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Autonomous exploration through deep reinforcement learning

Xiangda Yan (College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China)
Jie Huang (College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China)
Keyan He (College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China)
Huajie Hong (College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China)
Dasheng Xu (College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 11 April 2023

Issue publication date: 9 August 2023

167

Abstract

Purpose

Robots equipped with LiDAR sensors can continuously perform efficient actions for mapping tasks to gradually build maps. However, with the complexity and scale of the environment increasing, the computation cost is extremely steep. This study aims to propose a hybrid autonomous exploration method that makes full use of LiDAR data, shortens the computation time in the decision-making process and improves efficiency. The experiment proves that this method is feasible.

Design/methodology/approach

This study improves the mapping update module and proposes a full-mapping approach that fully exploits the LiDAR data. Under the same hardware configuration conditions, the scope of the mapping is expanded, and the information obtained is increased. In addition, a decision-making module based on reinforcement learning method is proposed, which can select the optimal or near-optimal perceptual action by the learned policy. The decision-making module can shorten the computation time of the decision-making process and improve the efficiency of decision-making.

Findings

The result shows that the hybrid autonomous exploration method offers good performance, which combines the learn-based policy with traditional frontier-based policy.

Originality/value

This study proposes a hybrid autonomous exploration method, which combines the learn-based policy with traditional frontier-based policy. Extensive experiment including real robots is conducted to evaluate the performance of the approach and proves that this method is feasible.

Keywords

Acknowledgements

The authors disclosed receipt of the following financial support for the research, authorship and publication of this article: This research was supported by NUDT, China.

Citation

Yan, X., Huang, J., He, K., Hong, H. and Xu, D. (2023), "Autonomous exploration through deep reinforcement learning", Industrial Robot, Vol. 50 No. 5, pp. 793-803. https://doi.org/10.1108/IR-12-2022-0299

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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