Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-24T08:47:14.587Z Has data issue: false hasContentIssue false
  • English
  • Français

A sparsity-based method for fault-tolerant manipulation of a redundant robot

Published online by Cambridge University Press:  08 March 2022

Zhan Li ,
Chunxu Li Open the ORCID record for Chunxu Li [Opens in a new window] ,
Shuai Li ,
Shuo Zhu  and
Hooman Samani
Zhan Li
Affiliation:
Computer Science Department, Swansea University, Swansea, UK
Chunxu Li*
Affiliation:
School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK
Shuai Li*
Affiliation:
College of Engineering, Swansea University, Swansea, UK
Shuo Zhu
Affiliation:
School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK
Hooman Samani
Affiliation:
School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK
*
*Corresponding authors. E-mails: [email protected]; [email protected]
*Corresponding authors. E-mails: [email protected]; [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

As an important part of the manufacturing industry, redundant robots can undertake heavy and tough tasks, which human operators are difficult to sustain. Such onerous and repetitive industrial manipulations, that is, positioning and carrying, impose heavy burdens on the load bearing for redundancy robots’ joints. Under the circumstances of long-term and intense industrial operations, joints of redundant robots are conceivably to fall into functional failure, which may possibly cause abrupt joint lock or freeze at unknown time instants. Therefore, task accuracy by end-effectors tends to diminish considerably and gradually because of broken-down joints. In this paper, a sparsity-based method for fault-tolerant motion planning of redundant robots is provided for the first time. The developed fault-tolerant redundancy resolution approach is defined as L1-norm based optimization with immediate variables involved to avoid discontinuity in the dynamic solution process. Meanwhile, those potential faulty joint(s) can be located by the designed fault observer with the proposed fault-diagnosis algorithm. The proposed fault-tolerant motion planning method with fault diagnosis is dynamically optimized by resultant primal dual neural networks with provable convergence. Moreover, the sparsity of joint actuation by the proposed method can be enhanced by around 43.87% and 36.51%, respectively, for tracking circle and square paths. Simulation and experimental findings on a redundant robot (KUKA iiwa) prove the efficacy of the developed defect tolerant approach based on sparsity.

Keywords

redundantkinematicsjoint failuremotion planning
Type
Research Article
Information
Robotica , Volume 40 , Issue 10 , October 2022 , pp. 3396 - 3414
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Zhan Li and Chunxu Li are the co-first authors

