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 propose XIT (Exploration-Exploitation Informed Trees), a sampling-based planner that balances exploration and exploitation by generating concurrent trajectories toward exploration-rich goals while collecting informative gas measurements en route. XIT draws batches of samples from an Upper Confidence Bound (UCB) information field derived from the current gas posterior and expands trees using a cost that trades off travel effort against gas concentration and uncertainty. 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 and real-world experiments demonstrate the benefits of XIT in terms of GDM quality and efficiency. Although developed for active GDM, XIT is readily applicable to other robotic information-gathering tasks in unknown environments that face the exploration and exploitation trade-off.

翻译：移动机器人气体分布建图（GDM）在危险气体泄漏应急响应中提供关键态势感知能力。然而，现有系统大多仍依赖遥操作，限制了可扩展性与响应速度。在未知杂乱环境中实现自主主动GDM具有挑战性，因为机器人需同时探索可通行空间、构建环境地图，并从稀疏化学测量中推断气体分布置信度。为此，我们将主动GDM建模为最优轨迹信息路径规划（IPP）问题，并提出XIT（探索-利用信息树）——一种基于采样的规划器，通过生成指向富探索目标的同时轨迹并在途中采集信息性气体测量值，实现探索与利用的平衡。XIT从当前气体后验导出的上置信界（UCB）信息场中批量采样，并利用权衡行进代价与气体浓度及不确定性的成本函数扩展树结构。为实现羽流感知探索，我们提出气体前沿概念（定义为与高浓度气体相邻的未观测区域），并开发波前气体前沿检测（WGFD）算法进行识别。高保真仿真与实物实验验证了XIT在GDM质量与效率方面的优势。尽管专为主动GDM设计，XIT可便捷应用于未知环境中面临探索-利用权衡的其他机器人信息采集任务。