XIT: Exploration and Exploitation Informed Trees for Active Gas Distribution Mapping in Unknown Environments
XIT: Exploration and Exploitation Informed Trees for Active Gas Distribution Mapping in Unknown Environments
This research presents XIT, a sampling-based planner designed for autonomous mobile robotic gas distribution mapping in unknown environments. The system addresses the challenge of simultaneously exploring traversable space, mapping the environment, and inferring gas distribution from sparse measurements by formulating active mapping as a next-best-trajectory informative path planning problem. Key contributions include the development of XIT, which uses an Upper Confidence Bound information field to balance travel effort with information acquisition, and the introduction of the 'gas frontier' concept alongside the Wavefront Gas Frontier Detection (WGFD) algorithm for identifying unobserved regions near high gas concentrations. High-fidelity simulations and a scaled real-world experiment demonstrate the method's benefits in terms of mapping quality, efficiency, and practicality.
The paper proposes XIT, a sampling-based planner for autonomous gas distribution mapping that couples exploration and exploitation via an Upper Confidence Bound information field and introduces the Wavefront Gas Frontier Detection algorithm to identify unobserved regions adjacent to high gas concentrations; high-fidelity simulations and a scaled real-world experiment support its practicality.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2602.13739v3 Announce Type: replace Abstract: 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 sp
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r 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.
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ace, 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 g
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as 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 Frontie
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Source：arXiv Robotics — research abstracts · arxiv.org