Decentralized Multi-Robot Exploration with Probabilistic Peer Intent and Multi-hop Plan Propagation

2026-09-18

AI Summary

本文提出了一种基于概率同伴意图和多跳计划传播的方法，以解决在通信受限环境下的去中心化多机器人探索问题。

Abstract

Efficient coordination under limited communication remains a key challenge in decentralized multi-robot exploration. While centralized approaches benefit from global information sharing, they are often impractical in large-scale or communication-constrained environments. Existing Monte Carlo Tree Search (MCTS)-based approaches, such as Decentralized Monte Carlo Exploration (DMCE), enable decentralized planning by taking peer intent into account. This peer intent is obtained by communicating sequences of planned waypoints with robots within direct communication range. In this work, we extend this idea by introducing Probabilistic Peer Intent (PPI), which converts peer trajectories into a continuous spatial representation of predicted intent and incorporates it into local MCTS action evaluation. We additionally study the effects of sharing peer intent beyond direct communication range by propagating plans over multiple hops. Experiments across multiple simulated environments and team sizes show that PPI and Multi-hop propagation can each improve decentralized exploration, with their relative benefits depending on environment structure and team size. We also demonstrate the real-world deployment of our method on three robots operating in different environment types.

Problem

• decentralized multi-robot exploration
• limited communication
• efficient coordination

Innovation

• Probabilistic Peer Intent
• Multi-hop Plan Propagation
• Decentralized Exploration
• Monte Carlo Tree Search