XIT: Exploration and Exploitation Informed Trees for Active Gas Distribution Mapping in Unknown Environments

2026-02-14

AI Summary

This work addresses the challenge of enabling mobile robots to efficiently coordinate exploration, mapping, and sparse gas distribution inference in unknown complex environments. The authors propose XIT, a novel approach that formulates the task as an informative path planning problem. It introduces the concept of a “gas front” and a Wavefront Gas Front Detection (WGFD) algorithm, coupled with UCB-based information field-guided trajectory sampling. A tree expansion strategy is designed to balance travel cost, gas concentration, and uncertainty, thereby achieving perception-driven synergistic optimization between exploration and exploitation. Experimental results demonstrate that XIT significantly improves both the quality and efficiency of gas distribution mapping in simulated and real-world scenarios, and the framework generalizes effectively to other information-gathering tasks involving exploration–exploitation trade-offs.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 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.

Problem

• gas distribution mapping
• active exploration
• unknown environments
• robotic sensing
• exploration-exploitation trade-off

Innovation

• Informative Path Planning
• Exploration-Exploitation Trade-off
• Gas Distribution Mapping
• Gas Frontier
• Sampling-based Planner