Multi-Robot Autonomous Exploration and Mapping Under Localization Uncertainty with Expectation-Maximization
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909130253402112 |
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| author | Huang, Yewei Lin, Xi Englot, Brendan |
| author_facet | Huang, Yewei Lin, Xi Englot, Brendan |
| contents | We propose an autonomous exploration algorithm designed for decentralized multi-robot teams, which takes into account map and localization uncertainties of range-sensing mobile robots. Virtual landmarks are used to quantify the combined impact of process noise and sensor noise on map uncertainty. Additionally, we employ an iterative expectation-maximization inspired algorithm to assess the potential outcomes of both a local robot's and its neighbors' next-step actions. To evaluate the effectiveness of our framework, we conduct a comparative analysis with state-of-the-art algorithms. The results of our experiments show the proposed algorithm's capacity to strike a balance between curbing map uncertainty and achieving efficient task allocation among robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04021 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Robot Autonomous Exploration and Mapping Under Localization Uncertainty with Expectation-Maximization Huang, Yewei Lin, Xi Englot, Brendan Robotics We propose an autonomous exploration algorithm designed for decentralized multi-robot teams, which takes into account map and localization uncertainties of range-sensing mobile robots. Virtual landmarks are used to quantify the combined impact of process noise and sensor noise on map uncertainty. Additionally, we employ an iterative expectation-maximization inspired algorithm to assess the potential outcomes of both a local robot's and its neighbors' next-step actions. To evaluate the effectiveness of our framework, we conduct a comparative analysis with state-of-the-art algorithms. The results of our experiments show the proposed algorithm's capacity to strike a balance between curbing map uncertainty and achieving efficient task allocation among robots. |
| title | Multi-Robot Autonomous Exploration and Mapping Under Localization Uncertainty with Expectation-Maximization |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.04021 |