UAV Motion Planning

UAV Two-Dimensional Path Planning in Real-Time using Fuzzy Logic

There are a variety of scenarios in which the mission objectives rely on an unmanned aerial vehicle (UAV) being capable of maneuvering in an environment containing obstacles in which there is little prior knowledge of the surroundings. With an appropriate dynamic motion planning algorithm, UAVs would be able to maneuver in any unknown environment towards a target in real time.

This research presents a methodology for two-dimensional motion planning of a UAV using fuzzy logic. The fuzzy inference system takes information in real time about obstacles (if within the agent’s sensing range) and target location and outputs a change in heading angle and speed.

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The FL controller was validated, and Monte Carlo testing was completed to evaluate the performance. Not only was the path traversed by the UAV often the exact path computed using an optimal method, the low failure rate makes the fuzzy logic controller (FLC) feasible for exploration. The FLC showed only a total of 3% failure rate, whereas an artificial potential field (APF) solution, a commonly used intelligent control method, had an average of 18% failure rate. These results highlighted one of the advantages of the FLC method: its adaptability to complex scenarios while maintaining low control effort.

Acknowledgements:

This work was supported by a Graduate Assistantship at the University of Cincinnati for the completion of an M.S. degree. The committee for this thesis was chaired by Dr. Kelly Cohen.

Publications:

Sabo and K. Cohen, “Fuzzy Logic Unmanned Aerial Vehicle Motion Planning,” Journal for Advances in Fuzzy Systems, Hindawi Publishing Corporation, Vol. 2012, June 2012. doi: 10.1155/2012/989051.

Sabo, K. Cohen, M. Kumar, and S. Abdallah, “Effectiveness of 2D Path Planning in Real Time using Fuzzy Logic,” 48th AIAA Aerospace Sciences Meeting, Orlando, FL, Jan. 2010, AIAA-2010-417.

Sabo, K. Cohen, M. Kumar, and S. Abdallah, “Path Planning for a Fire-Fighting Aircraft using Fuzzy Logic,” 47th AIAA Aerospace Sciences Meeting, Orlando, FL, Jan. 2009, AIAA-2009-1353.