This paper proposes a methodology for creating simplified structural schemes and forward geometric models for industrial robots with serial architecture, with the goal of reducing thermal deformation errors that negatively impact positioning accuracy during operation. Unlike classical approaches, the proposed methodology introduces modifications to the order of matrix multiplication and incorporates new parameters to create a forward geometric model that better corresponds to the deformation characteristics of these robots. Details are presented on how to build and employ this extended model and integrate it into a thermal error compensation algorithm. The implementation of the algorithm in a software application is presented along with experimental results that demonstrate its effectiveness. This work addresses a real phenomenon that occurs in industrial robot operation and has implications for improving the performance of robots in manufacturing applications.

This paper introduces a gesture-controlled conversational interface driven by a local AI model, aimed at improving accessibility and facilitating hands-free interaction within digital environments. The technology utilizes real-time hand gesture recognition via a typical laptop camera and connects with a local AI engine to produce customized learning materials. Users can peruse educational documents, obtain topic summaries, and generate automated quizzes with intuitive gestures, including lateral finger movements, a two-finger gesture, or an open palm, without the need for conventional input devices. Upon selection of a file, the AI model analyzes its whole content, producing a structured summary and a multiple-choice assessment, both of which are immediately saved for subsequent inspection. A unified set of gestures facilitates seamless navigating within the user interface and the opened documents. The system underwent testing with university students and faculty (n = 31), utilizing assessment measures such as gesture detection accuracy, command-response latency, and user satisfaction. The findings demonstrate that the system offers a seamless, hands-free user experience with significant potential for usage in accessibility, human–computer interaction, and intelligent interface design. This work advances the creation of multimodal AI-driven educational aids, providing a pragmatic framework for gesture-based document navigation and intelligent content enhancement.

Ensuring synchronization between real-world sensor data and industrial robotic simulations remains a critical challenge in digital twin and virtual commissioning applications. This study proposes an innovative method for integrating real sensor signals into RoboDK simulations, bridging the gap between virtual models and real-world dynamics. The proposed system utilizes an Arduino-based data acquisition module and a custom Python script to establish real-time communication between physical sensors and RoboDK’s simulation environment. Unlike traditional simulations that rely on predefined simulated signals or manually triggered virtual inputs, our approach enables dynamic real-time interactions based on live sensor data. The system supports both analog and digital signals and is validated through latency measurements, demonstrating an average end-to-end delay of 23.97 ms. These results confirm the feasibility of real sensor integration into RoboDK, making the system adaptable to various industrial applications. This framework provides a scalable foundation for researchers and engineers to develop enhanced simulation environments that more accurately reflect real industrial conditions.

Using Machine Learning (ML) in educational management transforms higher education strategy. This study examines students’ views on machine learning (ML) technologies and how they might be used to plan, monitor, and predict student performance. The Faculty of Industrial Engineering and Robotics surveyed 118 third-year undergraduates. It featured closed- and open-ended questions to collect quantitative and qualitative data. Descriptive statistics showed broad patterns, inferential tests (Chi-square, t-test, ANOVA) showed group differences, regression models predicted school outcomes, and exploratory factor analysis (EFA) and clustering found hidden attitudes and student profiles. A multi-method quantitative approach combining descriptive statistics, inferential tests, regression modeling, and exploratory techniques (EFA and clustering) was employed. The findings show that most students realize that ML may help them be more productive, adapt their study pathways, and learn about the future. Concerns remain regarding its accuracy, overreliance, and morality. The findings indicate that ML can both support and challenge educational management, depending on how responsibly it is implemented. Results show that institutions may utilize ML as a strategic tool to boost academic progress and make better judgments, provided they incorporate it responsibly and follow ethical rules and training.

This paper presents a software application developed in Python (v3.9) for obstacle avoidance trajectory planning in the RoboDK (v6.0) virtual environment. The proposed method automatically scans the virtual station, identifies obstacles, discretizes the workspace into a three-dimensional free-space-graph (FSG) and searches for candidate routes between start and finish points. Each route is then verified at the robot level by inverse kinematics and collision control, and the validated solutions can be transformed into preview curves, intermediate points and motion programs executable by the robot. The study includes an initial test scenario performed with the ABB IRB 6650-125/3.2 robot and randomly generated obstacles, followed by a series of benchmark tests performed in different virtual scenarios and with four different robot models. In the comparative tests, the proposed method was evaluated together with a rapidly exploring random tree (RRT) reference planner and the native probabilistic roadmap (PRM) planner, embedded RoboDK. The final scenario included a robotic cell with realistic objects. The results show that the application can identify valid executable routes and, in some cases, several alternative variants for the same pair of target points. Overall, the benchmark suggests that the analyzed methods have different strengths and should be viewed as complementary solutions. In the tested scenarios, RRT was the method with the lowest computational times, while the proposed method offered the possibility of generating several alternative routes. At the current stage, the application can thus be used as an offline programming tool but also as a research and analysis tool for planning robotic trajectories in the presence of static obstacles.

The management of end-of-life photovoltaic panels has become a focal point for circular economy initiatives, given the significant waste volumes generated by the global energy transition. This study addressed the challenge of identifying optimal recycling solutions characterized by conflicting objectives, such as material recovery efficiency, economic feasibility, and environmental impact. Given that photovoltaic waste contains valuable materials alongside elements requiring specialized handling, the selection of appropriate processing technologies has been prioritized by research and industrial sectors. To resolve these trade-offs, a hybrid Multi-Criteria Decision-Making (MCDM) framework was implemented, combining the Best–Worst Method (BWM) with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE II). The BWM was employed to determine criteria weights based on expert evaluations, focusing on the relationships between the most and least significant factors to ensure mathematical consistency. Subsequently, the PROMETHEE II facilitated a complete ranking of technological alternatives by calculating net preference flows, allowing for a nuanced comparative analysis of diverse recovery processes. Through this approach, the research established a clear performance hierarchy among established and emerging recycling pathways. These findings provided a structured quantitative basis for decision-makers to identify balanced solutions for industrial implementation, supporting long-term sustainability goals and the preservation of secondary raw materials.