  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.
