The article describes the use of embedded systems in the Industrial Internet of Things and its benefits for industrial robots. For this purpose, the article presents a case study, which deals with an embedded system using an advanced microcontroller designed to be placed directly on the robot. The proposed system is being used to collect information about industrial robot parameters that impact its behavior and its long-term condition. The device measures the robot’s surroundings parameters and its vibrations while working. Besides that, it also has an enormous potential to collect other parameters such as air pollution or humidity. The collected data are stored on the cloud platform and processed and analysed. The embedded system proposed in this article is conceived to be small and mobile, as it is a wireless system that can be easily applied to any industrial robot.

The paper presents a new version of the existing elastic band algorithm used for path finding, with application in the field of collaborative robotics and point-to-point movements. The algorithm places control points on the path and dynamically modifies the position of these control points in reaction to any obstacles located or moving in the workspace. The control points are updated in the robot space (TCP space), obstacles are represented by a set of grid-aligned voxels acquired by a camera system. Repulsive forces are created between the obstacles and the robot body (represented by a set of points covering evenly the surface of individual links) and transferred to the locations of the elastic band control points. The method is computationally effective and provides a smooth and length-optimal path while considering collisions in a more accurate manner than traditional usage of simple bounding volumes. The dynamic iterative updating of elastic band provides a clear principle for modification of the trajectory even close to the actual location of the robot as it is following the trajectory. The algorithm is verified on a set of practical experiments made on a physical UR3 robot with simulated obstacles, and the results are also compared to other commonly used methods for dynamic obstacle avoidance.

Industrial robotic workstations contribute substantially to the total energy demand of modern manufacturing, yet most existing energy-saving approaches focus on modifying robot trajectories, motion parameters, or the position of the robot’s base. This paper proposes a novel methodology for the automatic optimization of the spatial placement of a fixed technological trajectory within the robot workspace, without altering the task itself. The method combines pre-simulation filtering of infeasible configurations, large-scale energy simulation in ABB RobotStudio, and real measurement using a dual acquisition system consisting of the robot’s controller and an external power meter. A digital twin of the workstation is used to systematically evaluate thousands of candidate positions of a standardized trajectory. Experimental validation on an ABB IRB 1600–10/1.2 confirms a 23.4% difference in total energy consumption between two workspace configurations selected from the simulation study. The non-optimal configuration exhibits higher current draw, greater power variability, and a more intensive warm-up phase, indicating increased mechanical loading arising purely from geometric placement. By providing a scalable, trajectory-preserving approach grounded in digital-twin analysis and IoT-based measurement, this work establishes a data foundation for future AI-driven predictive and adaptive energy optimization in smart manufacturing environments.

Designing a robotic workplace is a time and knowledge intensive process. It requires systems engineers who specialise in the design of this type of workplace and have the appropriate know-how in this area. Due to the time-consuming nature and the small number of experts, a small number of solutio...