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Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles

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
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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

simultaneous localization and mapping (SLAM)reinforcement learningmultirobotsdynamic obstaclesrobot operating system (ROS)navigation

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Tsinghua Science and Technology
Volume 27 Issue 6,
December 2022
Pages 894-901
DOI: 10.26599/TST.2021.9010073
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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