Multi-Agent Motion Planning (MAMP) is a problem that seeks collision-free dynamically-feasible trajectories for multiple moving agents in a known environment while minimizing their travel time. MAMP is closely related to the well-studied Multi-Agent Path-Finding (MAPF) problem. Recently, MAPF methods have achieved great success in finding collision-free paths for a substantial number of agents. However, those methods often overlook the kinodynamic constraints of the agents, assuming instantaneous movement, which limits their practicality and realism. In this paper, we present a three-level MAPF-based planner called PSB to address the challenges posed by MAMP. PSB fully considers the kinodynamic capability of the agents and produces solutions with smooth speed profiles that can be directly executed by the controller. Empirically, we evaluate PSB within the domains of traffic intersection coordination for autonomous vehicles and obstacle-rich grid map navigation for mobile robots. PSB shows up to 49.79% improvements in solution cost compared to existing methods.

翻译：多智能体运动规划（MAMP）是一个在已知环境中为多个移动智能体寻找无碰撞且满足动力学可行轨迹，同时最小化其行进时间的问题。MAMP与广泛研究的多智能体路径规划（MAPF）问题密切相关。近年来，MAPF方法在为大量智能体寻找无碰撞路径方面取得了巨大成功。然而，这些方法常忽略智能体的动力学约束，假设瞬时运动，限制了其实用性和真实性。本文提出了一种基于MAPF的三级规划器PSB，以应对MAMP带来的挑战。PSB充分考虑智能体的动力学能力，生成具有平滑速度曲线的解，可由控制器直接执行。在自动驾驶车辆的交通路口协调和移动机器人的障碍密集网格地图导航场景中，实验评估表明：与现有方法相比，PSB的解成本提升最高达49.79%。