论文标题
非线性MPC内汽车障碍避免的Frenet-Cartesian模型表示
Frenet-Cartesian Model Representations for Automotive Obstacle Avoidance within Nonlinear MPC
论文作者
论文摘要
近年来，非线性模型预测控制（NMPC）已广泛用于解决汽车运动控制和计划任务。为了提出NMPC问题，可以使用不同的优势使用不同的坐标系。我们提出并比较了与NMPC相关的优化问题的公式，涉及单个非线性程序（NLP）中的笛卡尔和FRENET坐标框架（CCF/ FCF）。我们在更有利的坐标框架中指定成本和相撞避免限制，得出适当的配方并比较不同的障碍物约束。通过这种方法，我们利用了CCF中对手车辆约束的更简单配方，以及与FCF相关的道路成本和约束。与模拟框架中其他方法的比较突出了所提出方法的优势。
In recent years, nonlinear model predictive control (NMPC) has been extensively used for solving automotive motion control and planning tasks. In order to formulate the NMPC problem, different coordinate systems can be used with different advantages. We propose and compare formulations for the NMPC related optimization problem, involving a Cartesian and a Frenet coordinate frame (CCF/ FCF) in a single nonlinear program (NLP). We specify costs and collision avoidance constraints in the more advantageous coordinate frame, derive appropriate formulations and compare different obstacle constraints. With this approach, we exploit the simpler formulation of opponent vehicle constraints in the CCF, as well as road aligned costs and constraints related to the FCF. Comparisons to other approaches in a simulation framework highlight the advantages of the proposed approaches.
