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Design and Implementation of a GPS-Based Navigation System for Micro Air Vehicles

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Design and Implementation of a GPS-Based Navigation System for Micro Air Vehicles
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2008

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Aircraft ( jstor )
Altitude ( jstor )
Antennas ( jstor )
Desert pavements ( jstor )
Flight control ( jstor )
Global positioning systems ( jstor )
Navigation ( jstor )
Navigation systems ( jstor )
Payloads ( jstor )
Software ( jstor )

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University of Florida
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University of Florida
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Copyright the author. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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5/4/2002
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DESIGN AND IMPLEMENTATION OF A GPS-BASED NAVIGATION SYSTEM FOR MICRO AIR VEHICLES BY SCOTT M. KANOWITZ A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORID A IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2002

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ii ACKNOWLEDGMENTS I wish to thank Dr. Arroyo for providing me with th e opportunity to work alongside him, and for all he has taught me, not only in engineering, but in life, and Dr. Nechyba for his openness to ideas, however wrong they may be, and for his pa tience and guidance in th e way of my education and beyond. You two have my gra titude, and have no idea how much fun you have made this experience. I also wish to thank Dr. Schwartz for hi s undying pursuit of perfection, Dr. Ifju for providing me with the means and opport unity to work on this project and the members of the Machine Intelligence Laboratory with whom I have shared my workbench and ideas. I also wish to thank my parents for so many things, but mainly for not only telling me, but for providing me with the means to ensure that anything is possible, my brother for his guidance and example from which I live my life, and St ephanie, whose patience and understanding made this experience that much easier.

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iii TABLE OF CONTENTS page ACKNOWEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .ii LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi CHAPTERS 1 INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Micro Air Vehicles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Navigation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2.1 Ground Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.2 Aerial Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 OVERVIEW OF SYSTEM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Micro Air Vehicle Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Vision System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.4 Control Limitations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3 LIGHT-WEIGHT GPS NAVIGATION SYSTEM. . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Hardware Description. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.3 Software Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4 EXPERIMENTAL RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2 Initial Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.3 PID Controller Tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.4 Ground-Based Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

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iv5 FUTURE WORK AND DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 APPENDIX SCHEMATICS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 REFERENCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 BIOGRAPHICAL SKETCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

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v LIST OF FIGURES figure page 2-1Polygon estimation using horizon li ne separation. . . . . . . . . . . . . . . . . . . . . . . . 8 2-2Horizon estimation; (a) F itness surface; (b) pixel dist ribution in RGB space. . . 9 2-3Vision-based system control diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3-1REB-2000 GPS receiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3-2Radiometrix TX1 RX1 FM data transmitter re ceiver pair. . . . . . . . . . . . . . . . . 16 3-3Complete GPS on-board navigatio n package . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3-4FM data receiver board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3-5Software flow diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3-6Flight path bearing error illustration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3-7Vision-based navigation and GPS-based na vigation system integration. . . . . . 22 4-1GPS data from test path with map overlay . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4-2MAV developed for flight test s of navigation system. . . . . . . . . . . . . . . . . . . . 26 4-3PID controller tuning method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4-4New waypoint navigation method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 A-1Schematic for base station data receiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 A-2PCB layout of base station receiver. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 A-3Schematic for GPS receiver and data transmitte r . . . . . . . . . . . . . . . . . . . . . . . 38

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vi Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degr ee of Master of Science DESIGN AND IMPLEMENTATION OF A GPS-BASED NAVIGATION SYSTEM FOR MICRO AIR VEHICLES By Scott M. Kanowitz May 2002 Chairman: Dr. A. Antonio Arroyo Major Department: Electrical and Computer Engineering Micro air vehicles (MAVs) are becoming vastly popular in the areas of surveillance and reconnaissance for military and civilian use; however, their instability due to their small size renders them useful to only a handful of pilots . We propose implementing a GPS-based navigation system for use in autonomo us flight of micro air vehicles. Previous efforts in this area have produced a vision-based horizon tracking algorithm capable of sustained level flight with user input. Our goal is to improve on this flight system us ing information from a GPS receiver. In this thesis we first introduce the current vision-based naviga tion system and discuss its limitations. We next discuss the proposed improvements to the navigati on system through GPS. Then, we describe the design of the hardware system and software algo rithms for navigation and control. The GPSand vision-based navigation system has been successfu lly integrated and tested in multiple groundbased simulations at the University of Florida.

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1 CHAPTER 1 INTRODUCTION 1.1 Micro Air Vehicles Since the beginning of modern aviation the goal of research has been to produce larger faster winged vehicles. These designs ha ve succeeded in pushing the enve lope and creating superior passenger jets and fighter planes. Currently, however, efforts are emerging to tackle problems associated with designs derived from the other end of the spectrum. Small winged vehicles, or Micro Air Vehicles (MAVs), are being developed to participate in dozens of low-a ltitude surveillance missions not suitable for larger planes. MAVs hold a great potential for use in the su rveillance field. Equipp ed with small video cameras and transmitters, they can be used in areas too remote or dangerous for a human counterpart. Their small scale and low no ise enables them to blend in w ith the sky and surroundings, rendering them unnoticeable. Even at low altitude s, their strong resemblan ce to insects and birds enables MAVs to operate unnoticed. This trait le nds itself well to unobtrusive wildlife surveillance, as well as a variety of military applications. MAVs will become an integral part of the battle field, relaying real-time data to troops close by. They can be easily deployed by soldiers fo r short range reconnaissance work where battlefield information is to difficult or expensive to obtain quickly. Th is new capability will reduce casualties among military personal wh ile improving intelligence data. With the continuing trend in developing chea per faster and smaller electronics, MAVs can be outfitted to serve a variety of monitoring missions in addition to general surveillance. Equipped with the proper sensors, MAVs can locate areas of high radiation, monitor chemical spills, perform

