Learning-based decentralized control with collision avoidance for multi-agent systems
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909578860429312 |
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| author | Nieto, Omayra Yago Simoes, Alexandre Anahory Giribet, Juan I. Colombo, Leonardo J. |
| author_facet | Nieto, Omayra Yago Simoes, Alexandre Anahory Giribet, Juan I. Colombo, Leonardo J. |
| contents | In this paper, we present a learning-based tracking controller based on Gaussian processes (GP) for collision avoidance of multi-agent systems where the agents evolve in the special Euclidean group in the space SE(3). In particular, we use GPs to estimate certain uncertainties that appear in the dynamics of the agents. The control algorithm is designed to learn and mitigate these uncertainties by using GPs as a learning-based model for the predictions. In particular, the presented approach guarantees that the tracking error remains bounded with high probability. We present some simulation results to show how the control algorithm is implemented. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09730 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning-based decentralized control with collision avoidance for multi-agent systems Nieto, Omayra Yago Simoes, Alexandre Anahory Giribet, Juan I. Colombo, Leonardo J. Systems and Control Optimization and Control In this paper, we present a learning-based tracking controller based on Gaussian processes (GP) for collision avoidance of multi-agent systems where the agents evolve in the special Euclidean group in the space SE(3). In particular, we use GPs to estimate certain uncertainties that appear in the dynamics of the agents. The control algorithm is designed to learn and mitigate these uncertainties by using GPs as a learning-based model for the predictions. In particular, the presented approach guarantees that the tracking error remains bounded with high probability. We present some simulation results to show how the control algorithm is implemented. |
| title | Learning-based decentralized control with collision avoidance for multi-agent systems |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2504.09730 |