Hostname: page-component-89b8bd64d-46n74 Total loading time: 0 Render date: 2026-05-06T16:08:25.408Z Has data issue: false hasContentIssue false
  • English
  • Français

Physical Human–Robot Cooperation Based on Robust Motion Intention Estimation

Published online by Cambridge University Press:  23 September 2020

Konstantinos I. Alevizos ,
Charalampos P. Bechlioulis Open the ORCID record for Charalampos P. Bechlioulis [Opens in a new window]  and
Kostas J. Kyriakopoulos
Konstantinos I. Alevizos
Affiliation:
Control Systems Lab, School of Mechanical Engineering, National Technical University of Athens, 9 Heroon Polytechniou Street, Athens, 15780, Greece
Charalampos P. Bechlioulis*
Affiliation:
Control Systems Lab, School of Mechanical Engineering, National Technical University of Athens, 9 Heroon Polytechniou Street, Athens, 15780, Greece
Kostas J. Kyriakopoulos
Affiliation:
Control Systems Lab, School of Mechanical Engineering, National Technical University of Athens, 9 Heroon Polytechniou Street, Athens, 15780, Greece
*
*Corresponding author. E-mail: chmpechl@mail.ntua.gr
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Cooperative transportation by human and robotic coworkers constitutes a challenging research field that could lead to promising technological achievements. Toward this direction, the present work demonstrates that, under a leader–follower architecture, where the human determines the object’s desired trajectory, complex cooperative object manipulation with minimal human effort may be achieved. More specifically, the robot estimates the object’s desired motion via a prescribed performance estimation law that drives the estimation error to an arbitrarily small residual set. Subsequently, the motion intention estimation is utilized in the object dynamics to determine the interaction force between the human and the object. Human effort reduction is then achieved via an impedance control scheme that employs the aforementioned estimations. The feedback relies exclusively on the robot’s force/torque, position as well as velocity measurements at its end effector, without incorporating any other information on the task. Moreover, an adaptive control scheme is adopted to relax the need for exact knowledge of the object dynamics. Finally, an extension for multiple robotic coworkers is studied and verified via simulation, while extensive experimental results for the single robot case clarify the proposed method and corroborate its efficiency.

Keywords

RAAD2018pHRICooperative transportationPrescribed performanceImpedance control

Information

Type
Articles
Information
Robotica , Volume 38 , Special Issue 10: Human–Robot Interaction (HRI) , October 2020 , pp. 1842 - 1866
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

