Environment-Adaptable Printed-Circuit Board Positioning Using Deep Reinforcement Learning

Vision-based object positioning is very important in the electronic industry for assembly and inspection tasks. Many methods have been proposed to tackle the problem, either by traditional machine vision or by deep learning (DL) techniques. The traditional methods rely on template matching or feature point correspondence. They are computationally intensive and are easily affected by illumination changes and noise. DL models such as convolutional neural networks (CNNs) are computationally very efficient but are also sensitive against environmental changes. In this article, a deep reinforcement learning (DRL) model based on the Actor-Critic style Proximal Policy Optimization algorithm(s) (AC-PPO) is proposed. The proposed method is applied for the positioning of printed circuit boards (PCBs). The model uses as the current environment the sensed image and the reference template as a guide. It requires only a single manually marked template in the reference image. All possible training images are automatically and randomly generated during the neural network training without human intervention. The proposed reinforcement learning (RL) model is shown to be adaptive to environmental changes, including illumination, noise, de-focusing, and template occlusion, compared with the CNN regressor. Experimental results indicate that the proposed model on average can achieve estimation errors less than 1 pixel in translation and 1° in orientation, with fast evaluation for the real-time PCB positioning task.