Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree*
An efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the proposed approach engages in hierarchical inter- and int...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/21/7846 |
| _version_ | 1797548758037168128 |
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| author | Hyejeong Ryu |
| author_facet | Hyejeong Ryu |
| author_sort | Hyejeong Ryu |
| collection | DOAJ |
| description | An efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the proposed approach engages in hierarchical inter- and intra-regional searches. A navigable graph of an environment is constructed using segmented local grid maps and safe junction nodes. An inter-regional path is obtained using the navigable graph and a graph-search algorithm. A skeletonization-informed rapidly exploring random tree* (SIRRT*) efficiently computes converged intra-regional paths for each map segment. The sampling process of the proposed hierarchical path-planning algorithm is locally conducted only in the start and goal regions, whereas the conventional path-planning should process the sampling over the entire environment. The entire path from the start position to the goal position can be achieved more quickly and more robustly using the hierarchical approach than the conventional single-layered method. The performance of the hierarchical path-planning is analyzed using a publicly available benchmark environment. |
| first_indexed | 2024-03-10T15:04:12Z |
| format | Article |
| id | doaj.art-8653898c47544a0596f9802fdbc26c3f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:04:12Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8653898c47544a0596f9802fdbc26c3f2023-11-20T19:52:54ZengMDPI AGApplied Sciences2076-34172020-11-011021784610.3390/app10217846Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree*Hyejeong Ryu0Department of Mechatronics Engineering, Kangwon National University, Chuncheon KR24341, KoreaAn efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the proposed approach engages in hierarchical inter- and intra-regional searches. A navigable graph of an environment is constructed using segmented local grid maps and safe junction nodes. An inter-regional path is obtained using the navigable graph and a graph-search algorithm. A skeletonization-informed rapidly exploring random tree* (SIRRT*) efficiently computes converged intra-regional paths for each map segment. The sampling process of the proposed hierarchical path-planning algorithm is locally conducted only in the start and goal regions, whereas the conventional path-planning should process the sampling over the entire environment. The entire path from the start position to the goal position can be achieved more quickly and more robustly using the hierarchical approach than the conventional single-layered method. The performance of the hierarchical path-planning is analyzed using a publicly available benchmark environment.https://www.mdpi.com/2076-3417/10/21/7846path-planningmobile robotsrapidly exploring random treegrid map segmentationenvironmental graph structure |
| spellingShingle | Hyejeong Ryu Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree* Applied Sciences path-planning mobile robots rapidly exploring random tree grid map segmentation environmental graph structure |
| title | Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree* |
| title_full | Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree* |
| title_fullStr | Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree* |
| title_full_unstemmed | Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree* |
| title_short | Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree* |
| title_sort | hierarchical path planning for mobile robots using a skeletonization informed rapidly exploring random tree |
| topic | path-planning mobile robots rapidly exploring random tree grid map segmentation environmental graph structure |
| url | https://www.mdpi.com/2076-3417/10/21/7846 |
| work_keys_str_mv | AT hyejeongryu hierarchicalpathplanningformobilerobotsusingaskeletonizationinformedrapidlyexploringrandomtree |