开源CUDA库加速随机轨迹优化，支持多种控制算法与自定义模型。
MPPI-Generic: A CUDA Library for Stochastic Trajectory Optimization
- 基于CUDA的C++库，实现MPPI系列算法的高效并行计算。
- 在多款GPU上实测性能超越主流实现，满足实时控制需求。
- 支持用户灵活扩展动力学模型与代价函数，便于科研复用。
本文介绍了一款名为MPPI-Generic的新C++/CUDA库，用于GPU加速的随机轨迹优化。该库实现了模型预测路径积分控制（Model Predictive Path Integral Control）、管状模型预测路径积分控制（Tube-Model Predictive Path Integral Control）和鲁棒模型预测路径积分控制（Robust Model Predictive Path Integral Control），并可适配多种已有动力学模型与代价函数。研究人员可根据其API定义自定义动力学模型或代价函数，无需修改核心控制代码。此外，我们在多种GPU上对比了该库与其他主流实现的计算性能，验证了其在实际应用中具备实时计算能力。库代码可在 https://acdslab.github.io/mppi-generic-website/ 获取。
原文摘要 · Abstract (English)
This paper introduces a new C++/CUDA library for GPU-accelerated stochastic optimization called MPPI-Generic. It provides implementations of Model Predictive Path Integral control, Tube-Model Predictive Path Integral Control, and Robust Model Predictive Path Integral Control, and allows for these algorithms to be used across many pre-existing dynamics models and cost functions. Furthermore, researchers can create their own dynamics models or cost functions following our API definitions without needing to change the actual Model Predictive Path Integral Control code. Finally, we compare computational performance to other popular implementations of Model Predictive Path Integral Control over a variety of GPUs to show the real-time capabilities our library can allow for. Library code can be found at: https://acdslab.github.io/mppi-generic-website/ .
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