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An efficient self-motion scheme for redundant robot manipulators: a varying-gain neural self-motion approach

Published online by Cambridge University Press:  14 May 2021

Pengchao Zhang,
Xiaohui Ren and
Zhijun ZhangOpen the ORCID record for Zhijun Zhang[Opens in a new window]
Pengchao Zhang
Affiliation:
Key Laboratory of Industrial Automation of Shaanxi Province, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, ChinaSchool of Mechanical Engineering, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, China
Xiaohui Ren
Affiliation:
Key Laboratory of Industrial Automation of Shaanxi Province, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, ChinaSchool of Electrical Engineering, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, China
Zhijun Zhang*
Affiliation:
Key Laboratory of Industrial Automation of Shaanxi Province, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, ChinaGuangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou 510335, ChinaSchool of Automation Science and Engineering, East China Jiaotong University, Nanchang 330052, China
*
*Corresponding author. Email: auzjzhang@scut.edu.cn
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Abstract

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In order to achieve high efficient self-motion for a redundant robot manipulator, a novel quadratic programming and varying-gain recurrent neural network based varying-gain neural self-motion (VGN-SM) approach is proposed and developed. With VGN-SM, the convergence errors can be adaptively and efficiently converged to zero. For comparisons, a traditional fixed-parameter neural self-motion (FPN-SM) approach is also presented. Theoretical analysis shows that the proposed VGN-SM has higher accuracy than the traditional FPN-SM. Finally, comparative experiments between VGN-SM and FPN-SM are performed on a six degrees-of-freedom robot manipulator to verify the advantages of the novel VGN-SM.

Keywords

redundant robotself-motionvarying-gain recurrent neural networkzeroing neural networkconvergence

Information

Type
Article
Information
Robotica , Volume 39 , Issue 10 , October 2021, pp. 1897 - 1908
Copyright
© The Author(s), 2021. Published by Cambridge University Press

