A General Algorithm for Exploration with Gaussian Processes in Complex, Unknown Environments

Abstract

We propose a novel algorithm for efficient exploration with a single agent in unknown environments, populated with static obstacles. Every next position is computed by employing environment inference on top of path planning. The path planning process that ensures obstacle avoidance uses a modified version of the A* algorithm and the inference is performed by direct sensing and by predicting values at yet-not-visited positions using Gaussian processes. We have validated our algorithm with densely measured data of an indoor magnetic field with significant spatial variations in different obstacle setups. Additionally, it is shown that it outperforms a previously developed algorithm, for obstacle-free scenarios, and the random movement of the agent.