Optimizing the energy efficiency of robotic workstations is a key aspect of industrial automation. This study focuses on the analysis of the relationship between the position of the robot base and its energy consumption and time aspects. A number of 6-axis robots, including the ABB IRB 120 robot, were investigated in this research by combining measurements and simulations using the energy consumption measurement module in the ABB RobotStudio 2024.1.1 environment. The objective of this study was to develop an energy consumption model that can identify the optimal robot positions to minimize energy costs and time losses. The results suggest that the strategic positioning of the robot has a significant impact on its performance and efficiency. These results demonstrate that the ideal working distance of the robots is approximately 50% of its maximum range, and displacements along the X and Z axes affect the energy and time consumption. These findings suggest the existence of a trade-off between time and energy efficiency, providing a basis for further research into the optimization of robotic systems. Thus, this work offers new perspectives for the design of efficient robotic workstations for cross-sensory applications.

The efficient and sustainable design of robotic manufacturing cells is a critical aspect of modern industrial processes, for which energy and time efficiency play significant roles in achieving sustainability goals. In industrial practice, robotic cell design often involves methods such as predefined layout templates, empirical rules for positioning, and simulation-based validation. While these approaches provide a practical starting point, they may not fully account for the complex interdependencies between robot configuration, energy consumption, and operational efficiency. Consequently, opportunities for optimizing resource usage are frequently overlooked. This paper presents a novel methodology for optimizing the deployment of industrial robots and their peripherals, focusing on minimizing energy and time costs to enhance the sustainability of industrial processes. The proposed approach, grounded in experimental measurements and simulations, was validated through an experimental model of a welding robot station. The methodology integrates the analysis of the relationship between the robot base position, trajectory, and energy consumption. The results indicate that adjusting the relative positions of robots and work points can achieve energy savings of approximately six percent. Specifically, optimization reduced energy consumption by 1.6731 Wh per work cycle, translating to an annual savings of 0.8794 MWh for a 60 s clock cycle. These findings highlight the practical applicability of the proposed methodology, demonstrating its potential to significantly improve the energy and time efficiency of robotic workplaces.

This paper focuses on the input parameters that affect the resulting efficiency of a robotic workplace for industrial use. The aim of the investigation was to find which parameters most significantly affect cycle time and electricity consumption, and which way of movement is the most suitable for robotic operation. By finding the most suitable parameters, we obtain the information on how to build the most efficient robotic workplace. The investigation focused mainly on the energy consumption and layout of the robotic work cells components that significantly affect the cycle time and potentially save electric energy.

In the current ever-accelerating time of deployment of new production systems associated by various manipulating, technological and maintenance procedures, there is a demand from the point of view of the industrial sphere for shortening these operations and efficiency of the selected processes. It is the ability to visualize these processes using modern augmented reality (AR) technology that is the way to achieve the desired aspects. Visualization via smartphones, tablets, headsets in the AR environment allows users to provide comprehensive information with the exact procedure and information about the required tasks (assembly, disassembly, service operations, maintenance, wiring diagrams, etc.). The use of AR technology, also in view of the ongoing pandemic situation Covid 19, makes possible to involve less qualified operators in the industrial sphere. Deployment of the less skilled operators in the production sphere, using AR technology, can make up for the shortage of skilled labour that is currently lacking in every industry. In this way, it is also possible to keep the industrial sector in good condition without undesirable factors such as production shutdowns, long service operations, and poorly trained service personnel.