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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2208.09318 |
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| _version_ | 1866917638547963904 |
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| author | Faroni, Marco Pedrocchi, Nicola Beschi, Manuel |
| author_facet | Faroni, Marco Pedrocchi, Nicola Beschi, Manuel |
| contents | 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 rewards from previous samples. The paper demonstrates that the 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_09318 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Adaptive Hybrid Local-Global Sampling for Fast Informed Sampling-Based Optimal Path Planning Faroni, Marco Pedrocchi, Nicola Beschi, Manuel Robotics Systems and Control 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 rewards from previous samples. The paper demonstrates that the 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. |
| title | Adaptive Hybrid Local-Global Sampling for Fast Informed Sampling-Based Optimal Path Planning |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2208.09318 |