Joanna Gąbka

The paper presents the results of research focused on configuring a system for stereoscopic view capturing and processing. The system is being developed for use in staff training scenarios based on Virtual Reality (VR), where high-quality, distortion-free imagery is essential. This research addresses key challenges in image distortion, including the fish-eye effect and other aberrations. In addition, it considers the computational and bandwidth efficiency required for effective and economical streaming and real-time display of recorded content. Measurements and calculations were performed using a selected set of cameras, adapters, and lenses, chosen based on predefined criteria. A comparative analysis was conducted between the nearest-neighbour linear interpolation method and a third-order polynomial interpolation (ABCD polynomial). These methods were tested and evaluated using three different computational approaches, each aimed at optimizing data processing efficiency critical for real-time image correction. Images captured during real-time video transmission—processed using the developed correction techniques—are presented. In the final sections, the paper describes the configuration of an innovative VR-based training system incorporating an edge computing device. A case study involving a factory producing wheel rims is also presented to demonstrate the practical application of the system.

The article presents research focused on design, testing and evaluation of the Artificial Intelligence (AI) algorithms dedicated for Virtual Player (VP/bot) embedded in the Production Management Training Platform (TP). The innovative software enables complex teaching process in form of didactic games. The purpose of the study is to eliminate limitations of the prototype VP’s algorithmic structure which was primarily implemented in the Platform. The study comprise measurements aimed at comparing effectiveness and efficiency of the existing solution with the capabilities of the newly developed VP’s logic. The overview of the algorithmic concepts available for VPs in serious games was made indicating elements useful for the purpose of the analysed system. Considering drawbacks of the original bot’s engine architecture, the upgraded version was elaborated and tested in the simplified version of the game. The results of the verification show to prove dominance of the AI solution with Machine Learning over the existing hybrid approach. The training, evaluation and recruitment games offered by the Platform could successfully fill in the gap identified in the new paradigm of education for Industry 4.0. It assures teaching of multidiscipline knowledge as well as practical skills training in the competitive environment altogether with thorough evaluation procedure. The appropriate VP’s logic increases flexibility of the didactic game and enables to customize the learning process which is crucial for the successful education process aided with gaming tools.

The paper presents work aimed at building practical applications of virtual reality (VR) in manufacturing environments. It contains studies of the optical properties of cameras and lenses aimed at the selection of an optimal set (camera, adapter, lens) for the realization of recordings and video transmissions in stereoscopic format for VR. In response to the increasing trend in the number of applications of VR systems in the industry, works have been initiated with the purpose of building a system levelling image noise identified thus far as an obstacle to the effective utilization of VR in production systems. It was considered that picture error correction can significantly increase an already big data stream from the recordings. Based on it, a set of parameter values was defined which determined the selection of study equipment. Three research areas were set: the verification of the optical correctness, the study of image defects and their correction and the determination of the maximum optical resolution and the achievable image parameters in various lighting and environmental conditions. An example was presented for the application of a projected system for the monitoring of undesirable events/movement at work stands and key areas of production halls as well as training in the high risk production zones.

The paper presents GoShaper Platform serving as a training, recruitment and evaluation gaming tool in Production Management field. The complex composition of the game modules and functions was described altogether with the decision process, in the most elaborated Free Competition module, examining players skills in the competitive environment and challenging them with tasks such as factory locating (with consideration of suppliers placement), technology purchasing, layout planning, schedule building and production control, while competing with other manufacturers on the market. The description of the decision process complexity and its interdependencies embedded, gave overall view of algorithmic needs for Virtual Player (VP) to be integrated in the Platform. The review of the available AI solutions for serious games was included. The set of algorithms chosen and combined accordingly and innovative logic developed for VP were shown, and the entity was tested proving its potential. The VP built in gained significant advantage over real, well qualified player which proved the evolved dedicated logic with AI elements effectiveness. The possibilities of future algorithms upgrade were indicated.