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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.17174 |
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| _version_ | 1866914209577566208 |
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| author | Zhai, Chao Li, Yanlin |
| author_facet | Zhai, Chao Li, Yanlin |
| contents | It is always a challenging task for multi-agent systems to achieve efficient and robust coverage in uncertain environments. The absence of global positioning information on the uncertain environment introduces significant complexity to the spatially distributed design of coverage control algorithms. To address this issue, this paper proposes a coverage control formulation based on beacon-free rotary pointer partition mechanism. A partition dynamics is designed to enable the asymptotical consensus of multi-agent reference points, as well as the workload-balanced subdivision of coverage region. On this basis, a distributed coverage control algorithm is developed to drive each agent toward the optimal deployment of their respective subregions, thereby minimizing the coverage cost. Simulation results demonstrate that the proposed coverage control method can significantly improve overall coverage efficiency with workload balance among agents, and exhibit strong adaptability and robustness in uncertain environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17174 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Rotary Coverage Control of Multi-Agent Systems in Uncertain Environments Zhai, Chao Li, Yanlin Optimization and Control It is always a challenging task for multi-agent systems to achieve efficient and robust coverage in uncertain environments. The absence of global positioning information on the uncertain environment introduces significant complexity to the spatially distributed design of coverage control algorithms. To address this issue, this paper proposes a coverage control formulation based on beacon-free rotary pointer partition mechanism. A partition dynamics is designed to enable the asymptotical consensus of multi-agent reference points, as well as the workload-balanced subdivision of coverage region. On this basis, a distributed coverage control algorithm is developed to drive each agent toward the optimal deployment of their respective subregions, thereby minimizing the coverage cost. Simulation results demonstrate that the proposed coverage control method can significantly improve overall coverage efficiency with workload balance among agents, and exhibit strong adaptability and robustness in uncertain environments. |
| title | Distributed Rotary Coverage Control of Multi-Agent Systems in Uncertain Environments |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2512.17174 |