Brain-Machine Cooperative Control for Multi-Robot Formation

With the aging of society and the growing number of patients experiencing motor dysfunction, enhancing their quality of life and autonomy has become increasingly crucial. This paper proposes a brain-computer synergistic multi-robot formation control technique aimed at efficiently managing multi-robot formations through brain-computer interface (BCI) technology. The system framework includes a Leader Controller, Fol-lower Controller, Formation Decision Module, and Autonomous Navigation Module. Experimental validation demonstrates the system's performance in tasks such as formation transformation and autonomous navigation. Results confirm that the auxiliary controller effectively tracks user formation control intentions and accomplishes multi-robot formation tasks safely. Moreover, the autonomous navigation module significantly enhances system autonomy, reduces user control demands, and alleviates user control burdens. This research contributes to improving the quality of life for motor disorder patients by leveraging advanced BCI and robotics technologies to facilitate independent living and social participation through enhanced task assistance and reduced dependency on direct user control.

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Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1109/icus61736.2024.10840070
Primary Topic
Reinforcement Learning in Robotics
Type
article
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article

Brain-Machine Cooperative Control for Multi-Robot Formation

Weijie Fei, Luzheng Bi, Zhenge Yang, Peiyu Zhang
Reinforcement Learning in Robotics
article

Brain-Machine Cooperative Control for Multi-Robot Formation

Weijie Fei, Luzheng Bi, Zhenge Yang, Peiyu Zhang
article en

Abstract

With the aging of society and the growing number of patients experiencing motor dysfunction, enhancing their quality of life and autonomy has become increasingly crucial. This paper proposes a brain-computer synergistic multi-robot formation control technique aimed at efficiently managing multi-robot formations through brain-computer interface (BCI) technology. The system framework includes a Leader Controller, Fol-lower Controller, Formation Decision Module, and Autonomous Navigation Module. Experimental validation demonstrates the system's performance in tasks such as formation transformation and autonomous navigation. Results confirm that the auxiliary controller effectively tracks user formation control intentions and accomplishes multi-robot formation tasks safely. Moreover, the autonomous navigation module significantly enhances system autonomy, reduces user control demands, and alleviates user control burdens. This research contributes to improving the quality of life for motor disorder patients by leveraging advanced BCI and robotics technologies to facilitate independent living and social participation through enhanced task assistance and reduced dependency on direct user control.

Beijing Institute of Technology (CN)
Openalex Percentile: Top 76%
Reinforcement Learning in Robotics
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