文章基本信息

标题：SRRT* - a probabilistic optimal trajectory planner for problematic area structures
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作者：Robin Hess ; Robin Hess ; Roland Jerg 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：30
页码：331-336
DOI：10.1016/j.ifacol.2016.11.157
语种：English
出版社：Elsevier
摘要：Abstract: This paper extends the well known efficient RRT* algorithm to handle special environmental structures, such as narrow passages. By applying the watershed algorithm, inspired by image processing, a segmentation component is added to solve the given problem. Therefore the traditional watershed segmentation competes against the newly developed center marker watershed algorithm and the results of different experiments, combining both segmentation methods with the Rapidly Exploring Random Tree* (RRT*) algorithm to the Segmented Rapidly Exploring Random Tree* (SRRT*), are compared for several critical environments. It is shown, the whole trajectory planning process benefits from the segmentation and the SRRT*, using the new center marker watershed, outer performs the traditional RRT* method.
关键词：KeywordsOptimal TrajectoryPath PlanningAutonomous Mobile Robot