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Robotic High-precision Collision Detection and Force Estimation Under Unknown Load

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Abstract

Collision detection is a fundamental problem in the field of human-machine interaction. Inaccurate robotic models caused by unknown loads significantly impact the sensitivity of collision detection and can even result in algorithm failures. This study proposes a collision detection method, inspired by the concept of “interference cancellation” in communication, to overcome the limitations of existing methods in eliminating the influence of unknown loads. The method is based on the self-interference cancellation extended state observer (SICESO). The method establishes a relationship between the output of a feedback control system and the state of robotic motion through dynamics. Computational efficiency is enhanced by employing a reduced-order extended state observer (ESO) to construct an external force observer. The internal disturbance influence caused by dynamic model errors is removed by appropriately setting the observation positions of ESOs based on the delay response characteristics between the physical system and the control commands. The proposed method achieves accurate estimation of external forces independent of the load. Experimental results demonstrate that the proposed method effectively eliminates the influence of unknown loads, achieves collision detection within 0.001s, and accurately estimates external forces with an accuracy of 10−2 Nm. The proposed method has significantly improved sensitivity and robustness.

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References

  1. C.-C. Huo, Y. Zheng, W.-W. Lu, T.-Y. Zhang, D.-F. Wang, D.-S. Xu, and Z.-Y. Li, “Prospects for intelligent rehabilitation techniques to treat motor dysfunction,” Neural regeneration research, vol. 16, no. 2, pp. 264–269, February 2021.

    Article  Google Scholar 

  2. Z. M. Bi, C. Luo, Z. Miao, B. Zhang, W. J. Zhang, and L. Wang, “Safety assurance mechanisms of collaborative robotic systems in manufacturing,” Robotics Comput. Integr. Manuf., vol. 67, 102022, 2021.

    Article  Google Scholar 

  3. O. Abdelaziz, M. Luo, G. Jiang, and S. Chen, “Adaptive threshold for robot manipulator collision detection using fuzzy system,” SN Applied Sciences, vol. 2, no. 319, 2020.

  4. X. Fan, D. Lee, Y. Chen, C. Prepscius, V. Isler, L. Jackel, H. S. Seung, and D. Lee, “Acoustic collision detection and localization for robot manipulators,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9529–9536, 2020.

  5. F. Min, G. Wang, and N. Liu, “Collision detection and identification on robot manipulators based on vibration analysis,” Sensors, vol. 19, 1080, 2019.

    Article  Google Scholar 

  6. A. García and V. Feliu, “Force control of a single-link flexible robot based on a collision detection mechanism,” Control Theory and Applications, vol 147, no. 6, pp. 588–595, 2000.

    Article  Google Scholar 

  7. A. García, V. Feliu, and J. Somolinos, “Experimental testing of a gauge based collision detection mechanism for a new three-degree-of-freedom flexible robot,” J. Field Robotics, vol. 20, no. 6, pp. 271–284, 06 2003.

    Google Scholar 

  8. S. Haddadin, A. De Luca, and A. Albu-Schäffer, “Robot collisions: A survey on detection, isolation, and identification,” IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1292–1312, 2017.

    Article  Google Scholar 

  9. X. Zhang, J. Zhao, M. Zhang, and X. Lin, “Disturbance recognition and collision detection of manipulator based on momentum observer,” Sensors, vol. 20, no. 15, 4187, 2020.

    Article  Google Scholar 

  10. T. Xu, J. Fan, Q. Fang, Y. Zhu, J. Zhao, and Y. H. Kim, “A new robot collision detection method: A modified nonlinear disturbance observer based-on neural networks,” J. Intell. Fuzzy Syst., vol. 38, no. 1, pp. 175–186, 2020.

    Article  Google Scholar 

  11. T. Ren, Y. Dong, D. Wu, and K. Chen, “Collision detection and identification for robot manipulators based on extended state observer,” Control Engineering Practice, vol. 79, pp. 144–153, 2018.

    Article  Google Scholar 

  12. W. Li, Y. Han, J. Wu, and Z. Xiong, “Collision detection of robots based on a force/torque sensor at the bedplate,” IEEE/ASME Transactions on Mechatronics, vol. 25, no. 5, pp. 2565–2573, 2020.

    Article  Google Scholar 

  13. K. M. Park, J. Kim, J. Park, and F. C. Park, “Learning-based real-time detection of robot collisions without joint torque sensors,” IEEE Robotics and Automation Letters, vol. 6, no. 1, pp. 103–110, 2021.

    Article  Google Scholar 

  14. A.-N. Sharkawy, P. N. Koustoumpardis, and N. A. Aspragathos, “Human-robot collisions detection for safe humanrobot interaction using one multi-input-output neural network,” Soft Computing, vol. 24, pp. 6687–6719, 2020.

    Article  Google Scholar 

  15. P. Cao, Y. Gan, and X. Dai, “Model-based sensorless robot collision detection under model uncertainties with a fast dynamics identification,” International Journal of Advanced Robotic Systems, vol. 16, no. 3, 2019.

