Information-Guided Safe Reinforcement Learning for Autonomous Gas Source Localization using sUAS

Abstract:The autonomous localization of fugitive gas emissions using small Unmanned Aircraft Systems (sUAS) constitutes a fundamentally ill-posed inverse problem. In turbulent atmospheric boundary layers, highly intermittent scalar concentration fields violate the assumptions of classical gradient-based navigation, causing data-driven estimators to suffer from severe noise and spurious local minima. To address these challenges, we introduce an Information-Guided Safe Reinforcement Learning framework evaluated within a custom, GPU-accelerated 3D simulation environment coupling an Eulerian wind solver with a Lagrangian puff dispersion model. We identify a critical vulnerability in deterministic information-seeking planners - a Gramian bias where agents act greedily upon flawed early estimates, starving the estimator of spatial diversity. To systematically break this degeneracy, our architecture integrates a classical empirical observability Gramian (EMGR) planner with a learned Soft Actor-Critic (SAC) exploratory policy. A deterministic meta-supervisor actively monitors estimator reliability via Kullback-Leibler (KL) divergence, dynamically blending deterministic exploitation with learned exploration to steer the sUAS into high-information zones. Trained via a progressive curriculum and safeguarded by a strictly enforced Robust Control Barrier Function (RCBF), our RL framework achieves nearly 80% localization success on complex, mobile sources - drastically outperforming classical baselines (~30%) - while ensuring zero safety violations.

8 pages, 5 figures, 2 tables. Submitted to the 2026 IEEE Conference on Decision and Control (CDC). Code: this https URL

Robotics (cs.RO)

I.2.9; I.2.6; I.2.8

arXiv:2609.05569 [cs.RO]

(or arXiv:2609.05569v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.05569 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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