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2 forrest fire reconnaissance, mon itor volcanic activity, and survey natural disaster areas. The new electronics can also be used to im prove the navigation abilities of MAVs. The small size requirements of MAVs generate a variety of challenges in development not seen in their larger wing counterparts. These cha llenges fall into three br oad categories: (1) aerodynamic efficiency, (2) in creased wing loading and (3) stability and control [4]. Solutions for the first and second challenges are currently being de veloped in the Micro Air Vehicle Laboratory at the University of Florida in th e form of innovative designs inco rporating advanced materials [6]. In this thesis we propose solvin g the third challenge of stability an d control. We plan to implement a GPS-based navigation system into the existing vision-based naviga tion system to solve this challenge. The resulting flight control system will be capable of achieving fu lly autonomous flight, removing the human component from the control loop. 1.2 Navigation Systems The current GPS constellation whic h began operation in the early 1990Â’s allows for accurate land based navigation with meter accuracy [1]. Th is system is widely becoming the standard for land and air based navigation [9]. Within the Un ited States, GPS has been approved as an IFR supplemental navigation system for domestic en rout e phases of flight, and as a primary means for oceanic navigation. [12]. Presently, GPS is being added to the primary co mputer systems of large aircraft, increasing their navigation abilities. We feel GPS can also greatly alter the usability of MAVs. The lack of stability and control inherent in MA Vs renders them useful to only a handful of skilled pilots. With the proper navigation system, the MAVs can be telecontrolled by a computer, eliminating the major stability challenges of flight , and allowing any pilo t to focus on altitude and direction. With a GPS-based navigation system, the pilot can be further removed from the control loop. This system could fully control the flight of the aircraft allowing any person to operate the MAVs by simply programming a flight path.

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3 1.2.1 Ground Vehicles Efforts have been made in implementing GPS-based navigation in ground-based mobile robots with additional sensors as done by M. Betke and K. Gurvits [3], and L. Lin et al. [8]. Ymanashi University developed a system to navigate outdoor terrain based on an environment model. The navigation system is based on differential GPS (DGPS), and can achieve accuracies down to 5m. The robot begins a mission with a predetermined course based on GPS coordinates or waypoints. The robot then uses the DGPS system wi th an environment model for rough, high-level navigation and relies on vision and dead reckoning for localized navigation. This system is successful, and capable of navigating roads around Yamanashi University. At Tohoku University, a system was develope d to navigate a car based primarily on DGPS [13]. The car is equipped with a GPS unit, 3D sca nning laser rangefinder, and ultrasonic sensors. The DGPS unit is capable of accuracies in the 5m range. The system matches the GPS coordinates with an internal 3D map for rough positioning of the vehicle’s location. Because of the error inherent in the GPS, the car relies on image processing, a laser rang efinder, and shaft encoders for low level localized navigation. This system is su ccessful in navigating predetermined courses with available accurate 3D environmental maps. 1.2.2 Aerial Vehicles The main differences between ground-based an d flight-based vehicles are the static stability and degrees of freedom of the vehicles. Ground ve hicles are constrained to three degrees of freedom and are statically stable, while aerial vehi cles operate with six degrees of freedom and may not be statically stable [4]. As such, GPS-based control of MAVs and other aerial vehicles presents challenges unseen in the control of ground-based vehicles. While extensive work exists in GPS based cont rol of ground vehicles, small investigations have been made in the control of aerial vehicles as done by S. Fürst and E. Dickmanns [5] and E. M. Atkins et al. [2]. Efforts were made at UC Berkeley to develop a navigation system for an

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4 unmanned aerial vehicle [10]. This system relies primarily on computer vision with noisy updates of its present state coming from GPS. The system architecture consists of a strategic planner, a tactical planner, a trajectory planner, and a regu lation and dynamics layer. The strategic planner develops a coarse, self-optimized trajectory ba sed on predetermined waypoints. The tactical planner makes use of the GPS and internal sensors to update the planned path based on the appearance of new obstacles. This system is still in simulation with plans for implementation on an autonomous helicopter within the BE AR project at UC Berkeley. A navigation system for unmanned aircraft based primarily on GPS was developed at Northwestern Polytechnical University in China [14]. The system is b ased on either single receiver or DGPS navigation. The hardware design includes ai rcraft equipment and a ground station system. The aircraft equipment co nsists of an aircraft computer and GPS receiver. The ground station includes a GPS receiver for use in the DGPS sy stem and a ground station computer. Unmanned flight of this system is realized using the aircra ft computer with a predetermined flight plan. The base station is used only for DGPS corrections and telecontrol. This system flew successfully using a single receiver GPS. Although the previously explained GPS based navigation systems for mobile robots, cars, and planes were successful, they were implemented on systems mu ch larger than the MAV scale planes that are the focus of this thesis. The cl osest system resembling the navigation system to be implemented on MAVs was the unmanned aircraft developed at Northwestern Polytechnical Institute [14]. The payload capacity of the plane enabled an 8098 microcomputer to be flown with the GPS. Presently this is not possible given the payl oad capacity of MAVs. For the system we are developing, all of the computing will have to be done off-board. 1.3 Overview In this thesis we describe the GPS-based navi gation system we have developed and tested on MAVs. Chapter 2 introduces MAVs and the current vision-based navigation system used for flight

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5 control. Chapter 3 discusses the hardware and soft ware design of the GPS system and implementation on the MAV and base station. Chapter 4 descr ibes flight tests of the system and illustrates examples of ground-based tests. Finally, chap ter 5 offers some concluding discussions and thoughts for future work.

