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

2025-12-18

AI Summary

This work addresses the multi-robot cooperative gas source localization problem under dynamic, unknown numbers of airborne release sources. To tackle joint parameter estimation amid source birth, death, and merging, we propose a wind-aware distributed coverage control strategy that explicitly models anisotropic plume transport effects on sensing performance. We further develop a hybrid Bayesian inference framework integrating physics-informed state transition models with a superposition-based concentration measurement model, enabling dynamic topology inference of evolving sources. Simulation results demonstrate significantly improved convergence speed and separation of source beliefs. Real-world CO₂ release experiments on TurtleBot platforms validate high multi-source estimation accuracy (mean localization error < 0.35 m) and system scalability—supporting collaborative operation of at least eight robots.

Technology Category

• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Estimates parameters of unknown multiple airborne gas sources
• Guides robot deployment using wind-aware coverage control
• Improves multi-source belief separation and convergence speed

Innovation

• Hybrid Bayesian inference for multi-source probability density
• Wind-aware coverage control integrating belief and wind data
• Superposition-based measurement model for sparse concentration data