Multiple Unmanned Aerial Vehicle Cooperative Reconnaissance Based on Combinatorial Manta Ray Foraging Optimization

Unmanned aerial vehicles (UAVs) are widely used in reconnaissance missions due to their good maneuverability, low cost and high efficiency. When multiple UAVs complete reconnaissance tasks, they need accurate positioning and cooperation. In the case that some UAVs lack positioning equipment, cooperative reconnaissance of UAV groups in a certain spatial area can be realized. The innovation of this research lies in the proposal of a novel combinatorial metaheuristic optimization method, named Combinatorial Manta Ray Foraging Optimization (CMRFO). This method uniquely combines the Hungarian algorithm from combinatorial optimization with the manta ray foraging optimization from metaheuristic optimization to systematically address collaborative task planning problems for multiple UAVs. According to the requirements of the cooperative reconnaissance task, a model is established that is divided into three stages: estimation of the optimal reconnaissance position of the UAV, estimation of the position of the UAV itself and reconnaissance subtask assignment. In the experiment, through the optimization results of particle swarm optimization, the Harris eagle algorithm and Manta ray foraging optimization in position estimation, the superiority of the Manta ray foraging optimization algorithm is demonstrated, and the Hungarian algorithm is then used to obtain the optimal distribution results of reconnaissance subtasks. We also show the completion process of the multi-UAV cooperative reconnaissance task in some scenes where UAVs lack positioning equipment through graphical simulation and verify the proposed multiple unmanned aerial vehicle cooperative reconnaissance model.