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6 CHAPTER 2 OVERVIEW OF SYSTEM 2.1 Introduction The original development of a MAV stability control system focused on a computer vision approach. This system was develo ped to address the problems associ ated with current sensor technology, and conserve weight and payload volume to accommodate the needs of smaller MAVs. The system was inspired by the biological MAV co unterpart, the bird. In studying the nervous (control) system of birds the general observation is that birds rely heavily on sharp eyes and general vision to guide almost every aspect of their behavior [4]. An initial effort to develop a rudimentary form of vision control for flight was done by a University of Florida student, Gabriel Torres. He demonstrated using Cadmium Sulfide cells to sense the general orientation of the horizon on a television monitor. Later work was performed by a University of Florida student, Scott Ettinger, to develop a horizon tracki ng system using the onboard surveillance camera. This sy stem was successful and was capa ble of sustained flight through video noise and sky and ground color variation due to varying weather conditions. It became the current MAV vision-based navigation system. 2.2 Micro Air Vehicle Design In developing MAVs we again study the biolog ical MAV counterpart, the bird. Most large winged aircraft are designed with rigid fixed wings to avoid catastrophic failures due to structural dynamics. Birds on the other hand do not have rigid wings, and instead exhibit a great deal of flexibility in their wings. The design of MAVs makes us e of this flexible wing design to produce a passive mechanism called adaptive washout to suppress wind gustsÂ’ effects on their stability. To

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7 implement this flexible wing conc ept, we make use of carbon fiber construction techniques to produce lightweight durable aircrafts. The planes developed for use in this thesis have wingspans from 24in to 5in. For initial tests, a 24in MAV will be used since it has the highes t payload capacity. The 24in plane is capable of carrying 150g in addition to its primary flight systems including ser vos, a motor, receiver and batteries. Using a standard configura tion, the MAV is capable of sustai ned flight for up to 45min. This flight time is essential for close range surveillance missions. 2.3 Vision System The vision-based system tak es advantage of the surveillan ce capabilities of MAVs. With a color camera and transmitter already in cluded in the payload, the system does not rely on any additional payload to control the MAV. All the vision -based control work is done off-board using a base station computer on the ground. The vision-based system derives its control usin g a direct measurement of the aircraftÂ’s orientation with respect to the ground. The two degree s of freedom critical for stability in this measurement are the bank angle and the pitch angle . These two angles are determined directly from the horizon estimate of an image fro m a forward facing camera on the aircraft. The bank angle is determined as the inverse tangent of the slope of th e horizon line. The pitch angle is estimated to be closely proportional to the pe rcentage of the image above or below the line. The horizon estimating algorithm is based on the assumption that th e sky and ground sections of the image are distinctly different in color and texture, and the horizon can be approximated by a straight line separating these two regions. Using this approach, th e algorithm becomes the task of fitting two polygons to the sky and ground regions of the image as in Figure 2-1. The horizon line separation is used to determine these polygons for a statical modeling technique

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8 . The horizon estimation algorithm begins with a course search of the image, fitting horizon estimates based on previously defined search pa rameters. The various sky and ground regions resulting from this search are mode led as a Gaussian distribution in RGB space. Using the Gaussian model, the mean and covariance matrices of the two distributions are calculated and used in the cost function equation (2-1), where denotes the covariance matrix for ground pixels, and denotes the covariance matrix for sky pixels. This cost function is used for comput ing the line with the highest likelihood of being the best-fit horizon. (2-1) A typical fitness function surface is shown in Figure 2-2 (a), while Figure 2-2 (b) illustrates the distribution of sky pixels (blu e crosses) and ground pixels (green circles) in RGB space. Locating the best-fit horizon line search becomes a task of finding the global maximum on the fitness surface. Sky region Ground region Horizon line estimate Figure 2-1: Polygon estimation using horizon line separation GSF GSG 1G 2G 3++ 2S 1S 2S 3++ 2+++ 1 –=

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9 (a) (b) Figure 2-2: Horizon estimation; (a) Fitness surface; (b) pixel distribution in RGB space

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10 Once the system has identified the mo st likely estimate of the horizon, and are determined using the previously explained methods. Th e next stage in the navigation system uses these pitch and bank estimates in determining the proper actions to fly the MAV. The system takes input from the user via joystick control for the user desired horizon location. This desired horizon is compared with the estimated real horizon an d the resulting error fu nction is calculated. The flight control surface settings are determ ined from a controller op erating on the horizon error function. The proper location s of the flight control surfaces to achieve to the desired horizon location are calculated in this controller. These locations are transmitted to a receiver on the MAV through servo control commands di rectly from the computer, comple ting the control loop in Figure 2-3 . 2.4 Control Limitations The vision-based navigation system proved to be a useful tool in allowing unskilled pilots to fly a MAV. While the vision-based system is capable of flying through rudimentary human control, the goal of this thesis is to produce a fu lly autonomous navigation system for MAVs. This is Figure 2-3: Vision-based system control diagram Acquire and transmit image Capture image Calculate horizon estimate Get user desired input Calculate flight surface changes Transmit servo controls

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11 not possible using only the current vision-based system. A new GPS-based navigation system will have to be added in addition to the vision-based system to achieve fully autonomous flight capable of navigating a predetermined course. The current vision-based navigation system is capable of controlling the pitch and bank angles of the MAV. While this is necessary for controlled level flight, it only produces low-level navigation. The system was designed to rely on user input for high-level navigation. The user is responsible for controlling the aircraftÂ’s locatio n, namely altitude, la titude and longitude. By implementing a GPS-based navigation system we can eliminate the need for human interaction, and instead rely on the GPS for measurements of altitude, latitude and longitude . In addition to the primary navigation measurements, GPS can provid e us with measurements of ground speed and course. It is possible to determine the primary navigation measuremen ts through the vision system using optical flow analysis. This system would de termine such things as speed, course and location through estimates of pixel moveme nts in an image. While this wo uld achieve the goal of producing a minimal payload system by not adding add itional hardware to the MAV, it would not be as accurate as the GPS. The optical flow analysis system would be much more computationally intense needing faster computers, and would not be easily depl oyable. The current surveillance systems on the MAVs produce low quality noisy images po ssibly rendering any optical flow analysis system useless. The vision-based system uses a forward facing camera mounted on the MAV. These cameras can become shaken during or even before flight. Th e vision system is unable to account for the offcenter camera, and relies on a le vel forward facing image. If th is image becomes twisted due to flight or improper placement, th e level horizon will not lie on the center line of the image. This will cause the navigation system to co nstantly bank, climb, or dive to achieve a level horizon in the image leading to unstable flight. The GPS-based system can account for an unlevel image through

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12 high-level navigation measurements. These corrections can be fed into the vision-based system producing, a more robust navigation system.

