Multi-agent Motion Planning through Stationary State Search (Extended Abstract)

Jingtian Yan, Jiaoyang Li

Abstract: Multi-Agent Motion Planning (MAMP) finds various real-world applications in fields such as traffic management, airport operations, and warehouse automation. This work primarily focuses on its application in large-scale automated warehouses. Recently, Multi-Agent Path-Finding (MAPF) methods have achieved great success in finding collision-free paths for hundreds of agents within automated warehouse settings. However, these methods often use a simplified assumption about the robot dynamics, which limits their practicality and realism. In this paper, we introduce a three-level MAMP framework called PSS which incorporates the kinodynamic constraints of the robots. PSS combines MAPF-based methods with Stationary Safe Interval Path Planner (SSIPP) to generate high-quality kinodynamically-feasible solutions. Our method shows significant improvements in terms of scalability and solution quality compared to existing methods.