This paper presents the development and experimental validation of an adaptive pneumatic gripper for collaborative robotic palletizing of packages with varying mass and surface characteristics. The main objective is to determine the optimal gripping-force and minimum operating pressure required to ensure stable and safe handling without slippage. A dynamic mathematical model was developed, incorporating the effects of package mass, friction coefficient, contact surface area, and inertial forces during manipulation. Numerical analysis was performed for different friction conditions (μ = 0.30-0.90) and contact configurations, enabling the determination of the minimum required gripping-forces and corresponding operating pressures. Experimental validation was conducted on a real industrial system with a collaborative robot. The results show a linear relationship between pressure and gripping-force, described by F = 22.152 p − 17.535, with a high correlation coefficient (R2 ≈ 0.998). The maximum experimentally obtained gripping-force was approximately 70-75 N at a pressure of around 4 bar. Quantitative deviations between numerical and experimental results (65-75 %) were observed and corrected by introducing a calibration factor (kcorr ≈ 0.30). The proposed model and experimental system enable reliable optimization of gripping-force and improve manipulation stability under real industrial conditions. The main contribution of this study lies in the integration of analytical modelling, numerical optimization, and industrial experimental validation for collaborative robotic palletizing systems

The integration of technology in correctional facilities represents a paradigm shift in modern prison rehabilitation approaches globally, offering unprecedented opportunities to enhance inmate reintegration while addressing systemic challenges. This research examines the global phenomenon of digital rehabilitation, using the current state and prospects of technology adoption in Albanian prisons as an illustrative case study. By analysing both opportunities for rehabilitation enhancement and implementation challenges, this study highlights the broader implications of digital tools in correctional settings. Through a comprehensive analysis of recent developments from 2020 to 2025, including international cooperation initiatives, this study reveals significant potential for digital transformation in correctional systems. Key findings indicate that technology-enhanced rehabilitation programs can reduce recidivism rates by up to 23% compared to traditional methods, while digital education platforms show 68% success rates versus 35% for conventional approaches. However, implementation faces substantial barriers globally, including high costs (85% impact), ageing infrastructure (78% impact), and staff training requirements (72% impact). The research demonstrates that strategic   technology adoption, supported by international partnerships and phased implementation approaches, can transform prisons into modern rehabilitation-focused institutions. This study contributes to the growing body of international knowledge on correctional technology, providing insights that are transferable to other transitional contexts and informing global policy decisions.

The first industrial robots appeared in the production processes in the 1960s have continued to be implemented in manufacturing worldwide. The greatest application of industrial robots has been observed in three major industries: the automotive industry, the electrical and electronics industry, and the metal industry. The automotive industry was the first to adopt the most industrial robots extensively, and in recent years the electrical and electronics industry has followed. Together, these two sectors account for more than 60% of the total number of industrial robots deployed worldwide. Industrial robots have primarily been used to perform tasks that are physically demanding and hazardous to workers’ health, including welding operations, which are predominantly carried out in the automotive industry. To date, first-generation industrial robots have been the most widely implemented. These systems are typically enclosed by protective fences to ensure worker safety, occupy substantial floor space, and are relatively complex to reprogram. The development of advanced technologies — such as sensor systems, the Internet of Things (IoT), big data analytics, cloud computing, virtual and augmented reality (AR), artificial intelligence (AI), and advanced safety systems — has significantly contributed to the evolution of robotic technology. The present study presents current trends in the implementation of industrial robots and examines their role in welding processes.

This paper investigates the potential of Explainable Artificial Intelligence (XAI) methods to improve the interpretation of AI-based models in smart manufacturing applications. The study focuses on SHAP and LIME techniques, analyzing their ability to explain model decisions, identify the most influential input features, and provide insights into both individual predictions and overall model behaviour. A practical case study based on the Steel Plates Faults dataset is presented using a Random Forest classification model to investigate the application of explainable AI techniques for steel plate fault detection. The results demonstrate that SHAP and LIME methods provide valuable insights into model decision-making by identifying the key features influencing fault classification. These explainability approaches enhance model transparency and trustworthiness, supporting the development of more reliable AI-based solutions for industrial quality inspection and smart manufacturing applications.

Research and development of advanced technologies underpinning the implementation of Industry 4.0 are driving the transformation of traditional manufacturing systems into flexible production systems whose high level of integration and intelligence is based on digital technologies. Mobile and collaborative robotics, supported by artificial intelligence, are becoming central elements of intelligent manufacturing processes through their implementation, complementing other Industry 4.0 technologies. This paper provides a clear and comprehensive overview of the architecture of Industry 4.0 smart manufacturing systems based on the integration of autonomous mobile robots and collaborative robots, with particular emphasis on system hierarchy, data flows, and interoperability with existing industrial information systems. Patent trends over the past decade in robotics and artificial intelligence are analyzed, along with trends in the implementation of mobile and collaborative robots in manufacturing processes. The role of robotic systems in process monitoring, decision-support, and adaptive production control is examined, including their integration with IoT platforms, manufacturing execution systems, and AI-based analytical modules. The paper also discusses the technical and organizational challenges associated with implementing such architectures, as well as their potential benefits in terms of flexibility, scalability, and resilience of manufacturing systems. The proposed architectural approach provides a foundation for the further development of smart manufacturing environments and offers guidelines for the industrial application of mobile and collaborative robotics in accordance with Industry 4.0 principles.