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13 CHAPTER 3 LIGHT-WEIGHT GPS NAVIGATION SYSTEM 3.1 Introduction The integration of GPS navigatio n into the MAV control system consists of developing a hardware and software la yer. When designing the hardware sy stem we must consider the payload requirements of the MAV. This requirement is th e main restriction as to what processing will be done on the MAV and on the ground, and what hard ware will be used in the MAV. Therefore, the hardware system will consist of an on-board and off-b oard component. The on-board hardware will enab le the system to directly de termine the GPS coordinates of the MAV using a GPS receiver and antenna mounte d on the MAV. This system will not perform any navigation processing, and will only be used to gather data. The system will be responsible for collecting GPS data and transmitting it to the base station. The base station hardware will be responsible for receiving the GPS data transmissi ons and making them available for use in the computer. A software control system is needed on the base station to extract the GPS data from the MAV and determine the current flight path and ne w flight controls. The vision-based system currently uses a base statio n computer to process data from the video camera and produce flight controls. It was determined that the GPS-based sy stem should use the same base station computer as the vision system to enable ease of integratio n, and limit the amount of additional hardware. 3.2 Hardware Description To meet the small payload requirements of the MAV, we searched for a small lightweight GPS receiver with standard fu nctionality and limited user depend ence. To satisfy these require-

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14 ments, we used the Royaltek REB-2000 GPS receiver. This unit is an 11-channel GPS receiver transmitting NMEA update messages at 1HZ through a local serial port. The unit operates on 3.3V at less than 170mA, and weighs 8.6g. The weight of this receiver falls well within the payload requirements of the MAV. The documented error of the GPS receiver is in the range of 15m. The typical observed drift is around 7m to 10m. These error measurements might seem too large for raw navigation purposes, however, they are within control limits. The MAV must maintain considerable speed to stay airborne for proper operation. The average speed during test flights is around 40mph to 50mph. This amounts to around 20m of ground coverage per second. With GPS data updating at 1Hz, the drift due to error becomes tolerable since the MAV will al ways be outside the range of error by the time the next data set arrives. While th is is not precise navigation, it is sufficient for following a general flight path. An equally small GPS antenna was needed to in terface with the GPS receiver. When shrinking the size of a passive GPS antenna, signal degr adation becomes large, and it is difficult to produce usable satellite transmissions. We determin ed we would need an active antenna with a sizable gain that consumes minimal power. The GPS an tenna that meets our requirements is the Tri-M Micro Skymaster . This antenna has a 24dB gain with a maximum of 12mA current consumption. The antenna interfaces directly to the GPS receiv er through a MMCX right angle connector. For the purposes of this thesis, the antenna cable was shortened from 3ft. to 14in to reduce extra payload.

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15 To enable the base station to receive the serial NMEA messages from the MAV GPS hardware, a data transmission system was needed. An ap propriate baud rate should be around 4800 Bps to conform with the standard NMEA transmission protocols. The system sh ould also satisfy the inherent MAV qualities of long range, low weig ht and low noise succeptability. A low power system was also desired to maximize battery life. To satisfy the data transmission requir ements, a system was designed using the TX1\RX1 FM serial data transmitter/rece iver pair designed by Radiometrix . These units operate on the 173.25MHz FM band and can transmit at data rate s up to 10 KBps. The overall range of the system can approach 10Km with the proper antennas and data rate. The TX1 and RX1 operate at 3.3V, and have internal regulators. They consum e 10mA on average. These properties of the TX1/RX1 make them suitable for use in MAV applications. Figure 3-1: REB-2000 GPS receiver

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16 The TX1 FM data transmitter unit was interfaced directly to the REB-2000 GPS receiver as shown in Appendix, Figure A-3. The receiver boots up with a default data rate of 9600 Bps. While the data transmission system is capable of this high data rate, it is not suitable for use over long ranges. A system was developed to directly interface to the GPS receiver and initialize it to send the NMEA update messages at 4800 Bps, lowering the data rate of the FM data system, thereby increasing the possible range. This system was mounted on the MAV with the GPS antenna and data transmit antenna, adding a total of 57g to the MAV payload. Figure 3-2: Radiometrix TX1 RX1 FM data transmitter receiver pair

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17 To help improve the transmitted signal quality and range, a variety of transmit antennas were experimented with. To meet the MAV payload rest rictions, we decided to use a simple low-gain antenna system on the MAV. While this system mi ght limit the transmit quality of the data, it is necessary to maintain the flight characteristics of the MAV. To ensure long range data transmission would be possible with this limited transmission an tenna, we decided the base station should use a large high-gain antenna since there are no weight restriction on the ground. The first antenna used on the MAV for data transmission was a quarter wave whip antenna made from 20 gauge wire. The quarter wave antenn a produced insufficient results due to the lack of an appropriate ground plane. The best possible antenna that satisfies th e weight restrictions of the MAV and produces better transmission characteri stics is a half-wave antenna. This antenna consists of a quarter wave signal antenna and a qu arter wave ground antenna attached on the leading edges of the MAVÂ’s wings. While the antenna is not extremely powerful, it is capable of long Figure 3-3: Complete GPS on-board navigation package

