黄郑 王红星 翟学锋 王永强 高超

摘 要：为了解决输电线路无人机巡检作业易受恶劣天气影响、远距离遥控不及时、通信稳定性差等问题，提出了一种输电线路无人机自主巡检方法。首先，利用卡尔曼滤波算滤除原始线路中的轨迹噪点，采用垂距法对轨迹冗余点进行过滤，优化巡检航线;其次，提出基于巡检对象差异特性的动态优化定位方法提升无人机巡检质量。以某220 kV输电线路杆塔进行测试，测试结果表明，提出的方法解决了航线采集以及定位优化问题，贯穿航线学习到自主巡检的流程之中，兼顾了无人机自主巡检效率以及巡检精确度。

关键词：输电线路;无人机自主巡检;航线自学习;定位优化

Abstract：In order to solve the problems such as vulnerable to bad weather， untimely remote control and poor communication stability， the uav autonomous inspection method is proposed in this paper. Firstly， kalman filter was used to filter the track noise points in the original line， and the offset method was used to filter the track redundancy points， so as to optimize the inspection route. Secondly， a dynamic optimization positioning method based on the difference characteristics of inspection objects is proposed to improve the quality of uav inspection. A 220kV transmission line tower was used for the test. The test results showed that the method proposed in this paper effectively solved the problems of route acquisition and positioning optimization， learned the process of autonomous inspection throughout the route， and took into account the autonomous inspection efficiency and accuracy.

Key words：transmission line; uav autonomous inspection; route self-learning; location optimization

輸电线路穿越复杂、偏远的地区，在长期运行过程中极易遭受自然灾害和人为破坏，进而引发严重的电力事故，影响电网安全与稳定运行[1]。随着无人机巡检技术的逐渐成熟，其在电力系统、地质勘探、环境监测等各个领域得到了广泛应用[2-4]。目前，无人机仍需要专业人员操作，无人控制、完全自主作业能力尚未实现。

无人机自主巡检关键在于自动获取“无人机巡检轨迹航线”数据，而当前学术领域对该方向研究大部分集中在轨迹跟踪控制上[5-7]，对于飞行控制之前轨迹数据获取方式研究相对较少。现有航线学习策略往往是通过飞手控制无人机模拟一段作业轨迹航线，并在飞行过程中通过不断触发、记录位置点状态来获取目标轨迹线路。……