Abstract: Mechanical devices used in industrial sorting are typically designed for specific application scenarios and products,often exhibiting poor versatility and intelligence when faced with unordered mixed object grasping.Current point cloud matching grasping technologies based on 3D structured light cameras have improved flexible production capabilities to a certain extent.How-ever,they are constrained by high hardware costs,limited feature description capabilities,high computational complexity,and sensitivity to occlusions,making it difficult to meet the demands of unordered mixed object grasping.In recent years,deep learning-based grasping technologies,represented by GraspNet,have developed rapidly,achieving pose estimation through binocular ca-meras.Nevertheless,these methods still suffer from suboptimal target selection strategies,limitations in pose scoring mechanisms,and significant pose localization errors.To address these challenges,this study proposes an improved three-stage grasping algorithm.In the first stage,the YOLOv10 object detection model is fused with the SAM segmentation model,combined with an optimized target selection algorithm that prioritizes unobstructed and closer targets,effectively solving the problem of poor target selection strategies in stacked and occluded scenarios.In the second stage,the GraspNet pose estimation framework is enhanced by introducing a pose filtering mechanism based on point cloud surface normals and reconstructing the scoring mechanism to obtain high-precision grasping poses.In the third stage,a pose fine-tuning strategy is designed using a hierarchical control architecture of “hover alignment-vertical grasping” to effectively eliminate cumulative errors during execution,ultimately addressing the issue of inaccurate real-world grasping.Experimental results demonstrate that this method significantly improves grasping efficiency,operational reliability,and cross-scenario generalization capabilities in complex environments.Moreover,by replacing 3D structured light cameras with binocular cameras,the system cost is significantly reduced,providing a cost-effective solution for industrial automation.

Key words: Unordered mixed objects grasping, Pose estimation, Target selection, Pose optimization, Binocular camera