A Multi-stage and Adaptive Collaborative Search Planning Method for Moving Targets with Multi-UAV

Aiming at the problem of UAV cluster coordinated search for multi-motion targets in a specific region, a multi-stage, adaptive multi-UAV coordinated search planning method for moving targets is proposed by considering the flight constraints of UAV swarms, detection probability of sensors, false alarm probability, and other characteristics. Only short-term benefits and long-term benefits are adopted in the UAV block fast search, as well as coordinative gains is considered in the full-area collaborative search. An environmental infographic including a revisit pheromone map is designed to integrate revisit into the infographic. A mathematical programming model for collaborative searching with multi-UAV is formulated, and the model is solved based on the rolling planning architecture and path pruning strategy. Numerical examples of typical UAV cluster cooperative search verify the effectiveness of the method in this paper. The results of the numerical simulation show that this approach is capable of detecting a greater number of targets with fewer misjudgments, thereby effectively enhancing the efficiency of multi-UAV collaborative searching.