Indoor positioning systems (IPS) are essential for mobile robot navigation in environments where global positioning systems (GPS) are unavailable, such as hospitals, warehouses, and intelligent infrastructure. While current surveys may limit themselves to specific technologies or fail to provide practical application-specific details, this review summarizes IPS developments directed specifically towards mobile robotics. It examines and compares a breadth of approaches that vary across non-radio frequency, radio frequency, and hybrid sensor fusion systems, through the lens of performance metrics that include accuracy, delay, scalability, and cost. Distinctively, this work explores emerging innovations, including synthetic aperture radar (SAR), federated learning, and privacy-aware AI, which are reshaping the IPS landscape. The motivation stems from the’ increasing complexity and dynamic nature of indoor environments, where high-precision, real-time localization is essential for safety and efficiency. This literature review provides a new conceptual, cross-border pathway for research and implementation of IPS in mobile robotics, addressing both technical and application-related challenges in sectors related to healthcare, industry, and smart cities. The findings from the literature review allow early career researchers, industry knowledge workers, and stakeholders to provide secure societal, human, and economic integration of IPS with AI and IoT in safe expansions and scale-ups.

This study presents an innovative framework to improve the accessibility and usability of collaborative robot programming. Building on previous research that evaluated the feasibility of using a domain-specific language based on behaviour-driven development, this paper addresses the limitations of earlier work by integrating additional features like a drag-and-drop Blockly web interface. The system enables end users to define and execute robot actions with minimal technical knowledge, making it more adaptable and intuitive. Additionally, a gesture-recognition module facilitates multimodal interaction, allowing users to control robots through natural gestures. The system was evaluated through a user study involving participants with varying levels of professional experience and little to no programming background. Results indicate significant improvements in user satisfaction, with the system usability scale overall score increasing from 7.50 to 8.67 out of a maximum of 10 and integration ratings rising from 4.42 to 4.58 out of 5. Participants completed tasks using a manageable number of blocks (5 to 8) and reported low frustration levels (mean: 8.75 out of 100) alongside moderate mental demand (mean: 38.33 out of 100). These findings demonstrate the tool’s effectiveness in reducing cognitive load, enhancing user engagement and supporting intuitive, efficient programming of collaborative robots for industrial applications.

The engineering-to-order (ETO) sector, driven by the demands of new energy transition markets, is witnessing rapid innovation, especially in the design of complex systems of turbomachinery components. ETO involves tailoring products to meet specific customer requirements, often posing coordination challenges in integrating engineering and production. Meeting customer demands for short lead times without imposing high price premiums is a key industry challenge. This article explores the application of artificial neural networks as an enabler for design automation to deliver a first tentative optimal design solution in a short period of time with respect to more computationally demanding optimization methods. The research, conducted in collaboration with an energy company operating in the Oil & Gas and energy transition markets, focuses on the design process of reciprocating compressors as a means of study to develop and validate the developed methodology. Three case studies corresponding to as many representative jobs related to reciprocating compressor cylinders have been analyzed. The results indicate that the proposed method performs well within its training boundaries, delivering optimal solutions and providing reasonably accurate predictions for target configurations beyond these boundaries. However, in cases requiring a creative redesign using artificial neural networks may lead to errors that exceed acceptable tolerance levels. In any case, this methodology can significantly assist design engineers in the efficient design of complex systems of components, resulting in reduced operating and lead times.