This paper improves the performance of RRT∗-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∗) in several simulated and real-world scenarios. An open-source, ROS-compatible implementation of the algorithm is publicly available.
Adaptive hybrid local–global sampling for fast informed sampling-based optimal path planning
Faroni M.Primo
Membro del Collaboration Group
;Pedrocchi N.Co-ultimo
Membro del Collaboration Group
;Beschi M.Co-ultimo
Conceptualization
2024
Abstract
This paper improves the performance of RRT∗-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∗) in several simulated and real-world scenarios. An open-source, ROS-compatible implementation of the algorithm is publicly available.File | Dimensione | Formato | |
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