迈向信息优化的多智能体路径寻找：一种减少智能体间信息共享的混合框架
Towards Information-Optimized Multi-Agent Path Finding: A Hybrid Framework with Reduced Inter-Agent Information Sharing
AI总结：
本文提出了一种减少智能体间信息共享的混合框架IO-MAPF，通过强化学习和轻量级集中协调器实现高效多智能体路径寻找，在信息受限条件下保持高成功率。
摘要：
多智能体路径寻找（MAPF）仍然是机器人和自主系统中的关键问题，其中智能体必须在共享空间中高效导航并避免冲突。传统集中式算法利用全局信息提供高质量的解决方案，但在大规模场景中由于冲突的组合爆炸而扩展性差。相反，具有本地信息的分布式方法，特别是基于学习的方法，通过使用放松的信息可用性来提供更好的扩展性，但往往以牺牲解决方案质量为代价。在实际部署中，信息是一种受限资源：广播完整的智能体状态和目标可能引发隐私问题，消耗有限的带宽，并需要额外的传感和通信硬件，从而增加成本和能耗。我们专注于如何在最小化智能体间信息共享的同时保持解决方案可行性的问题。为此，我们提出了一个以信息为中心的MAPF问题表述，并引入了一种混合框架IO-MAPF，该框架集成了去中心化的路径规划与轻量级的集中协调器。在该框架中，智能体使用强化学习（RL）独立规划，而中央协调器提供最小化、有针对性的信号，如静态冲突单元指示器或短冲突轨迹，这些信号动态共享以支持高效的冲突解决。我们引入了信息单位（IU）度量来量化信息使用，并表明我们的警报驱动设计在信息共享方面相比最先进的算法减少了2倍到23倍，同时保持了高成功率，证明在强信息受限、隐私保护条件下，可靠的MAPF是可行的。我们通过模拟和硬件实验展示了我们算法的有效性。
英文摘要：
Multi-agent pathfinding (MAPF) remains a critical problem in robotics and autonomous systems, where agents must navigate shared spaces efficiently while avoiding conflicts. Traditional centralized algorithms with global information provide high-quality solutions but scale poorly in large-scale scenarios due to the combinatorial explosion of conflicts. Conversely, distributed approaches that have local information, particularly learning-based methods, offer better scalability by operating with relaxed information availability, yet often at the cost of solution quality. In realistic deployments, information is a constrained resource: broadcasting full agent states and goals can raise privacy concerns, strain limited bandwidth, and require extra sensing and communication hardware, increasing cost and energy use. We focus on the core question of how MAPF can be solved with minimal inter-agent information sharing while preserving solution feasibility. To this end, we present an information-centric formulation of the MAPF problem and introduce a hybrid framework, IO-MAPF, that integrates decentralized path planning with a lightweight centralized coordinator. In this framework, agents use reinforcement learning (RL) to plan independently, while the central coordinator provides minimal, targeted signals, such as static conflict-cell indicators or short conflict trajectories, that are dynamically shared to support efficient conflict resolution. We introduce an Information Units (IU) metric to quantify information use and show that our alert-driven design achieves 2x to 23x reduction in information sharing, compared to the state-of-the-art algorithms, while maintaining high success rates, demonstrating that reliable MAPF is achievable under strongly information-restricted, privacy-preserving conditions. We demonstrate the effectiveness of our algorithm using simulation and hardware experiments.
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