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.

This paper is dedicated to soft grippers, robot tools with a wide application area in various activities where an accurate and delicate grabbing movement is required such as routine manipulation tasks with fragile objects, operation in unknown or dangerous environments, and manipulation with unknown shape objects, as well as exploring the depths of the sea or harvesting vegetables in agriculture. The main goal of this paper is to review and systematize the main ideas about and achievements of soft grippers published from 2015 to 2024. The paper provides a statistical analysis of the performed research and systematized advancements of soft grippers according to their operating principle, forces and effects that enable their operation, and the properties of potential manipulation objects. Grippers inspired by nature are also discussed, as most successful solutions are based on ideas derived from nature. This study discusses the latest achievements of soft grippers and their various applications and presents a unique distribution of soft grippers according to the physical principle of the forces they act on, according to the size of the object to be grasped, and according to technological realizations. The results of this analysis can be useful for practical gripper users aiming to improve their workplace and find optimal design solutions, for gripper manufacturers or developers, or for scientists of material sciences looking for applications for their products.

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.

Positioning accuracy in robotics is a key issue for the manufacturing process. One of the possible ways to achieve high accuracy is the implementation of machine learning (ML), which allows robots to learn from their own practical experience and find the best way to perform the prescribed operation. Usually, accuracy improvement methods cover the generation of a positioning error map for the whole robot workspace, providing corresponding correction models. However, most practical cases require extremely high positioning accuracy only at a few essential points on the trajectory. This paper provides a methodology for the online deep Q-learning-based approach intended to increase positioning accuracy at key points by analyzing experimentally predetermined robot properties and their impact on overall accuracy. Using the KUKA-YouBot robot as a test system, we perform accuracy measurement experiments in the following three axes: (i) after a long operational break, (ii) using different loads, and (iii) at different speeds. To use this data for ML, the relationships between the robot’s operating time from switching on, load, and positioning accuracy are defined. In addition, the gripper vibrations are evaluated when the robot arm moves at various speeds in vertical and horizontal planes. It is found that the robot’s degrees of freedom (DOFs) clearances are significantly influenced by operational heat, which affects its static and dynamic accuracy. Implementation of the proposed ML-based compensation method resulted in a positioning error decrease at the trajectory key points by more than 30%.

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.