Citation
A* Path Planning Algorithm for Obstacle Avoidance Applied to Single Agent Indirect Herding

Material Information

Title:
A* Path Planning Algorithm for Obstacle Avoidance Applied to Single Agent Indirect Herding
Creator:
Greene, Max Lewis
Publication Date:
Language:
English

Notes

Abstract:
Certain network systems are comprised of agents with uncertain nonlinear dynamics that are indirectly controlled and herded by the influence of other agents, during which collision and interference between agents may occur. In some circumstances, the uncontrollable (target) agents and controllable (herder) agents should not collide or interfere, such as in munition collection or arrangement. This thesis evaluates the implementation of an A* path planning algorithm used to direct one target agent toward a goal location while navigating around other uncontrolled agents (obstacles). A heuristic approach is developed for the A* algorithm that enables obstacle avoidance and generates the least cost path to the desired goal location. It is possible to implement an A* algorithm to plan a path for this specific single agent herding problem. While this method may be implemented to herd other objects with uncertain nonlinear dynamics, it requires system-dependent tuning and reliable control of the target agent. ( en )
General Note:
Awarded Bachelor of Science in Mechanical Engineering, summa cum laude, on May 8, 2018. Major: Mechanical Engineering
General Note:
College or School: College of Engineering
General Note:
Advisor: Warren Dixon. Advisor Department or School: Mechanical and Aerospace Engineering

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright Max Lewis Greene. 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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UF Undergraduate Honors Theses

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