论文标题
基于抽样的神经网络动力学的非线性MPC，并应用于自动驾驶运动计划
Sampling-Based Nonlinear MPC of Neural Network Dynamics with Application to Autonomous Vehicle Motion Planning
论文作者
论文摘要
机器学习模型的控制已成为广泛的机器人应用程序的重要范式。在本文中，我们提出了一种基于抽样的非线性模型预测控制（NMPC），以控制神经网络动力学。我们将其设计分为两个部分：1）将基于常规优化的NMPC制定为贝叶斯状态估计问题，以及2）使用粒子过滤/平滑来实现估计。通过基于采样的原则实施，这种方法可以在控制动作空间中进行有效的搜索，以实现最佳控制，并促进计算克服神经网络动态引起的挑战。我们将拟议的NMPC方法应用于自动驾驶汽车的运动计划。具体问题考虑了建模为神经网络以及动态跨道驾驶场景的非线性未知车辆动力学。在案例研究中，该方法在成功的运动计划中显示出重要的有效性。
Control of machine learning models has emerged as an important paradigm for a broad range of robotics applications. In this paper, we present a sampling-based nonlinear model predictive control (NMPC) approach for control of neural network dynamics. We show its design in two parts: 1) formulating conventional optimization-based NMPC as a Bayesian state estimation problem, and 2) using particle filtering/smoothing to achieve the estimation. Through a principled sampling-based implementation, this approach can potentially make effective searches in the control action space for optimal control and also facilitate computation toward overcoming the challenges caused by neural network dynamics. We apply the proposed NMPC approach to motion planning for autonomous vehicles. The specific problem considers nonlinear unknown vehicle dynamics modeled as neural networks as well as dynamic on-road driving scenarios. The approach shows significant effectiveness in successful motion planning in case studies.
