Multi-UAV cooperative search path planning method under target directional avoidance maneuvering conditions

Abstract

The configuration of an unmanned aerial vehicle (UAV) swarm is flexible and offers significant cost advantages, making it widely used in various task scenarios. One typical application of UAV swarm is regional target search, which involves obtaining target information through collaborative search by multiple machines. This article focuses on the dynamic target search problem with known probabilities and adversarial and avoidance capabilities. Based on Bayesian theory, the posterior probability of the target is calculated by considering its existence probability and detection process, and the probability graph is updated accordingly. Considering the evasion ability of the target, the UAV's search process towards the target is modeled as a Markov chain, and the impact of target evasion actions on the probability distribution of target existence is analyzed. A probability transition model for target existence under adversarial conditions is established, and the probability graph is predicted. To improve the probability of discovering targets within a limited area and time, a multi-UAV collaborative search path planning method is designed. Simulation results, compared with different algorithms, demonstrate that the proposed method can effectively simulate multi-UAV collaborative search scenarios under adversarial conditions, generate efficient search paths, and exhibit strong adaptability to different scenarios.