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Authors: Daniel Gleason and Michael Jenkin

Affiliation: Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Canada

Keyword(s): Robot Navigation, Reinforcement Learning, Navigating Mazes.

Abstract: Developing a navigation function for an unknown environment is a difficult task, made even more challenging when the environment has complex structure and the robot imposes nonholonomic constraints on the problem. Here we pose the problem of navigating an unknown environment as a reinforcement learning task for an Ackermann vehicle. We model environmental complexity using a standard characterization of mazes, and we show that training on complex maze architectures with loops (braid and partial braid mazes) results in an effective policy, but that for a more efficient policy, training on mazes without loops (perfect mazes) is to be preferred. Experimental results obtained in simulation are validated on a real robot operating both indoors and outdoors, assuming good localization and a 2D LIDAR to recover the local structure of the environment.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Gleason, D. and Jenkin, M. (2022). Nonholonomic Robot Navigation of Mazes using Reinforcement Learning. In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO; ISBN 978-989-758-585-2; ISSN 2184-2809, SciTePress, pages 369-376. DOI: 10.5220/0011123600003271

@conference{icinco22,
author={Daniel Gleason. and Michael Jenkin.},
title={Nonholonomic Robot Navigation of Mazes using Reinforcement Learning},
booktitle={Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO},
year={2022},
pages={369-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011123600003271},
isbn={978-989-758-585-2},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - Nonholonomic Robot Navigation of Mazes using Reinforcement Learning
SN - 978-989-758-585-2
IS - 2184-2809
AU - Gleason, D.
AU - Jenkin, M.
PY - 2022
SP - 369
EP - 376
DO - 10.5220/0011123600003271
PB - SciTePress

- Science and Technology Publications, Lda.
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