基于多传感器的无人机配电网架空线路自主巡检和姿态控制

针对配电网架空线路无人机巡检过程中人工干预较多且效率低下的问题，文中提出了一种基于多传感器融合的无人机自主巡检与姿态控制方法。该方法采用MinkUNet深度学习模型对线路通道点云数据进行精确分类，结合杆塔与导线建模方法以及三维航线规划方法，实现无人机巡检路径的自动规划。在飞行执行阶段，融合毫米波雷达和深度相机数据，结合基于贝叶斯理论的占位栅格地图更新策略，有效识别环境中新出现的障碍物，并通过改进的人工势场法实现自主避障。为了在定位信号受到干扰时确保拍照质量，提出基于轻量化YOLOv8网络的目标识别模型与云台控制策略，实现对巡检目标的精确自动对准拍照。通过在真实环境和仿真环境中的实验验证，文中提出的方法更适合复杂的配电网巡检场景。

Autonomous inspection technology of overhead lines of distribution netword and attitude control of UAVs based on multi-sensor fusiion

In response to the issues of excessive manual intervention and low efficiency in the unmanned aerial vehicle (UAV) inspection of overhead power distribution lines, this paper proposed a method for autonomous inspection and attitude control of UAVs based on multi-sensor fusion. The method employs a deep learning model based on MinkUNet for segmentation of corridor pointcloud followed by modelling of powerpoles and transmission lines and three-dimensional flight path planning. During the flight execution phase, filtered millimeter wave radar data and depth camera data are fused under a Bayesian theory-based occupancy grid map update strategy to detect newly emerged obstacles. An improved artificial potential field method is utilized for autonomous obstacle avoidance. To compensate the locating drifts when positioning signals are disrupted, a lightweight YOLOv8-based target recognition model and gimbals control strategy are introduced to enable precision alignment and photography of the inspection targets. The experimental results based on real-world and simulated environments demonstrated that the proposed methods are effective and robust for complex power distribution network inspection scenarios.