This paper presents a scalable multi-robot motion planning algorithm called Conflict-Based Model Predictive Control (CB-MPC). Inspired by Conflict-Based Search (CBS), the planner leverages a similar high-level conflict tree to efficiently resolve robot-robot conflicts in the continuous space, while reasoning about each agent's kinematic and dynamic constraints and actuation limits using MPC as the low-level planner. We show that tracking high-level multi-robot plans with a vanilla MPC controller is insufficient, and results in unexpected collisions in tight navigation scenarios. Compared to other variations of multi-robot MPC like joint, prioritized, and distributed, we demonstrate that CB-MPC improves the executability and success rate, allows for closer robot-robot interactions, and reduces the computational cost significantly without compromising the solution quality across a variety of environments. Furthermore, we show that CB-MPC combined with a high-level path planner can effectively substitute computationally expensive full-horizon multi-robot kinodynamic planners.

翻译：本文提出了一种名为基于冲突的模型预测控制（CB-MPC）的可扩展多机器人运动规划算法。受基于冲突的搜索（CBS）启发，该规划器利用类似的高层冲突树在连续空间中高效解决机器人间的冲突，同时通过将MPC作为底层规划器来处理每个智能体的运动学与动力学约束以及执行机构限制。研究表明，使用标准MPC控制器跟踪高层多机器人规划方案在紧凑导航场景中存在不足，会导致意外碰撞。与联合式、优先式和分布式等其他多机器人MPC变体相比，我们证明CB-MPC能提升可执行性和成功率，支持更紧密的机器人间交互，并在不降低解决方案质量的前提下显著降低各类环境中的计算成本。此外，我们进一步表明，CB-MPC结合高层路径规划器可有效替代计算开销高昂的全时域多机器人运动学动力学规划器。