SPARC: Spatial-Aware Path Planning via Attentive Robot Communication

TL;DR

This paper introduces RMHA, a spatial-aware attention mechanism for multi-robot path planning that improves coordination efficiency by prioritizing messages from nearby robots, leading to significant success rate gains.

Contribution

The paper presents RMHA, a novel attention mechanism that explicitly incorporates spatial relations into robot communication, enhancing decentralized path planning performance.

Findings

RMHA achieves ~75% success rate in large-scale scenarios.

Outperforms baseline by over 25 percentage points in high-density environments.

Distance-relation encoding is key to improved success rates.

Abstract

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