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18 range transmission. If an appropriate powerful antenn a is used on the base station, the strength of the transmission antenna is not extremely important. The base station was designed to enable th e computer to receiv e the transmitted NMEA messages from the MAV GPS receiver. The RX1 FM data receiver was interfaced to the computerÂ’s serial port, thereby allowing the machin e to receive GPS data from the MAV, as shown in Appendix, Figure A-1. The system was designed to use a 5/8 wave omnidirectional whip antenna to maximize the receive strength of the base station while not limitin g the antenna position. 3.3 Software Description The software package for the GPS-based navigati on system consists of three main control structures: (1) data input and ex traction, (2) control system and (3) vision system interface. These systems interact to gather data from the GPS receiver on the MAV, determine proper flight control changes to achieve the desired mission goals an d interface with the current vision system. The software is based on the flow diagram in Figure 3-5. Figure 3-4: FM data receiver board

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19 The data input and extraction sy stem is the first step in the program flow. The system is designed to directly interface with the GPS rece iver and the base statio n data receiver. The GPS receiver is initialized to transmit NMEA GPS se ntences at 4800 Bps with a 1Hz update frequency. The data input and extraction software reads these NMEA sentences from the RS-232 port and decodes them for use in the other parts of the soft ware package. The system is extremely robust to account for invalid data due to signal degradati on when flying at large distances from the base station. Included in the data in put and extraction system is the data display c onsole. This section provides the user with all the data transmitted from the GPS receiver, as well as the current navigation data being used in the flight cont rol system. It also enables the us er to make changes to the flight path on the fly. The software control system is the next step in the program flow. This system first takes the raw GPS data and converts it into th e proper units for use in the fli ght controller. It then uses the desired flight plan to determine the necessary navigation controls for achieving autonomous flight. Figure 3-5: Software flow diagram Navigation engine Display engineFlight controller Receive data Extract NMEA sentence info

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20 The flight plan consists of a number of waypoints for the MAV to traverse. The user designs a course based on latitude, longit ude and altitude measurements at discrete points on the path. The system is designed around a central PID controllers based on the PID control equation (3-1) and uses two individual PID systems to control th e direction, or bearing, and the altitude. The errors for the PID system are determined from the current GPS position data and the predetermined flight plan. The altitude is taken directly from the GPS GPGGA NMEA sentence. The position and course data are taken from the GPS GP RMC NMEA sentence. The bearing controller is updated using the error between the current bear ing to the desired point and current course as shown in Figure 3-6. The altitude controller is updated using the error between the current and desired altitude. The proportion , integration and differentiation constants were tuned all tuned by hand during tests. mt Kpet Kie d0 t Kddet dt -----------++ = Kp Ki Kd

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21 The bearing error is based on the current course and the bearing to the next requested waypoint in the predetermined flight path, and is calculated using the Great Circle equations for navigation in a three dimensional environment. The bearing to the next requested waypoint is calculated by first determining the dist ance to the next waypoint using equation (3-2). This distance is used in equation (3-3) to determine the absolu te bearing off north. The resulting bearing is used as the desired bearing in dete rmining the PID error measure [11]. (3-2) (3-3) The final phase of the program flow is the vi sion system interface. The vision system was designed around user input to de termine the desired pitch and bank angles. Without user input the Waypoints Current course Desired course Bearing error MAV Figure 3-6: Flight path bearing error illustration dlat1 lat2 sin sin lat1 cos + lat2 cos lon1lon2– cos acos = c lat2 sin lat1 sin – d cos lat1 cos d sin ----------------------------------------------------------------------acos =

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22 system would continue to fly on a level straight course. The GPS vision system interface take the place of the user input. The interface provides the desired pitch and bank angles based on th e GPS system PID controllers. The control output from the altitude PID system is used in determining the desired pitch percentage for the vision interface. The control output from the bearing PID system is used in determining the bank angle for the vision system interface. The vision system uses the desired GPS pitch and bank angles in the main flight co ntroller to determine the proper control surface changes. This enables the system to maintain the main flight control interface in the vision system for ease of integration. It also allows the vision system to continue legacy support for user input when GPS is unavailable. The new navigation sy stem resulting from the integration of the legacy vision-based navigation system and new GPS-based navigation system is illustrated in Figure 3-7. The system was designed and asse mbled based on the specifications defined in this chapter. Schematics illustrating the hardwa re design are given in the Ap pendix. Each sub-system was Navigation state system GPS position estimate Horizon estimate Flight control Data acquisition Figure 3-7: Vision-based navigatio n and GPS-based navigation system integration

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23 developed independently and tested for validation. When completed, the navigation system represents a complete control loop for autonomous navigation of a MAV based on computer vision and GPS with limited additional payload.

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24 CHAPTER 4 EXPERIMENTAL RESULTS 4.1 Introduction With the hardware and software layers complete, we began t esting of the individual components before integrating the entire system. We be gan by testing the feas ibility of the GPS system based on the accuracy of th e GPS data. This focused mainly on th e positional errors inherent in the standard positioning se rvice. Next, we tested the hardwa re capabilities. The GPS receiver was checked for valid data and the da ta transmission range was tested . This was performed by building a MAV designed for large payloads, and installin g the GPS hardware. The MAV was then flown to test the transmission range. After all the indivi dual systems were workin g within specifications, they were integrated to test th e autonomous navigation capabilities . The high-level control systems were calibrated and tuned resulting in a system capable of autonomo us flight based on a predetermined course of waypoints. 4.2 Initial Testing Initial testing of the GPS receiver did not yiel d suitable results. It was determined that the antenna cable shortening was unsucessfull. The ampl ified antenna is designed to drive at least 3ft of cable. With the cable shortened, the signal amplifier would cause noise interference in the GPS receiver. To counter this problem, we replaced th e active antenna with a pa ssive L1 substrate. This antenna is not amplified and is de signed for short length cables. To test the accuracy of the GPS data and the data logging software, we performed an experiment with the GPS unit in a car. We attached the receiver to the car, and drove along streets at the University of Florida. We logged various waypoints along the path producing the map overlay in

