Multi-robot Multi-source Localization in Complex Flows with Physics-Preserving Environment Models
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 49 references that are in the map. First posted on arXiv 17 Sep 2025.
Builds on
12 of 49 references.
References · 12
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Citing papers per year: 2026: 1.
1 paper.