To address the challenges of detecting the variable pose of bolster springs within the confined space of a bogie frame and identifying potential entrapment interactions between the springs and surrounding obstacles, this study proposes a method for identifying the trapped state of bolster springs based on heterogeneous information fusion. This approach improves the accuracy of pose detection and enables precise perception of trapped states through a constructed identification model. Initially, the principles of multisensor joint calibration, heterogeneous information fusion, and the construction of the bolster spring trap identification model are presented. Subsequently, the architecture and loss function of RTMDet were optimized to achieve accurate detection of the planar pose of the bolster spring based on image data. Statistical filtering and Random Sample Consensus (RANSAC) were employed for the segmentation of external spring point clouds and the initial pose estimation. Additionally, pose optimization was performed based on projection roundness and a genetic algorithm. Finally, the identification experiment for the trapped state of the bolster spring was successfully completed based on the proposed model. The results indicated that the method achieved a root mean square error (RMSE) of 1.47 mm for position and 0.84° for orientation, with an accuracy of 94.4% in classifying whether the spring is trapped or not trapped. Compared to mainstream methods, this approach significantly improves the accuracy of bolster spring pose detection. This advancement provides a novel identification strategy for workpieces trapped in confined complex environments, offering potential applications in the intelligent perception system design of robots for workpiece assembly and disassembly in the equipment manufacturing industry.
Liu et al. (Thu,) studied this question.