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New repetitive motion planning scheme with cube end-effector planning precision for redundant robotic manipulators

Published online by Cambridge University Press:  31 August 2021

Limin Shen Open the ORCID record for Limin Shen [Opens in a new window]  and
Yuanmei Wen
Limin Shen*
Affiliation:
Department of Mechanical and Electronic Engineering, Guangzhou Railway Polytechnic, Guangzhou 510430, China
Yuanmei Wen
Affiliation:
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China E-mail: shenlimin2019@126.com
*
*Corresponding author. E-mail: slmluck@126.com
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Abstract

Repetitive motion planning (RMP) is important in operating redundant robotic manipulators. In this paper, a new RMP scheme that is based on the pseudoinverse formulation is proposed for redundant robotic manipulators. Such a scheme is derived from the discretization of an existing RMP scheme by utilizing the difference formula. Then, theoretical analysis and results are presented to show the characteristic of the proposed RMP scheme. That is, this scheme possesses the characteristic of cube pattern in the end-effector planning precision. The proposed RMP scheme is further extended and studied for redundant robotic manipulators under joint constraint. Based on a four-link robotic manipulator, simulation results substantiate the effectiveness and superiority of the proposed RMP scheme and its extended one.

Keywords

Repetitive motion planningpseudoinverse formulationtheoretical analysisredundant robotic manipulatorsjoint constraint
Type
Research Article
Information
Robotica , Volume 40 , Issue 4 , April 2022 , pp. 1233 - 1254
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Jin, L., Li, S., Yu, J. and He, J., “Robot manipulator control using neural networks: A survey,” Neurocomputing 285, 23–34 (2018).CrossRefGoogle Scholar
Ruiz, A. G., Santos, J. C., Croes, J., Desmet, W. and da Silva, M. M., “On redundancy resolution and energy consumption of kinematically redundant planar parallel manipulators,” Robotica 36(6), 809–821 (2018).CrossRefGoogle Scholar
Li, S., Jin, L. and Mirza, M. A., Kinematic Control of Redundant Robot Arms Using Neural Networks (Wiley, Hoboken, 2019).CrossRefGoogle Scholar
Mu, Z., Liu, T., Xu, W., Lou, Y. and Liang, B., “Dynamic feedforward control of spatial cable-driven hyper-redundant manipulators for on-orbit servicing,” Robotica 37(1), 18–38 (2019).CrossRefGoogle Scholar
Khan, A. H., Li, S., and Luo, X., “Obstacle avoidance and tracking control of redundant robotic manipulator: An RNN-based metaheuristic approach,” IEEE Trans. Ind. Informat. 16(7), 4670–4680 (2020).CrossRefGoogle Scholar
Hassan, A., El-Habrouk, M., and Deghedie, S., “Inverse kinematics of redundant manipulators formulated as quadratic programming optimization problem solved using recurrent neural networks: A review,” Robotica, in press (2020).Google Scholar
Craig, J. J., Introduction to Robotics: Mechanics and Control (Pearson/Prentice Hall Upper Saddle River, NJ, 2005).Google Scholar
Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics: Modeling, Planning and Control (Springer-Verlag, London, 2009).CrossRefGoogle Scholar
Sturz, Y. R., Affolter, L. M. and Smith, R. S., “Parameter identification of the KUKA LBR iiwa robot including constraints on physical feasibility,” IFAC-Papers OnLine 50(1), 6863–6868 (2017).CrossRefGoogle Scholar
Mao, Y., Lu, Q. and Xu, Q., “Visual Servoing Control Based on EGM Interface of An ABB Robot,” Proceeding of Chinese Automation Congress (2018) pp. 3260–3264.Google Scholar