  16. M. Makarov, A. Caldas, M. Grossard, P. Rodríguez-Ayerbe, and D. Dumur, “Adaptive filtering for robust proprioceptive robot impact detection under model uncertainties,” IEEE/ASME Transactions on Mechatronics, vol. 19, no. 6, pp. 1917–1928, 2014.

    Article  Google Scholar 

  17. C.-N. ho and J.-B. Song, “Collision detection algorithm robust to model uncertainty,” International Journal of Control, Automation and Systems, vol. 11, pp. 776–781, 2013.

    Article  Google Scholar 

  18. B.-j. Jung, J. C. Koo, H. R. Choi, and H. Moon, “Humanrobot collision detection under modeling uncertainty using frequency boundary of manipulator dynamics,” Journal of Mechanical Science and Technology, vol. 28, pp. 4389–4395, 2014.

    Article  Google Scholar 

  19. Z. Li, J. Ye, and H. Wu, “Collision detection algorithm for robots with unknown payload,” ROBOT, vol. 42, no. 1, pp. 29–38, 2020.

    Google Scholar 

  20. S. A. B. Birjandi and S. Haddadin, “Model-adaptive high-speed collision detection for serial-chain robot manipulators,” IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6544–6551, 2020.

    Article  Google Scholar 

  21. L. Sun and Y. Liu, “Extended state observer augmented finite-time trajectory tracking control of uncertain mechanical systems,” Mechanical Systems and Signal Processing, vol. 139, 106374, 2020.

    Article  Google Scholar 

  22. D. Yoo, S. T. Yau, and Z. Q. Gao, “Optimal fast tracking observer bandwidth of the linear extended state observer,” International Journal of Control, vol. 80, no. 1, pp. 102–111, 2007.

    Article  MathSciNet  Google Scholar 

  23. A. De Luca, A. Albu-Schaffer, S. Haddadin, and G. Hirzinger, “Collision detection and safe reaction with the dlr-iii lightweight manipulator arm,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1623–1630, 2006.

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Authors and Affiliations

  1. School of Mechanical Engineering, Dalian Jiaotong University, Dalian, 116025, China

    Yu Du & Xiaojing Tian

  2. School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China

    Hongxiang Song, Dong Liu, Ming Cong & Jingyuan Wu

Authors
  1. Yu Du
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  3. Dong Liu
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Correspondence to Dong Liu.

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The authors declare that there is no competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.

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This work was supported in part by Joint Open Fund Program of Liaoning Science and Technology Plan (2022-KF-12-06), “Take the lead” Science and Technology Research Project of Liaoning Province under Grant 2021JH1/10400104, and the Fundamental Research Funds for the Central Universities under Grant DUT22RC(6)003.

Yu Du received her M.S. degree from Dalian University of Technology, China, in 2007. She then joined the SIASUN Robot and Automation Co. Ltd, China. Mrs. Du has received a Ph.D. degree in Mechanical Engineering at University of British Columbia, Canada. She is currently the CEO of Dalian Dahuazhongtian Technology Co., Ltd., China. Her main research interests include robotics and automation, intelligent control.

Hongxiang Song received his B.S. degree from the College of Electromechanical Engineering at Qingdao University of Science and Technology, Qingdao, China, in 2018. Mr. Song has received an M.S. degree in mechanical engineering at Dalian University of Technology, Dalian, China, in 2021. His research interests include intelligent robotics and human-machine interaction.

Dong Liu received his Ph.D. degree in mechatronics engineering from Dalian University of Technology, Dalian, China, in 2014. He is currently an Associate Professor at the School of Mechanical Engineering, Dalian University of Technology. He was a Research Fellow in Electrical & Computer Engineering at the National University of Singapore, Singapore, from 2015 to 1016, a visiting scholar in Mechanical Engineering at University of British Columbia, Canada, from 2011 to 2012. His research interests include intelligent robotics and system, and human-machine interaction.

Ming Cong received his Ph.D. degree from Shanghai Jiao Tong University, China, in 1995. Since 2003, he joined the faculty of the School of Mechanical Engineering at Dalian University of Technology, China. Prof. Cong was an Outstanding Expert enjoying special government allowances approved by the State Council, an advanced worker of Intelligent Robot theme in the field of automation by National High Technology Research and Development Program (863), and a member of industrial robot expert group of the fifth intelligent robot theme for the 863. His research interests include robotics and automation, intelligent control, and biomimetic robots.

Jingyuan Wu received his B.S. degree from Southwest Jiaotong University, Chengdu, China, in 2021. He is currently pursuing an M.S. degree in mechanical engineering at Dalian University of Technology, Dalian, China. His main research interests include robotics and automation.

Xiaojing Tian received her Ph.D. degree in mechanical manufacturing and automation from the University of Dalian Jiaotong University, Dalian, China, in 2018. She is currently an Associate Professor with the School of Mechanical Engineering, Dalian Jiaotong University, Dalian. Her main research interests include digital image processing, robotics and automation.

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Du, Y., Song, H., Liu, D. et al. Robotic High-precision Collision Detection and Force Estimation Under Unknown Load. Int. J. Control Autom. Syst. (2024). https://doi.org/10.1007/s12555-022-1045-0

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  • DOI: https://doi.org/10.1007/s12555-022-1045-0

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