多无人机3D侦察路径规划

针对现有路径规划方案忽略侦察区域优先级以及缺乏对侦察区域中新发生事件的跟踪,而导致规划路径不能适应动态环境和无法根据侦察区域重要性来执行优先侦察等问题。首先,提出将侦察区域重要性作为必要优化指标,与无人机能耗和飞行风险值等7个优化指标加权联合,构成路径优化过程中评估生成路径的多目标效用函数,从而使得规划路径可以反映侦察区域优先级特性。然后,提出了基于事件检测的侦察区域重要性值更新机制以提升路径规划方案对动态环境的适应性。最后,采用粒子群算法求解最优路径。仿真结果表明,利用所提路径规划方案生成的侦察路径能最大限度优先覆盖重要侦察区域,并且所提更新机制能够跟踪侦察区域中发生的新事件。

3D surveillance path planning for multi-UAVs

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.