Adaptive Exploration-Exploitation Balancing for Robotic Gas Source Seeking via Time Progress and Spatial Dispersion

Gas source localization is critical for industrial safety monitoring, disaster rescue, and environmental protection. This paper presents an adaptive source-seeking planner that explicitly balances exploration and exploitation under uncertainty. At each step, the robot samples candidate goal points and scores them by combining (i) an exploitation term derived from a Gaussian-like dispersion model and (ii) an exploration term computed as frontier-based information gain. To avoid search stagnation, a time-dependent penalization is introduced to reduce the attractiveness of early sampled goals, discouraging long-distance revisits. Moreover, the explorationexploitation weight is adapted online using the spatial variance of a high-probability candidate set: dispersed candidates trigger stronger exploration, while concentrated candidates promote rapid exploitation toward the source. Simulation and real-robot experiments demonstrate that the proposed algorithm improves search efficiency in complex environments.