Optimized Design and Deep Vision-Based Operation Control of a Multi-Functional Robotic Gripper for an Automatic Loading System

Abstract

This study presents an optimized design and vision-guided control strategy for a multi-functional robotic gripper integrated into an automatic loading system for warehouse environments. The system adopts a modular architecture, including standardized platforms, transport containers, four collaborative 6-DOF robotic arms, and a multi-sensor vision module. Methodologically, we first developed three gripper prototypes, selecting the optimal design (30° angle between the gripper and container side) through workspace and interference analysis. A deep vision-based recognition system, enhanced by an improved YOLOv5 algorithm and multi-feature fusion, was employed for real-time object detection and pose estimation. Kinematic modeling and seventh-order polynomial trajectory planning ensured smooth and precise robotic arm movements. Key results from simulations and experiments demonstrated a 95.72% success rate in twist lock operations, with a positioning accuracy of 1.2 mm. The system achieved a control cycle of 35 ms, ensuring efficiency compared with non-vision-based methods. Practical implications include enabling fully autonomous container handling in logistics, reducing labor costs, and enhancing operational safety. Limitations include dependency on fixed camera setups and sensitivity to extreme lighting conditions.