Time-Coordinated Motion Planning for Multiple Unmanned Vehicles Using Distributed Model Predictive Control and Sequential Convex Programming

This paper introduces an integrated approach for time-coordinated motion planning of multiple unmanned vehicles using distributed model predictive control (DMPC) and sequential convex programming (SCP). This approach employs a unified framework that integrates trajectory planning and tracking into a single optimization problem, effectively expanding the domain of attraction for the MPC controller and addressing the challenge of time-coordination among multiple vehicles. Non-uniform discrete time scales are introduced to mitigate the dimensionality of the optimization problem, thereby enhancing computational efficiency. By combining the ability of DMPC to distribute computational efforts across multiple vehicles with the iterative convexification method of SCP, our approach efficiently handles the complexities of non-linear optimization. Theoretical analysis has confirmed the feasibility and stability of the proposed method. Based on this approach, the time-coordinated sequential convex programming-based distributed model predictive control (TC-SCP-DMPC) algorithm is proposed. Numerical simulations are conducted to validate the effectiveness and efficiency of the proposed algorithm in achieving time-coordinated control of multiple unmanned vehicles.