In recent advancements in robotics, Artificial Intelligence (AI) methods such as Deep Learning, Deep Reinforcement Learning (DRL), Transformers, and Large Language Models (LLMs) have significantly enhanced robotic capabilities. Key AI models driving advancements in robotic vision include Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), the DEtection Transformers (DETR), the YOLO family of algorithms, segmentation techniques, and 3D vision technologies. Deep Reinforcement Learning (DRL), an AI technique where agents learn optimal behaviors through trial and error interactions with their environment, enables robots to perform complex tasks autonomously. Transformers, originally developed for natural language processing, have been adapted to robotics for tasks involving sequence prediction and data understanding, improving perception and decision-making processes. LLMs leverage vast amounts of text data to enhance robot-human interaction, enabling robots to understand and generate human-like language, thus improving their communicative and collaborative abilities in various applications. The integration of these AI methods enhances the adaptability, efficiency, and overall performance of robotic systems, paving the way for more sophisticated and intelligent autonomous agents.

Currently, the world is facing major changes. Research and development of innovations in new technologies, the rapid pace of implementation of these innovations and especially digitization and automation, play a major role in shaping the future world. Technological innovations promise the transformation of the world we live in all its dimensions. However, in order for the benefits of innovation to be adequately exploited, it is necessary for us as a society to adapt to the coming changes. We must also keep in mind that these changes come at a time previously marked by uncertainty, turbulent changes and hyper competitiveness. The development and implementation of new technologies in business is motivated by a number of technical and economic reasons: improving the quality of finished products (machining, etc.), increasing productivity and reducing the share of work (assembly process), increasing the degree of homogeneity of product quality in all production processes related to the application of robotic technology, increasing the level of safety, reducing labor engagement in routine and reproducible processes, minimizing total production costs and maintenance costs of the device in the production process, all with the purpose of adequate responses to competition challenges and increasingly stringent customer requirements. Although the concept of Industry 4.0 is already widely used in developed countries, it is a relatively new concept in the Western Balkans, including Bosnia and Herzegovina. Most company managers understand the benefits of "smart" production and are familiar with new trends in the industry, intend to gradually introduce smart solutions, methods and technologies, and only a small number of companies in Bosnia and Herzegovina currently implement the concept of Industry 4.0. The paper presents the results of research on the application of Industry 4.0 technologies in all branches of the economy in Bosnia and Herzegovina and especially the representation of Industry 4.0 in small, medium and large enterprises. Detection of awareness of certain groups about the concepts of Industry 4.0 was performed, and the research method itself is based on an online survey.

Due to COVID-19 pandemic, there is an increasing demand for mobile robots to substitute human in disinfection tasks. New generations of disinfection robots could be developed to navigate in high-risk, high-touch areas. Public spaces, such as airports, schools, malls, hospitals, workplaces and factories could benefit from robotic disinfection in terms of task accuracy, cost, and execution time. The aim of this work is to integrate and analyse the performance of Particle Swarm Optimization (PSO) algorithm, as global path planner, coupled with Dynamic Window Approach (DWA) for reactive collision avoidance using a ROS-based software prototyping tool. This paper introduces our solution – a SLAM (Simultaneous Localization and Mapping) and optimal path planning-based approach for performing autonomous indoor disinfection work. This ROS-based solution could be easily transferred to different hardware platforms to substitute human to conduct disinfection work in different real contaminated environments.