Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Computational principles of mobile robotics
Computational principles of mobile robotics
Robot Motion Planning
A genetic-fuzzy approach for mobile robot navigation among moving obstacles
International Journal of Approximate Reasoning
A layered goal-oriented fuzzy motion planning strategy for mobile robot navigation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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 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, 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.