This article introduces an innovative inspection system designed to check the condition of the welding electrode tip. The system combines 2D laser scanning with advanced analytical algorithms, enabling fast, accurate, and automated inspection. Developed PC software controls the synchronized movement of welding electrodes and the scanner, allowing the creation of 3D point clouds representing the surface of the electrodes. These data are analysed to identify deformations, contamination, and surface imperfections. The inspection results are communicated with the welding control system, contributing to improved weld quality and efficiency in industrial production processes. This innovation has the potential to change how the condition of welding electrodes is monitored in industrial sectors.

This paper aims to describe a method of non-destructive testing in highly variable production using a robotic ultrasound diagnostic system. Highly variable production involves producing products of different shapes and dimensions, which requires flexible positioning and the adaptation of diagnostic technology. Typically, highly variable products are made in small batches. Implementing automation tools, including robotic systems, can meet this requirement, even in diagnostic processes that demonstrate the required quality level. This paper deals with the design, simulation, optimization, and verification of an innovative robotic ultrasound diagnostic system for non-destructive testing in highly variable production environments. The robotic positioning of the diagnosed object and the ultrasound probe achieves the automatic adaptation of the system. Robots can automatically position the ultrasound probe relative to the diagnosed object using laser distance measurements in the water environment of a diagnostic vessel. Computer processing of the data measured by the ultrasound probe enables the evaluation of data and the documentation of quality criterion fulfillment in digital form. Setting the technology parameters, monitoring the technology status, and displaying the quality control results are enabled by a human–machine interface system also described in this paper.

The aim of this research is to design and validate methods for detecting specific objects, namely vehicles, in LiDAR point clouds obtained from large‐scale urban mapping. Accurate detection of vehicles in such data is challenging because outdoor environments contain a mixture of objects with varying geometry, density, and reflectivity. The proposed approach focuses on reliable vehicle segmentation and classification while maintaining low computational complexity and high interpretability. The processing pipeline combines terrain filtering, modified Euclidean clustering that incorporates LiDAR intensity information, and PointNet‐based classification. To evaluate the proposed approach under real‐world conditions, a dedicated urban LiDAR dataset (Danubia) containing several hundred vehicles was created using a mobile mapping platform. Experimental results demonstrate that the proposed method achieves a vehicle classification success rate of 92.50%.