Two key algorithms for intelligent inspection robots in electric bicycle charging sheds
Abstract The deployment of intelligent inspection robots in electric bicycle charging sheds is critical for preventing fire hazards, yet faces challenges in navigating narrow passages and recognizing small components. This paper proposes two enhanced algorithms to address these issues: (1) a multi-r...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-99825-9 |
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| author | Yingjian An Ge Wei |
| author_facet | Yingjian An Ge Wei |
| author_sort | Yingjian An |
| collection | DOAJ |
| description | Abstract The deployment of intelligent inspection robots in electric bicycle charging sheds is critical for preventing fire hazards, yet faces challenges in navigating narrow passages and recognizing small components. This paper proposes two enhanced algorithms to address these issues: (1) a multi-root node RRT* (MS-RRT*) for efficient narrow-channel path planning, and (2) an improved SOLOv2-based instance segmentation method for small-target recognition. The MS-RRT* introduces dynamic secondary root nodes with constrained expansion cycles, significantly increasing the probability of traversing narrow channels while reducing sampling nodes in obstacles by 29.36% compared to classical RRT*. For component recognition, the enhanced SOLOv2 algorithm augments feature pyramid outputs with larger hierarchical maps, improving small-target accuracy (e.g., button detection from 52.9% to 62.5%) without compromising processing speed. Experimental results demonstrate that the proposed MS-RRT* achieves a 100% exploration success rate in narrow channels, outperforming state-of-the-art methods in both efficiency and robustness. The improved SOLOv2 also surpasses Mask R-CNN in multi-category component recognition, ensuring reliable inspection in complex scenarios. These advancements collectively enable 24/7 automated monitoring, addressing critical safety demands in real-world charging infrastructure. |
| format | Article |
| id | doaj-art-56c990ec767b45219d09de4f78feb00a |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-56c990ec767b45219d09de4f78feb00a2025-08-20T03:09:34ZengNature PortfolioScientific Reports2045-23222025-05-0115111210.1038/s41598-025-99825-9Two key algorithms for intelligent inspection robots in electric bicycle charging shedsYingjian An0Ge Wei1Shanghai Construction Management Vocational Technical CollegeShanghai Communications PolytechnicAbstract The deployment of intelligent inspection robots in electric bicycle charging sheds is critical for preventing fire hazards, yet faces challenges in navigating narrow passages and recognizing small components. This paper proposes two enhanced algorithms to address these issues: (1) a multi-root node RRT* (MS-RRT*) for efficient narrow-channel path planning, and (2) an improved SOLOv2-based instance segmentation method for small-target recognition. The MS-RRT* introduces dynamic secondary root nodes with constrained expansion cycles, significantly increasing the probability of traversing narrow channels while reducing sampling nodes in obstacles by 29.36% compared to classical RRT*. For component recognition, the enhanced SOLOv2 algorithm augments feature pyramid outputs with larger hierarchical maps, improving small-target accuracy (e.g., button detection from 52.9% to 62.5%) without compromising processing speed. Experimental results demonstrate that the proposed MS-RRT* achieves a 100% exploration success rate in narrow channels, outperforming state-of-the-art methods in both efficiency and robustness. The improved SOLOv2 also surpasses Mask R-CNN in multi-category component recognition, ensuring reliable inspection in complex scenarios. These advancements collectively enable 24/7 automated monitoring, addressing critical safety demands in real-world charging infrastructure.https://doi.org/10.1038/s41598-025-99825-9Intelligent inspection robotRRT* algorithmSOLOv2 algorithmCharging sheds |
| spellingShingle | Yingjian An Ge Wei Two key algorithms for intelligent inspection robots in electric bicycle charging sheds Scientific Reports Intelligent inspection robot RRT* algorithm SOLOv2 algorithm Charging sheds |
| title | Two key algorithms for intelligent inspection robots in electric bicycle charging sheds |
| title_full | Two key algorithms for intelligent inspection robots in electric bicycle charging sheds |
| title_fullStr | Two key algorithms for intelligent inspection robots in electric bicycle charging sheds |
| title_full_unstemmed | Two key algorithms for intelligent inspection robots in electric bicycle charging sheds |
| title_short | Two key algorithms for intelligent inspection robots in electric bicycle charging sheds |
| title_sort | two key algorithms for intelligent inspection robots in electric bicycle charging sheds |
| topic | Intelligent inspection robot RRT* algorithm SOLOv2 algorithm Charging sheds |
| url | https://doi.org/10.1038/s41598-025-99825-9 |
| work_keys_str_mv | AT yingjianan twokeyalgorithmsforintelligentinspectionrobotsinelectricbicyclechargingsheds AT gewei twokeyalgorithmsforintelligentinspectionrobotsinelectricbicyclechargingsheds |