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Human-Robot Collaborative Manipulation with the Suppression of Human-caused Disturbance

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Abstract

A method for robot control is proposed in this paper to suppress human-caused disturbance in human-robot collaborative manipulation. In the method, the robot control algorithms are chosen according to the impacts of human motion on the manipulated objects: the modified model predictive controller is used when the impact is large, and the impedance controller is used when the impact is small. In the modified model predictive control, the human motion in a specific direction is considered as disturbance, the disturbance is observed, predicted, and its impact on the manipulated objects’ stability is estimated. The robot control parameters are then optimized based on the estimation. A series of simulation and physical experiments are conducted. The results show that the modified model predictive control shows better stability than the impedance control and model predictive control. Specifically, the maximum displacement of the manipulated objects decreases by 70% compared with the impedance control and 44% compared with the model predictive control.

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References

  1. Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems–A review of model predictive control (MPC). Bui. and Envi. 72, 343–355 (2014)

    Article  Google Scholar 

  2. Agravante, D.J., Cherubini, A., Bussy, A., Gergondet, P., Kheddar, A.: Collaborative human-humanoid carrying using vision and haptic sensing. Proc. IEEE Int. Conf. Robot. Autom, pp. 607–612 (2014)

  3. Agravante, D.J., Cherubini, A., Sherikov, A., Wieber, P.B., Kheddar, A.: Human-Humanoid Collaborative carrying. J. Pro. Con. 61, 1–14 (2019)

    Google Scholar 

  4. Berger, E., Vogt, D., Haji-Ghassemi, N., Jung, B., Amor, H.B.: Inferring guidance information in cooperative human-robot tasks. Proc. IEEE-RAS Int. Conf. on Humanoid Robots. pp. 15–17 (2013)

  5. Bussy, A., Kheddar, A., Crosnier, A., Keith, F.: Human-humanoid haptic joint object transportation case study. Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 3633–3638 (2012)

  6. Chipalkatty, R., Daepp, H., Egerstedt, M., Book, W.: Human-in-the-loop: MPC for shared control of a quadruped rescue robot. Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 4556–4561 (2011)

  7. Chipalkatty, R.: Human-in-the-loop control for cooperative human-robot tasks. https://smartech.gatech.edu/handle/1853/43649https://smartech.gatech.edu/handle/1853/43649. Accessed 25 April 2020 (2012)

  8. Chipalkatty, R., Droge, G., Egerstedt, M.B.: Less is more: Mixed-initiative model-predictive control with human inputs. IEEE Tran. on Robo. 29, 695–703 (2013)

    Article  Google Scholar 

  9. Erhart, S., Sieber, D., Hirche, S.: An impedance-based control architecture for multi-robot cooperative dual-arm mobile manipulation. Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. 29, 315–322 (2013)

    Google Scholar 

  10. Faulwasser, T., Weber, T., Zometa, P., Findeisen, R.: Implementation of nonlinear model predictive path-following control for an industrial robot. IEEE Tran. on Cont. Sys. Tech. 25, 1504–1551 (2016)

    Google Scholar 

  11. Ghadirzadeh, A., Bütepage, J., Maki, A., Kragic, D., Björkman, M.: A sensorimotor reinforcement learning framework for physical human-robot interaction. Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 2682–2688 (2016)

  12. Goodwin, G.C., Kong, H., Mirzaeva, G., Seron, M.M.: Robust model predictive control: reflections and opportunities. J. Con. and Dec. 1, 115–148 (2014)

    Google Scholar 

  13. He, W., Li, Z., Chen, C.L.P.: A survey of human-centered intelligent robots: issues and challenges. IEEE/CAA J. Autom. Sinica. 4, 602–609 (2017)

    Article  Google Scholar 

  14. Lanini, J., Razavi, H., Urain, J., Ijspeert, A.: Human intention detection as a multiclass classification problem: application in physical Human–Robot interaction while walking. IEEE Robot. Autom. Lett. 3, 4171–4178 (2018)

    Article  Google Scholar 

  15. Latella, C., Lorenzini, M., Lazzaroni, M., Romano, F., Traversaro, S., Akhras, M.A., Pucci, D., Nori, F.: Towards real-time whole-body human dynamics estimation through probabilistic sensor fusion algorithms. Auton. Robot. 43, 1591–1603 (2019)

