UAV Swarm Search Path Planning Method Based on Probability of Containment

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...

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Bibliographic Details
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
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Summary: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.
ISSN:2504-446X
2504-446X
DOI:10.3390/drones8040132