Evolutionary Image Processing for an Intelligent Power Module Inserter Robust to Different Lighting Environments

Abstract

This paper proposes a genetic algorithm-based image-processing method that enables an intelligent power module (IPM) inserter to be robust to different lighting environments. In a previous study, we developed an IPM inserter based on a 6-axis parallel link robot. The robot grasps the IPM and inserts it securely into the printed circuit board; this process facilitated by three-dimensional visual feedback control from two cameras. Image processing is used to acquire the positions of the IPM pin tips and the holes on the board. However, because the parameters used in the image processing all had fixed values, there were cases where the system could not cope with changes in the lighting environment. To address this issue, in this paper, we propose a method that uses a genetic algorithm to automatically obtain a set of image-processing parameter values that are optimized for the environment. The method also incorporates a proposed technique for speeding up insertion by conventional inserters. Overall, the proposed method can speed up insertion by approximately 40% and achieves a high insertion success rate even when the lighting conditions change.