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运动智能体寻找无碰撞且满足动力学可行性的轨迹，同时最小化其运动时间的问题。该问题与广泛研究的多智能体路径寻优（MAPF）问题密切相关。近年来，MAPF方法在为数以百计的智能体寻找无碰撞路径方面取得了显著成功。然而，这些方法通常忽略智能体的运动学约束，假设瞬时运动，这限制了其实用性和真实性。本文提出一种基于MAPF的三层级规划器PSB，以应对MAMP带来的挑战。PSB充分考虑了智能体的运动学能力，能生成具有平滑速度轮廓的解决方案，可直接由控制器执行。通过实验，我们在自动驾驶车辆交通路口协调与移动机器人障碍密集栅格地图导航两个场景中评估了PSB。与现有方法相比，PSB在解成本上实现了最高49.79%的改进。