Guo, D., Xu, F. and Yan, L., “New pseudoinverse-based path-planning scheme with PID characteristic for redundant robot manipulators in the presence of noise,” IEEE Trans. Control Syst. Technol. 26(6), 2008–2019 (2018).Google Scholar
Guo, D., Li, K. and Liao, B., “Bi-criteria minimization with MWVN-INAM type for motion planning and control of redundant robot manipulators,” Robotica 36(5), 655–675 (2018).CrossRefGoogle Scholar
Filho, P. P. R., da Silva, S. P. P., Praxedes, V. N., Hemanth, J. and de Albuquerque, V. H. C., “Control of singularity trajectory tracking for robotic manipulator by genetic algorithms,” J. Comput. Sci. 30, 55–64 (2019).CrossRefGoogle Scholar
Fu, L. and Zhao, J., “Maxwell model-based null space compliance control in the task-priority framework for redundant manipulators,” IEEE Access 8, 35892–35904 (2020).CrossRefGoogle Scholar
Zhang, Y. and Zhang, Z., Repetitive Motion Planning and Control of Redundant Robot Manipulators (Springer-Verlag, New York, 2013).CrossRefGoogle Scholar
Klein, C. A. and Huang, C. H., “Review of pseudoinverse control for use with kinematically redundant manipulators,” IEEE Trans. Syst., Man, Cybern. 13(3), 245–250 (1983).CrossRefGoogle Scholar
Shamir, T., “The singularities of redundant robot arms,” Int. J. Robot. Res. 9(1), 113–121 (1990).CrossRefGoogle Scholar
Shamir, T. and Yomdin, Y., “Repeatability of redundant manipulators: mathematical solution of the problem,” IEEE Trans. Autom. Control 33(11), 1004–1009 (1988).Google Scholar
Roberts, R. G. and Maciejewski, A. A., “Nearest optimal repeatable control strategies for kinematically redundant manipulators,” IEEE Trans. Robot. Autom. 8(3), 327–337 (1992).CrossRefGoogle Scholar
De Luca, A., Lanari, L., and Oriolo, G., “Control of Redundant Robots on Cyclic Trajectories,” Proceeding of IEEE International Conference on Robotics and Automation (1992) pp. 500–506.Google Scholar
Assal, S. F. M., “Neural Network Learning from Hint for the Cyclic Motion of the Constrained Redundant Arm,” Proceeding of IEEE International Conference on Robotics and Automation (2006) pp. 3679–3684.Google Scholar
Benzaoui, M. and Chekireb, H., “Cyclic Control of Redundant Robot under Constraint with the Self-Motion Method,” Proceeding of International Multi-Conference on Systems, Signals and Devices (2008) pp. 1–7.Google Scholar
Shen, L. and Wen, Y., “Investigation on the discretization of a repetitive path planning scheme for redundant robot manipulators,” IEEE Access 8, 23895–23903 (2020).CrossRefGoogle Scholar
Guo, D., Li, Z., Khan, A. H., Feng, Q. and Cai, J., “Repetitive motion planning of robotic manipulators with guaranteed precision,” IEEE Trans. Ind. Informat. 17(1), 356–366 (2021).CrossRefGoogle Scholar
Li, Z., Xu, F., Guo, D., Wang, P. and Yuan, B., “New P-type RMPC scheme for redundant robot manipulators in noisy environment,” Robotica 38(5), 775–786 (2020).CrossRefGoogle Scholar
Li, Z., Liao, B., Xu, F. and Guo, D., A new repetitive motion planning scheme with noise suppression capability for redundant robot manipulators. IEEE Trans. Syst., Man, Cybern., Syst., in press (2020).CrossRefGoogle Scholar
Xie, Z., Jin, L., Luo, X., Li, S. and Xiao, X., “A data-driven cyclic-motion generation scheme for kinematic control of redundant manipulators,” IEEE Trans. Syst, Control. Tech., in press (2020).CrossRefGoogle Scholar
Jin, L., Xie, Z., Liu, M., Ke, C., Li, C. and Yang, C., “Novel joint-drift-free scheme at acceleration level for robotic redundancy resolution with tracking error theoretically eliminated,” IEEE/ASME Tran. Mech., in press (2020).CrossRefGoogle Scholar
