(). check [ this missionStep ]= this check [0]; this missionStep ++; main .getPlayer(). missionStep = this missionStep ; } } } } Map.java int ROAD = 1; int FRINGE = 4; int GRASS = 99; public void mapInitialize(){ for ( int i =0; i < w ; i ++){ for ( int j =0; j < h ; j ++){ gridCell [ i ][ j ]. cost = GRASS ; // fringe } } for ( int i = 0; i < w ; i ++){ for ( int j =250; j <350; j ++){ gridCell [ i ][ j ]. cost = FRINGE ; // road horizontal } } for ( int i =250; i <350; i ++){ for ( int j =0; j < h ; j ++){ gridCell [ i ][ j ]. cost = FRINGE ; // road vertical } } for ( int i =350; i <600; i ++){ for ( int j =550; j < h ; j ++){ gridCell [ i ][ j ]. cost = FRINGE ; }

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88 } for ( int i = 270; i <280; i ++){ for ( int j =0; j < h ; j ++){ gridCell [ i ][ j ]. cost = ROAD ; // road horizontal } } for ( int i =320; i <330; i ++){ for ( int j =0; j < h ; j ++){ gridCell [ i ][ j ]. cost = ROAD ; // road vertical } } for ( int i = 270; i <280; i ++){ for ( int j =0; j < h ; j ++){ gridCell [ j ][ i ]. cost = ROAD ; // road horizontal } } for ( int i =320; i <330; i ++){ for ( int j =0; j < h ; j ++){ gridCell [ j ][ i ]. cost = ROAD ; // road vertical } } for ( int i =280; i <320; i ++){ for ( int j =280; j <320; j ++){ gridCell [ j ][ i ]. cost = ROAD ; // road vertical } } for ( int i =0; i <260; i ++){ for ( int j =297; j <303; j ++){ gridCell [ i ][ j ]. cost = GRASS ; // west } } for ( int i =340; i <600; i ++){ for ( int j =297; j <303; j ++){ gridCell [ i ][ j ]. cost = GRASS ; // east } } for ( int i =297; i <303; i ++){ for ( int j =0; j <260; j ++){ gridCell [ i ][ j ]. cost = GRASS ; // north } } for ( int i =297; i <303; i ++){ for ( int j =340; j <600; j ++){ gridCell [ i ][ j ]. cost = GRASS ; // south } } }

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89 LIST OF REFERENCES A. Elfes (1989). Using occupancy grids for mobile robot perception and navigation. Computer(Long. Beach. Calif)., 22(6) A. P asha (2003). Path planning for nonholonomic vehicles and its application to University of Florida A. Stentz (1994). Optimal and efficient path planning for partially known environments. Proc. 1994 IEEE Int. Conf. Robot. Autom. 3310 3317. C. Coulter ( 1992 ). Implementation of the pure pursuit path tracking algorithm. Report CMU RI TR 92 01, Ca rnegie Mellon University, Pittsburg PA C D. Crane., D. G. Armstrong., M. Ahmed., S. Solanki., D. Macarthur., E. Zawodny., S. Gray., T. Petroff., M. Grifis., C. Evans (200 5 ). Development of an integrated sensor system for obstacle detection and terrain evaluation for application to unmanned ground vehicles. Proc. SPIE 5804, Unmanned Ground Vehicle Techonology, 7 156, C. D. Crane ., D. G. Armstrong., R. Touchton., T. Galluzzo., S. Solanki., J. Lee., D. Kent., M. Ahmed., R. Montane., S. Ridgeway., S. Velat., G. Garcia., M. Griffis., S. Unmanned Ground Vehicle for the 2005 DARPA Grand Challenge. The 2005 DARPA Gr and Challenge, 23 311 347. C. L. Shin., T. T. Lee., & W. A Gruver. (1990). A unified approach for robot motion planning with moving polyhedral obstacles. IEEE trans. Syst., Man, Cybern, 20(4) 903 915 D. Arbuckle ., A. Howard., & M. Mataric. (2002). Tempor al occupancy grids: a method for classifying the spatio temporal properties of the environment. IEEE/RSJ International Conference on Intelligent Robots and System, 1 409 414. E. F. Krause. (1987). Taxicab Geometry: An Adventure in Non Euclidean Geometry. Dover Publication E. W. Dijkstra. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1, 269 271. E Zawodny (2003). Multiple vehicle positioning simulation and optimization Thesis, University of Florida J. Borenstein., & Y. Koren. (1989). Real time obstacle avoidance for fast mobile robots. IEEE trans. on System, Man, and Cybernetics, 19(5) 1179 1989

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90 J. Borenstein., & Y. Koren. (1991). The vector field histogram fast obstacle avoidance for mobile robots. IEEE trans. on Robotics and Automation, 7(3) 278 288 K. Fujimura., & H. Samet. (1989). A hierarchical strategy for path planning among moving obstacles. IEEE trans. On Robotics and, 5(1) 61 69 K. O. Arras., J. Persson., N. Tomatis., & R. Siegwart. (2002 ). Real time obstacle avoidance for polygonal robots with a reduced dynamic window. Proceedings 2002 IEEE International Conference on Robotics and Automation, 3 3050 3055. M. Erdmann., & T. Lozano Perez. (1986). On multiple moving objects. Proc. 1986 IEEE Int. Conf Robotics Automat 1419 1424 Nak Young Ko., & Bum Hee Lee. (1996). Avoidability measure in moving obstacle avoidance problem and its use for motion planning. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 3 1 296 1303 N. C. Griswold., & J. Eem. (1990). Control for mobile robots in the presence of moving objects. IEEE trans. Robotics and Automation, 6(2) 263 268 N.H.C. Yung., & C. Ye. (1998). Avoidance of moving obstacles throught behavior fusion and motion prediction. Conference of Systems, Man, and Cybernetics, 4 3424 342 9. P. Shamsinejad., M. Saraee Bailey. & F. Sheikholeslam. (2010). A new path planner for autonomous mobile robots based on genetic algorithm. Int. Conf. Comput. Sci. Inf. Technol. 3 115 120. R. D. Ahmad Abu Hatab. (2013). Dynamic Modelling of Differential Drive Mobile Robots using Langrange and Newton Euler Methodologies: A Unified Framework. Adv. Robot. Autom 2(2) S. Ge., & Y. Cui. (2002). Dynamic Motion Planning for Mobile Robots Using Potential Field Method Auton. Robots, 13(3) 207 222.

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91 BIOGRAPHICA L SKETCH degrees in Korea Military Academy. As an officer after his graduation, he served as a platoon leader in the DMZ and as a web programming officer in Headquarter of Republic of Kore University of Florida supported by the government. Under the direction of his advisor, Carl D. Crane III, he participated in various classes and projects. His current research goal is to create autonomous vehicles for army. After graduation, he will continue his military service and will go to the institute for weapons development research. His interest s are related to real time path planning, artificial intelligence, and recognition.