5G信号约束下多无人机协同电力巡检路径规划技术

在多无人机协同电力巡检任务中，现有的路径规划方法普遍忽略了信号质量的影响，致使其难以在大范围的电力巡检任务中得到有效应用。因此，针对由于通信受限所引起的检测效果下降问题，提出了5G信号约束下的多无人机协同电力巡检路径规划方法。首先，基于5G信号传输特性，建立了面向电力巡检大尺度空间的传播损耗模型；继而，基于遗传算法架构，提出了综合5G信号质量、飞行里程、巡检目标共同约束的多无人机路径规划方法；最后，对基于5G信号的多无人机协同电力巡检路径规划方法进行了仿真验证。结果表明，相较于传统方法，约束后信号质量较差路径的飞行长度减少了452%，并且无人机会在距离相差较小的情况下优先巡检信号较强的杆塔，进而提升巡检任务的检测效果，从而可以保证在大范围环境下的使用。

Path planning technology of multi-UAV cooperative power patrol under 5G signal constraint

In the multi-UAV coordinated power inspection task, the existing path planning methods generally ignore the impact of signal quality, which makes it difficult to be effectively applied in a wide range of power inspection tasks. Therefore, aiming at the problem that the detection effect is degraded due to the limited communication, this paper proposes a path planning method for multi UAV cooperative power inspection under 5G signal constraint. Firstly, based on the 5G signal transmission characteristics, a propagation loss model for power inspection in large scale space is established. Then, based on the genetic algorithm architecture, a path planning method for multiple UAVs is proposed, which integrates the constraints of 5G signal quality, flight mileage and patrol objectives. Finally, the path planning method of multi UAV cooperative power inspection based on 5G signal is simulated and verified. The results show that, compared with the traditional methods, the flight length of the path with poor signal quality after the constraint is reduced by 45.2%, and the vehicles will give priority to the tower with strong signal when the distance difference is small, thereby improving the detection effect of the patrol task, so as to ensure the use in a wide range of environments.