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.

Systems and Control (eess.SY); Robotics (cs.RO)

arXiv:2506.13624 [eess.SY]

(or arXiv:2506.13624v2 [eess.SY] for this version)

https://doi.org/10.48550/arXiv.2506.13624 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.