One of the characteristics of today's world economic trends is market expansion, i. e. the need for constant modification of existing products through application of new technologies, which should ensure competitiveness in turbulent market conditions. The role of industrial and service robots is essential in such conditions of manufacturing processes. An industrial and service robot is an essential constituent element of the new manufacturing lines, which are designed on a high degree of automation with the characteristics of flexibility, where it is very difficult to observe the robot and its effect outside the whole manufacturing line. The inclusion of industrial and service robots in existing production facilities increases significantly the performance of existing equipment, ensuring its efficiency. This reduces or postpones the need for new or larger investments, since certain situations have shown that the acquisition of robots is much more economical than purchasing new machines. The application of robotic systems in manufacturing processes, whether it is a fully automated process or highly automated manufacturing line (the so-called fixed automation or a possible flexible manufacturing cell), is observed through: materials handling and supporting machines, process operations, assembly tasks, and tasks of products inspection. The paper describes the dissemination of industrial and service robots in the world in the period 2009 to 2014, through the application in specific operations in the manufacturing process.

Industry 4.0 marks a new phase of industrial transformation, driven by the integration of advanced technologies such as industrial robotics, the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and cyber‑physical systems (CPS). The Republic of Korea and Singapore are global frontrunners in this domain, ranking first and second worldwide in robot density per 10,000 manufacturing workers. This paper explores how the strategic integration of robotics with key Industry 4.0 technologies contributes to smart manufacturing and enhanced industrial performance. Using a comparative case study approach, the research analyzes national policies, investments in R&D and education, 5G infrastructure, and support for innovation ecosystems that have enabled these countries to develop flexible, automated, and intelligent production systems. Findings indicate that both Korea and Singapore have successfully combined robotics with IoT, big data analytics, and cloud platforms to create efficient and adaptive manufacturing environments. The study emphasizes that robotization alone is not sufficient; its effectiveness depends on alignment with broader digital transformation strategies. Based on longitudinal data from 2013 to 2023, sourced from the International Federation of Robotics (IFR), the OECD, and national innovation agencies, the research highlights how coordinated implementation of Industry 4.0 technologies fosters sustainable and globally competitive manufacturing.

The integration of human-robot collaboration (HRC) into industrial and service environments demands efficient and adaptive robotic systems capable of executing diverse tasks, including pick-and-place operations. This paper investigates the application of Soft Actor-Critic (SAC) and Conservative Q-Learning (CQL)—two deep reinforcement learning (DRL) algorithms—for the learning and optimization of pick-and-place actions within HRC scenarios. By leveraging SAC’s capability to balance exploration and exploitation, the robot autonomously learns to perform pick-and-place tasks while adapting to dynamic environments and human interactions. Moreover, the integration of CQL ensures more stable learning by mitigating Q-value overestimation, which proves particularly advantageous in offline and suboptimal data scenarios. The combined use of CQL and SAC enhances policy robustness, facilitating safer and more efficient decision-making in continually evolving environments. The proposed framework combines simulation-based training with transfer learning techniques, enabling seamless deployment in real-world environments. The critical challenge of trajectory completion is addressed through a meticulously designed reward function that promotes efficiency, precision, and safety. Experimental validation demonstrates a 100 % success rate in simulation and an 80 % success rate on real hardware, confirming the practical viability of the proposed model. This work underscores the pivotal role of DRL in enhancing the functionality of collaborative robotic systems, illustrating its applicability across a range of industrial environments.

It is well known that with the emergence of Industry 4.0, the focus was placed on the digitalization and automation of industrial processes through technologies such as the Internet of Things (IoT), Big Data, artificial intelligence (AI) and robotics, which led us in the direction of smart production processes with the goal of ‘’smart factories’’. Unlike Industry 4.0, Industry 5.0 emphasizes the importance of humanization of technology, where people and robots work together in a harmonious environment. The paper examines whether advanced robotic technology can be synergistically integrated with human creativity to create more efficient, innovative and sustainable production practices. The paper explores the key elements that enable the integration of robotic technology and human creativity, including collaborative robots (cobots), artificial intelligence that supports creative processes and advanced sensor systems. Collaborative robots, designed to work safely alongside humans, take over routine and physically demanding tasks, freeing up time for workers to focus on creative and strategic activities. AI technologies analytically support human decisions, enabling faster and more informed innovation. Ethical and safety aspects of robotic technology integration are discussed, emphasizing the need for a transparent and responsible approach. The application of robotic technology in industry brings significant benefits, including increased productivity, cost reduction, improved worker safety and more sustainable development. The key to the success of Industry 5.0 is in creating a balanced synergy between technology and human creativity. By harmonizing automation with humanization, industry can achieve new levels of innovation and efficiency, adapting to the dynamic needs of the global marketplace. This approach ensures not only technological progress, but also social responsibility, thus laying the foundations for a sustainable and prosperous future for the industry.

