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A New Positioning Method for Climbing Robots Based on 3D Model of Transmission Tower and Visual Sensor.
Liu, Yansheng; You, Junyi; Du, Haibo; Chang, Shuai; Xu, Shuiqing.
Affiliation
  • Liu Y; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
  • You J; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
  • Du H; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
  • Chang S; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
  • Xu S; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
Sensors (Basel) ; 22(19)2022 Sep 26.
Article in En | MEDLINE | ID: mdl-36236386
ABSTRACT
With the development of robot technology and the extensive application of robots, the research on special robots for some complex working environments has gradually become a hot topic. As a special robot applied to transmission towers, the climbing robot can replace humans to work at high altitudes to complete bolt tightening, detection, and other tasks, which improves the efficiency of transmission tower maintenance and ensures personal safety. However, it is mostly the ability to autonomously locate in the complex environment of the transmission tower that limits the industrial applications of the transmission tower climbing robot. This paper proposes an intelligent positioning method that integrates the three-dimensional information model of transmission tower and visual sensor data, which can assist the robot in climbing and adjusting to the designated working area to guarantee the working accuracy of the climbing robots. The experimental results show that the positioning accuracy of the method is within 1 cm.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Robotics Limits: Humans Language: En Journal: Sensors (Basel) Year: 2022 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Robotics Limits: Humans Language: En Journal: Sensors (Basel) Year: 2022 Document type: Article Affiliation country: China