A PSO-Based Cooperative Search Method for Multi-UAVs with Collision Avoidance

Since Unmanned Aerial Vehicle (UAV) swarm emerged with the merit of high efficiency, flexibility and robustness in search tasks, related research has been in the spotlight in recent years. In this paper, an adaptive cooperative search method with the ability of collision avoidance for multi-UAVs is presented on the basis of the well-known Particle Swarm Optimization (PSO) algorithm and the efficient topological planning method EGO-Swarm. Remarkably, apart from the introduction of adaptive strategy to compensate for the easy tendency to local optimum of classic PSO, a mechanism of autonomous path planning is innovatively incorporated into PSO algorithm to eliminate possible collisions. This, while inheriting the intrinsic advantages of PSO like few parameters, simple implications and low computational complexity, facilitates the practical application of UAVs in nonideal search environments. Simulation shows that our method proposed is well suited for rapid target localization in scenes dense with obstacles, while confliction among UAVs can also be avoided swiftly.