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arXiv · 2303.01619

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

Ardalan Tajbakhsh·Lorenz T. Biegler·Aaron M. Johnson
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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.

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Ardalan Tajbakhsh, Lorenz T. Biegler, Aaron M. Johnson. 2023-03-02. Conflict-Based Model Predictive Control for Scalable Multi-Robot Motion Planning. https://arxiv.org/abs/2303.01619

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