01542nas a2200217   4500000000100000000000100001008004100002260001200043653002600055653001300081653001300094653001300107100001600120700001500136245011100151856008000262300001000342490000600352520095200358022001401310       2022                            d  c06/202210aProbabilistic Roadmap10aSampling10aPlanning10aRobotics1 aLhilo Kenye1 aRahul Kala00aOptimistic Motion Planning Using Recursive Sub- Sampling: A New Approach to Sampling-Based Motion Planning  uhttps://www.ijimai.org/journal/sites/default/files/2022-05/ijimai_7_4_8.pdf  a87-990 v73 aSampling-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.  a1989-1660