Industry 4.0 represents a new chapter in the development of manufacturing systems, where digitalization, automation, and the application of advanced technologies become key drivers of competitiveness. The textile industry, traditionally characterized by manual processes, is undergoing a profound transformation through the integration of next-generation robotics. This paper analyzes the significance and impact of robotic implementation within the Industry 4.0 framework on process efficiency, flexibility, and sustainability in textile production. Special attention is given to the application of collaborative and autonomous robots, which enable smart work organization, optimized transport and storage, and adaptive production flow management. The study highlights both the benefits brought by the adoption of advanced robotic systems and the challenges encountered during their implementation, such as the need for digital competencies among the workforce and high investment costs. Through the analysis of current trends and examples of good practice, the paper points to key development directions aimed at enhancing innovation, sustainability, and global competitiveness of the textile sector. The conclusion emphasizes the necessity of a strategic approach and continuous investment in new technologies to ensure a successful transition toward the smart factory of the future.

The rapid evolution of Industry 4.0 is fundamentally reshaping the global automotive sector, positioning digitalization, automation, and robotics as core drivers of innovation and competitiveness. This paper examines the implementation and impact of Industry 4.0 technologies in three leading vehicle-producing countries with distinct industrial trajectories - China, India, and the United States. Through a comparative approach, the study explores the relationship between annual vehicle production, the intensity of industrial robot adoption, and the integration of smart manufacturing solutions. Special attention is given to robotics-both industrial and collaborative-as a key enabler of efficiency, flexibility, and innovation in production systems. The analysis also highlights the fundamental components of Industry 4.0, including cyber-physical systems, the Internet of Things (IoT), digital factories, artificial intelligence (AI), and digital twins, which collectively enable the synergy between humans, machines, and data. The paper presents recent trends in robotization and digital integration within automotive manufacturing, accompanied by an overview of national policies and investment priorities. Findings reveal that China leads in absolute vehicle output and robot installations, the United States focuses on highly automated and digitally connected production systems, while India is rapidly developing its capacities through selective and adaptive implementation of Industry 4.0 technologies. The study concludes that differing approaches to digital transformation are shaping unique models of competitiveness, technological sovereignty, and sustainable development in the automotive industry.

Picture this: a world where machines can decode the intricate rhythms of the human body, tracing electrical patterns from the brain and heart to uncover hidden signs of disease. Artificial intelligence has brought this vision closer to reality, transforming electroencephalography (EEG) and electrocardiography (ECG) analysis into a sophisticated fusion of data science and medicine. Yet, the journey is far from complete. Biomedical signals are notoriously complex—drenched in noise, prone to variability, and demanding meticulous preprocessing before they reveal their secrets. This review embarks on a deep dive into the essential preprocessing and feature engineering techniques that refine raw EEG and ECG data, making them suitable for intelligent analysis. From signal filtering to wavelet transformations, each step in the pipeline plays a crucial role in shaping AI’s ability to detect meaningful patterns. Particular attention is given to recurrent neural networks (RNNs), which excel in capturing the temporal dependencies hidden within these signals but come with their own set of computational hurdles. Beyond technical refinement, the discussion extends into the future—how can multimodal AI enhance clinical diagnostics?

The implementation of new Industry 4.0 technologies in robotics (mobile and collaborative robotics) with artificial intelligence (AI) is reshaping maintenance planning in advanced manufacturing. This paper analyzes the application of robotic systems combining collaborative robots (cobots) and autonomous mobile robots (AMRs) as support for predictive maintenance. Predictive maintenance is based on continuous real-time visual monitoring with the goal of managing faults. A mixed-methods approach was used, combining quantitative metrics such as downtime reduction, mean time to repair, and return on investment with qualitative staff assessments. The results of implementing robotic systems to support predictive maintenance indicate a significant reduction in production downtime, increased operational efficiency, and faster resolution of faults in the manufacturing process. In addition to technical efficiency, the study analyzes the economic feasibility, stability, and challenges of implementing AI vision systems within Industry 4.0. Compared to previously published studies in this field, this work is distinguished by the implementation of a cobot and an AMR in a unified system for visual inspection and control, with real-time data used for predictive maintenance. The system is connected to Computerized Maintenance Management Systems software for maintenance planning and monitoring and Enterprise Resource Planning software for real-time business activity planning. The results demonstrate that the integration of advanced robotics, computer vision, and machine learning algorithms enables the transformation of the traditional reactive approach into a proactive asset management model, thereby ensuring a long-term sustainable increase in reliability, safety, and competitiveness of the manufacturing processes.