Research on AGV Driving Stability from Multi-Modal Crack Perception to Vibration Constraint Speed Decision

Abstract

Pavement cracks pose significant challenges to the driving stability and operational safety of automated guided vehicles (AGV) in industrial and logistics environments. The complex geometry of cracks and their nonlinear coupling with vehicle dynamics make conventional rule-based or single-modal approaches insufficient for reliable engineering decision-making. To address this issue, this study proposes an artificial intelligence–driven multi-modal perception and engineering analysis framework for AGV speed optimization under representative operating conditions. From the artificial intelligence perspective, an improved lightweight instance segmentation model based on YOLO11 is developed by integrating a dynamic upsampling strategy, a hybrid multi-scale feature representation module, and a large-kernel attention mechanism, enabling robust and fine-grained crack extraction in complex pavement scenes. In addition, a multi-modal learning strategy is adopted to fuse two-dimensional visual features with three-dimensional point cloud-derived geometric parameters, allowing accurate quantification of crack width, depth, and surface damage. From the engineering analysis perspective, the relationship between AI-extracted crack geometric characteristics and AGV dynamic responses is established to investigate the influence of pavement defects on vehicle vibration behavior. A triaxial vibration acquisition system is constructed under controlled experimental conditions, and the relationship between crack severity, vibration characteristics, and driving speed is quantitatively analyzed. Based on vibration constraints, a hierarchical speed optimization strategy is formulated for different crack levels. Experimental results demonstrate that the proposed method achieves accurate crack perception and effective geometric feature characterization, providing a quantitative basis for AGV speed adjustment under different pavement conditions.