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原文摘要

Mobile robotic gas distribution mapping (GDM) provides critical situational awareness during emergency responses to hazardous gas releases. However, most systems still rely on teleoperation, limiting scalability and response speed. Autonomous active GDM is challenging in unknown and cluttered environments, because the robot must simultaneously explore traversable space, map the environment, and infer the gas distribution belief from sparse chemical measurements. We address this by formulating active GDM as a next-best-trajectory informative path planning (IPP) problem and develop XIT (Exploration and Exploitation Informed Trees), a sampling-based planner that grows a single shared tree toward multiple frontier goals, coupling exploration of unresolved regions with informative gas sampling en route. XIT draws a batch of samples from an Upper Confidence Bound (UCB) information field derived from the current gas posterior and expands the tree using a cost that trades off travel effort against information acquisition. To enable plume-aware exploration, we introduce the gas frontier concept, defined as unobserved regions adjacent to high gas concentrations, and propose the Wavefront Gas Frontier Detection (WGFD) algorithm for their identification. High-fidelity simulations demonstrate the benefits of XIT in terms of GDM quality and efficiency, and a scaled real-world experiment demonstrates its practicality. Although developed for active GDM, XIT is applicable to other robotic information-gathering tasks in unknown environments that face the exploration and exploitation trade-off.