References

Yu, X., He, W., Li, Q., Li, Y. and Li, B., “Human-robot co-carrying using visual and force sensing,” IEEE Trans. Ind. Electron. 68(9), 8657–8666 (2021).CrossRefGoogle Scholar
Mu, Z., Zhang, B., Xu, W., Li, B. and Liang, B., “Fault Tolerance Kinematics and Trajectory Planning of a 6-DOF Space Manipulator Under a Single Joint Failure,” In: 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR) (IEEE, 2016) pp. 483–488.CrossRefGoogle Scholar
Lewis, C. L. and Maciejewski, A. A., “Fault tolerant operation of kinematically redundant manipulators for locked joint failures,” IEEE Trans. Robotic. Autom. 13(4), 622–629 (1997).CrossRefGoogle Scholar
Li, K. and Zhang, Y., “Fault-tolerant motion planning and control of redundant manipulator,” Control Eng. Pract. 20(3), 282–292 (2012).CrossRefGoogle Scholar
Sharkawy, A.-N., Koustoumpardis, P. N. and Aspragathos, N., “A recurrent neural network for variable admittance control in human–robot cooperation: Simultaneously and online adjustment of the virtual damping and inertia parameters,” Int. J. Intell. Robot. Appl. 4(4), 441–464 (2020).CrossRefGoogle Scholar
Yu, X., He, W., Li, H. and Sun, J., “Adaptive fuzzy full-state and output-feedback control for uncertain robots with output constraint,” IEEE Trans. Syst. Man Cybern. Syst. 51(11), 6994–7007 (2021).CrossRefGoogle Scholar
Yu, X., Li, B., He, W., Feng, Y., Cheng, L. and Silvestre, C., “Adaptive-constrained impedance control for human-robot co-transportation,” IEEE Trans. Cybern., 1–13 (2021).CrossRefGoogle Scholar
Chen, P., Xiang, J. and Wei, W., “A unified weighted least norm method for redundant manipulator control,” Int. J. Adv. Robot. Syst. 13(19), 1–10 (2016).CrossRefGoogle Scholar
Zhang, Y., Wang, J. and Xia, Y., “A dual neural network for redundancy resolution of kinematically redundant manipulators subject to joint limits and joint velocity limits,” IEEE Trans. Neural Netw. 14(3), 658–667 (2003).CrossRefGoogle ScholarPubMed
Zhang, Z., Zheng, L., Yu, J., Li, Y. and Yu, Z., “Three recurrent neural networks and three numerical methods for solving a repetitive motion planning scheme of redundant robot manipulators,” IEEE/ASME Trans. Mechatron. 22(3), 1423–1434 (2017).CrossRefGoogle Scholar
Jin, L., Li, S., Luo, X., Li, Y. and Qin, B., “Neural dynamics for cooperative control of redundant robot manipulators,” IEEE Trans. Ind. Inform. 14(9), 3812–3821 (2018).CrossRefGoogle Scholar
Chen, Y., McInroy, J. E. and Yi, Y., “Optimal, fault-tolerant mappings to achieve secondary goals without compromising primary performance,” IEEE Trans. Robotic. Autom. 19(4), 680–691 (2003).CrossRefGoogle Scholar
Roberts, R. G., Yu, H. G. and Maciejewski, A. A., “Fundamental limitations on designing optimally fault-tolerant redundant manipulators,” IEEE Trans. Robot. 24(5), 1224–1237 (2008).CrossRefGoogle Scholar
Ben-Gharbia, K. M., Maciejewski, A. A. and Roberts, R. G., “A kinematic analysis and evaluation of planar robots designed from optimally fault-tolerant jacobians,” IEEE Trans. Robot. 30(2), 516–524 (2014).CrossRefGoogle Scholar
Xie, B. and Maciejewski, A. A., “Kinematic design of optimally fault tolerant robots for different joint failure probabilities,” IEEE Robot. Autom. Lett. 3(2), 827–834 (2018).CrossRefGoogle Scholar
Li, K. and Zhang, Y., “Fault-tolerant motion planning and control of redundant manipulator,” Control Eng. Pract. 20(3), 282–292 (2012).CrossRefGoogle Scholar
Li, K., Yang, J., Yuan, C., Xu, J., Dai, X. and Luo, J., “Fault-Tolerant Motion Planning of Redundant Manipulator with Initial Position Error,” In: 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) (2018) pp. 533–538.Google Scholar
Li, Z., Li, C., Li, S. and Cao, X., “A fault-tolerant method for motion planning of industrial redundant manipulator,” IEEE Trans. Ind. Inform. 16(12), 7469–7478 (2020).CrossRefGoogle Scholar
Gao, Z., Cecati, C. and Ding, S. X., “A survey of fault diagnosis and fault-tolerant techniques—Part i: Fault diagnosis with model-based and signal-based approaches,” IEEE Trans. Ind. Electron. 62(6), 3757–3767 (2015).CrossRefGoogle Scholar
Costa, M. A., Wullt, B., Norrlöf, M. and Gunnarsson, S., “Failure detection in robotic arms using statistical modeling, machine learning and hybrid gradient boosting,” Measurement 146(7), 425–436 (2019).CrossRefGoogle Scholar
Bach, F., Jenatton, R., Mairal, J. and Obozinski, G., “Convex optimization with sparsity-inducing norms,” Optim. Mach. Learn. 5, 19–53 (2011).Google Scholar
Xu, J. and Wang, J., “Effective motion planning of manipulator based on SDPS-RRTConnect,” Robotica, 1–13 (2021).CrossRefGoogle Scholar
Polvara, R., Sharma, S., Wan, J., Manning, A. and Sutton, R., “Autonomous vehicular landings on the deck of an unmanned surface vehicle using deep reinforcement learning,” Robotica 37(11), 1867–1882 (2019).CrossRefGoogle Scholar
Bascetta, L., Ferretti, G. and Scaglioni, B., “Closed form Newton–Euler dynamic model of flexible manipulators,” Robotica 35(5), 1006–1030 (2017).CrossRefGoogle Scholar
Asif, M. S. and Romberg, J., “Fast and accurate algorithms for re-weighted -norm minimization,” IEEE Trans. Signal Process 61(23), 5905–5916 (2013).CrossRefGoogle Scholar
Selesnick, I., “Sparse regularization via convex analysis,” IEEE Trans. Signal Process. 65(17), 4481–4494 (2017).CrossRefGoogle Scholar
Boyd, S. and Vandenberghe, L.. Convex Optimization (Cambridge University Press, 2004).CrossRefGoogle Scholar
Gao, X.-B., “Exponential stability of globally projected dynamic systems,” IEEE Trans. Neural Netw. 14(2), 426–431 (2003).Google ScholarPubMed
Gao, X.-B. and Liao, L.-Z., “A neural network for monotone variational inequalities with linear constraints,” Phys. Lett. A 307(2), 118–128 (2003).CrossRefGoogle Scholar

Li et al. supplementary material

Li et al. supplementary material

Download Li et al. supplementary material(Video)
Video 2.4 MB