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Tsinghua Science and Technology 2022, 27(6): 894-901 https://doi.org/10.26599/TST.2021.9010073
Open Access | Issue | Published: 21 June 2022

Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles

Show Author's Information Kaidong Zhao,  Li Ning( )
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
University of Chinese Academy of Sciences, Beijing 100049, China
Keywords:
reinforcement learning, navigation, simultaneous localization and mapping (SLAM), multirobots, dynamic obstacles, robot operating system (ROS)
Cite this article:
Zhao K, Ning L. Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles. Tsinghua Science and Technology, 2022, 27(6): 894-901. https://doi.org/10.26599/TST.2021.9010073
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Abstract Full text About this article

With the continuous development of robotics and artificial intelligence, robots are being increasingly used in various applications. For traditional navigation algorithms, such as Dijkstra and A *, many dynamic scenarios in life are difficult to cope with. To solve the navigation problem of complex dynamic scenes, we present an improved reinforcement-learning-based algorithm for local path planning that allows it to perform well even when more dynamic obstacles are present. The method applies the gmapping algorithm as the upper layer input and uses reinforcement learning methods as the output. The algorithm enhances the robots’ ability to actively avoid obstacles while retaining the adaptability of traditional methods.


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About this article

Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles

Show Author's information Kaidong Zhao, Li Ning( )
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
University of Chinese Academy of Sciences, Beijing 100049, China

Abstract

With the continuous development of robotics and artificial intelligence, robots are being increasingly used in various applications. For traditional navigation algorithms, such as Dijkstra and A *, many dynamic scenarios in life are difficult to cope with. To solve the navigation problem of complex dynamic scenes, we present an improved reinforcement-learning-based algorithm for local path planning that allows it to perform well even when more dynamic obstacles are present. The method applies the gmapping algorithm as the upper layer input and uses reinforcement learning methods as the output. The algorithm enhances the robots’ ability to actively avoid obstacles while retaining the adaptability of traditional methods.

Keywords: reinforcement learning, navigation, simultaneous localization and mapping (SLAM), multirobots, dynamic obstacles, robot operating system (ROS)

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Publication history

Received: 11 June 2021
Revised: 22 August 2021
Accepted: 02 September 2021
Published: 21 June 2022
Issue date: December 2022

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© The author(s) 2022.

Acknowledgements

This work was supported in part by the National Key Research and Development Project of China (No. 2019YFB2102500) and the Natural Science Foundation of Hebei Province (No. F2018201115).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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