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.