    Article  Google Scholar 

  16. Liu, P., Khaled, E., Simon, P., Nazmul, H., Gerhard, N.: Towards real-time robotic motion planning for grasping in cluttered and uncertain environments. Towards Autonomous Robotic Systems:, 19th Annual Conference., pp. 481 (2018)

  17. Liu, P., Yu, H., Cang, S.: Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances. Nonlinear Dyn. 98, 1447–1464 (2019)

    Article  Google Scholar 

  18. Liu, P., Yu, H., Cang, S.: Optimized adaptive tracking control for an underactuated vibro-driven capsule system. Nonlinear Dynamics. 94, 1803–1817 (2018)

    Article  Google Scholar 

  19. Lucia, S., Finkler, T., Engell, S.: Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty. J. Pro. Con. 23, 1306–1319 (2013)

    Article  Google Scholar 

  20. Nikolaidis, S., Kuznetsov, A., Hsu, D., Srinivasa, S.: Formalizing human-robot mutual adaptation: a bounded memory model. ACM/IEEE Int. Conf. Hum.-Robot Interact., pp. 75–82 (2016)

  21. Parietti, F., Asada, H.H.: Dynamic analysis and state estimation for wearable robotic limbs subject to human-induced disturbances. Proc. IEEE Int. Conf. Robot. Autom., pp. 3880–3887 (2013)

  22. Peternel, L., Petrič, T., Oztop, E., Babič, J.: Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach. Auton. Robot. 36, 123–136 (2014)

    Article  Google Scholar 

  23. Shyam, R.A., Lightbody, P., Das, G., Liu, P., Gomez-Gonzalez, S., Neumann, G.: Improving Local Trajectory Optimisation using Probabilistic Movement Primitives. Proc. IEEE/RSJ. Int. Conf. Intell. Robots Syst., pp. 2666–2671 (2019)

  24. Rozo, L., Calinon, S., Caldwell, D.G.: Learning force and position constraints in human-robot cooperative transportation. Proc. IEEE Int. Sym. Inte. Com., pp. 619–624 (2014)

  25. Rozo, L., Calinon, S., Caldwell, D.G., Jimenez, P., Torras, C.: Learning physical collaborative robot behaviors from human demonstrations. IEEE Trans. Robo. 32, 513–527 (2016)

    Article  Google Scholar 

  26. Safavi, A., Huynh, L., Rahmat-Khah, H., Zahedi, E., Zadeh, M.H.: A novel MPC approach to optimize force feedback for human-robot shared control. Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 3018–3023 (2015)

  27. Saltık, M.B., Özkan, L., Ludlage, J.H., Weiland, S., Van den Hof, P.M.: An outlook on robust model predictive control algorithms: Reflections on performance and computational aspects. J. Pro. Con. 61, 77–102 (2018)

    Article  Google Scholar 

  28. Schaal, S.: Dynamic movement primitives-a framework for motor control in humans and humanoid robotics. Proceedings of the International Symposium on Adaptive Motion of Animals and Machines., pp.261–280 (2006)

  29. Stückler, J., Behnke, S.: Following human guidance to cooperatively carry a large object. Proc. IEEE-RAS. Int. Conf. on Humanoid Robots., pp. 2218–223 (2011)

  30. Vogt, D., Stepputtis, S., Grehl, S., Jung, B., Amor, H.B.: A system for learning continuous human-robot interactions from human-human demonstration. Proc. IEEE Int. Conf. Robot. Autom., pp. 2882–2889 (2017)

  31. Zhou, T., Wachs, J.P.: Early prediction for physical human robot collaboration in the operating room. Auton. Robot. 42, 977–995 (2018)

    Article  Google Scholar 

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Funding

This study was supported in part by the Manned Aerospace Research Project of China [grant number 060601].

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Conceptualization: [Shiqi Li], [Haipeng Wang]; Methodology: [Haipeng Wang]; Writing - original draft preparation: [Haipeng Wang]; Writing - review and editing: [Haipeng Wang]; Funding acquisition: [Shiqi Li]; Simulation: [Haipeng Wang], [Shuai Zhang]; Experiment: [Haipeng Wang], [Shuai Zhang]; Supervision: [Shiqi Li].

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Correspondence to Haipeng Wang.

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Li, S., Wang, H. & Zhang, S. Human-Robot Collaborative Manipulation with the Suppression of Human-caused Disturbance. J Intell Robot Syst 102, 73 (2021). https://doi.org/10.1007/s10846-021-01429-8

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