This article focuses on the integration of the Internet of Things (IoT) and the Internet of Robotic Things, representing a dynamic research area with significant potential for industrial applications. The Internet of Robotic Things (IoRT) integrates IoT technologies into robotic systems, enhancing their efficiency and autonomy. The article provides an overview of the technologies used in IoRT, including hardware components, communication technologies, and cloud services. It also explores IoRT applications in industries such as healthcare, agriculture, and more. The article discusses challenges and future research directions, including data security, energy efficiency, and ethical issues. The goal is to raise awareness of the importance of IoRT and demonstrate how this technology can bring significant benefits across various sectors.

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.

Reliable wireless communication is essential for mobile robotic systems operating in dynamic environments, particularly in the context of smart mobility and cloud-integrated urban infrastructures. This article presents an experimental study analyzing the impact of robot motion dynamics on wireless network performance, contributing to the broader discussion on data reliability and communication efficiency in intelligent transportation systems. Measurements were conducted using a quadruped robot equipped with an onboard edge computing device, navigating predefined trajectories in a laboratory setting designed to emulate real-world variability. Key wireless parameters, including signal strength (RSSI), latency, and packet loss, were continuously monitored alongside robot kinematic data such as speed, orientation (roll, pitch, yaw), and movement patterns. The results show a significant correlation between dynamic motion—especially high forward velocities and rotational maneuvers—and degradations in network performance. Increased robot speeds and frequent orientation changes were associated with elevated latency and greater packet loss, while static or low-motion periods exhibited more stable communication. These findings highlight critical challenges for real-time data transmission in mobile IoRT (Internet of Robotic Things) systems, and emphasize the role of network-aware robotic behavior, interoperable communication protocols, and edge-to-cloud data integration in ensuring robust wireless performance within smart city environments.

Accurate trajectory learning and reconstruction represent a core challenge in mobile robotics, particularly in environments affected by sensor noise, drift, and incomplete data. Addressing this challenge is essential for reliable navigation and motion control in real-world Internet of Robotic Things (IoRT) systems. This paper presents a data-driven framework for learning and reconstructing mobile robot trajectories using LSTM autoencoders. Trajectory data were collected from both simulation and real-world experiments with a Unitree GO1 quadruped robot, preprocessed through normalization, sequence padding, and trajectory boundary flags, and then used to train recurrent neural network models. The proposed architecture employs bidirectional LSTM layers and a custom loss function combining reconstruction, velocity, and boundary terms to improve trajectory stability. Experimental results show stable reconstruction accuracy across simulated and real-world datasets, with the position RMSE reduced from 0.92 m to 0.60 m and the yaw MAE improved from 0.49 rad to 0.17 rad on the most complex trajectory. The evaluation was conducted in controlled indoor conditions and offline mode, which defines the current scope of validation. Future work will extend the analysis to larger and more diverse environments and investigate extensions such as attention mechanisms, sensor fusion, and online learning to enhance adaptability in real-world deployment.

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.

Depth cameras have become an integral part of a wide range of applications, including industrial inspection, reverse engineering, human–robot collaboration, and biomechanical analysis. Their practical use, however, is limited by a narrow field of view and self-occlusions, which often prevent complete surface coverage of the scanned object. This paper introduces a methodology for systematically determining the optimal placement of a depth camera and an auxiliary planar mirror to maximize surface visibility. To this end, a simulation tool was developed that models camera rays, their reflections from the mirror, and their intersections with objects. The proposed method combines geometric visibility analysis with an optimization procedure that searches for configurations with maximum coverage. Validation was performed on a set of twenty objects ranging from simple geometric primitives to complex reference models. While a standalone camera achieved an average coverage of 51.68 %, the addition of a single planar mirror increased this value to 85.46 %, with complete coverage achieved for several objects. A complementary user study demonstrated that manually chosen configurations exhibit high variability and do not outperform the algorithmic design. The results confirm that the use of mirrors provides a cost-effective solution for overcoming occlusion-related limitations without resorting to multi-camera systems, and that the proposed methodology delivers consistent performance across diverse object geometries. The approach is particularly applicable in industrial inspection, robotic vision, and other domains where complete geometric information is essential.