电力输电线路无人机巡检航线智能规划方法

Intelligent path planning method for unmanned serial vehicle inspection of power transmission lines

针对无人机在巡检输电线路时的路径规划效率问题,提出一种能够自主进行智能路径规划来避开障碍物的无人机巡检新方法。通过采集输电线路的点云数据,选择最佳的点云分割方法对采集到的点云数据进行实时语义分割,将具有语义属性的点云数据合并为体素。使用欧几里德符号距离场实时重建障碍物表面与周围障碍物之间的距离和角度,利用二维图像识别技术精确拍摄目标照片,识别输电通道中的绝缘子来调整无人机的框架角度和焦距。将所提方法与一些先进的方法进行实验对比,发现利用无人机自动巡检方法得到的位置误差小于10 cm;与人工巡检相比,采用所提出方法时效率提高57.98%~62.88%,可应用于输电线路的大规模巡检任务。

In order to improve the path planning efficiency of unmanned aerial vehicle(UAV)when patrolling transmission lines,a new method is proposed to enable UAV to independently carry out intelligent path planning to avoid surrounding obstacles during patrolling.By collecting the point cloud data of transmission lines,the best point cloud segmentation method is selected to perform real-time semantic segmentation of the collected point cloud data,and the point cloud data with semantic attributes are merged into voxels.Euclidean symbolic distance fields(ESDFs)are used to reconstruct the distance and angle between the obstacle surface and surrounding obstacles in real time,and the two-dimensional image recognition technology is used to accurately take target photos and identify insulators in the transmission channel to adjust the frame angle and focus of the UAV.The experimental comparison between the proposed method and some of the most advanced methods shows that the position error of the UAV automatic patrol method is less than 10 cm.Compared with manual inspection,the efficiency of the proposed method can be improved by 57.98%~62.88%,which can be applied to large-scale inspection of transmission lines.