期刊名称：International Journal of Interactive Multimedia and Artificial Intelligence
印刷版ISSN：1989-1660
出版年度：2022
卷号：7
期号：4
页码：87-99
DOI：10.9781/ijimai.2022.04.001
语种：English
出版社：ImaI-Software
摘要：Sampling-based motion planning in the field of robot motion planning has provided an effective approach to finding path for even high dimensional configuration space and with the motivation from the concepts of sampling based-motion planners, this paper presents a new sampling-based planning strategy called Optimistic Motion Planning using Recursive Sub-Sampling (OMPRSS), for finding a path from a source to a destination sanguinely without having to construct a roadmap or a tree. The random sample points are generated recursively and connected by straight lines. Generating sample points is limited to a range and edge connectivity is prioritized based on their distances from the line connecting through the parent samples with the intention to shorten the path. The planner is analysed and compared with some sampling strategies of probabilistic roadmap method (PRM) and the experimental results show agile planning with early convergence.
