Automatic Rebar Binding Method for PC Components based on RGB-D Information

Aiming at the problem of the low efficiency of manual steel bar binding in precast concrete components, this paper proposes an automatic steel bar binding system for PC components that integrates RGB-D information. By improving the YOLOX-s network to detect the status of binding points, extract the area of unbound points, get the skeleton of the image, combine the image point cloud information, and the hand–eye position relationship to locate the three-dimensional position information of the binding points. The RRT-connect algorithm plans the binding trajectory, and the robotic arm executes the process. The model’s mAP is 97.91%, and the FPS is 38.76[Formula: see text]fps. The results show that the average time to locate the binding point is about 108[Formula: see text]ms, and the average time for completing a binding is about 15[Formula: see text]s, meeting real-time binding requirements.

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Publication Details

Journal
International Journal of Humanoid Robotics
Published
2025-09-25
DOI
https://doi.org/10.1142/s0219843625400079
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
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article

Automatic Rebar Binding Method for PC Components based on RGB-D Information

X.R. Wu, Gang Xiao, Xin Tong, Sheng Zheng et al.
International Journal of Humanoid Robotics
Industrial Vision Systems and Defect Detection
article

Automatic Rebar Binding Method for PC Components based on RGB-D Information

X.R. Wu, Gang Xiao, Xin Tong, Sheng Zheng, Zhian Li, Bihong Lin
article en

Abstract

Aiming at the problem of the low efficiency of manual steel bar binding in precast concrete components, this paper proposes an automatic steel bar binding system for PC components that integrates RGB-D information. By improving the YOLOX-s network to detect the status of binding points, extract the area of unbound points, get the skeleton of the image, combine the image point cloud information, and the hand–eye position relationship to locate the three-dimensional position information of the binding points. The RRT-connect algorithm plans the binding trajectory, and the robotic arm executes the process. The model’s mAP is 97.91%, and the FPS is 38.76[Formula: see text]fps. The results show that the average time to locate the binding point is about 108[Formula: see text]ms, and the average time for completing a binding is about 15[Formula: see text]s, meeting real-time binding requirements.

International Journal of Humanoid Robotics
Fujian Research Institute of Light Industry (CN), Fujian University of Technology (CN)
Openalex Percentile: Top 63%
Industrial Vision Systems and Defect Detection
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