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Fast and efficient motion planning using directional sampling-based generalized shape expansion
Zinage V.,
Published in American Institute of Aeronautics and Astronautics Inc, AIAA
Pages: 1 - 15
Motion planning is a fundamental area of research in robotics and aerospace applications. Sampling-based methods provide an efficient solution to an otherwise challenging problem in motion planning. Recently, a sampling-based motion planner was presented based on the notion of novel 3D generalized shape to plan for an optimal collision-free path in 3D workspace and was termed as 3D-Generalized Shape Expansion (3D-GSE) algorithm. This was found to exhibit superior performance in terms of computational time and path costs over other well-established seminal algorithms in literature. Considering that a suitable directional sampling feature could potentially lead to a further superior performance of the 3D-GSE algorithm, this paper proposes two sampling schemes, namely basic and augmented directional sampling, and presents the 3D-GSE-D and 3D-GSE-AD algorithms, respectively. These algorithms, by default, have the benefits of the 3D-GSE algorithm. In addition, both the basic and augmented directional schemes sample random points with more preference towards the direction of the Goal point leading to lower path cost on average. While the basic directional scheme suffers from a higher computational time when the obstacle density is high along the direction towards the Goal, the augmented directional scheme is free from this drawback. Probabilistic analysis and extensive numerical simulation studies justify the performance of the 3D-GSE-D and the superiority of the 3D-GSE-AD in performance in terms of computational time efficiency and shortest path cost when compared with the 3D-GSE, existing directional and other seminal algorithms. © 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
About the journal
JournalData powered by TypesetAIAA Scitech 2021 Forum
PublisherData powered by TypesetAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Impact Factor1.868
Open AccessNo
Citation Styleunsrt
Sherpa RoMEO Archiving PolicyGreen