Abstract
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 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.
Highlights
Over the years, autonomous robots and vehicles are being used to perform missions that are considered “dull, dirty, and dangerous” in both military and civil operations, such as operations in nuclear power plants, for the exploration of Mars, to investigate behind enemy lines in battle, wild-fire surveillance, border patrols, and weather forecasting [1, 2]
All environments were formed such that a plausible solution could be found. This means that the unmanned aerial vehicle (UAV) can adequately navigate around obstacles and that targets are at a “safe” distance from the obstacles
The fuzzy inference system was verified on various, stationary obstacles and for moving targets
Summary
Over the years, autonomous robots and vehicles are being used to perform missions that are considered “dull, dirty, and dangerous” in both military and civil operations, such as operations in nuclear power plants, for the exploration of Mars, to investigate behind enemy lines in battle, wild-fire surveillance, border patrols, and weather forecasting [1, 2]. As the trend develops toward the increasing use of UAVs, it becomes necessary to allocate and control them effectively. Unmanned air vehicles (UAVs) are incapable of being rerouted or retasked in flight, which is crucial as the mission objectives change, threats evolve, environment changes, or where there is no prior information about the scenario. Due to these limitations, it has been identified that a main concern for UAV growth is autonomous and intelligent control [3]. It is imperative that these UAVs are able to conduct its mission with a certain level of autonomy. Incorporating intelligence would help UAVs to act and react more like their manned counterparts
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