Vision-based robotic peg-in-hole research: integrating object recognition, positioning, and reinforcement learning

The peg-in-hole task is important in robotics. Visual inspection is a crucial method for recognition and positioning during this task. Currently, vision-based peg-in-hole techniques suffer from limited applicability and low positioning accuracy. Therefore, this study introduced a general vision-based approach for robotic peg-in-hole tasks. This approach delineates the process into two stages. First, during the object recognition, positioning, and approach phases, a coarse adjustment technique for the assembly pose of the robot's end effector was proposed based on object recognition. This method determines the pose of the hole to be assembled through ellipse fitting, thereby guiding the robot to approach the hole. Second, a Q-learning-based method was introduced to fine adjust the robot end effector's pose and position. Q-learning was applied to the scenario of small-scale adjustment of the robotic peg-in-hole, the reward function based on the pixel area of the gap and the included angle between central axes of peg and hole are designed. Finally, the feasibility and efficacy of this method are substantiated through a series of assembly experiments.