Multi-robot optimized sampling-base cooperative collision avoidance method in Lidar naviation

Junlang Huang; Zhihua Zhang; Zhuoxin Wang; Zuguang Zhou; Yimin Zhou; Chi-Man Vong

doi:10.1117/12.2662007

26 October 2022 Multi-robot optimized sampling-base cooperative collision avoidance method in Lidar naviation

Junlang Huang, Zhihua Zhang, Zhuoxin Wang, Zuguang Zhou, Yimin Zhou, Chi-Man Vong

Proceedings Volume 12452, 5th International Conference on Informatics Engineering and Information Science (ICIEIS 2022); 1245206 (2022) https://doi.org/10.1117/12.2662007
Event: 5th International Conference on Informatics Engineering and Information Science (ICIEIS 2022), 2022, Changsha, Hunan, China

Abstract

In multi-robot systems with dynamic and complex environments, robots are required to avoid not only the static objects but also other moving robots. To solve this problem, this paper presents an implementation of cooperative collision avoidance architecture based on optimized sampling-based collision avoidance paradigm. In our work, localization error is considered and bounded in adaptive Monte-Carlo localization process. Plus, we employ velocity obstacle paradigm in predicting collisions. Subsequently, by using Sampling-based planner and optimization theory, we get an optimizing velocity selection policy. Furthermore, we also introduce our distributed multi-robot system model in this paper. By applying the cooperative collision avoidance method in Gazebo self-driving car simulation environment and ROS mobile robots, it is illustrated that our approach is applicable and well-performed.

Citation Download Citation

Junlang Huang, Zhihua Zhang, Zhuoxin Wang, Zuguang Zhou, Yimin Zhou, and Chi-Man Vong "Multi-robot optimized sampling-base cooperative collision avoidance method in Lidar naviation", Proc. SPIE 12452, 5th International Conference on Informatics Engineering and Information Science (ICIEIS 2022), 1245206 (26 October 2022); https://doi.org/10.1117/12.2662007

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