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Fuzzy Logic Unmanned Air VehicleMotion Planning Open Access Deposited

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Date Uploaded: 02/13/2017
Date Modified: 04/05/2017

There are a variety of scenarios in which the mission objectives rely on an unmanned aerial vehicle (UAV) being capable ofmaneuvering in an environment containing obstacles in which there is little prior knowledge of the surroundings. With an appropriate dynamicmotion planning algorithm, UAVs would be able tomaneuver in any unknown environment towards a target in real time. This paper 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. The FL controller was validated, andMonte 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.

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  • Advances in Fuzzy Systems
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  • This work was part of a pilot "mediated-deposit model" where library staff found potential works, later submitted for faculty review

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Identifier: 10.1155/2012/989051
Link: http://dx.doi.org/10.1155/2012/989051

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Permanent link to this page: https://scholar.uc.edu/show/bc386v206