Optimistic Motion Planning Using Recursive Sub- Sampling: A New Approach to Sampling-Based Motion Planning

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Abstract
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.
Year of Publication
2022
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
7
Issue
Regular Issue
Number
4
Number of Pages
87-99
Date Published
06/2022
ISSN Number
1989-1660
URL
https://www.ijimai.org/journal/sites/default/files/2022-05/ijimai_7_4_8.pdf
DOI
10.9781/ijimai.2022.04.001
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