提出db-LaCAM，让多机器人快速规划支持复杂动力学。
db-LaCAM: Fast and Scalable Multi-Robot Kinodynamic Motion Planning with Discontinuity-Bounded Search and Lightweight MAPF
- 用预计算运动基元+可调不连续性约束，结合MAPF速度与动力学感知。
- 50个机器人场景下速度提升10倍，解质量相当。
- 适合飞行器、带拖车车辆等需动力学精确控制的多机系统。
当前最先进的多机器人动力学运动规划方法因计算负担重，难以扩展至超过几个机器人的场景，导致规划速度慢。本文提出 discontinuity-Bounded LaCAM（db-LaCAM），将现代多智能体路径规划（MAPF）算法的高效性与动力学规划器的动态感知能力相结合，以解决上述问题。db-LaCAM利用预计算的动力学一致运动基元生成时域运动序列，并允许用户定义相邻动作间的最大不连续性。该方法在运动基元层面具有分辨率完备性，支持任意机器人动力学模型。大量实验表明，db-LaCAM可高效扩展至最多50个机器人，相比现有最优方法运行时间最高降低90%，同时保持相近解的质量。该方法在2D与3D环境中验证，涵盖单轮式（unicycle）和3D双积分器动力学模型。进一步通过两组物理实验，成功实现了飞行机器人团队与带拖车汽车机器人团队的安全轨迹执行。
原文摘要 · Abstract (English)
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.
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