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.
Unmanned Air Vehicle (UAV) teams are anticipated to provide surveillance support through algorithms, software, and automation. It is desirable to have algorithms that compute effective and efficient routes for multiple UAVs across a variety of missions. These algorithms must be realizable, practical, and account for uncertainties. In surveillance missions, UAVs act as mobile wireless communication nodes in a larger, underlying network consisting of targets where information is to be collected and base stations where information is to be delivered. The role of UAVs in these networks has primarily been to maintain or improve connectivity while undervaluing routing efficiency. Moreover, many current routing strategies for UAVs ignore communication constraints even though neglecting communication can lead to suboptimal tour designs. Generating algorithms for autonomous vehicles that work effectively despite these communication restrictions is key for the future of UAV surveillance missions. A solution is offered here based on a variation of the traditional vehicle routing problem and a simple communication model. In this work, the new routing formulation is defined, analyzed, and a heuristic approach is motivated and described. Simulation results show that the heuristic algorithm gives near-optimal results in real-time, allowing it to be used for large problem sizes and extended to dynamic scenarios.