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Title: LES: Locally Exploitative Sampling for Robot Path Planning
Sampling-based algorithms solve the path planning problem by generating random samples in the search space and incrementally growing a connectivity graph or a tree. Conventionally, the sampling strategy used in these algorithms is biased towards exploration to acquire information about the search-space. In contrast, this work proposes an optimization-based procedure that generates new samples so as to improve the cost-to-come value of vertices in a given neighborhood. The application of the proposed algorithm adds an exploitative bias to sampling and results in a faster convergence1 to the optimal solution compared to other state-of-the-art sampling techniques. This is demonstrated using benchmarking experiments performed for 7 DOF Panda and 14 DOF Baxter robots.  more » « less
Award ID(s):
2008686
PAR ID:
10467352
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3503-2365-8
Page Range / eLocation ID:
1551 to 1557
Subject(s) / Keyword(s):
robotics path-planning sampling-based methods
Format(s):
Medium: X
Location:
London, United Kingdom
Sponsoring Org:
National Science Foundation
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