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Mr.MSTE: Multi-robot Multi-Source Term Estimation with Wind-Aware Coverage Control
arXiv:2512.17001v1 Announce Type: new
Abstract: This paper presents a Multi-Robot Multi-Source Term Estimation (MRMSTE) framework that enables teams of mobile robots to collaboratively sample gas concentrations and infer the parameters of an unknown number of airborne releases. The framework is built on a hybrid Bayesian inference scheme that represents the joint multi-source probability density and incorporates physics-informed state transitions, including source birth, removal, and merging induced by atmospheric dispersion. A superposition-based measurement model is naturally accommodated, allowing sparse concentration measurements to be exploited efficiently. To guide robot deployment, we introduce a wind-aware coverage control (WCC) strategy that integrates the evolving multi-source belief with local wind information to prioritize regions of high detection likelihood. Unlike conventional coverage control or information-theoretic planners, WCC explicitly accounts for anisotropic plume transport when modelling sensor performance, leading to more effective sensor placement for multi-source estimation. Monte Carlo studies demonstrate faster convergence and improved separation of individual source beliefs compared to traditional coverage-based strategies and small-scale static sensor networks. Real-world experiments with CO2 releases using TurtleBot platforms further validate the proposed approach, demonstrating its practicality for scalable multi-robot gas-sensing applications.
Abstract: This paper presents a Multi-Robot Multi-Source Term Estimation (MRMSTE) framework that enables teams of mobile robots to collaboratively sample gas concentrations and infer the parameters of an unknown number of airborne releases. The framework is built on a hybrid Bayesian inference scheme that represents the joint multi-source probability density and incorporates physics-informed state transitions, including source birth, removal, and merging induced by atmospheric dispersion. A superposition-based measurement model is naturally accommodated, allowing sparse concentration measurements to be exploited efficiently. To guide robot deployment, we introduce a wind-aware coverage control (WCC) strategy that integrates the evolving multi-source belief with local wind information to prioritize regions of high detection likelihood. Unlike conventional coverage control or information-theoretic planners, WCC explicitly accounts for anisotropic plume transport when modelling sensor performance, leading to more effective sensor placement for multi-source estimation. Monte Carlo studies demonstrate faster convergence and improved separation of individual source beliefs compared to traditional coverage-based strategies and small-scale static sensor networks. Real-world experiments with CO2 releases using TurtleBot platforms further validate the proposed approach, demonstrating its practicality for scalable multi-robot gas-sensing applications.
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