In this paper a self-developed controller algorithm is presented with the goal of handling a basic parking maneuver. One of the biggest challenges of autonomous vehicle control is the right calibration and finding the right vehicle models for the given conditions. As a result of many other research, model predictive control (MPC) structures have started to become the most promising control technique. During our work we implemented an MPC function from white paper. Considering the low-speed conditions of a parking maneuver we use a kinematic bicycle model as the basis of the controller. The algorithm has two main inputs: a planned trajectory and the vehicle state feedback signals. The controller is realized as a Simulink model, and it is integrated into a complete autonomous control system using ROS framework. The results are validated through multiple steps: using Simulink only with a pure kinematic bicycle plant model; using LGSVL simulation framework containing a real vehicle model and the entire software chain; the controller is prepared for real vehicle tests.
翻译:本文介绍了自开发的控制器算法,目的是处理基本的泊车操作。自动车辆控制的最大挑战之一是正确校准和为特定条件找到正确的车辆模型。由于许多其他研究,模型预测控制(MPC)结构开始成为最有希望的控制技术。我们在工作中从白皮书中应用了MPC功能。考虑到泊车操作的低速度条件,我们使用运动自行车模型作为控制器的基础。该算法有两个主要投入:计划轨迹和车辆状态反馈信号。控制器作为Simmlink模型实现,并用ROS框架纳入完整的自动控制系统。结果通过多个步骤得到验证:仅使用纯运动自行车厂模型使用Simmlink;使用包含真实车辆模型和整个软件链的LGSVL模拟框架;控制器为实际车辆测试作准备。