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Neural-learning-enhanced Cartesian Admittance control of robot with moving RCM constraints

Published online by Cambridge University Press:  20 December 2022

Hang Su Open the ORCID record for Hang Su [Opens in a new window] ,
Yunus Schmirander ,
Sarah Elena Valderrama-Hincapié ,
Wen Qi Open the ORCID record for Wen Qi [Opens in a new window] ,
Salih Ertug Ovur  and
Juan Sandoval
Hang Su
Affiliation:
Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, Italy
Yunus Schmirander
Affiliation:
Department of Computer Science, University of Innsbruck, Innsbruck, Austria
Sarah Elena Valderrama-Hincapié
Affiliation:
Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, Italy
Wen Qi*
Affiliation:
School of Future Technology, South China University of Technology, Guangzhou, China Pazhou Lab, Guangzhou, China
Salih Ertug Ovur
Affiliation:
Department of Electrical and Electronic Engineering, Imperial College London, London, UK
Juan Sandoval
Affiliation:
Department of GMSC, Pprime Institute, CNRS, ENSMA, University of Poitiers, UPR 3346, Poitiers, France
*
*Corresponding author. E-mail: wenqi@scut.edu.cn
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Abstract

In this manuscript, a scheme for neural-learning-enhanced Cartesian Admittance control is presented for a robotic manipulator to deal with dynamic environments with moving remote center of motion (RCM) constraints. Although some research has been implemented to address fixed constrained motion, the dynamic moving movement constraint is still challenging. Indeed, the moving active RCM constraints generate uncertain disturbance on the robot tool shaft with unknown dynamics. The neural-learning-enhanced decoupled controller with disturbance optimisation is employed and implemented to maintain the performance under the kinematic uncertain and dynamic uncertain generated. In addition, the admittance Cartesian control method is introduced to control the robot, providing compliant behaviour to an external force in its operational space. In this proposed framework, a neural-learning-enhanced disturbance observer is investigated to calculate the external factor operating on the end effector premised on generalised momentum in order to ensure accuracy. Finally, the experiments are implemented using a redundant robot to validate the efficacy of the suggested approach with moving RCM constraints.

