Optimizing Crowd-Aware Multi-Agent Path Finding through Local Broadcasting with Graph Neural Networks

arXiv:2309.10275v2 Announce Type: replace-cross
Abstract: Multi-Agent Path Finding (MAPF) in crowded environments presents a challenging problem in motion planning, aiming to find collision-free paths for all agents in the system. MAPF finds a wide range of applications in various domains, including aerial swarms, autonomous warehouse robotics, and self-driving vehicles. Current approaches to MAPF generally fall into two main categories: centralized and decentralized planning. Centralized planning suffers from the curse of dimensionality when the number of agents or states increases and …

abstract aerial agent agents applications arxiv autonomous broadcasting collision cs.ai cs.lg cs.ma cs.ro domains driving environments free graph graph neural networks motion planning multi-agent networks neural networks path planning robotics self-driving through type warehouse warehouse robotics