Communication Strategies for Environment Exploration Using a Frontier-Guided Decentralised MCTS

Multi-robot exploration of unknown environments is challenging, especially in scenarios with constrained communication. Decentralised Monte Carlo Tree Search (Dec-MCTS) is a promising online planning approach that enables robots to collaboratively explore an environment while handling communication failures. In this work, we propose a communication strategy for a frontier-guided version of the Dec-MCTS that is robust to communication loss and limited range while maintaining efficient exploration. Through extensive experimental evaluations, we demonstrate that limiting communication does not significantly impact performance and that discarding outdated information can negatively affect exploration efficiency. We also study how information propagation can positively or negatively impact the mission depending on the environment.