Calibration-Free Gas Source Localization with Mobile Robots: Source Term Estimation Based on Concentration Measurement Ranking

2026-05-13

AI Summary

This study addresses the challenge of gas source localization by mobile robots in hazardous environments, where low-cost gas sensors suffer from nonlinear responses and environmental interference, rendering accurate concentration measurements difficult without frequent calibration—a requirement often impractical in real-world deployments. To overcome this limitation, the paper proposes a novel calibration-free gas source localization method that leverages the dynamic relative ranking of measured concentrations rather than their absolute values. By comparing the observed ranking against predictions derived from a physical gas dispersion model, the approach constructs a probabilistic distribution over potential source locations. Integrating probabilistic inference with online data processing, the method achieves robust and accurate localization in both high-fidelity simulations and physical experiments, outperforming existing calibration-dependent techniques and demonstrating the feasibility and robustness of calibration-free gas source localization.

Abstract

Efficient Gas Source Localization (GSL) in real-world settings is crucial, especially in emergency scenarios. Mobile robots equipped with low-cost, in-situ gas sensors offer a safer alternative to human inspection in hazardous environments. Probabilistic algorithms enhance GSL efficiency with scattered gas measurements by comparing gas concentration measurements gathered by robots to physical dispersion models. However, accurately deriving gas concentrations from data acquired with low-cost sensors is challenging due to the nonlinear sensor response, environmental dependencies (e.g., humidity, temperature, and other gas influences), and robot motion. Mitigating these disturbance factors requires frequent sensor calibration in controlled environments, which is often impractical for real-world deployments. To overcome these issues, we propose a novel feature extraction algorithm that leverages the relative ranking of gas measurements within the dynamically accumulated dataset. By comparing the rank differences between gathered and modeled values, we estimate the probabilistic distribution of source locations across the entire environment. We validate our approach in high-fidelity simulations and physical experiments, demonstrating consistent localization accuracy with uncalibrated gas sensors. Compared to existing methods, our technique eliminates the need for gas sensor calibration, making it well-suited for real-world applications.

Problem

• Gas Source Localization
• Mobile Robots
• Low-cost Gas Sensors
• Sensor Calibration
• Concentration Measurement

Innovation

• calibration-free
• gas source localization
• concentration ranking
• mobile robots
• probabilistic estimation