去中心化多机器人探索中的概率性同伴意图与多跳计划传播

中文摘要 AI 辅助

在有限通信条件下的高效协调仍然是去中心化多机器人探索中的关键挑战。虽然集中式方法受益于全局信息共享，但在大规模或通信受限的环境中往往不切实际。现有的基于蒙特卡洛树搜索（MCTS）的方法，如去中心化蒙特卡洛探索（DMCE），通过考虑同伴意图来实现去中心化规划。这种同伴意图是通过与直接通信范围内的机器人通信计划航点序列来获得的。在这项工作中，我们通过引入概率性同伴意图（PPI）来扩展这一思想，该意图将同伴轨迹转换为预测意图的连续空间表示，并将其纳入局部MCTS动作评估中。我们还研究了通过多跳传播计划来共享超出直接通信范围的同伴意图的效果。在多个模拟环境和团队规模上的实验表明，PPI和多跳传播各自都能改善去中心化探索，其相对收益取决于环境结构和团队规模。我们还展示了我们的方法在三种不同环境类型中运行的三台机器人上的实际部署。

英文摘要

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.

发表机构

• Indian Institute of Technology Bombay（印度理工学院孟买分校）

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