论文标题
加速RRT*及其对自动停车的评估
Accelerated RRT* and its evaluation on Autonomous Parking
论文作者
论文摘要
通常通过计算几何方程来找到无碰撞路径的自主停车路径，但是在高度限制空间的挑战性情况下，几何方法可能变得无法使用。我们提出了一种基于快速探索的随机树星（RRT*）的算法，该算法即使在高度受约束的环境中起作用，并改进了基于RRT*的基于RRT*的算法，该算法加速了计算时间并降低了最终路径成本。我们改进的RRT*算法在不到0.15秒的情况下，在95％的情况下发现了一条平行停车操作的路径。
Finding a collision-free path for autonomous parking is usually performed by computing geometric equations, but the geometric approach may become unusable under challenging situations where space is highly constrained. We propose an algorithm based on Rapidly-Exploring Random Trees Star (RRT*), which works even in highly constrained environments and improvements to RRT*-based algorithm that accelerate computational time and decrease the final path cost. Our improved RRT* algorithm found a path for parallel parking maneuver in 95 % of cases in less than 0.15 seconds.
