Gas-Source Efficiently Active Searching in Unfamiliar Environments

Abstract

Searching Gas Source actively and efficiently in unknown hazard environments is an important but challenging issue. Using mobile robots to autonomously search and navigate to gas source location provides a promising way. Existing methods are mostly based on the modularization framework which investigates the gas-source search and robot navigation tasks independently, leading to a decoupled approach that results in higher collision risks and lower navigation efficiency. Moreover, existing robot navigation techniques grapple with the intricacies of navigating through unknown environments. To tackle these complexities, we introduce an integrated framework that merges gas source localization with robot navigation. This unified structure, underpinned by an end-to-end learning approach, resolves the inherent conflicts between gas exploration and collision avoidance. Our approach aggregates the local observations (raw 3D-LiDAR data) and the expert guidance information (gas distribution), and directly generates navigation actions by implementing the reinforcement learning with a novel reward function based on region dynamic guidances, thus effectively addressing the challenges of active gas source searching in unknown environments. Simulation results underscore the adaptability of our method to diverse unknown environments, along with its superior gas source searching capabilities compared to conventional approaches. Finally, we conduct real-world experiments to demonstrate our feasibility.