Mr.MSTE: Multi-robot Multi-Source Term Estimation with Wind-Aware Coverage Control

TL;DR

This paper introduces a multi-robot framework for estimating multiple airborne gas sources using a hybrid Bayesian approach and wind-aware coverage control, achieving faster convergence and better source separation in real-world tests.

Contribution

It develops a novel multi-robot multi-source estimation framework with physics-informed Bayesian inference and a wind-aware coverage control strategy that explicitly considers plume transport.

Findings

Faster convergence in source estimation compared to traditional methods.

Improved source separation and localization accuracy.

Validated effectiveness through real-world CO2 release experiments.

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.