References

Mohammadi, A., Rezapour, E., Maggiore, M. and Pettersen, K. Y., “Maneuvering control of planar snake robots using virtual holonomic constraints,” IEEE Trans. Cont. Syst. Technol. 24(3), 884–899 (2016).CrossRefGoogle Scholar
Zhang, Z., Yan, Z. and Yang, H., “A varying parameter recurrent neural network for solving nonrepetitive motion problems of redundant robot manipulators,” IEEE Trans. Cont. Syst. Technol. 27(6), 2680–2687 (2018).CrossRefGoogle Scholar
Li, K. and Zhang, Y., “State adjustment of redundant robot manipulator based on quadratic programming,” Robotica 30(3), 477–489 (2012).CrossRefGoogle Scholar
Chyan, G. S. and Ponnambalam, S., “Obstacle avoidance vontrol of redundant robots using variants of particle swarm optimization,” Robot. Comput.-Integr. Manufact. 28(2), 147–153 (2012).Google Scholar
Jin, M., Zhou, C. and Liu, Y., “Hybrid Impedance Control of 7-DOF Redundant Manipulator with Dual Compliant Surface,” IEEE International Conference on Mechatronics and Automation (2015) pp. 1424–1429.Google Scholar
Guo, D. and Zhang, Y., “Acceleration-level inequality-based MAN scheme for obstacle avoidance of redundant robot manipulators,” IEEE Trans. Ind. Electron. 61(12), 6903–6914 (2014).CrossRefGoogle Scholar
Zergeroglu, E., Dawson, D. D., Walker, I. W. and Setlur, P., “Nonlinear Tracking Control of Kinematically Redundant Robot Manipulators,” Proceedings of the 2000 American Control Conference, ACC (2004) pp. 129–132.Google Scholar
Xu, W., Zhang, J., Liang, B. and Li, B., “Singularity snalysis and avoidance for robot manipulators with non-spherical wrists,” IEEE Trans. Ind. Electron. 63(1), 277–290 (2016).CrossRefGoogle Scholar
Li, X. and Cheah, C. C., “Global task-space adaptive control of robot,” Automatica 49(1), 58–69 (2013).CrossRefGoogle Scholar
Zhang, Z., Zheng, L., Yu, J. and Li, Y., “Three recurrent neural networks and three numerical methods for solving a repetitive motion planning scheme of redundant,” IEEE/ASME Trans. Mechatron. 22(3), 1423–1434 (2017).CrossRefGoogle Scholar
Hollerbach, J. and Suh, K., “Redundancy resolution of manipulators through torque optimization,” IEEE J. Robot. Automat. 3(4), 308–316 (1987).CrossRefGoogle Scholar
Suh, K. and Hollerbach, J., “Local Versus Global Torque Optimization of Redundant Manipulators,” IEEE International Conference on Robotics and Automation (1987) pp. 619–624.Google Scholar
Zhang, Y., Ge, S. S. and Lee, T. H., “A unified quadratic-programming-based dynamical system approach to joint torque optimization of physically constrained redundant manipulators,” IEEE Trans. Syst. Man Cybernet. 34(5), 2126–2132 (2004).CrossRefGoogle ScholarPubMed
Zhang, Y., “On the LVI-based Primal-Dual Neural Network for Solving Online Linear and Quadratic Programming Problems,” Proceedings of the American Control Conference (2005) pp. 1351–1356.Google Scholar
Puga, J. P. and Chiang, L. E., “Optimal trajectory planning for a redundant mobile manipulator with non-holonomic constraints performing push-Pull tasks,” Robotica 26(3), 385–394 (2008).CrossRefGoogle Scholar
Khatib, O. and Bowling, A., “Optimization of the Inertial and Acceleration Characteristics of Manipulators,” Proceedings of IEEE International Conference on Robotics and Automation (1996) pp. 2883–2889.Google Scholar
Klein, C. A. and Huang, C. H., “Review of pseudoinverse control for use with kinematically redundant manipulators,” IEEE Trans. Syst. Man Cybernet. SMC-13(2), 245–250 (1983).CrossRefGoogle Scholar
Hussain, R., Qureshi, A., Mughal, R. A., Ijaz, R., Rashid, N., Tiwana, M. I and Iqbal, J. “Inverse Kinematics Control of Redundant Planar Manipulator with Joint Constraints Using Numerical Method,” International Conference on Control, Automation and Systems (2015) pp. 806–810.Google Scholar
Qiu, C., Cao, Q. and Miao, S., “An on-line task modification method for singularity avoidance of robot manipulators,” Robotica 27(4), 539–546 (2009).CrossRefGoogle Scholar
Wang, J., Hu, Q. and Jiang, D., “A lagrangian network for kinematic control of redundant manipulators,” IEEE Trans. Neural Netw. 10(5), 1123–1132 (1999).CrossRefGoogle Scholar
Ding, H. and Tso, S. K., “A fully neural-network-based planning scheme for torque minimization of redundant manipulators,” IEEE Trans. Ind. Electron. 46(1), 199–206 (1999).CrossRefGoogle Scholar
Ding, H. and Wang, J., “Recurrent neural networks for minimum infinity-norm kinematic control of redundant manipulators,” IEEE Trans. Syst. Man Cybernet. A Syst. Hum. 29(3), 269–276 (1999).CrossRefGoogle Scholar
Burdick, J. W., “On the Tnverse Kinematics of Redundant Manipulators: Characterization of the Self-Motion Manifolds,” Proceedings of the 4th International Conference on Advanced Robotics Columbus (1989) pp. 25–34.Google Scholar
Euler, J. A., Dubey, R. V. and Babcock, S. M., “Self motion determination based on actuator velocity bounds for redundant manipulators,” J. Robot. Syst. 6(4), 417–425 (1989).CrossRefGoogle Scholar
Rahmanian-Shahri, N. and Troch, I., “Collision-avoidance for redundant robots through control of the self-motion of the manipulator,” J. Intell. Robot. Syst. 16, 123–149 (1996).CrossRefGoogle Scholar
Zhang, Y., Tan, N., Tan, Z. and Chen, J., “Quadratic-optimization based self-motion planning with no target-configuration assigned and verified via PA10 simulations,” J. Jinan Univ. 31(3), 229–234 (2010).Google Scholar
Zhang, Y., Zhu, H., Tan, Z. and Cai, B., “Self-Motion Planning of Redundant Robot Manipulators Based on Quadratic Program and Shown via PA10 Example,” International Symposium on Systems and Control in Aerospace and Astronautics (2008).CrossRefGoogle Scholar
Zhang, Z., Lu, Y. and Zheng, L., “A new varying-parameter convergent-differential neural-network for solving time-varying convex QP problem constrained by linear-equality,” IEEE Trans. Automat. Cont. 63(12), 4110–4125 (2018).CrossRefGoogle Scholar