ALPHA: 高構造領域における注意機構ベースの長期的経路探索
ALPHA: Attention-based Long-horizon Pathfinding in Highly-structured Areas
各エージェントが局所情報と大域的な曖昧情報を組み合わせ、他エージェントの経路意図を短期予測することで、視野制限による近視眼的判断を克服するGraph Transformerベースのマルチエージェント経路探索フレームワークを提案。
著者: Chengyang He, Tianze Yang, Tanishq Duhan, Yutong Wang, Guillaume Sartoretti
分類: cs.RO
原文アブストラクト
The multi-agent pathfinding (MAPF) problem seeks collision-free paths for a team of agents from their current positions to their pre-set goals in a known environment, and is an essential problem found at the core of many logistics, transportation, and general robotics applications. Existing learning-based MAPF approaches typically only let each agent make decisions based on a limited field-of-view (FOV) around its position, as a natural means to fix the input dimensions of its policy network. However, this often makes policies short-sighted, since agents lack the ability to perceive and plan for obstacles/agents beyond their FOV. To address this challenge, we propose ALPHA, a new framework combining the use of ground truth proximal (local) information and fuzzy distal (global) information to let agents sequence local decisions based on the full current state of the system, and avoid such myopicity. We further allow agents to make short-term predictions about each others' paths, as a means to reason about each others' path intentions, thereby enhancing the level of cooperation among agents at the whole system level. Our neural structure relies on a Graph Transformer architecture to allow agents to selectively combine these different sources of information and reason about their inter-dependencies at different spatial scales. Our simulation experiments demonstrate that ALPHA outperforms both globally-guided MAPF solvers and communication-learning based ones, showcasing its potential towards scalability in realistic deployments.
