Recently, the need to produce from soft materials or components in extra-large sizes has appeared, requiring special solutions that are affordable using industrial robots. Industrial robots are suitable for such tasks due to their flexibility, accuracy, and consistency in machining operations. However, robot implementation faces some limitations, such as a huge variety of materials and tools, low adaptability to environmental changes, flexibility issues, a complicated tool path preparation process, and challenges in quality control. Industrial robotics applications include cutting, milling, drilling, and grinding procedures on various materials, including metal, plastics, and wood. Advanced robotics technologies involve the latest advances in robotics, including integrating sophisticated control systems, sensors, data fusion techniques, and machine learning algorithms. These innovations enable robots to adapt better and interact with their environment, ultimately increasing their accuracy. The main focus of this study is to cover the most common industrial robotic machining processes and to identify how specific advanced technologies can improve their performance. In most of the studied literature, the primary research objective across all operations is to enhance the stiffness of the robotic arm’s structure. Some publications propose approaches for planning the robot’s posture or tool orientation. In contrast, others focus on optimizing machining parameters through the utilization of advanced control and computation, including machine learning methods with the integration of collected sensor data.

Autonomous systems are becoming increasingly relevant in our everyday life. The transportation field is no exception and the smart cities concept raises new tasks and challenges for the development of autonomous systems development which has been progressively researched in literature. One of the main challenges is communication between different traffic objects. For instance, a mobile robot system can work as a standalone autonomous system reacting to a static environment and avoiding obstacles to reach a target. Nevertheless, more intensive communication and decision making is needed when additional dynamic objects and other autonomous systems are present in the same working environment. Traffic is a complicated environment consisting of vehicles, pedestrians, and various infrastructure elements. To apply autonomous systems in this kind of environment it is important to integrate object localization and to guarantee functional and trustworthy communication between each element. To achieve this, various sensors, communication standards, and equipment are integrated via the application of sensor fusion and AI machine learning methods. In this work review of vehicular communication systems is presented. The main focus is the researched sensors, communication standards, devices, machine learning methods, and vehicular-related data to find existing gaps for future vehicular communication system development. In the end, discussion and conclusions are presented.

Since the development of industrial robots, they have been used to enhance efficiency and reduce the need for manual labor. Industrial robots have become a universal tool across all economic sectors, with the integration of software that is extremely important for the effective operation of machines and processes. Robotic action accuracy is currently experiencing rapid development in all robot-involving activities. Currently, a significant breakthrough has been observed in modifying algorithms and controlling robot actions, as well as in monitoring and planning software and hardware compatibility to prevent errors in real-time. The integration of the Internet of Things, machine learning, and other advanced techniques has enhanced the intelligent features of industrial robots. As industrial automation advances, there is an increasing demand for precise control in a variety of robotic arm applications. It is essential to refine current solutions to address the challenges posed by the high connectivity, complex computations, and various scenarios involved. This review examines the application of vision-based models, particularly YOLO (You Only Look Once) variants, in object detection within industrial robotic environments, as well as other machine learning models for tasks such as classification and localization. Finally, this review summarizes the results presented in selected publications, compares represented methods, identifies challenges in prospective object-tracking technologies, and suggests future research directions.

One of the primary applications of industrial robots is in various manufacturing processes, such as milling, grinding, and additive manufacturing. To achieve the desired precision in tool trajectory performance when machining specific parts, it is necessary to calibrate the tool centre point (TCP) of the robot for each manufacturing process. The development of industrial robot tool trajectories is a multipurpose task. It encompasses issues related to robot geometry, path interpolation type, and trajectory waypoints approximation. The primary objective of this study is to establish a camera-based methodology for evaluating trajectory-following accuracy in industrial robots. The present paper proposes the use of a high-speed motion camera system for non-contact tracking of TCP trajectories. By capturing the robot’s end-effector motion in real-time and under actual trajectory tracking conditions, this technique enables a clearer understanding of how trajectory execution accuracy varies with velocity, trajectory geometry, trajectory interpolation, and robot kinematics. Provided analysis of two industrial robot types opened interesting findings related to the dependencies between the implementation of first- and second-degree interpolations. To illustrate this point, the implementation of second-degree interpolation ensures a more consistent velocity in the trajectory. This contrasts with first-degree interpolation, which is more challenging to achieve and is susceptible to variations in curvature. Conversely, the utilization of first-degree interpolation facilitates enhanced performance accuracy for smaller curvatures. The results of the experimental research confirm the initial hypothesis regarding the influence of interpolation mode and pave the way for future uses of this information for machine learning algorithms.