The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
권호 표지
DOI QR코드

DOI QR Code

Robot-Human Task Sharing System for Assembly Process

조립 공정을 위한 로봇-사람 간 작업 공유 시스템

  • Minwoo Na (Mechanical Engineering, Korea University) ;
  • Tae Hwa Hong (Mechatronics, Korea University) ;
  • Junwan Yun (Mechanical Engineering, Korea University) ;
  • Jae-Bok Song (Mechanical Engineering, Korea University)
  • Received : 2023.05.08
  • Accepted : 2023.09.04
  • Published : 2023.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Assembly tasks are difficult to fully automate due to uncertain errors occurring in unstructured environments. When assembling parts such as electrical connectors, advances in grasping and assembling technology have made it possible for the robot to assemble the connectors without the aid of humans. However, some parts with tight assembly tolerances should be assembled by humans. Therefore, task sharing with human-robot interaction is emerging as an alternative. The goal of this concept is to achieve shared autonomy, which reduces the efforts of humans when carrying out repetitive tasks. In this study, a task-sharing robotic system for assembly process has been proposed to achieve shared autonomy. This system consists of two parts, one for robotic grasping and assembly, and the other for monitoring the process for robot-human task sharing. Experimental results show that robots and humans share tasks efficiently while performing assembly tasks successfully.

Keywords

Acknowledgement

This research was supported by the MOTIE under the Industrial Foundation Technology Development Program supervised by the KEIT (No. 20008613)

References

  1. W.-C. Chang, "Robotic Assembly of Smartphone Back Shells with Eye-in-Hand Visual Servoing," Robotics and Computer-Integrated Manufacturing, vol. 50, pp. 102-113, 2018, DOI: 10.1016/j.rcim.2017.09.010.
  2. M. Selvaggio, M. Cognetti, S. Nikolaidis, S. Ivaldi, and B. Siciliano, "Autonomy in Physical Human-Robot Interaction: A Brief Survey," IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7989-7996, Oct., 2021, DOI: 10.1109/LRA.2021.3100603.
  3. D. Kragic, J. Gustafson, H. Karaoguz, P. Jensfelt, and R. Krug, "Interactive, Collaborative Robots: Challenges and Opportunities," The Twenty-Seventh International Joint Conference on Artificial Intelligence, pp. 18-25, 2018, DOI: 10.24963/ijcai.2018/3.
  4. P. J. Besl and N. D. Mckay, "A method for registration of 3-D shape," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, Feb., 1992, DOI: 10.1109/34.121791.
  5. K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask R-CNN," 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980-2988, 2017, DOI: 10.1109/ICCV.2017.322.
  6. J. Yun, Y. G. Kim, and J. B. Song, "Sim-to-Real Reinforcement Learning-based Square Peg Alignment Method," The 37th ICROS Annual Conference (ICROS 2022), pp. 534-535, Jun., 2022, [Online], https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11103704.
  7. N. Hogan, "Impedance Control: An Approach to Manipulation: Part III-Applications," Journal of Dynamic Systems, Measurement, and Control, vol. 107, no. 1, pp. 8-16, 1985, DOI: 10.1115/1.3140713.
  8. J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement," arXiv:1804.02767, 2018, DOI: 10.48550/arXiv.1804.02767.
  9. M. Na, T. J. Kim, and J. B. Song, "Similarity-based Assembly Success/Failure Estimation Algorithm," Transactions of the KSME A, vol. 44, no. 1, pp. 71-77, Jan., 2020, DOI: 10.3795/KSME-A.2020.44.1.071.