加速的RRT * 及其在自主泊车中的应用
Accelerated RRT* and its evaluation on Autonomous Parking
摘要：
为自主停车寻找无碰撞路径通常通过计算几何方程来执行，但是在空间高度受限的挑战性情况下，几何方法可能变得不可用。我们提出了一种基于快速探索随机树星（RRT*）的算法，该算法即使在高度约束的环境中也能工作，并对基于RRT* 的算法进行了改进，从而加快了计算时间并降低了最终路径成本。我们的改进RRT* 算法发现，在95%的情况下，在不到0.15秒的平行停车机动的路径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
