带局部通信的可扩展多智能体迷宫遍历
Scalable Multi-Agent Maze Traversal with Local Communication
- Technical University of Darmstadt（达姆施塔特工业大学）
- The University of Sheffield（谢菲尔德大学）
- University of Bristol（布里斯托尔大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对通信受限的未知迷宫环境，提出基于局部通信与主从机制的分布式多智能体遍历算法，其性能接近最优全知策略，且在多智能体场景下优于朴素基线。
AI中文摘要：
洞穴网络、管道系统及类似迷宫环境对通信受限的未知场景下的多智能体导航构成重大挑战。本文提出一种分布式算法，使智能体能够协同遍历未知的、可能带环的图。智能体按顺序进入指定起始节点，任务是定位并抵达未公开的目标，同时避免碰撞。它们通过局部通信协调，采用主从关系及主从切换。任意时刻仅一个智能体执行探索，该智能体运行单智能体迷宫求解器。本文证明该算法是完备的，其总耗时（makespan）在智能体数量维度上渐近等价于最优全知策略的总耗时，并推导了其时间与空间复杂度。对最多625个智能体的仿真显示，随着智能体数量增加，平均燃料总和呈下降趋势，且所提方法优于所有智能体独立执行单智能体求解器的朴素基线方法。
英文摘要：
Cave networks, pipe systems, and similar maze-like environments pose significant challenges for multi-agent navigation in unknown settings with limited communication. We propose a distributed algorithm that enables agents to collectively traverse an unknown, possibly cyclic graph. Agents enter sequentially at a designated start node and are tasked to localize and reach an undisclosed goal while avoiding collisions. They coordinate via local communication using leader-follower relationships and leader switching. At any moment in time, exploration is performed by only one of the agents, which runs a single-agent maze solver. We prove that the algorithm is complete, that its makespan is asymptotically equivalent (in the number of agents) to that of an optimal full-knowledge strategy, and derive its time and space complexity. Simulations with up to $625$ agents show a decreasing average sum-of-fuels as the number of agents increases and demonstrate that the proposed approach outperforms a naïve baseline in which all agents independently execute the single-agent solver.