Keywords

adaptive neural controlCartesian admittance controlnull spacemoving remote center of motion
Type
Research Article
Information
Robotica , Volume 41 , Issue 4 , April 2023 , pp. 1231 - 1243
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Zhou, M., Yu, Q., Huang, K., Mahov, S., Eslami, A., Maier, M., Lohmann, C. P., Navab, N., Zapp, D., Knoll, A. and Nasseri, M. A. , “Towards robotic-assisted subretinal injection: A hybrid parallel-serial robot system design and preliminary evaluation,” IEEE Trans. Ind. Electron. 67(8), 6617–6628 (2019).CrossRefGoogle Scholar
Chen, L., Jiang, Z., Cheng, L., Knoll, A. C. and Zhou, M., “Deep reinforcement learning based trajectory planning under uncertain constraints,” Front. Neurorobotics 16, 1–10 (2022).CrossRefGoogle ScholarPubMed
Okamura, A. M., “Haptic feedback in robot-assisted minimally invasive surgery,” Curr. Opin. Urol. 19(1), 102–107 (2009).CrossRefGoogle ScholarPubMed
Su, H., Hu, Y., Karimi, H. R., Knoll, A., Ferrigno, G. and De Momi, E., “Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results,” Neural Netw. 131, 291–299 (2020).CrossRefGoogle ScholarPubMed
Su, H., Qi, W., Schmirander, Y., Ovur, S. E., Cai, S. and Xiong, X., “A human activity-aware shared control solution for medical human-robot interaction,” Assembly Autom. 42(3), ahead–of-print (2022).CrossRefGoogle Scholar
Su, H., Mariani, A., Ovur, S. E., Menciassi, A., Ferrigno, G. and De Momi, E., “Toward teaching by demonstration for robot-assisted minimally invasive surgery,” IEEE Trans. Autom. Sci. Eng. 18(2), 484–494 (2021).CrossRefGoogle Scholar
Guo, K., Su, H. and Yang, C., “A small opening workspace control strategy for redundant manipulator based on RCM method,” IEEE Trans. Contr. Syst. Technol. 30(6), 2717–2725 (2022).CrossRefGoogle Scholar
Ortmaier, T. and Hirzinger, G., “Cartesian Control Issues for Minimally Invasive Robot Surgery,” In: Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000), vol. 1 (IEEE, 2000) pp. 565-571 Google Scholar
From, P. J., “On the kinematics of robotic-assisted minimally invasive surgery,” Model. Identification Contr. 34(2), 69–82 (2013).CrossRefGoogle Scholar
Sandoval, J., Poisson, G. and Vieyres, P., “Improved Dynamic Formulation for Decoupled Cartesian Admittance Control and RCM Constraint,” In: 2016 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2016) pp. 1124–1129.CrossRefGoogle Scholar
Ge, S. S., Hang, C. C., Lee, T. H. and Zhang, T.. Stable Adaptive Neural Network Control, vol. 13. (Springer Science & Business Media, New York, NY, USA, 2013).Google Scholar
Zhang, X., Pan, W., Scattolini, R., Yu, S. and Xu, X., “Robust tube-based model predictive control with koopman operators,” Automatica 137, 110114 (2022).CrossRefGoogle Scholar
Zhang, X., Liu, J., Xu, X., Yu, S. and Chen, H., “Robust Learning-Based Predictive Control for Discrete-Time Nonlinear Systems with Unknown Dynamics and State Constraints,” In: IEEE Trans. Syst. Man Cybernet. (2022).Google Scholar
Wen, C., Zhou, J., Liu, Z. and Su, H., “Robust adaptive control of uncertain nonlinear systems in the presence of input saturation and external disturbance,” IEEE Trans. Automat. Contr. 56(7), 1672–1678 (2011).CrossRefGoogle Scholar
Zhang, X., Jiang, Y., Lu, Y. and Xu, X., “A receding-horizon reinforcement learning approach for kinodynamic motion planning of autonomous vehicles,” IEEE Trans. Intell. Veh. 7(3), 556–568 (2022).CrossRefGoogle Scholar
He, W., Dong, Y. and Sun, C., “Adaptive neural impedance control of a robotic manipulator with input saturation,” IEEE Trans. Syst. Man Cybern. Syst. 46(3), 334–344 (2016).CrossRefGoogle Scholar
Chen, X., Chen, C., Wang, Y., Yang, B., Ma, T., Leng, Y. and Fu, C., “A piecewise monotonic gait phase estimation model for controlling a powered transfemoral prosthesis in various locomotion modes,” IEEE Robot. Automat. Lett. 7(4), 9549–9556 (2022).CrossRefGoogle Scholar
Chen, X., Zhang, K., Liu, H., Leng, Y. and Fu, C., “A probability distribution model-based approach for foot placement prediction in the early swing phase with a wearable imu sensor,” IEEE Trans. Neur. Syst. Rehabil. 29, 2595–2604 (2021).CrossRefGoogle ScholarPubMed
Ott, C.. “Cartesian Impedance Control: The Rigid Body Case,” In: Cartesian Impedance Control of Redundant and Flexible-Joint Robots (Springer, Berlin, Heidelberg, Germany, 2008) pp. 29–44.Google Scholar
Yang, C., Wang, X., Cheng, L. and Ma, H., “Neural-learning-based telerobot control with guaranteed performance,” IEEE Trans. Cybernet. 47(10), 3148–3159 (2017).CrossRefGoogle ScholarPubMed
Li, Z., Deng, C. and Zhao, K., “Human-cooperative control of a wearable walking exoskeleton for enhancing climbing stair activities,” IEEE Trans. Ind. Electron. 67(4), 3086–3095 (2019).CrossRefGoogle Scholar
Li, Z., Xu, C., Wei, Q., Shi, C. and Su, C.-Y., “Human-inspired control of dual-arm exoskeleton robots with force and impedance adaptation,” IEEE Trans. Syst. Man Cybernet. Syst. 50(12), 5296–5305 (2018).CrossRefGoogle Scholar
Li, Z., Zhao, K., Zhang, L., Wu, X., Zhang, T., Li, Q., Li, X. and Su, C.-Y., “Human-in-the-loop control of a wearable lower limb exoskeleton for stable dynamic walking,” IEEE/ASME Trans. Mechatron. 26(5), 2700–2711 (2020).CrossRefGoogle Scholar
Khatib, O., “A unified approach for motion and force control of robot manipulators: The operational space formulation,” IEEE J. Robot. Automat. 3(1), 43–53 (1987).CrossRefGoogle Scholar
Albu-Schaffer, A., Ott, C., Frese, U. and Hirzinger, G., “Cartesian Impedance Control of Redundant Robots: Recent Results with the Dlr-Light-weight-arms,” In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA’03, vol. 3 (IEEE, 2003) pp. 3704–3709. Google Scholar
Shuzhi, S. G., Hang, C. C. and Woon, L., “Adaptive neural network control of robot manipulators in task space,” IEEE Trans. Ind. Electron. 44(6), 746–752 (1997).CrossRefGoogle Scholar
Buerger, S. P. and Hogan, N., “Complementary stability and loop shaping for improved human-robot interaction,” IEEE Trans. Robot. 23(2), 232–244 (2007).CrossRefGoogle Scholar
Zhang, L., Li, Z. and Yang, C., “Adaptive neural network based variable stiffness control of uncertain robotic systems using disturbance observer,” IEEE Trans. Ind. Electron. 64(3), 2236–2245 (2017).CrossRefGoogle Scholar
Schreiber, G., Stemmer, A. and Bischoff, R., “The Fast Research Interface for the Kuka Lightweight Robot,” In: IEEE Workshop on Innovative Robot Control Architectures for Demanding (Research) Applications How to Modify and Enhance Commercial Controllers (ICRA 2010) (2010) pp. 15–21.Google Scholar
Zhong, H., Wang, Y., Miao, Z., Li, L., Fan, S. and Zhang, H., “A homography-based visual servo control approach for an underactuated unmanned aerial vehicles in gps-denied environments,” IEEE Trans. Intell. Veh., 1–1 (2022).Google Scholar