RGB-D Bin-Picking System for Ergonomic Automotive Clip Assembly: 3D Annotation, Deep Learning, and 6-DOF Pose

Automotive seat cover assembly entails the manual collection of plastic J-shaped clips from boxes located at a non-ergonomic distance from the sewing stations. Operators are compelled to inadvertently pick up multiple components at once, and make actions that involve repetitive stretching. The daily, consistent repetition leads to misassembly of the critical seat to frame connectors, and adds on to physical stress. This study showcases a fully integrated RGB-D bin-picking solution that uses depth-dependent grasp planning and deep learning object detection for interlocked plastic clip handling. GraspAnnotator Pro, a dual-modality bespoke software solution developed for this work, allows for model training on point cloud and RGB data for cluttered environment 6D pose estimation. This is achieved through a custom integrated annotation tool that simplifies the labeling of grasp pose and object boundary assignments. The system reduces strain on operators by automatically positioning parts within ergonomic zones and using fault-tolerant handling integrated with assembly verification. Real-world deployment validation over 6 weeks of continuous operation accumulated 3,000 pick-and-place cycles across 10 distinct J-shaped wire harness components, achieving a 93.7% first-attempt success rate with an average cycle time of 10.6 seconds. The system demonstrates a 42% reduction in cycle time compared to manual methods (18.3 seconds) with significant ergonomic improvements.