让机器人通信更懂距离，提升密集环境下的路径规划成功率。
SPARC: Spatial-Aware Path Planning via Attentive Agent Communication
- 用曼哈顿距离增强注意力机制，动态优先处理近邻消息。
- 在30%障碍密度下，128个机器人的零样本测试成功率达75%。
- 适合需要高效协同的多机器人密集场景部署。
高效的通信对去中心化多机器人路径规划（MRPP）至关重要，但现有学习型通信方法对所有邻近机器人同等对待，忽略了空间远近差异，导致在拥堵区域协调效果减弱。本文提出关系增强多头注意力（RMHA），将成对曼哈顿距离显式嵌入注意力权重计算中，使每个机器人能动态优先接收空间相关邻居的信息。结合距离约束注意力掩码与GRU门控消息融合，RMHA可无缝集成至MAPPO框架，实现稳定端到端训练。在40x40网格上，从8个训练机器人零样本泛化至128个测试机器人，在30%障碍密度下，成功率约达75%，优于最佳基线超25个百分点。消融实验表明，距离关系编码是高密度环境下性能提升的关键因素。
原文摘要 · Abstract (English)
Efficient communication is critical for decentralized Multi-Robot Path Planning (MRPP), yet existing learned communication methods treat all neighboring robots equally regardless of their spatial proximity, leading to diluted attention in congested regions where coordination matters most. We propose Relation enhanced Multi Head Attention (RMHA), a communication mechanism that explicitly embeds pairwise Manhattan distances into the attention weight computation, enabling each robot to dynamically prioritize messages from spatially relevant neighbors. Combined with a distance-constrained attention mask and GRU gated message fusion, RMHA integrates seamlessly with MAPPO for stable end-to-end training. In zero-shot generalization from 8 training robots to 128 test robots on 40x40 grids, RMHA achieves approximately 75 percent success rate at 30 percent obstacle density outperforming the best baseline by over 25 percentage points. Ablation studies confirm that distance-relation encoding is the key contributor to success rate improvement in high-density environments. Index Terms-Multi-robot path planning, graph attention mechanism, multi-head attention, communication optimization, cooperative decision-making
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