Cooperative area search for multiple UAVs based on RRT and decentralized receding horizon optimization

This paper presents a decentralized method to the problem of multiple unmanned aerial vehicles (UAVs) cooperative search of an unknown area. Firstly, based on search map model, the multiple UAVs cooperative search problem is posed as a receding horizon (RH) optimization decision problem, and a RH based UAV search decision process is proposed. Then, this centralized online optimization problem is partitioned into several UAV subsystems optimization problems and solved in a parallel manner using a Nash optimality based decentralized RH optimization method, and particle swarm optimization (PSO) is used for subsystem optimization. Next, by introducing the heuristic information and improving the extension of node, a modified rapidly-exploring random tree (RRT) based path planning algorithm is presented to the UAV search path planning. It is shown by simulation that the proposed method can reduce the size of multiple UAVs optimization decision problem, and lead to an efficient cooperative search for multiple UAVs.