References

Hirata, Y., Kume, Y., Wang, Z. and Kosuge, K., “Decentralized Control of Multiple Mobile Manipulators Based on Virtual 3-D Caster Motion for Handling an Object in Cooperation with a Human,” Proceedings - IEEE International Conference on Robotics and Automation, vol. 1 (2003) pp. 938–943.Google Scholar
Hirata, Y. and Kosuge, K., “Distributed Robot Helpers Handling a Single Object in Cooperation with a Human,” Proceedings - IEEE International Conference on Robotics and Automation, vol. 1 (2000) pp. 458–463.Google Scholar
Mörtl, A., Lawitzky, M., Kucukyilmaz, A., Sezgin, M., Basdogan, C. and Hirche, S., “The role of roles: Physical cooperation between humans and robots,” Int. J. Robot. Res. 31(13), 1656–1674 (2012).CrossRefGoogle Scholar
Corteville, B., Aertbelien, E., Bruyninckx, H., De Schutter, J. and Van Brussel, H., “Human-Inspired Robot Assistant for Fast Point-to-Point Movements,” Proceedings - IEEE International Conference on Robotics and Automation (2007) pp. 3639–3644.Google Scholar
Maeda, Y., Hara, T. and Arai, T., “Human-Robot Cooperative Manipulation with Motion Estimation,” Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No. 01CH37180), vol. 4 (IEEE, 2001) pp. 2240–2245.Google Scholar
Thobbi, A., Gu, Y. and Sheng, W., “Using Human Motion Estimation for Human-Robot Cooperative Manipulation,” In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2011) pp. 2873–2878.Google Scholar
Roveda, L., “A User-Intention Based Adaptive Manual Guidance with Force-Tracking Capabilities Applied to Walk-Through Programming for Industrial Robots,” In: 2018 15th International Conference on Ubiquitous Robots (UR) (2018).CrossRefGoogle Scholar
Roveda, L., Haghshenas, S., Caimmi, M., Pedrocchi, N. and Molinati Tosatti, L., “Assisting operators in heavy industrial tasks: On the design of an optimized cooperative impedance fuzzy-controller with embedded safety rules,” Front. Robot. AI 6, 75 (2019).CrossRefGoogle Scholar
Roveda, L., Maskani, J., Franceschi, P., Abdi, A., Braghin, F., Tosatti, L. M. and Pedrocchi, N., “Model-based reinforcement learning variable impedance control for human-robot collaboration,” J. Intell. Robot. Syst., 1–17 (2020).CrossRefGoogle Scholar
Li, Y. and Ge, S. S., “Human-robot collaboration based on motion intention estimation,” IEEE/ASME Trans. Mechatron. 19(3), 1007–1014 (2014).CrossRefGoogle Scholar
Evrard, P., Gribovskaya, E., Calinon, S., Billard, A. and Kheddar, A., “Teaching Physical Collaborative Tasks: Object-Lifting Case Study with a Humanoid,” In: 9th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS09 (2009) pp. 399–404.Google Scholar
Rozo, L., Bruno, D., Calinon, S. and Caldwell, D. G., “Learning Optimal Controllers in Human-Robot Cooperative Transportation Tasks with Position and Force Constraints,” In: IEEE International Conference on Intelligent Robots and Systems, vol. 2015-December (2015) pp. 1024–1030.Google Scholar
Medina, J. R., Lawitzky, M., Mörtl, A., Lee, D. and Hirche, S., “An Experience-Driven Robotic Assistant Acquiring Human Knowledge to Improve Haptic Cooperation,” In: IEEE International Conference on Intelligent Robots and Systems (2011) pp. 2416–2422.Google Scholar
Uchiyama, M. and Dauchez, P., “A Symmetric Hybrid Position/Force Control Scheme for the Coordination of Two Robots,” Proceedings of the 1988 IEEE International Conference on Robotics and Automation (IEEE, 1988) pp. 350–356.Google Scholar
Khatib, O., “Object Manipulation in a Multi-Effector Robot System,” Proceedings of the 4th International Symposium on Robotics Research, vol. 4 (MIT Press, 1988) pp. 137–144.Google Scholar
Rahman, M. M., Ikeura, R. and Mizutani, K., “Investigation of the impedance characteristic of human arm for development of robots to cooperate with humans,” JSME Int. J. Ser. C: Mech. Syst. Mach. Elem. Manuf. 45(2), 510–518 (2002).CrossRefGoogle Scholar
Walker, I. D., Freeman, R. A. and Marcus, S. I., “Analysis of motion and internal loading of objects grasped by multiple cooperating manipulators,” Int. J. Robot Res. 10(4), 396–409 (1991).CrossRefGoogle Scholar
Hollerbach, J., Khalil, W. and Gautier, M., “Model Identification,” In: Springer Handbook of Robotics (Springer, 2008) pp. 321–344.CrossRefGoogle Scholar
Bechlioulis, C. P. and Rovithakis, G. A., “Robust partial-state feedback prescribed performance control of cascade systems with unknown nonlinearities,” IEEE Trans. Automat. Cont. 56(9), 2224–2230 (2011).CrossRefGoogle Scholar
Sontag, E. D., Mathematical Control Theory (Springer, London, UK, 1998).CrossRefGoogle Scholar
Kosuge, K. and Oosumi, T., “Decentralized Control of Multiple Robots Handling an Object,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems (vol. 1, 1996) pp. 318–323.Google Scholar
Kosuge, K., Oosumi, T. and Chiba, K., “Load Sharing of Decentralized-Controlled Multiple Mobile Robots Handling a Single Object,” Proceedings of the IEEE International Conference on Robotics and Automation (vol. 4, 1997) pp. 3373–3378.Google Scholar
Kosuge, K., Oosumi, T. and Seki, H., “Decentralized Control of Multiple Manipulators Handling an Object in Coordination Based on Impedance Control of Each Arm,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems (vol. 1, 1997) pp. 17–22.Google Scholar
Siciliano, B. and Villani, L., Robot Force Control, vol. 540 (Springer Science & Business Media, 2012).Google Scholar
Villani, L. and De Schutter, J., “Force Control,” In: Springer Handbook of Robotics (Springer, 2008) pp. 161–185.CrossRefGoogle Scholar
Caccavale, F., Siciliano, B. and Villani, L., “Quaternion-Based Impedance with Nondiagonal Stiffness for Robot Manipulators,” Proceedings of the 1998 American Control Conference, vol. 1. (IEEE, 1998) pp. 468–472.CrossRefGoogle Scholar
Wang, Z. and Schwager, M., “Multi-Robot Manipulation Without Communication,” In:Distributed Autonomous Robotic Systems (Springer, 2016) pp. 135–149.CrossRefGoogle Scholar
Hsu, P., “Coordinated control of multiple manipulator systems,” IEEE Trans. Robot. Automat. 9(4), 400–410 (1993).Google Scholar
Mavridis, C., Alevizos, K., Bechlioulis, C. P. and Kyriakopoulos, K. J., “Human-Robot Collaboration Based on Robust Motion Intention Estimation with Prescribed Performance,” In: 2018 European Control Conference, Limassol, Cyprus, vol. 1 (2018) pp. 249–254.Google Scholar
Ioannou, P. A. and Sun, J., Robust Adaptive Control (Courier Corporation, 2012).Google Scholar