A novel optimal path-planning and following algorithm for wheeled robots on deformable terrains

Hamid Taghavifar, Subhash Rakheja, Giulio Reina

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)
    181 Downloads (Pure)


    An immense body of research has focused on path-planning and following of wheeled robots in unstructured surfaces. Nonholonomic robots traveling over deformable terrains together with complex operating conditions, however, pose further challenges in terms of a higher demand for robustness and optimality. In this paper, a Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm is employed for planning an optimal path of a wheeled robot, so as to ensure shortest path from the starting point to the target location together with safety through guaranteed avoidance of collisions with static and dynamic obstacles. The fundamental terramechanics concepts are employed to derive essential forces and moments acting on the wheeled robot. Subsequently, a kineto-dynamic model of the robot is developed for designing a novel robust control algorithm based on an exponential-integral-sliding mode (EISMC) scheme and a RBF-NN approximator. The results revealed that the proposed algorithm is responsive and robust to withstand adverse effects of structured and unstructured uncertainties by using the designed adaptation law according to the Lyapunov stability theorem. The effectiveness of the proposed algorithm is also validated against several reported frameworks.

    Original languageEnglish
    Pages (from-to)147-157
    Number of pages11
    JournalJournal of Terramechanics
    Early online date29 Dec 2020
    Publication statusPublished - Aug 2021

    Bibliographical note

    NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Terramechanics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Terramechanics, 96, (2017)
    DOI: 10.1016/j.jterra.2020.12.001

    © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/


    • Artificial Intelligence
    • Path-planning
    • PSO
    • Terrain
    • Terramechancis

    ASJC Scopus subject areas

    • Mechanical Engineering


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