MPPI-Generic:用于随机优化的 CUDA 库

Bogdan Vlahov, Jason Gibson, Manan Gandhi, Evangelos A. Theodorou
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引用次数: 0

摘要

本文介绍了一个用于 GPU 加速随机优化的新 C++/CUDA 库,名为 MPPI-Generic。它提供了模型预测路径积分控制、管模型预测路径积分控制和鲁棒模型预测路径积分控制的实现,并允许这些算法在许多已有的动力学模型和成本函数中使用。此外,研究人员还可以根据我们的 API 定义创建自己的动力学模型或成本函数,而无需更改实际的模型预测路径积分控制代码。最后,我们将模型预测路径积分控制在各种 GPU 上的计算性能与其他流行的实现进行了比较,以展示我们的库可以实现的实时功能。库代码请访问:https://acdslab.github.io/mppi-generic-website/ 。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MPPI-Generic: A CUDA Library for Stochastic Optimization
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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