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高压输电线路多无人机自主协同巡线设计与测试 被引量:44

Design and Testing on Autonomous Multi-UAV Cooperation for High-voltage Transmission Line Inspection
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摘要 针对目前巡检高压输电线路的固定翼无人机不能精细化巡查,而多旋翼无人机运动速度慢且需要人工遥控操作导致安全风险较大的问题,提出多架多旋翼无人机自主协同精细化巡线方案。探讨了利用两架部署在导线两侧的无人机通过机间通信协同巡检高压输电杆塔和导线的方法,并采用机器学习技术和多智能体一致性控制算法设计无人机自主飞行控制器。根据设计结果开发一站四机系统验证巡线方案,实验部署两机为一组,两组并行作业。结果表明该系统按预期完成高压输电线路的巡检作业,未发生误报缺陷,相比单个遥控操作的多旋翼无人机巡检方案,多机协同方案无需人工干预飞行,总巡线速度超过单机最高时速,双机协同对单个杆塔的平均巡检时间小于遥控单机巡检时间的一半。 To solve the problems in high-voltage transmission line inspection, such as fixed wing unmanned aerial vehicles (UAVs) are incapable of fine inspection and multi rotor UAVs move slowly and require remote pilot which may pose safety risks, a scheme for inspecting high voltage transmission lines with multiple cooperating autonomous multi rotor UAVs is proposed. A cooperative method is discussed for inspecting high-voltage towers and transmission conductors by deploying two networked UAVs at both sides of the power line, respectively. Machine learning and multi-agent consensus control algorithms are employed to design the autopilot controller for the UAVs. According to the above work, four four rotor UAVs and a ground station are built to validate the proposed methods. In the experiment, two pairs of UAVs are deployed to inspect the hlgh-voltage transmission line in parallel with each other. The result shows that the UAV-station system can finish the inspection work as expected, and are free from misreported defects. Compared to the single remote-pilot multi rotor UAV when inspecting high voltage transmission lines, the two UAV cooperation scheme features autopilot, and the average speed of inspection is even higher than the maximal speed of single UAV. Moreover, the average time of inspecting a transmission tower is less than half of single remote-pilot UAV' s.
出处 《电力系统自动化》 EI CSCD 北大核心 2017年第10期117-122,共6页 Automation of Electric Power Systems
基金 高等学校博士学科点专项科研基金资助项目(20130185110023)~~
关键词 多旋翼无人机 高压线巡线 多无人机协同 机器学习 一致性 multi-rotor UAV high-voltage transmission line inspection cooperation of multi UAV machine learning consensus
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