Environmental Map Learning with Multiple-Robots

Abstract

This paper explores decision-making processes in robotic systems tasked with reconstructing scalar fields through sensing in uncertain environments. Each robot must handle noisy perception and operate within specific environmental and physical constraints. The complexity increases in multiagent scenarios, where robots must not only plan their actions but also anticipate the movements and strategies of other agents. Effective coordination is crucial to prevent collisions and minimize redundant tasks. To address this challenge, we propose an online, distributed multi-robot sampling algorithm that combines Monte Carlo Tree Search (MCTS) with Gaussian regression. In this approach, each robot iteratively selects its next sampling point while exchanging limited information with other robots and predicting their future actions. Predictions about other robots future actions are computed with a MCTS that is recomputed at each iteration to incorporate all information collected up to that point. We evaluate the performance of our method across diverse environments and team sizes, comparing it to algorithmic alternatives.