通过图神经网络局部通信优化人群感知的多智能体路径规划
Optimizing Crowd-Aware Multi-Agent Path Finding through Local Communication with Graph Neural Networks
AI总结：
本文提出CRAMP，一种基于图神经网络局部通信的人群感知分散式强化学习方法，用于解决拥挤环境下的多智能体路径规划问题，在完工时间、碰撞次数和成功率上显著优于现有方法。
摘要：
在拥挤环境中进行多智能体路径规划（MAPF）是运动规划中的一个具有挑战性的问题，其目标是为系统中的所有智能体找到无碰撞路径。MAPF在各个领域有着广泛的应用，包括空中集群、自主仓库机器人和自动驾驶车辆。当前的MAPF方法通常分为两大类：集中式规划和分散式规划。当智能体数量或状态数量增加时，集中式规划会受到维度灾难的影响，因此在大型复杂环境中扩展性不佳。另一方面，分散式规划使智能体能够在部分可观测环境中进行实时路径规划，表现出隐式协调。然而，它们在密集环境中存在收敛速度慢和性能下降的问题。在本文中，我们提出了CRAMP，一种新颖的人群感知的分散式强化学习方法，通过图神经网络（GNN）实现智能体之间的高效局部通信，从而增强拥挤环境中的态势感知能力和决策能力。我们在模拟环境中测试了CRAMP，并证明我们的方法在多个指标上优于最先进的MAPF分散式方法。与先前方法相比，CRAMP在完工时间和碰撞次数方面将解的质量提高了最多59%，在成功率方面提高了最多35%。
英文摘要：
Multi-Agent Path Finding (MAPF) in crowded environments presents a challenging problem in motion planning, aiming to find collision-free paths for all agents in the system. MAPF finds a wide range of applications in various domains, including aerial swarms, autonomous warehouse robotics, and self-driving vehicles. Current approaches to MAPF generally fall into two main categories: centralized and decentralized planning. Centralized planning suffers from the curse of dimensionality when the number of agents or states increases and thus does not scale well in large and complex environments. On the other hand, decentralized planning enables agents to engage in real-time path planning within a partially observable environment, demonstrating implicit coordination. However, they suffer from slow convergence and performance degradation in dense environments. In this paper, we introduce CRAMP, a novel crowd-aware decentralized reinforcement learning approach to address this problem by enabling efficient local communication among agents via Graph Neural Networks (GNNs), facilitating situational awareness and decision-making capabilities in congested environments. We test CRAMP on simulated environments and demonstrate that our method outperforms the state-of-the-art decentralized methods for MAPF on various metrics. CRAMP improves the solution quality up to 59% measured in makespan and collision count, and up to 35% improvement in success rate in comparison to previous methods.
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