UAV Swarm Search Path Planning Method Based on Probability of Containment

• Published in: Drones (Basel) 2024-04, Vol.8 (4), p.132
• Main Authors: Fan, Xiangyu, Li, Hao, Chen, You, Dong, Danna
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Algorithms; Analysis; Collaboration; Containment; differential evolution algorithm; Disaster relief; Drone aircraft; Drones; Earthquakes; Efficiency; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; Methods; Operators (mathematics); Optimization algorithms; Planning; Probability distribution; probability of containment; probability of detection; search track; Searching; Trajectory optimization; Trajectory planning; unmanned aerial vehicle swarm; Unmanned aerial vehicles
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones8040132

To improve the search efficiency of the unmanned aerial vehicle (UAV) swarm in disaster areas, the target distribution probability graph in the prior information is introduced, and a drone cluster search trajectory planning method based on probability of containment (POC) is proposed. Firstly, based on the concept of probability of containment in search theory, a task area division method for polygonal and circular areas is constructed, and the corresponding search trajectory is constructed. Then, the influence of factors, including probability of containment, probability of detection, and probability of success on search efficiency, is sorted out, and the objective function of search trajectory optimization is constructed. Subsequently, an adaptive mutation operator is used to improve the differential evolution algorithm, thus constructing a trajectory optimization process based on the improved adaptive differential evolution algorithm. Through simulation verification, the proposed method can achieve a full coverage search of the task area and a rapid search within a limited time, and can prioritize the coverage of areas with a high target existence probability as much as possible to achieve a higher cumulative success probability. Moreover, the time efficiency and accuracy of the solution are high.