For randomly stacked workpieces in bins, boxes, or totes, traditional 2D vision often struggles to accurately obtain height, orientation, and occlusion relationships. The 3D Vision Bin Picking Solution captures 3D point cloud data to identify the position, orientation, and stacking relationship of different workpieces. Combined with diversified grasping strategies, collision detection, AI deep learning, and robot motion path planning, the system guides robots to automatically select and pick target workpieces from complex piles. The solution is widely applicable to random picking of automotive parts, machined metal parts, die castings, forgings, electronic components, and various irregular workpieces.
High-quality 3D imaging, application algorithms, and coordinated robot control enable reliable 3D vision performance in complex industrial environments.
High-quality industrial 3D cameras directly capture object contours and spatial poses, overcoming the positioning limitations of traditional 2D vision in complex stacking scenarios.
Supports random stacking, mutual occlusion, and varied workpiece poses, reducing the need for positioning fixtures and manual part arrangement.
Depending on workpiece features, template/model matching, geometric feature recognition, AI segmentation, and other algorithms can be combined to identify randomly stacked parts and improve positioning accuracy.
Selects appropriate grasping strategies based on workpiece pose, graspable areas, gripper structure, and bin conditions, improving single-pick success rate and overall bin-clearing rate.
Builds a simulation environment based on robot, gripper, bin, and workstation models to plan robot grasping poses and motion paths, reducing collision and part-scrap risks.