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Article

Learning-Based Autonomous UAV System for Electrical and Mechanical (E&M) Device Inspection

1
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong
2
Interdisciplinary Division of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong
*
Author to whom correspondence should be addressed.
Academic Editors: Hyun Myung and Yang Wang
Sensors 2021, 21(4), 1385; https://doi.org/10.3390/s21041385
Received: 12 December 2020 / Revised: 6 February 2021 / Accepted: 13 February 2021 / Published: 16 February 2021
(This article belongs to the Special Issue Robotic Sensing for Smart Cities)
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. View Full-Text
Keywords: UAV; autonomous inspection; object detection; deep learning UAV; autonomous inspection; object detection; deep learning
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MDPI and ACS Style

Feng, Y.; Tse, K.; Chen, S.; Wen, C.-Y.; Li, B. Learning-Based Autonomous UAV System for Electrical and Mechanical (E&M) Device Inspection. Sensors 2021, 21, 1385. https://doi.org/10.3390/s21041385

AMA Style

Feng Y, Tse K, Chen S, Wen C-Y, Li B. Learning-Based Autonomous UAV System for Electrical and Mechanical (E&M) Device Inspection. Sensors. 2021; 21(4):1385. https://doi.org/10.3390/s21041385

Chicago/Turabian Style

Feng, Yurong; Tse, Kwaiwa; Chen, Shengyang; Wen, Chih-Yung; Li, Boyang. 2021. "Learning-Based Autonomous UAV System for Electrical and Mechanical (E&M) Device Inspection" Sensors 21, no. 4: 1385. https://doi.org/10.3390/s21041385

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