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：本文提出了一种可扩展的多机器人运动规划算法——基于冲突的模型预测控制（CB-MPC）。受基于冲突搜索（CBS）的启发，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冲突，同时以模型预测控制（MPC）作为底层规划器，考虑每个智能体的运动学和动力学约束以及执行器限制。研究表明，使用普通MPC控制器跟踪高层多机器人规划结果是不够的，在密集导航场景中会导致意外碰撞。与联合规划、优先级规划和分布式规划等其他多机器人MPC变体相比，我们证明了CB-MPC能够提高可执行性和成功率，允许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，显著降低各类环境下的计算成本。此外，我们还展示了CB-MPC结合高层路径规划器能够有效替代计算成本高昂的全时域多机器人动力学规划器。