Industrial robots are mostly designed for tasks where working conditions are well known and do not change over time. However, there are many examples where robots require vision sensor-based control in order to perform complex operations and react to changes in the environment. Other areas where there is need for combining vision sensors with manipulators are quality inspection tasks of large or complex structured objects. The paper presents an overview of the methods used in robotic vision systems, taking into account their specific requirements and advantages. Experimental studies of a selected 2D/3D sensors mounted on industrial manipulators are presented including analysis of collected 3D points clouds. Additionally, a system of a reconfigurable manipulator that interchangeably uses various sensors and tools in complex inspection tasks is presented.

The paper presents the possibilities of a hybrid vision method based on simultaneous analysis of infrared and vision images for surface inspection of hot aluminium in a manufacturing process. The system consists of a NIR/SWIR camera and a high resolution visual camera, and a computer based image analysis system. The simultaneous analysis of infrared and vision images will enable surface inspection for detecting defects in temperature range from 200°C to 600°C. Thermal images present temperature distribution on the surface, and contain information about the manufacturing process. The analysis of thermograms enables to find areas of temperature irregularity caused by increased friction loads, as well as areas of inhomogeneous emissivity caused by surface defects. Furthermore, information captured by the vision camera is used to detect surface defects. The software developed enables the overlaying of images. The proposed simultaneous thermovision and vision imaging can be applied in industry for in-line monitoring of aluminium extrusion processes.

A method for quality inspection of food packaging based on hybrid imaging and machine-learning techniques is presented. The proposed inspection system integrates thermal and visible-light imaging, enabling detection and classification of faults such as weak seals, creases and contamination. For the purpose of the study data acquisition is automated with the use of an industrial manipulator, ensuring repeatability and consistent positioning of samples. Using the acquired images, the temperature distribution in the sealing area and selected process parameters, a predictive model for burst-pressure testing was developed. The proposed workflow includes attribute selection, hyperparameter optimization and the application of regression algorithms. The proof-of-concept results demonstrate a strong alignment between predicted and measured values, as well as high model stability. The best-performing model, ElasticNet, achieved an R² of 0.815 and an MAE of 0.028 kgf/cm², confirming its potential for non-destructive quality control of packaging.