Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong.
Interdisciplinary Division of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong.
Sensors (Basel). 2021 Feb 16;21(4):1385. doi: 10.3390/s21041385.
The inspection of electrical and mechanical (E&M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E&M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.
在过去十年中，由于其灵活性和机动性，使用无人机 (UAV) 检查电气和机械 (E&M) 设备已成为越来越受欢迎的选择。无人机有可能减少人类在目视检查任务中的参与，从而提高效率并降低风险。本文提出了一种用于自主执行 E&M 设备检查的无人机系统。所提出的系统依赖于基于学习的检测进行感知、多传感器融合进行定位以及进行完全自主检查的路径规划。感知方法利用二维对象检测器生成的语义和空间信息。然后，将该信息与深度测量值融合以进行对象状态估计。不需要关于目标设备的位置和类别的先验知识。通过使用四旋翼平台进行飞行实验验证了系统设计。结果表明，所提出的无人机系统能够自主执行检查任务，并确保稳定且无碰撞的飞行。
