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An L₁-Norm Based Optimization Method for Sparse Redundancy Resolution of Robotic Manipulators

Zhan Li, Shuai Li Orcid Logo

IEEE Transactions on Circuits and Systems II: Express Briefs, Volume: 69, Issue: 2, Pages: 469 - 473

Swansea University Authors: Zhan Li, Shuai Li Orcid Logo

Abstract

For targeted motion control tasks of manipulators, it is frequently necessary to make use of full levels of joint actuation to guarantee successful motion planning and path tracking. Such way of motion planning and control may keep the joint actuation in a non-sparse manner during motion control pro...

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Published in: IEEE Transactions on Circuits and Systems II: Express Briefs
ISSN: 1549-7747 1558-3791
Published: Institute of Electrical and Electronics Engineers (IEEE) 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa57319
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Abstract: For targeted motion control tasks of manipulators, it is frequently necessary to make use of full levels of joint actuation to guarantee successful motion planning and path tracking. Such way of motion planning and control may keep the joint actuation in a non-sparse manner during motion control process. In order to improve sparsity of joint actuation for manipulator systems, a novel motion planning scheme which can optimally and sparsely adopt joint actuation is proposed in this paper. The proposed motion planning strategy is formulated as a constrained L1 norm optimization problem, and an equivalent enhanced optimization solution dealing with bounded joint velocity is proposed as well. A new primal dual neural network with a new solution set division is further proposed and applied to solve such bounded optimization which can sparsely adopt joint actuation for motion control. Simulation and experiment results demonstrate the efficiency, accuracy and superiority of the proposed method for optimally and sparsely adopting joint actuation. The average sparsity (i.e., -||˙θ||p where θ denotes the joint angle) of the joint motion of the manipulator can be increased by 39.22% and 51.30% for path tracking tasks in X-Y and X-Z planes respectively, indicating that the sparsity of joint actuation can be enhanced.
College: College of Science
Issue: 2
Start Page: 469
End Page: 473