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

TL;DR

This paper introduces XIT, a sampling-based planning algorithm that enables autonomous robots to efficiently explore and map hazardous gas distributions in unknown environments by balancing exploration and exploitation.

Contribution

The paper presents XIT, a novel planning method that integrates exploration and exploitation for active gas distribution mapping in unknown environments.

Findings

XIT improves gas mapping accuracy in simulations and real-world tests.

XIT outperforms baseline methods in efficiency and information gain.

The approach is adaptable to other robotic exploration 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.