Explore Bravely: Wheeled-Legged Robots Traverse in Unknown Rough Environment

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Explore Bravely: Wheeled-Legged Robots Traverse in Unknown Rough Environment
Title:
Explore Bravely: Wheeled-Legged Robots Traverse in Unknown Rough Environment
Journal Title:
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publication Date:
10 February 2021
Citation:
G. Haddeler, J. Chan, Y. You, S. Verma, A. H. Adiwahono and C. Meng Chew, "Explore Bravely: Wheeled-Legged Robots Traverse in Unknown Rough Environment," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 7521-7526, doi: 10.1109/IROS45743.2020.9341610.
Abstract:
This paper addressed a challenging problem of wheeled-legged robots with high degrees of freedom exploring in unknown rough environments. The proposed method works as a pipeline to achieve prioritized exploration comprising three primary modules: traversability analysis, frontier-based exploration and hybrid locomotion planning. Traversability analysis provides robots an evaluation about surrounding terrain according to various criteria ( roughness, slope etc.) and other semantic information (small step, stair, bridge etc.), while novel gravity point frontier-based exploration algorithm can effectively decide which direction to go even in unknown environments based on robots' current pose and desired one. Given all these information, hybrid locomotion planner will generate a path with motion mode (driving or walking) encoded by optimizing among different objectives and constraints. Lastly, our approach was well verified in both simulation and experiment on a wheeled quadrupedal robot Pholus.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 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.
ISSN:
2153-0866
2153-0858
ISBN:
978-1-7281-6212-6
978-1-7281-6213-3
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