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 aims to prove the hypothesis, that an approach direction influences repeatability at target point of a trajectory. Unlike most researches that deal with absolute accuracy, this paper is focused on determining the achievable repeatability and the influence of the direction of approach on it. To prove the hypothesis, several measurements are performed under different conditions, on industrial robot ABB IRB1200. To verify and confirm the result obtained from the resolvers located on the individual axes of the robot, the measurements are replicated using high-speed digital image correlation cameras. Using an external measuring device, the real repeatability of the robot endpoint is determined. The measurement proved the correctness of the hypothesis, i.e., the dependence of the approach direction on repeatability was proved. Furthermore, real deviations were measured and the extent of this influence on the robot repeatability was determined.

This paper presents an approach to compensate for the effect of thermal expansion on the structure of an industrial robot and thus to reduce the repeatability difference of the robot in cold and warm conditions. In contrast to previous research in this area that deals with absolute accuracy, this article is focused on determining achievable repeatability. To unify and to increase the robot repeatability, the measurements with highly accurate sensors were performed under different conditions on an industrial robot ABB IRB1200, which was equipped with thermal sensors, mounted on a pre-defined position around joints. The performed measurements allowed to implement a temperature-based prediction model of the end effector positioning error. Subsequent tests have shown that the implemented model used for the error compensation proved to be highly effective. Using the methodology presented in this article, the impact of drift can be reduced by up to 89.9%. A robot upgraded with a compensation principle described in this article does not have to be warmed up as it works with the same low repeatability error in the entire range of the achievable temperatures.

The article describes the process of development of an essentially new wheel suitable both for moving on flat ground and for travelling on stairs. The stair-climbing wheel is composed of rotary circular segments arranged around a shared carrier with arms to form a complete circular profile of the wheel adapted for moving on flat ground; for travelling on stairs, individual segments are rotated by an appropriate angle to touch down tangentially on the stepping surface of the stairs. The dimensions of individual segments, the centre of rotation of individual segments and the angle of their partial turn have been chosen so that the length of the arc along which the circular segment rolls is equal to the length of the stepping surface of an average stair, and, at the same time, the circular segment touches down tangentially on the stepping surface while the wheel turns around the edge of the previous segment. Using the rotation angle of the turnable segments, the wheel can be adapted to the height of non-standard stairs. The segments can be inclined in both directions for bidirectional movement of the wheel up and down the stairs. An undercarriage equipped with these wheels can be used in the field of exploratory robots and for the transportation of persons and materials on stairs.

We describe a method for robotic cell optimization by changing the placement of the robot manipulator within the cell in applications with a fixed end-point trajectory. The goal is to reduce the overall robot joint wear and to prevent uneven joint wear when one or several joints are stressed more than the other joints. Joint wear is approximated by calculating the integral of the mechanical work of each joint during the whole trajectory, which depends on the joint angular velocity and torque. The method relies on using a dynamic simulation for the evaluation of the torques and velocities in robot joints for individual robot positions. Verification of the method was performed using CoppeliaSim and a laboratory robotic cell with the collaborative robot UR3. The results confirmed that, with proper robot base placement, the overall wear of the joints of a robotic arm could be reduced from 22% to 53% depending on the trajectory.

In situations of a confined workplace with a lot of obstacles and a complicated required trajectory of the endpoint of an industrial or collaborative robot, it may be impossible to find a suitable robot and its position within the workplace to fulfill the given task. In some cases, it could be favorable to design a custom manipulator arm with an unusual kinematic structure or shapes of some of its links. This article presents a novel way of finding the optimal lengths and shapes of two crucial links of a manipulator arm, where the target lengths are as short as possible to reduce mass, and the shape in the form of a Bézier curve is chosen to avoid collisions. The chosen type of kinematic structure of the manipulator arm is fixed and is based on the most typical structure of existing industrial robots, with six degrees of freedom. Two algorithm variants were proposed; one method uses iterations to find the solution based on in-depth collision analysis, and the second method uses the particle swarm optimization algorithm. Both methods were implemented in a simulation system and verified in several testing workplaces.