Calibration-Free Gas Source Localization with Mobile Robots: Source Term Estimation Based on Concentration Measurement Ranking
Wanting Jin, Agatha Duranceau, Izzet Kagan Erunsal, Alcherio Martinoli
AI summary
Problem
Real-world gas source localization with mobile robots is hindered by the impractical need for frequent sensor calibration due to nonlinear sensor responses, environmental dependencies, and motion-induced artifacts that distort absolute concentration readings.
Approach
The method extracts a rank-based gas feature from dynamically accumulated sensor data and compares the relative ranking of measurements against a data-driven plume model within a probabilistic source term estimation framework.
Key results
- High-fidelity MOX sensor simulation capturing nonlinear response and slow dynamics
- Novel rank-based gas feature encoding relative concentration rankings within the dataset
- Integration of the feature into a probabilistic Source Term Estimation framework for benchmarking
- Consistent localization accuracy in simulations and physical experiments using uncalibrated sensors
Why it matters
Eliminates the need for frequent sensor calibration, enabling robust, real-world deployment of mobile robots for hazardous gas leak detection.
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 concentra- tion 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.