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25 Figure 4-1. The errors in the path/road match up in Figure 4-1 are mostly due to inconsistencies in the map, however, GPS positional e rrors play a small role. This init ial test proved GPS can be used with the hardware and software design to pr oduce accurate navigation measurements for the MAV. The errors shown in this test are within th e range necessary to prod uce autonomous flight. The next phase of testing called for a check of the communication ab ilities between the GPS receiver, the video transmitter and the base station computer. This requires developing a high payload capacity MAV on which to instal the GPS unit. The MAV developed for this and all future flight tests, shown in Figure 4-2, is based on the TooGruven wing design produced in the MAV laboratory at the University of Florida. It has a 24in wingspan with a payload capacity of 150g in addition to servos, a receive r, a motor and batteries. Figure 4-1: GPS data from test path with map overlay longitudelatitude

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26 The camera, used for general surveillance and the vision-base d navigation system was traditionally placed on the tail of the MAV. While th is placement produced usable images, it was directly in-line with the propelle r. Depending on the arrangement of the tail, the propeller would appear in the top or bottom regions of the image. This is not a problem when used for general surveillance, however, it does affect the horizon id entification algorithm. The propeller changes the pixel distribution of the sky and ground regions, resulting in some false horizon estimates. For this reason, the camera was placed under the wing, out of the way of the propeller, as seen in Figure 42 The GPS unit was installed on th e MAV with the half wave ante nna placed along the leading edges of each wing to reduce drag. The GPS rece iver antenna was placed on the top of the frontend of the MAV behind the propeller. The MAV w as flown in a large field at varying altitudes and distances to simulate aspects of an autonomous mission. Close range tests of the system demonstrated superior communication abilities. Longer range tests pro duced the same superior results Figure 4-2: MAV developed for flight tests of navigation system

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27 not seen in the original laboratory tests. The actual data was not nearly as noisy as expected with practically no transmission errors. The GPS unit was capable of transmission throug hout the entire range of testing. The limiting factor in the navigation system was shown to be the video transmission unit. The range of the GPS data transmitter is well beyond the range of the video transmitter. This system will fail to produce a valid image well before the GPS system fa ils. Transmission of GPS data was even possible outside the range of the servo controller. With all the individual hardware and softwa re systems working within specifications the autonomous navigation system was put into place for testing. Initial lab tests were performed to test the navigation capabilities of GPS while the legacy vision-based sy stem was controlling the MAV. The test was setup with the GPS-based navi gation system receiving data, while the visionbased system tracked the horizon and received user input via joystick. The GPS-based system would suggest fight controls to the user. These su ggested controls were not processed by the main navigation system and were only used for testing. This trial run showed the impr oved capabilities of the naviga tion system when integrating the GPS-based system. With a properly tuned contro ller, the GPS-based system is capable of eliminating the need for user input to navigate a pred etermined path. The system will be able to provide control updates equal to or better than a human. 4.3 PID Controller Tuning With all the systems integrated and tested, the PID control el ement of the GPS-based system was to be tuned. The difficulty in tuning this co ntroller is the potential for a catastrophic crash due to non-dampening control. The MAV can not be flown directly by the autonomous navigation system until this controller was properly tuned, howe ver, tuning the controller requires flying the MAV.

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28 Initial trials for tuning the PI D controller were performed in simulation in the laboratory. A fixed set of waypoints were arranged at various locations around the laboratory. The flight control system was run with the GPS receiver stationary on the roof. We would observe the controllerÂ’s actions for maintaining stable flight, and attempt to correct the PID controller based on our observations. These initial tests of the PID system exhibited th e difficulty in tuning the gains of the controller. We would need to develop a system capa ble of indirect control of the MAV. This system would suggest flight controls to the user while not actually cont rolling the plane. The user could then observe these suggestions, and update the PID gains as necessary. To properly tune the PID system while main taining stability of the MAV, we developed a process where a pilot would fly the MAV through a predetermined course using the vision-based system and the joystick. The GPS-based system would suggest a horizon position based on the course, as shown in Figure 4-3. Observations woul d be made as to the instability of the PID system, and the proper changes would be made. Th e system would then be reset to observe the changes.

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29 Tuning of the PID system using this method will not be as difficu lt as initially expected. Slight changes to the gain values should result in a suggested horizon loca tion similar to the userÂ’s input. As long as the output valu es of the PID controller are within the range of joystick values the system can not become unstable. This is due mainly to the original design of the flight controller, which was based solely on user input. The specifications of the flight controller enab le any pilot to indirectly fly the MAV. The pilotÂ’s input, while used as the basis for navigation, is not allowed to cause instability in the MAVÂ’s flight. These same specifications are used when processing GPS suggested horizon locations. If the GPS desired navigation updates cause any instability in the MAVÂ’s flight, they will be ignored until stable horizo n locations are achieved. Horizon estimate User desired horizon GPS suggested horizon Vision estimate GPS estimate User input Figure 4-3: PID controller tuning method

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30 4.4 Ground-Based Testing After the PID system was tuned to produce hor izon location suggestions within specifications, the waypoint navigation system was tested. Va rious courses were selected to test the different aspects of the MAV’s navigations capabilities. The courses ranged from one to four waypoints. Some problems with the navigatio n system were discovered during these tests and corrected for later tests. The first test was based on a course of four waypoints arranged in a square pattern around the testing area with a constant altitude. The MAV was moved, by hand, along the course on the ground. This data arrangement wo uld allow us to observe the syst em’s response to sharp 90° turns and long straight paths. The navigation system produced unexpected results in this test. After the navigation system gained control, horizon location su ggestions forced a turn in the direction of the first waypoint. Once the area around this waypoint was reached, the system began to react erratically, suggesting sharp left and right turns. We immediately halte d the navigation system to determine the cause of the erratic control. Investigations into the high-l evel control strategies reveal ed erroneous assumptions that could account for the chaotic control observed. Th e navigation system maintains a list of the waypoints to traverse with a holder for the current desired location. The system will not change the current desired location to the next waypoint un til the current waypoint has been registered in memory. Given the speed of the MAV, the GPS positional errors and th e GPS data update frequency, there is a small likelihood that the current desired waypoint will be registered. The system will most likely fly the MAV past the current desi red waypoint and make immediate sharp turns in an attempt to reverse the MAV. Th is might result in the MAV flying in a circular pattern around the waypoint without ever reaching it. This error is the possible cau se for the sudden erratic control observed during the previous test.

