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Performance analysis of path planners for car-like vehicles toward automatic parking control

机译:汽车状车辆路径规划者对自动停车控制的绩效分析

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摘要

Acar parking control problem is difficult because of its non-holonomic constraints and complicated environmental geometry. We proposed Korea University Path Planner (KPP) in our prior work. KPP is an appropriate scheme for a car-like mobile robot in a parking environment. The purpose of this paper is to investigate the advantages of KPP through both quantitative and qualitative analyses. For comparison, numerical simulations have been carried out by the application of KPP and the conventional Rapidly exploring random tree method. This paper shows that KPP exhibits outstanding performance from the viewpoints of travel time and computational time.
机译:由于其非完全约束和复杂的环境几何形状,ACAR停车控制问题很难。 我们提出了韩国大学道路规划师(KPP)。 KPP是一个适用于停车环境中的汽车移动机器人的适当方案。 本文的目的是通过定量和定性分析来研究KPP的优点。 为了比较,通过kpp的应用和常规快速探索随机树方法进行了数值模拟。 本文显示KPP从旅行时间和计算时间的观点表现出出色的性能。

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