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Pose Estimation of Six-axis Industrial Robots Based on Deep Learning

Published: 31 December 2021 Publication History

Abstract

Industrial robot pose estimation has important applications in industrial robot safety detection and abnormal pose analysis. Aiming at the current problems of low accuracy of industrial robot pose estimation, this paper proposes a pose estimation method of six-axis industrial robots based on deep learning. Firstly, this paper uses the object detection method to detect the six joint axes of the industrial robot, and then the detected results are converted into the pose of the industrial robot. Additionally, this paper constructs an image dataset for industrial robot pose. Our method lays the foundation for industrial robot safety detection and abnormal pose analysis. The experimental results on the self-constructed dataset show that our method can accurately estimate the pose of the industrial robot, and the mean average precision (mAP) is 82.98%. It also outperforms previous industrial robot pose estimation methods by a significant margin of 29.98% performance gain.

References

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Chengzhi Shang. Design and implementation of industrial robot safety system based on machine vision[D]. Chongqing University, 2018.
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Na Li. Research and implementation of industrial robot path planning for humanrobot collaboration safety assurance[D]. Wuhan University of Technology, 2018.
[3]
Zhou F, Chi Z, Zhuang C, Ding H. (2019) 3D Pose Estimation of Robot Arm with RGB Images Based on Deep Learning. In: Yu H., Liu J., Liu L., Ju Z., Liu Y., Zhou D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science, vol 11743. Springer, Cham. https://doi.org/10.1007/978-3-030-27538-9_46
[4]
J. Redmon, S. Divvala, R. Girshick and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779--788.
[5]
T. Gulde, D. Ludl, and C. Curio, "RoPose: CNN-based 2D Pose Estimation of Industrial Robots," in IEEE 14th International Conference on Automation Science and Engineering [C]. IEEE, 2018.
[6]
Shixiang Zhang. Analysis and countermeasures on accidents of industrial robots[J]. Industrial Safety and Environmental Protection, 2002(03):26--29.
[7]
Yuqi Peng. Pose estimation of industrial robots based on deep learning[J]. Wuhan University of Technology, 2020.
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Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao. YOLOv4: Optimal Speed and Accuracy of Object Detection[C]. CVPR, 2020
[9]
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., & Fu, C. Y., et al. (2016). SSD: Single Shot MultiBox Detector. European Conference on Computer Vision. Springer, Cham.

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  1. Pose Estimation of Six-axis Industrial Robots Based on Deep Learning

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    EITCE '21: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering
    October 2021
    1723 pages
    ISBN:9781450384322
    DOI:10.1145/3501409
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 31 December 2021

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    Author Tags

    1. Deep learning
    2. Pose estimation
    3. Six-axis industrial robots

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    EITCE '21 Paper Acceptance Rate 294 of 531 submissions, 55%;
    Overall Acceptance Rate 508 of 972 submissions, 52%

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