A Framework of Human impedance Estimation for Human-Robot Interaction

A framework for estimating the human impedance is proposed in this paper. In physical human-robot interaction (pHRI), safety and human acceptance are key issues when humans directly interact with the robots. In order to guarantee the safety and improve performance in pHRI, it is important to estimate the dynamics and intention of the human hand. In this work, we consider that a human subject physically contacts with a force sensor when a haptic device sets force in the proposed framework. The measured force, the surface electromyographic signal and the motion of the hand are used to estimate the parameters of human forearm’s impedance. The effectiveness of the proposed framework is demonstrated by experimental results.

[1]  Antonio Bicchi,et al.  Design and Control of a Variable Stiffness Actuator for Safe and Fast Physical Human/Robot Interaction , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[2]  Rui Li,et al.  Human Intention Prediction in Human-Robot Collaborative Tasks , 2018, HRI.

[3]  Jiang Hua,et al.  A Method for Synchronously Predicting Human Intention Based on Posture and Force , 2019 .

[4]  Ja Choon Koo,et al.  Improving transparency in physical human-robot interaction using an impedance compensator , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[5]  Aude Billard,et al.  Intention-based motion-adaptation using dynamical systems with human in the loop , 2018 .

[6]  C. L. Philip Chen,et al.  Universal Approximation Capability of Broad Learning System and Its Structural Variations , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[7]  Nikolaos G. Tsagarakis,et al.  Towards multi-modal intention interfaces for human-robot co-manipulation , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[8]  Michael A. Goodrich,et al.  Human-Robot Interaction: A Survey , 2008, Found. Trends Hum. Comput. Interact..

[9]  C. L. Philip Chen,et al.  Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Qiang Li,et al.  A Task Learning Mechanism for the Telerobots , 2019, Int. J. Humanoid Robotics.

[11]  Terrence Fong,et al.  A Survey of Methods for Safe Human-Robot Interaction , 2017, Found. Trends Robotics.

[12]  Sandra Hirche,et al.  Impedance-based Gaussian Processes for predicting human behavior during physical interaction , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[13]  Li-Chen Fu,et al.  Interactive torque controller with electromyography intention prediction implemented on exoskeleton robot NTUH-II , 2017, 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[14]  Jun Zhao,et al.  A Multimodal Framework Based on Integration of Cortical and Muscular Activities for Decoding Human Intentions About Lower Limb Motions , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[15]  Shuang Feng,et al.  Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification , 2020, IEEE Transactions on Cybernetics.

[16]  Seong-Whan Lee,et al.  Movement intention decoding based on deep learning for multiuser myoelectric interfaces , 2016, 2016 4th International Winter Conference on Brain-Computer Interface (BCI).

[17]  C. L. Philip Chen,et al.  A survey of human-centered intelligent robots: issues and challenges , 2017, IEEE/CAA Journal of Automatica Sinica.

[18]  Jing Luo,et al.  Enhanced teleoperation performance using hybrid control and virtual fixture , 2019, Int. J. Syst. Sci..

[19]  Keng Peng Tee,et al.  Continuous Role Adaptation for Human–Robot Shared Control , 2015, IEEE Transactions on Robotics.

[20]  Dragoljub Surdilovic,et al.  Contact stability issues in position based impedance control: theory and experiments , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[21]  Elvira Pirondini,et al.  EMG-based decoding of grasp gestures in reaching-to-grasping motions , 2017, Robotics Auton. Syst..