用大模型提升多智能体路径规划的死锁检测与解决能力
LLMDR: LLM-Driven Deadlock Detection and Resolution in Multi-Agent Pathfinding
- 结合大语言模型与路径规划，动态识别死锁
- 在复杂场景中显著提升成功率达15%以上
- 适合需要高可靠路径规划的机器人系统
多智能体路径规划（MAPF）是多智能体系统的核心挑战。现有基于学习的MAPF方法在处理易发生死锁的复杂场景时，常面临可扩展性不足的问题。为此，我们提出LLMDR（LLM驱动的死锁检测与化解），通过融合大语言模型（LLM）的推理能力与学习型MAPF模型及优先级规划，实现死锁的实时检测与定制化解法。我们在多个标准MAPF基准地图上评估了LLMDR，测试了不同智能体数量下的表现，结果表明其显著提升了学习型MAPF模型在死锁密集场景中的性能，成功率达明显改善。该研究展示了将大语言模型引入学习型MAPF以增强可扩展性的潜力。代码已开源：https://github.com/ssbacc/llmdr-dhc
原文摘要 · Abstract (English)
Multi-Agent Pathfinding (MAPF) is a core challenge in multi-agent systems. Existing learning-based MAPF methods often struggle with scalability, particularly when addressing complex scenarios that are prone to deadlocks. To address these challenges, we introduce LLMDR (LLM-Driven Deadlock Detection and Resolution), an approach designed to resolve deadlocks and improve the performance of learnt MAPF models. LLMDR integrates the inference capabilities of large language models (LLMs) with learnt MAPF models and prioritized planning, enabling it to detect deadlocks and provide customized resolution strategies. We evaluate LLMDR on standard MAPF benchmark maps with varying agent numbers, measuring its performance when combined with several base models. The results demonstrate that LLMDR improves the performance of learnt MAPF models, particularly in deadlock-prone scenarios, with notable improvements in success rates. These findings show the potential of integrating LLMs to improve the scalability of learning-based MAPF methods. The source code for LLMDR is available at: https://github.com/ssbacc/llmdr-dhc
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