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An efficient follow-the-leader strategy for continuum robot navigation and coiling

Mohammad, Abdelkhalick; Russo, Matteo; Fang, Yihua; Dong, Xin; Axinte, Dragos; Kell, James

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Authors

Matteo Russo

Yihua Fang

DRAGOS AXINTE dragos.axinte@nottingham.ac.uk
Professor of Manufacturing Engineering

James Kell



Abstract

Efficient path planning for hyper-redundant continuum and snake-like robots is a challenging task due to limited sensing capabilities, high computational loads, multiple possible solutions, and non-linear models. This paper presents a new approach to snake robot navigation and coiling, with an algorithm that enables online step-by-step position adjustment with a follow-the-leader strategy, significantly improving the performance of the robot when compared to previous methods. The proposed algorithm is demonstrated on a 16-degree-of-freedom snake-like robot for inspection and maintenance tasks in nuclear facilities.

Citation

Mohammad, A., Russo, M., Fang, Y., Dong, X., Axinte, D., & Kell, J. (2021). An efficient follow-the-leader strategy for continuum robot navigation and coiling. IEEE Robotics and Automation Letters, 6(4), 7493 - 7500. https://doi.org/10.1109/LRA.2021.3097265

Journal Article Type Article
Acceptance Date Jun 18, 2021
Online Publication Date Jul 14, 2021
Publication Date Oct 1, 2021
Deposit Date Aug 5, 2021
Publicly Available Date Mar 28, 2024
Journal IEEE Robotics and Automation Letters
Electronic ISSN 2377-3774
Peer Reviewed Peer Reviewed
Volume 6
Issue 4
Pages 7493 - 7500
DOI https://doi.org/10.1109/LRA.2021.3097265
Keywords Artificial Intelligence; Control and Optimization; Computer Science Applications; Computer Vision and Pattern Recognition; Mechanical Engineering; Human-Computer Interaction; Biomedical Engineering; Control and Systems Engineering
Public URL https://nottingham-repository.worktribe.com/output/5899795
Publisher URL https://ieeexplore.ieee.org/document/9484758
Additional Information © 2021 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.

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