Learning safe control for multi-robot systems: Methods, verification, and open challenges

Garg, Kunal; Zhang, Songyuan; So, Oswin; Dawson, Charles; Fan, Chuchu

doi:10.1016/j.arcontrol.2024.100948

Cited by 6 publications

References 119 publications

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Intelligent multi-robot collaborative transport system

Li,

Lin,

et al. 2024

Urban Lifeline

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The research on multi-robot systems has been divided into various fields, such as communication, navigation, task allocation, and collaborative transport. While significant progress has been made in each area, there has been limited research integrating these fields to build a fully autonomous multi-robot collaborative transport system. Therefore, we identify the key issues and propose a multi-robot collaborative transport system founded on ROS1 and conduct validation in a simulated environment, laying a solid foundation for the system to run on real robots. The primary contributions of this study include three key areas: (1) modeling and validating robot collaborative transport, (2) developing a visual task allocation system leveraging FastDDS service, and (3) resolving path collision issues in multi-robot navigation through both traditional methods and reinforcement learning techniques. Extensive experimental evaluations demonstrate that the proposed intelligent multi-robot collaborative transport system can autonomously navigate to target points for collaborative transport task. Performance assessments, based on the error between the target point and the object’s arrival point as well as the transport trajectory error, reveal that the system effectively completes the assigned tasks.

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