Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search
This addresses the challenge of realistic motion planning for differential drive robots in applications like warehouse automation, representing an incremental improvement over simplified models.
The paper tackles the problem of multi-agent motion planning for differential drive robots by introducing a three-level framework called MASS, which combines MAPF-based methods with stationary state search to generate kinodynamically-feasible plans, showing up to 400% improvements in throughput compared to existing methods.
Multi-Agent Motion Planning (MAMP) finds various applications in fields such as traffic management, airport operations, and warehouse automation. In many of these environments, differential drive robots are commonly used. These robots have a kinodynamic model that allows only in-place rotation and movement along their current orientation, subject to speed and acceleration limits. However, existing Multi-Agent Path Finding (MAPF)-based methods often use simplified models for robot kinodynamics, which limits their practicality and realism. In this paper, we introduce a three-level framework called MASS to address these challenges. MASS combines MAPF-based methods with our proposed stationary state search planner to generate high-quality kinodynamically-feasible plans. We further extend MASS using an adaptive window mechanism to address the lifelong MAMP problem. Empirically, we tested our methods on the single-shot grid map domain and the lifelong warehouse domain. Our method shows up to 400% improvements in terms of throughput compared to existing methods.