Parallel Branch Model Predictive Control on GPUs

Abstract

We present a GPU-based solver for trajectory planning problems using branch Model Predictive Control. Building on iterative LQR methods, we adopt a multiple-shooting formulation for the system dynamics and use an augmented Lagrangian method to handle general stage-wise constraints. This design enables straightforward warm-starting. The constraint-handling capability of our solver is validated on two challenging trajectory planning problems. In addition, we develop two tailored inner LQR solvers that exploit the tree-sparse structure. The solvers offer different levels of parallelism, making them appropriate for different tree sizes. The numerical results demonstrate that, compared to a high-performance CPU-based solver, our approach achieves superior performance on large-scale problems.

8 pages, 7 figures