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

翻译：高效的通信对于分散式多机器人路径规划（MRPP）至关重要，然而现有基于学习的通信方法对所有邻近机器人一视同仁，忽略了它们的空间邻近性，导致在需要高度协调的拥挤区域中注意力被稀释。我们提出关系增强多头注意力（RMHA）机制，该机制将成对曼哈顿距离显式嵌入注意力权重计算中，使每个机器人能够动态优先选择空间相关邻居的信息。结合距离约束注意力掩码和GRU门控消息融合，RMHA可与MAPPO无缝集成，实现稳定的端到端训练。在从8个训练机器人零样本泛化至40×40网格中128个测试机器人的实验中，当障碍物密度为30%时，RMHA达到约75%的成功率，较最优基线提升超过25个百分点。消融实验表明，距离关系编码是高密度环境下成功率提升的关键因素。关键词-多机器人路径规划，图注意力机制，多头注意力，通信优化，协同决策