arXiv:2603.02845v3 Announce Type: replace-cross 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 relevan
The increasing complexity of multi-robot systems and demand for efficient decentralized operations necessitate more sophisticated communication protocols that account for spatial relevance, a problem current methods struggle with.
This development improves multi-robot coordination efficiency, critical for applications ranging from logistics and automation to defense, by enabling robots to prioritize relevant spatial information.
Robot communication systems will move beyond uniform attention, dynamic spatial relevance will be explicitly embedded in multi-robot path planning, leading to more robust and scalable decentralized systems.
- · Robotics companies
- · Logistics and automation sector
- · Defense contractors
- · AI/ML researchers
- · Manufacturers of less sophisticated multi-robot systems
Multi-robot systems will achieve higher efficiency and precision in congested, dynamic environments.
New applications for autonomous multi-robot deployments will become feasible due to enhanced coordination capabilities.
This could accelerate the adoption of multi-robot systems in complex tasks, potentially displacing human labor in certain industries sooner than anticipated.
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Read at arXiv cs.AI