Multi-robot exploration improves with probabilistic peer intent sharing
Decentralized Multi-Robot Exploration with Probabilistic Peer Intent and Multi-hop Plan Propagation
RoboticsMultiagent Systems
Summary
Coordinating multiple robots to explore an area can be hard when they cannot communicate easily. The authors developed a way for robots to share their planned paths in a way that predicts where others might go, even beyond direct communication range. This helps each robot plan better and explore more efficiently without needing constant contact. They tested this approach in simulations and with real robots in different environments.
What this means in practice
- •For robotics engineers: Coordinate multiple robots to explore large or communication-limited areas more effectively by sharing intent probabilistically over several communication hops.
- •For search and rescue teams: Deploy teams of robots that maintain efficient exploration and coverage even when communication networks are unreliable or sparse.
Authors
Saurbh Singh Jamwal, Nived Chebrolu, Shivaram Kalyanakrishnan
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.