Conflict-Based Model Predictive Control for Scalable Multi-Robot Motion Planning

TL;DR

This paper introduces CB-MPC, a scalable multi-robot motion planning algorithm that combines conflict-based search with model predictive control to improve success rates, reduce computation, and enable closer interactions in multi-robot systems.

Contribution

The paper proposes Conflict-Based Model Predictive Control (CB-MPC), integrating conflict-based high-level planning with MPC for scalable multi-robot motion planning.

Findings

CB-MPC improves success rate and executability.

CB-MPC allows closer robot interactions.

CB-MPC reduces computational costs significantly.

Abstract

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.