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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.14228 |
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| _version_ | 1866914043148632064 |
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| author | Shaffer, Benjamin Edwards, Victoria Kinch, Brooks Trask, Nathaniel Hsieh, M. Ani |
| author_facet | Shaffer, Benjamin Edwards, Victoria Kinch, Brooks Trask, Nathaniel Hsieh, M. Ani |
| contents | Source localization in a complex flow poses a significant challenge for multi-robot teams tasked with localizing the source of chemical leaks or tracking the dispersion of an oil spill. The flow dynamics can be time-varying and chaotic, resulting in sporadic and intermittent sensor readings, and complex environmental geometries further complicate a team's ability to model and predict the dispersion. To accurately account for the physical processes that drive the dispersion dynamics, robots must have access to computationally intensive numerical models, which can be difficult when onboard computation is limited. We present a distributed mobile sensing framework for source localization in which each robot carries a machine-learned, finite element model of its environment to guide information-based sampling. The models are used to evaluate an approximate mutual information criterion to drive an infotaxis control strategy, which selects sensing regions that are expected to maximize informativeness for the source localization objective. Our approach achieves faster error reduction compared to baseline sensing strategies and results in more accurate source localization compared to baseline machine learning approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14228 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-robot Multi-source Localization in Complex Flows with Physics-Preserving Environment Models Shaffer, Benjamin Edwards, Victoria Kinch, Brooks Trask, Nathaniel Hsieh, M. Ani Robotics Machine Learning Source localization in a complex flow poses a significant challenge for multi-robot teams tasked with localizing the source of chemical leaks or tracking the dispersion of an oil spill. The flow dynamics can be time-varying and chaotic, resulting in sporadic and intermittent sensor readings, and complex environmental geometries further complicate a team's ability to model and predict the dispersion. To accurately account for the physical processes that drive the dispersion dynamics, robots must have access to computationally intensive numerical models, which can be difficult when onboard computation is limited. We present a distributed mobile sensing framework for source localization in which each robot carries a machine-learned, finite element model of its environment to guide information-based sampling. The models are used to evaluate an approximate mutual information criterion to drive an infotaxis control strategy, which selects sensing regions that are expected to maximize informativeness for the source localization objective. Our approach achieves faster error reduction compared to baseline sensing strategies and results in more accurate source localization compared to baseline machine learning approaches. |
| title | Multi-robot Multi-source Localization in Complex Flows with Physics-Preserving Environment Models |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2509.14228 |