Zhu, T. orcid.org/0009-0009-6297-3282, Mao, J. orcid.org/0000-0001-8979-2646, Han, L. orcid.org/0000-0002-4023-3322 et al. (2 more authors) (2024) Real-Time Dynamic Obstacle Avoidance for Robot Manipulators Based on Cascaded Nonlinear MPC With Artificial Potential Field. IEEE Transactions on Industrial Electronics, 71 (7). pp. 7424-7434. ISSN 0278-0046
Abstract
Nowadays, the realization of obstacle avoidance for robot manipulators are generally based on offline path planning, which may be insufficient for real-time dynamic obstacle avoidance scenarios. In order to achieve safe and smooth avoidance of dynamic obstacles, a low-latency motion planning algorithm, which takes into account the dynamic planning is of practical significance. Toward this end, this article proposes a cascaded nonlinear model predictive control (MPC) assigned with a two-stage optimization problem. Specially, the high-level MPC combines artificial potential field as a motion planner to generate foresight smooth trajectories. The low-level MPC acts as a trajectory tracker and a safety protector, taking along hard constraints to avoid collisions and singularities. In addition, a super-twisting observer is deployed to enhance the motion estimation accuracy of dynamic obstacles. Experimental results show that the proposed approach is beneficial to achieve safe and smooth dynamic obstacle avoidance in real-world scenarios.
Metadata
Item Type: | Article |
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Authors/Creators: | |
Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Artificial potential field (APF), dynamic obstacle avoidance, model predictive control (MPC), robot manipulators, super-twisting observer (STO). |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Nov 2023 09:49 |
Last Modified: | 24 May 2024 00:10 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/tie.2023.3306405 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205260 |