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.
From: Chenghuai Lin [view email]
[v1]
Mon, 16 Jun 2025 15:53:18 UTC (1,237 KB)
[v2]
Tue, 18 Aug 2026 00:28:03 UTC (1,876 KB)
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