A Camera-Based Position Correction System for Autonomous Production Line Inspection.

Visual inspection is an important task in manufacturing industries in order to evaluate the completeness and quality of manufactured products. An autonomous robot-guided inspection system was recently developed based on an offline programming (OLP) and RGB-D model system. This system allows a non-expert automatic optical inspection (AOI) engineer to easily perform inspections using scanned data. However, if there is a positioning error due to displacement or rotation of the object, this system cannot be used on a production line. In this study, we developed an automated position correction module to locate an object's position and correct the robot's pose and position based on the detected error values in terms of displacement or rotation. The proposed module comprised an automatic hand-eye calibration and the PnP algorithm. The automatic hand-eye calibration was performed using a calibration board to reduce manual error. After calibration, the PnP algorithm calculates the object position error using artificial marker images and compensates for the error to a new object on the production line. The position correction module then automatically maps the defined AOI target positions onto a new object, unless the target position changes. We performed experiments that showed that the robot-guided inspection system with the position correction module effectively performed the desired task. This smart innovative system provides a novel advancement by automating the AOI process on a production line to increase productivity.

Autonomous Robot-Guided Inspection System Based on Offline Programming and RGB-D Model.

Automatic optical inspection (AOI) is a control process for precisely evaluating the completeness and quality of manufactured products with the help of visual information. Automatic optical inspection systems include cameras, light sources, and objects; AOI requires expert operators and time-consuming setup processes. In this study, a novel autonomous industrial robot-guided inspection system was hypothesized and developed to expedite and ease inspection process development. The developed platform is an intuitive and interactive system that does not require a physical object to test or an industrial robot; this allows nonexpert operators to perform object inspection planning by only using scanned data. The proposed system comprises an offline programming (OLP) platform and three-dimensional/two-dimensional (3D/2D) vision module. A robot program generated from the OLP platform is mapped to an industrial manipulator to scan a 3D point-cloud model of an object by using a laser triangulation sensor. After a reconstructed 3D model is aligned with a computer-aided design model on a common coordinate system, the OLP platform allows users to efficiently fine-tune the required inspection positions on the basis of the rendered images. The arranged inspection positions can be directed to an industrial manipulator on a production line to capture real images by using the corresponding 2D camera/lens setup for AOI tasks. This innovative system can be implemented in smart factories, which are easily manageable from multiple locations. Workers can save scanned data when new inspection positions are included based on cloud data. The present system provides a new direction to cloud-based manufacturing industries and maximizes the flexibility and efficiency of the AOI setup process to increase productivity.