电力工程技术 (May 2024)

Precise positioning and identification of omnidirectional inspection robot for substation secondary equipment

  • LI Jie,
  • DONG Linjie,
  • TANG Xiaobing,
  • ZHANG Wenbing,
  • WANG Xingsong

DOI
https://doi.org/10.12158/j.2096-3203.2024.03.025
Journal volume & issue
Vol. 43, no. 3
pp. 234 – 243

Abstract

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Substation secondary equipment is monitored through inspection robots, providing an important means to enhance power equipment automation and intelligent management, thereby ensuring the safe operation of power engineering equipment. In this paper, a Mecanum wheeled omnidirectional mobile robot is developed for automatically inspecting secondary equipment in substations. It possesses autonomous navigation, positioning, and identification capabilities, significantly enhancing inspection efficiency and the accuracy of protection plate state identification. The Mecanum wheels enable the inspection robot to move flexibly and adjust its attitude within narrow working environments. Meanwhile, a multi-track lifting platform facilitates image acquisition and state identification of the secondary equipment pressure plate, covering a height range of 350-1 800 mm. The robot employs the lidar-based simultaneous localization and mapping (SLAM) method for autonomous positioning and navigation, supplemented by a vision-based path extraction and tracking algorithm for precise position correction at set points. Moreover, a color recognition-based image arrangement and state recognition method is proposed to accurately identify and assess the connection state of the secondary equipment protection plate. Experimental results demonstrate that the substation secondary equipment inspection robot, utilizing Mecanum wheels, achieves successful autonomous navigation and precise positioning, with maximum deflection angles and distances during path tracking processes being ±3° and ±8 mm, respectively. Additionally, the plate recognition method, combining machine vision and color recognition, achieves an outstanding recognition accuracy rate exceeding 95.80%, thereby elevating the level of robot automation in inspection operations.

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