用GPU加速的分支模型预测控制，提升轨迹规划效率。
Parallel Branch Model Predictive Control on GPUs
- 采用多打靶法与增广拉格朗日法处理约束，支持快速热启动。
- 相比高性能CPU求解器，在大规模问题上性能更优。
- 针对树状稀疏结构设计两种并行求解器，适配不同规模问题。
我们提出一种基于GPU的轨迹规划求解器，采用分支模型预测控制（branch MPC）。在迭代LQR方法基础上，使用多打靶法建模系统动力学，并利用增广拉格朗日法处理通用阶段约束，实现简便的热启动。该求解器在两个具有挑战性的轨迹规划问题上验证了其约束处理能力。此外，我们开发了两种针对树状稀疏结构优化的内层LQR求解器，提供不同程度的并行性，适用于不同大小的树结构。数值结果表明，相较于高性能CPU求解器，本方法在大规模问题上表现出更优性能。
原文摘要 · Abstract (English)
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.
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