A Study on Peg-in-Hole Insertion Based on Misalignment Error Estimation Network

Ongoing research is being conducted on the autonomous operation of robots for manufacturing automation. However, it faces challenges due to uncertainty in unstructured environments. Particularly, tasks with frequent contact, such as peg-in-hole assembly, pose a risk of hazardous forces on the robot system due to uncertainty-induced collisions. To address this, we propose a regression learning model to infer the orientation error angles of the peg and hole based on contact data. We demonstrate its effectiveness in overcoming jamming caused by misalignment in peg-in-hole insertion by integrating it with a traditional parallel force/position controller and a learned misalignment error estimation network (MEN). Experimental results show that the system integrated with MEN achieves a 100
^%
insertion success rate and a faster average duration compared to the single system, demonstrating stable peg-in-hole task without jamming.