A 3D Parallel Multi-target Search Coordination Control Strategy for Swarm UAVS

A parallel coordinated search control strategy of 3D swarm unmanned aerial vehicle (UAV) is proposed to solve the multi-target search problem in an unknown environment. First, two strategies-closed-up regulation and cooperation are introduced with the use of the self-organizing task division of the target response threshold-based swarm robots. An improved target response threshold self-organizing task division mode of seed group size homogenization is proposed to handle the problem of uneven distribution of UAVS subgroups, such that the efficiency of parallel search of UAVS can be implicitly improved. Second, the coordinated control algorithm of swarm robot based on 2D extended particle swarm optimization is applied to 3D swarm UAV, whereby an improved adaptive inertia weight is developed, and a 3D extended particle swarm optimization with an improved adaptive inertia weight (IAEPSO) is proposed to alleviate the local optimum problem and low searching efficiency. Finally, two improved methods (ITRT+IAEPSO) are used in the parallel search of swarm UAVS. Compared with the traditional parallel multi-target search extended particle swarm optimization (TRT+EPSO), the proposed methods can reduce the search time and energy consumption of system and the numerical simulations are performed to verify the effectiveness of the proposed methods.