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

TL;DR

Active gas distribution mapping in unknown environments is tackled with Exploration-Exploitation Informed Trees (XIT), a sampling-based IPP planner that jointly balances environmental exploration and gas exploitation. XIT uses a gas-informed information field $\oldsymbol{\mathcal{I}}(\boldsymbol{x})$ to drive informed sampling and multi-direction tree expansion toward frontier-based goals, while a trajectory cost $J(\sigma)$ incorporates gas concentration and uncertainty. A novel gas-frontier concept and Wavefront Gas Frontier Detection (WGFD) guide plume-aware exploration, and theoretical results prove probabilistic completeness and almost-sure AO under a mixture sampling regime. In high-fidelity simulations and a real-world CO$_2$ experiment, XIT with gas-frontier prioritization consistently improves RMSE and differential entropy in critical gas regions without sacrificing map completeness, showing practical potential for autonomous, plume-aware GDM and applicability to other unknown-environment information-gathering tasks.

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.

Figures (12)

• Figure 1: Overview of the XIT planner for active GDM. The figure illustrates a robot constructing a gas distribution map from concentration measurements in real time and planning gas-exploitive, distance-aware trajectories toward both gas frontiers and environmental frontiers. The three candidate paths generated by XIT are shown in grey, with the next-best trajectory (NBT) highlighted in green, representing the path with the highest expected information value, balancing gas dispersion inference and spatial exploration where possible.
• Figure 2: System overview of the proposed active framework. On the left (a) is a general system architecture for the mobile robotic application. Key functional components in the compute unit include localisation and mapping for navigation and occupancy information (summarised as the module), the function, and the module, which selects the . The process is expanded in (b), where we also illustrate a gas polluted environment and the corresponding mean and uncertainty maps produced during . The concept of gas frontiers is visualised using yellow markers along the boundary of the estimated gas plume, while conventional frontiers outlining the unknown occupancy map are in red. Both types are used by to steer and terminate tree expansion in exploration rich regions, while a -based cost function, derived from the current gas model, guides paths through inferred high value gas areas. A separate example is included to illustrate the characteristic trajectories and tree structure generated by .
• Figure 3: Effect of the distance weight $\alpha$ on an isolated trajectory. The three panels use the same scenario, informed sample size ($N$), start, and goal, with $\alpha = 0$ (a), $\alpha = 0.005$ (b), and $\alpha = 1$ (c). Larger $\alpha$ yields a shorter path that de-emphasises high-information corridors, trading exploitation for reduced travel.
• Figure 4: Example trajectories generated by in three synthetic scenarios using the information field, cost function, and heuristic setup described in Section \ref{['subsec:xit_exploitation']}. For the illustrative trajectories, $N=300$ was used. The right-hand side of each example shows the corresponding asymptotic convergence results over ten simulations for each $N$.
• Figure 5: Cell-level depiction of gas frontiers and their role in path planning within an active task over three sequential time steps. (a) The robot enters from the bottom left and finds itself within a critical gas region above $\tau_{\text{gas}}$ based on its updated belief. Two gas frontiers are identified, and one is selected as the . (b) Upon reaching the gas frontier, the updated belief reveals that the area is no longer a priority as no new gas frontiers emerged based on the estimate, suggesting a less concentrated region. It now targets the other previously identified gas frontier from (a), which has remained unresolved. (c) Resolving the second gas frontier reveals a high-concentration zone that generates new a new gas frontier, which can be explored next to continue the adaptive plume probing process.

Theorems & Definitions (3)

• Theorem 1: Probabilistic completeness of
• Theorem 2: Asymptotic optimality of XIT
• Remark 3