Research on Target Ranging Method for Live-Line Working Robots

Author:

Hua Guoxiang12,Chen Guo3,Luo Qingxin3,Yan Jiyuan1

Affiliation:

1. School of Automation, Wuxi University, Wuxi 214105, China

2. School of electrical and Electronic Engineering, North China Electric Power University, Beijing 100096, China

3. School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China

Abstract

Due to the operation of live-line working robots at elevated heights for precision tasks, a suitable visual assistance system is essential to determine the position and distance of the robotic arm or gripper relative to the target object. In this study, we propose a method for distance measurement in live-line working robots by integrating the YOLOv5 algorithm with binocular stereo vision. The camera’s intrinsic and extrinsic parameters, as well as distortion coefficients, are obtained using the Zhang Zhengyou calibration method. Subsequently, stereo rectification is performed on the images to establish a standardized binocular stereovision model. The Census and Sum of Absolute Differences (SAD) fused stereo matching algorithm is applied to compute the disparity map. We train a dataset of transmission line bolts within the YOLO framework to derive the optimal model. The identified bolts are framed, and the depth distance of the target is ultimately calculated. And through the experimental verification of the bolt positioning, the results show that the method can achieve a relative error of 1% in the proximity of positioning. This approach provides real-time and accurate environmental perception for symmetrical structural live-line working robots, enhancing the stability of these robots.

Funder

Open Subjects of State Key Laboratories of China

Publisher

MDPI AG

Reference28 articles.

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3. Kumar, G.A., Lee, J.H., Hwang, J., Park, J., Youn, S.H., and Kwon, S. (2020). LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles. Symmetry, 12.

4. Cao, W.M. (2014). Research on Visual Control Method for High Voltage Transmission Line Deicing Robot, Hunan University.

5. Obstacle identification and localization of high-voltage transmission line inspection robot;Zhang;China Electr. Power,2019

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