Distributed Linear Quadratic Gaussian for Multi-Robot Coordination with Localization Uncertainty
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912309116403712 |
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| author | Tasooji, Tohid Kargar Khodadadi, Sakineh |
| author_facet | Tasooji, Tohid Kargar Khodadadi, Sakineh |
| contents | This paper addresses the problem of distributed coordination control for multi-robot systems (MRSs) in the presence of localization uncertainty using a Linear Quadratic Gaussian (LQG) approach. We introduce a stochastic LQG control strategy that ensures the coordination of mobile robots while optimizing a performance criterion. The proposed control framework accounts for the inherent uncertainty in localization measurements, enabling robust decision-making and coordination. We analyze the stability of the system under the proposed control protocol, deriving conditions for the convergence of the multi-robot network. The effectiveness of the proposed approach is demonstrated through experimental validation using Robotrium simulation experiments, showcasing the practical applicability of the control strategy in real-world scenarios with localization uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Linear Quadratic Gaussian for Multi-Robot Coordination with Localization Uncertainty Tasooji, Tohid Kargar Khodadadi, Sakineh Systems and Control Multiagent Systems Robotics This paper addresses the problem of distributed coordination control for multi-robot systems (MRSs) in the presence of localization uncertainty using a Linear Quadratic Gaussian (LQG) approach. We introduce a stochastic LQG control strategy that ensures the coordination of mobile robots while optimizing a performance criterion. The proposed control framework accounts for the inherent uncertainty in localization measurements, enabling robust decision-making and coordination. We analyze the stability of the system under the proposed control protocol, deriving conditions for the convergence of the multi-robot network. The effectiveness of the proposed approach is demonstrated through experimental validation using Robotrium simulation experiments, showcasing the practical applicability of the control strategy in real-world scenarios with localization uncertainty. |
| title | Distributed Linear Quadratic Gaussian for Multi-Robot Coordination with Localization Uncertainty |
| topic | Systems and Control Multiagent Systems Robotics |
| url | https://arxiv.org/abs/2504.03126 |