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.

翻译：多智能体运动规划在交通管理、机场运营和仓储自动化等领域具有广泛应用。在这些场景中，差分驱动机器人是常用设备。这类机器人的运动学动力学模型仅允许原地旋转和沿当前朝向运动，且受速度和加速度限制。然而，现有基于多智能体路径规划的方法通常采用简化的机器人运动学动力学模型，这限制了其实用性和真实性。本文提出名为MASS的三层框架来解决这些挑战。MASS将基于MAPF的方法与我们提出的静止状态搜索规划器相结合，生成高质量且满足运动学动力学约束的路径。我们进一步通过自适应窗口机制扩展MASS，以解决终身多智能体运动规划问题。实验部分，我们在单次网格地图场景和终身仓储场景中测试了所提方法。与现有方法相比，我们的方法在吞吐量指标上实现了高达400%的性能提升。