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31 To correct for the single-point navigation assum ptions, a new control st ate was added to the navigation control loop. As befo re, the system will maintain a flight path towards the current desired waypoint, however, the rule for deciding whether the desired waypoint has been achieved will be different. The waypoint will be changed once the MAV has penetrat ed an area around the waypoint. The navigation system will continually check the distance from the current location to the desired waypoint to determine if the MAV has penetrated the area, as shown in Figure 4-4. This will allow for the various data discrepancies di scussed above while traver sing a general course. The MAV is not required to pass over the actual waypoint in this strategy. With the navigation software updates in place, a second flight test was attempted based on the course used in the first test. The navigation system immediately suggested a heading to follow a direct course to the first waypoint. After passing through the area surrounding the waypoint, the system suggested a turn to move to the next waypoint. The system continued in this fashion, eventually traversing all four waypoints. Additional tests were performed to check va rious mission complexities. Desired altitudes were set at different levels throughout the test s. The number of waypoints in the course was also increased, resulting in wider more complex courses. In each of the tests, the system achieved the flight goals necessary to seemingly pr oduce stable autonomous flight. Waypoint Waypoint location achieved Waypoint location not achieved d1d2Figure 4-4: New waypoint navigation method

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32 The tests discussed in this ch apter exhibited the capabilities of the autonomous navigation system. While computer controlled flight was limite d to ground-based simulation on simple paths, it set the basis for a higher-level system. A successful system is no w in place to enable the MAV to perform a variety of autonomous missions while airborne. A pilot is only needed for take-offs and landings.

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33 CHAPTER 5 FUTURE WORK AND DISCUSSION The stability and control systems developed to meet the goals of this thesis, while successful, have only taken the first steps towards creating a fully autonomous MAV. The GPS-based flight control system was only developed to test the possibilities of autono mous flight. Due to the success of the system, MAVs can be now altered to achieve a variety of missions. The navigation system, however, can be greatly improved upon through hardware and so ftware additions and improvements. This thesis focused on control of the MAV while in flight. It pl anned for the pilot to transfer control to the navigation system after stable fli ght had been achieved. Wh en the system completed the flight path the pilot would regain control of the MAV to pe rform a controlled landing. If the MAV navigation system is ever to be used in commercial applications, take-offs and landings should be automated, completely removing the pilot from the co ntrol loop. This implementation should only require software modifications since all the necessary navigation data is already available. The system only made use of limited GPS capabilities. A lthough the limited accuracies available through the standard po sitioning service did not hinder th e systemÂ’s capabilities, they can be improved upon to produce even greater loca lization and navigation ca pabilities for surveillance and reconnaissance missions. DGPS presents th e ability to achieve much smaller positioning errors. This system makes use of an additional r eceiver of ground based satellite signal corrections to provide accuracies down to 1m. DGPS can be im plemented either directly on the MAV or on the base station. If implemented on the MAV a DGPS receiver will be added to the payload. In an

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34 effort to maintain light-weight payloads, DGPS can be implemented on the base station with the signal corrections calculated on the b ase station or radioed to the MAV GPS unit. The original design for the vision-based navi gation system included a base station computer because no system existed that was small or fast en ough to be placed on-board the MAV. Presently, these computer systems are being developed, and will become availa ble in the near future. These systems are expected to be faster than the orig inal base station computer and small enough to fit in the MAV. The MAV makes use of two transmitters to send video and GPS da ta to the base station computer. If these transmitters are removed, the additional payload capacity and battery capacity available will be able to handle a computer sy stem on-board the MAV. With radio interference removed from the control structure and data bandwidth greatly improved due to the removal of data transmission, the possibilities of sensors and flight control are endless. The autonomous flight control system developed for this thesis was only designed to test the capabilities of GPS-based navigation. The system was tested using redundant parts on a large stable MAV to provide easy debugging capabilities. MAVs exist in much smalle r sizes than that used for the purposes of this thesis. A smaller, lighter GPS-based system can be developed for implementation on one of these smaller MAVs. This will enable more covert surveillance abilities of autonomous flight systems. The autonomous flight system made use of a predetermined set of waypoints as a desired flight path. The system used th ese points as independent goals along a flight path. No processing was performed to account for the lo cation of future or past points. Only the current destination was used. An improved control system can be achieved by implementing path planning and curve fitting algorithms into the flight control structure. If this is implemented, the MAV will be capable of determining intermediate points along the loosely defined flig ht path producing smoother flight and possibly conserving energy.

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35 The GPS-based navigation system developed fo r this thesis performed within the defined specifications. When included with the vision-b ased navigation system, the resulting system will be capable of fully autonomous flight. While GP S has been used to produce autonomous aviation, it has never been used in this capacity or on this scale. To our knowledge, these were the first tests of their kind, being completely autonomous through the use of computer vision and GPS.