Xie, Z., Jin, L., Luo, X., Sun, Z. and Liu, M., “RNN for repetitive motion generation of redundant robot manipulators: An orthogonal projection-based scheme,” IEEE Trans. Netw, Neural. Syst, Learning ., in press (2020).Google Scholar
Zhang, Y., Li, S., Zou, J. and Khan, A. H., “A passivity-based approach for kinematic control of manipulators with constraints,” IEEE Trans. Ind. Informat. 16(5), 3029–3038 (2020).CrossRefGoogle Scholar
Xie, Z., Jin, L., Du, X., Xiao, X., Li, H. and Li, S., “On generalized RMP scheme for redundant robot manipulators aided with dynamic neural networks and nonconvex bound constraints,” IEEE Trans. Ind. Informat. 15(9), 5172–5181 (2019).CrossRefGoogle Scholar
Yang, M., Zhang, Y., Huang, H., Chen, D. and Li, J., “Jerk-Level Cyclic Motion Planning and Control for Constrained Redundant Robot Manipulators using Zhang Dynamics: Theoretics,” Proceeding of Chinese Control Decision Conference (2018) pp. 450–455.Google Scholar
Chen, D. and Zhang, Y., “Jerk-level synchronous repetitive motion scheme with gradient-type and zeroing-type dynamics algorithms applied to dual-arm redundant robot system control,” Int. J. Syst. Sci. 48(13), 2713–2727 (2017).CrossRefGoogle Scholar
Zhang, Y., Qi, Z., Li, J., Qiu, B. and Yang, M., “Stepsize domain confirmation and optimum of ZeaD formula for future optimization,” Numer. Algor. 81, 561–574 (2019).CrossRefGoogle Scholar
Mathews, J. H. and Fink, K. D., Numerical Methods using MATLAB, 4th edn. (Prentice Hall, New Jersey, 2004).Google Scholar
Zhang, Y., Jin, L., Guo, D., Yin, Y. and Chou, Y., “Taylor-type 1-step-ahead numerical differentiation rule for first-order derivative approximation and ZNN discretization,” J. Comput. Appl. Math. 273, 29–40 (2017).Google Scholar
Zhang, Y., Yang, M., Huang, H., Xiao, M. and Hu, H., “New discrete solution model for solving future different-level linear inequality and equality with robot manipulator control,” IEEE Trans. Ind. Informat. 15(4), 1975–1984 (2019).Google Scholar
Yang, M., Zhang, Y., Hu, H. and Qiu, B., “General 7-instant DCZNN model solving future different-level system of nonlinear inequality and linear equation,” IEEE Trans. Neural Netw. Learning Syst. 31(9), 3204–3214 (2020).CrossRefGoogle ScholarPubMed
Griffiths, D. F. and Higham, D. J., Numerical Methods for Ordinary Differential Equations: Initial Value Problems (Springer, London, 2010).CrossRefGoogle Scholar
Chaumette, F. and Marchand, E., “A redundancy-based iterative approach for avoiding joint limits: application to visual servoing,” IEEE Trans. Robot. Autom. 17(5), 719–730 (2001).CrossRefGoogle Scholar
Colome, A. and Torras, C., “Closed-Loop inverse kinematics for redundant robots: comparative assessment and two enhancements,” IEEE/ASME Trans. Mechatron. 20(2), 944–955 (2015).CrossRefGoogle Scholar
Atawnih, D., Papageorgiou, D. and Doulgeri, Z., “Kinematic control of redundant robots with guaranteed joint limit avoidance,” Robot. Auton. Syst. 79, 122–131 (2016).CrossRefGoogle Scholar
Ortenzi, D., Muthusamy, R., Freddi, A., Monteriu, A. and Kyrki, V., “Dual-arm cooperative manipulation under joint limit constraints,” Robot. Auton. Syst. 99, 110–120 (2018).Google Scholar
Xu, F., Li, Z., Nie, Z., Shao, H. and Guo, D., “New recurrent neural network for online solution of time-dependent underdetermined linear system with bound constraint,” IEEE Trans. Ind. Informat., 15(4), 2167–2176 (2019).CrossRefGoogle Scholar
Guo, D., Li, S. and Stanimirovic, P. S., “Analysis and application of modified ZNN design with robustness against harmonic noise,” IEEE Trans. Ind. Informat. 16(7), 4627–4638 (2020).CrossRefGoogle Scholar