论文标题
基于运动原语的基础运动动力学RRT，用于在复杂环境中自动驾驶汽车导航
Motion Primitives Based Kinodynamic RRT for Autonomous Vehicle Navigation in Complex Environments
论文作者
论文摘要
在这项工作中，我们为具有未知动态的真实自动驾驶汽车实施了一个猛击辅助导航模块。导航目标是在遵循系统动力学的同时，沿着无冲突轨迹达到所需的目标配置。具体来说，我们使用基于激增的Hector SLAM来构建环境地图，检测障碍物以及跟踪车辆穿过各种状态时对轨迹的一致性。为了进行运动计划，我们在一组生成的运动原语上快速探索随机树（RRT）来搜索动态可行的轨迹序列和无碰撞路径到达目标。我们展示了复杂的操作，例如使用呈现的方法在受约束的环境中进行平行停车，垂直停车以及真实车辆的运动。
In this work, we have implemented a SLAM-assisted navigation module for a real autonomous vehicle with unknown dynamics. The navigation objective is to reach a desired goal configuration along a collision-free trajectory while adhering to the dynamics of the system. Specifically, we use LiDAR-based Hector SLAM for building the map of the environment, detecting obstacles, and for tracking vehicle's conformance to the trajectory as it passes through various states. For motion planning, we use rapidly exploring random trees (RRTs) on a set of generated motion primitives to search for dynamically feasible trajectory sequences and collision-free path to the goal. We demonstrate complex maneuvers such as parallel parking, perpendicular parking, and reversing motion by the real vehicle in a constrained environment using the presented approach.
