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Accelerating sampling-based optimal path planning via adaptive informed sampling
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DOI：10.1007/s10514-024-10157-5.png)
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This paper improves the performance of RRT * \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {RRT}<^>*$$\end{document} -like sampling-based path planners by combining admissible informed sampling and local sampling (i.e., sampling the neighborhood of the current solution). An adaptive strategy regulates the trade-off between exploration (admissible informed sampling) and exploitation (local sampling) based on online reward from previous samples. The paper demonstrates that the resulting algorithm is asymptotically optimal and has a better convergence rate than state-of-the-art path planners (e.g., Informed-RRT * \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>*$$\end{document} ) in several simulated and real-world scenarios. An open-source, ROS-compatible implementation of the algorithm is publicly available.
Keyword:
Motion planning
Sampling-based algorithms
Informed sampling
Optimal path planning
Informed-RRT*
期刊
IF:
4.3
论文数:
1.7K
被引数:
5.0K
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