The last decade saw the emergence of highly autonomous, flexible, re-configurable Cyber-Physical Systems. Research in this domain has been enhanced by the use of high-fidelity simulations, including Digital Twins, which are virtual representations connected to real assets. Digital Twins have been used for process supervision, prediction, or interaction with physical assets. Interaction with Digital Twins is enhanced by Virtual Reality and Augmented Reality, and Industry 5.0-focused research is evolving with the involvement of the human aspect in Digital Twins. This paper aims to review recent research on Human-Centric Digital Twins (HCDTs) and their enabling technologies. A systematic literature review is performed using the VOSviewer keyword mapping technique. Current technologies such as motion sensors, biological sensors, computational intelligence, simulation, and visualization tools are studied for the development of HCDTs in promising application areas. Domain-specific frameworks and guidelines are formed for different HCDT applications that highlight the workflow and desired outcomes, such as the training of AI models, the optimization of ergonomics, the security policy, task allocation, etc. A guideline and comparative analysis for the effective development of HCDTs are created based on the criteria of Machine Learning requirements, sensors, interfaces, and Human Digital Twin inputs.

Technological advancements in various domains have broadened the application horizon of robotics to an incredible extent. Highlighting a very recent application area, this paper presents a comprehensive review of robotics application in food industry. Robots essentially have the potential to transform the processes in food processing and handling, palletizing and packing and food serving. Therefore, recent years witnessed tremendously increased trend of robots deployment in food sector. Consequently, the aspects related with robot kinematics, dynamics, hygiene, economic efficiency, human-robot interaction, safety and protection and operation and maintenance are of critical importance and are discussed in the present review. A comparison of actual robots being used in the industry is also presented. The review reveals that the food serving sector is the new potential area in which ample research opportunities exist by integrating advancements from various technology domains. It is anticipated that wider dissemination of research developments in ‘robo-food’ will stimulate more collaborations among the research community and contribute to further developments.

An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling.

The paper identifies the need for human robot collaboration for conventional light weight and heavy payload robots in future manufacturing environment. An overview of state of the art for these types of robots shows that there exists no solution for human robot collaboration. Here, we consider cyber physical systems, which are based on human worker participation as an integrated role in addition to its basic components. First, the paper identifies the collaborative schemes and a formal grading system is formulated based on four performance indicators. A detailed sensor catalog is established for one of the collaboration schemes, and performance indices are computed with various sensors. This study reveals an assessment of best and worst possible ranges of performance indices that are useful in the categorization of collaboration levels. To illustrate a possible solution, a hypothetical industrial scenario is discussed in a production environment. Generalizing this approach, a design methodology is developed for such human robot collaborative environments for various industrial scenarios to enable solution implementation.