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UAV Two-Dimensional Path Planning In Real-Time Using Fuzzy Logic

Sabo, Chelsea
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313755639

Abstract Details

2011, MS, University of Cincinnati, Engineering and Applied Science: Aerospace Engineering.
There are a variety of scenarios in which the mission objectives rely on a UAV being capable of maneuvering in an environment containing obstacles in which there is little prior knowledge of the surroundings. In these situations, not only can these obstacles be dynamic, but sometimes there is no way to plan ahead of the mission to avoid them. Additionally, there are many situations in which it is desirable to send in an exploratory robot where the environment is dangerous/ contaminated and there is a great deal of uncertainty. These scenarios could either be too risky to send people or not available to humans. With an appropriate dynamic motion planning algorithm in these situations, robots or UAVs would be able to maneuver in any unknown and/or dynamic environment towards a target in real-time. An autonomous system that can handle these varying conditions rapidly and efficiently without failure is imperative to the future of unmanned aerial vehicle (UAV). This paper presents a methodology for two-dimensional path planning of a UAV using fuzzy logic. This approach is selected due to its ability to emulate human decision making and relative ease of implementation. 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 for both simple (polygon obstacles in a sparse space) and complex environments (i.e. non-polygon obstacles, symmetrical/concave obstacles, dense environments, etc). Additionally, Monte Carlo testing was completed to evaluate the performance of the control method. 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. Also, the APF method failed about 1/3 of the time for very dense environments (the FLC only had 5% failure rate). These results highlighted one of the advantages of the FLC method: its adaptability to additional rules while maintaining low control effort. Furthermore, the solutions showed superior results when compared to the APF solutions when compared to distance traversed. Overall, the FLC produced solutions that were on average only about 7.7% greater distance traveled (as opposed to 9.7% for the APF).
Kelly Cohen, PhD (Committee Chair)
Shaaban Abdallah, PhD (Committee Member)
Manish Kumar, PhD (Committee Member)
86 p.
Aerospace Materials
UAV; Path Planning; Fuzzy Logic; Online; Real-Time; Motion Planning

Recommended Citations

Citations

  • Sabo, C. (2011). UAV Two-Dimensional Path Planning In Real-Time Using Fuzzy Logic [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313755639

    APA Style (7th edition)

  • Sabo, Chelsea. UAV Two-Dimensional Path Planning In Real-Time Using Fuzzy Logic. 2011. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313755639.

    MLA Style (8th edition)

  • Sabo, Chelsea. "UAV Two-Dimensional Path Planning In Real-Time Using Fuzzy Logic." Master's thesis, University of Cincinnati, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313755639

    Chicago Manual of Style (17th edition)

ucin1313755639
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© 2011, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.