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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]
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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

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Footnotes

Zhan Li and Chunxu Li are the co-first authors

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