Distributed MPC for multi-vehicle systems moving in formation

This work presents a novel distributed model predictive control (DMPC) strategy for controlling multi-vehicle systems moving in formation. The vehicles' motion trajectories are parameterized as polynomial splines and by exploiting the properties of the B-spline basis functions, constraints on the trajectories are efficiently enforced. The computations for solving the resulting optimization problem are distributed among the agents by the Alternating Direction Method of Multipliers (ADMM). In order to reduce the computation time and the amount of inter-vehicle interaction, only one ADMM iteration is performed per control update. In this way the method converges over the subsequent control updates. Simulations for various nonholonomic vehicle types and an experimental validation on in-house developed robotic platforms prove the capability of the proposed approach. A supporting software toolbox is provided that implements the proposed approach and that facilitates its use.