Versatile Robotic System for Assembly Tasks Using Flexible Mechanism

We propose an assembly system involving two robotic arms with flexible mechanisms. These flexible joints provide passive compliance, allowing the robots to absorb positional errors between assembly parts and their respec- tive destinations. Our machine learning-based object detection system shows robustness against variations in color, shape, and ambient lighting, achieving high positional accuracy. This system achieves the execution of diverse assembly tasks using a single setup, facilitated by a universal gripper and machine learning capabilities. Furthermore, the flexible joints minimize the need for precise trajectory planning. In our study, we implemented both an instruction support system and a position error correction system using object detection. We conducted real-world assembly experiments to evaluate these systems, focusing on tasks such as screw tightening and belt drive assembly, with alignment achieved through vision feedback and force feedback control.