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36 APPENDIX SCHEMATICS 1 2 34 A B C D 4 3 2 1 D C B A Title NumberRevision Size Letter Date: 15-Mar-2002 Sheet of File: C:\Documents and Settings\Scott Kanowitz\My Documents\MIL\mav\gps board v1.2\gps v1 Drawn By: R1 IN 13 R2 IN 8 T1 IN 11 T2 IN 10 GND 15 V+ 2 V6 VCC 16 R1 OUT 12 R2 OUT 9 T1 OUT 14 T2 OUT 7 C1+ 1 C1 3 C2+ 4 C2 5 U4 MAX232ACPE(16) RF in 1 RF gnd 2 RF gnd 3 EN 4 RSSI 5 Gnd 6 Vcc 7 AF Out 8 RXD 9 U3 RX1 RF in +5v Reg TXD 1 6 2 7 3 8 4 9 5 J2 DB9 1 2 3 4 J1 Power in (7-9V) 1 2 J3 Antenna in RF in V in +5v Reg TXD RS-232 TXD RS-232 TXD RS-232 RXD RS-232 RXDRXD 1 2 J4 TX interface C1 .1uF Q1 2N222 C2 .1uF Q2 2N222 +5v Reg R1 RES1 R2 RES1 +3v Reg +3v Reg RXD RXD-3V RXD-3V RX Non-Inverting Level Converter + C6 1uF + C7 1uF +C4 1uF +C5 1uF +5v Reg +5v Reg Vcc 1 5V Out 3 GND 2U1 LM7805 5V REG +5v Reg +3v Reg C3 10uF Vcc 3 3.3V out 2 GND 1U2 MC33269T-3.3V REG R3 4.7K +5v Reg D1 LED Figure A-1: Schematic for base station data receiver

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37 Figure A-2: PCB layout of base station receiver

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38 1 2 34 A B C D 4 3 2 1 D C B A Title NumberRevision Size Letter Date:15Mar-2002 Sheet of File: C:\Documents and Settings\Scott Kanowitz\My Documents\MIL\mav\gps receiver for thesis. D Drawn By: Vcc 3 3.3V out 2 GND 1U1 MC33269T-3.3V REG 1 2 J1 CON2 C1 CAP RF GND 1 RF GND 3 RF OUT 2 VCC 5 GND 6 TXD 7 EN 4 C2 TX1 VCC 1 TXA 2 RXA 3 GND 4 GPIOA 5 TXB 6 RXB 7 TIME MARK 8 RESET 9 VANT 10 VBAT 11 BOOTSET 12 C3 REB-2000 1 2 J2 CON2 VCC VCC VCC VCC TX TX RX RX Half wave antenna Figure A-3: Schematic for GPS receiver and data transmitter

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39 REFERENCES [1]M. P. Ananda, H. Bernstein, K. E. Cunningham , W. A. Feess and E. G. Stroud, “Global Positioning (GPS) Autonomous Navigation,” IEEE PLANS '90: Positio n Location and Navigation Symposium Record, pp. 497-508, 1990. [2]E. M. Atkins, R. H. Miller, T. VanPelt, K. D. Shaw, W. B. Ribbens, P. D. Washabaugh, and D. S. Bernstein, “Solus: An Autonomous Aircra ft for Flight Control and Trajectory Planning Research,” Proc. of the American Control Conference , pp. 689-693, 1998. [3]M. Betke and K. Gurvits, “Mobile Robot Localization Using Landmarks,” Proc. of the 1994 IEEE International Conference on Robotics and Automation , Volume 2, pp. 135-142, 1994. [4]S. M. Ettinger, “Design and Implementation of Autonomous Vision-Guided Micro Air Vehicles,” M.S. Thesis, Electrical and Computer Engineering, University of Florida, 2001. [5]S. Fürst and E. Dickmanns, “A Vision Based Navigation System for Autonomous Aircraft,” Robotics and Autonomous Systems 28, pp. 173-184, 1999. [6]D.A. Jenkins, P. Ifju, M. Abdulrahim and S. Olipra, “Assessment of Controlability of Micro Air Vehicles,” Proc. Sixteenth Int. Conference on Unmanned Air Vehicle Systems, Bristol, United Kingdom, paper 27, 2001. [7]S. Kotani, K. Kaneko, T. Shinoda and H. Mori, “Mobile Robot Navigation Based on Vision and DGPS Information,” Proc. of the 1998 IEEE Internatio nal Conference on Robotics Automation, pp. 2524-2529, 1998. [8]L. Lin, T. Hancock and J. Judd, “A Robust Landmark-Based System for Vehicle Location Using Low-Bandwidth Vision,” Robotics and Autonomous Systems 25, pp. 19-32, 1998. [9]G. Lu, “Development of a GPS Multi-Antenn a System for Attitude Determination,” Ph.D. Dissertation, Dept. of Geomatics Engineering, University of Calgary, Canada, 1995. [10]B. Sinopoli, M. Micheli, G. Donato and T. J. Koo, “Vision Based Navigation for an Unmanned Aerial Vehicle,” Proc. of the 2001 IEEE Interna tional Conference on Robotics and Automation, pp. 1757-1764, 2001. [11]J. Setfan, “Navigating With GPS,” Ci rcuit Cellar, issue 123, pp. 22-27, 2000. [12]K. L. Van Dyke, “The World After SA: Benefits to GPS Integrity,” IEEE 2000: Position Location and Navigation Symposium, pp. 387-394, 2000.

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40 [13]L. Wang, T. Emura and T. Ushiwata, “Autom atic Guidance of a Vehicle Based on DGPS and a 3D Map,” Proc. of the 2000 IEEE Intelligent Transportation Systems Conference, pp. 131136, 2000. [14]Y. Wang, X. Li and Y. Huang, “Navigatio n of a Pilotless Aircraft Via GPS,” IEEE AES Systems Magazine, vol. 11, issue 8, pp. 16-20, 1996.

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41 BIOGRAPHICAL SKETCH Scott Kanowitz was born in Ho llywood, Florida, in 1978. After graduating from high school in 1996, he attended the University of Florida. In 2000, Scott earned a Bachelor of Science in computer engineering from the University of Florida. He has since worked in the Machine Intelligence Laboratory pursuing a Master of Science in elec trical engineering while attending the Warrington College of Business Administration to pursue a Master of Science in business management.