State-of-the-art multi-robot kinodynamic motion planners struggle to handle more than a few robots due to high computational burden, which limits their scalability and results in slow planning time. In this work, we combine the scalability and speed of modern multi-agent path finding (MAPF) algorithms with the dynamic-awareness of kinodynamic planners to address these limitations. To this end, we propose discontinuity-Bounded LaCAM (db-LaCAM), a planner that utilizes a precomputed set of motion primitives that respect robot dynamics to generate horizon-length motion sequences, while allowing a user-defined discontinuity between successive motions. The planner db-LaCAM is resolution-complete with respect to motion primitives and supports arbitrary robot dynamics. Extensive experiments demonstrate that db-LaCAM scales efficiently to scenarios with up to 50 robots, achieving up to ten times faster runtime compared to state-of-the-art planners, while maintaining comparable solution quality. The approach is validated in both 2D and 3D environments with dynamics such as the unicycle and 3D double integrator. We demonstrate the safe execution of trajectories planned with db-LaCAM in two distinct physical experiments involving teams of flying robots and car-with-trailer robots.

翻译：当前先进的多机器人运动动力学运动规划器由于计算负担过重，难以处理超过少数机器人的场景，这限制了其可扩展性并导致规划时间缓慢。本研究结合现代多智能体路径规划（MAPF）算法的可扩展性与速度优势，以及运动动力学规划器的动态感知能力，以应对这些局限。为此，我们提出间断有界LaCAM（db-LaCAM），该规划器利用预计算且符合机器人动力学的运动基元集合生成时域长度的运动序列，同时允许用户定义连续运动间的间断。db-LaCAM规划器相对于运动基元具有分辨率完备性，并支持任意机器人动力学模型。大量实验表明，db-LaCAM能高效扩展至最多50个机器人的场景，相比先进规划器实现高达十倍的运行速度提升，同时保持相当的求解质量。该方法在二维和三维环境中均得到验证，涵盖如独轮车模型和三维双积分器等多种动力学模型。我们通过两组不同的物理实验——涉及飞行机器人团队和带拖挂的汽车机器人团队——验证了db-LaCAM所规划轨迹的安全执行能力。