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- W4226201217 abstract "The basic rapidly exploring random tree (RRT) path-planning algorithm uses a uniform random sampling over the entire free space of the environment in the process of producing new points for the RRT tree. That approach achieves good results in environments with simple and distant obstacles but becomes far more challenging in large and complex environments with narrow passages between obstacles. This paper presents an improvement of the sampling method used in the RRT search and path-planning algorithm to increase its ability to explore in large, complex, and dense environments. The main idea is to make the sampling over a dynamic space that grows with the RRT tree, which increases the ability to explore and finding narrow passages in large and complicated environments. To illustrate the benefits of dynamic sampling, a theoretical analysis of the fundamental principle with results from a simulated experiment is presented as well." @default.
- W4226201217 created "2022-05-05" @default.
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- W4226201217 date "2022-01-01" @default.
- W4226201217 modified "2023-09-27" @default.
- W4226201217 title "Dynamic sampling RRT for improved performance in large environments" @default.
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- W4226201217 doi "https://doi.org/10.1063/5.0075556" @default.
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