关键词: 多无人机路径规划, 3D路径规划, 侦察区域优先级, 粒子群算法

Abstract: To solve the problems that the existing path planning schemes lack environmental adaptivity and cannot execute the surveillance task according to the surveillance area priority, this paper proposes a multi-unmanned aerial vehicles (UAVs) 3D surveillance path planning scheme. In this scheme, we first introduce the surveillance area importance (SAI) value to symbolize the surveillance area priority, then combine it with other seven optimization indices to design the multi-objective utility function which is utilized to determine the fitness of generated trajectories. After that, an event detection based SAI value updating mechanism is proposed to enhance the environmental adaptivity. Finally, the particle swarm optimization (PSO) is used to derive the optimal trajectories. Simulation results validate that the trajectories generated by the proposed scheme can give priority to surveille the important areas first and the SAI value updating mechanism can help to trace the new events occurred in the operation area.

Key words: multi-unmanned aerial vehicles (UAVs) path planning, 3D path planning, surveillance area priority, particle swarm optimization (PSO)