In a collaborative scenario, the communication between humans and robots is a fundamental aspect to achieve good efficiency and ergonomics in the task execution. A lot of research has been made related to enabling a robot system to understand and predict human behaviour, allowing the robot to adapt its motion to avoid collisions with human workers. Assuming the production task has a high degree of variability, the robot’s movements can be difficult to predict, leading to a feeling of anxiety in the worker when the robot changes its trajectory and approaches since the worker has no information about the planned movement of the robot. Additionally, without information about the robot’s movement, the human worker cannot effectively plan own activity without forcing the robot to constantly replan its movement. We propose a novel approach to communicating the robot’s intentions to a human worker. The improvement to the collaboration is presented by introducing haptic feedback devices, whose task is to notify the human worker about the currently planned robot’s trajectory and changes in its status. In order to verify the effectiveness of the developed human-machine interface in the conditions of a shared collaborative workspace, a user study was designed and conducted among 16 participants, whose objective was to accurately recognise the goal position of the robot during its movement. Data collected during the experiment included both objective and subjective parameters. Statistically significant results of the experiment indicated that all the participants could improve their task completion time by over 45% and generally were more subjectively satisfied when completing the task with equipped haptic feedback devices. The results also suggest the usefulness of the developed notification system since it improved users’ awareness about the motion plan of the robot.

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.

Soft gripping, in which the gripper adapts to differently shaped objects, is in great demand for use in unknown or dynamically changing environments and is one of the main research subjects in soft robotics. Several systems have already been created, one of which is a passive shape-adaptable finger based on the FinRay effect. The geometric shape of this finger ensures that the finger wraps around the object it grips. FinRay fingers have been studied in several studies, which have changed the internal structure and examined how gripping force’s dependence on finger deformation changes. So far, however, no specific way has been determined to evaluate the proposed finger regarding its ability to wrap around the object. This work comes up with a new and simple method to evaluate the finger’s wrapping around the object mathematically. Based on this evaluation method, several different patterns of the internal structure of FinRay fingers were tested. The fingers were first tested in a simulation program, which simulated a steel roller indentation with a diameter of 20 mm in the middle of the finger’s contact surface. Based on the simulation results, selected types of structure were made by the Fused Filament Fabrication method from a flexible filament and tested on a real test rig to verify the results of the simulation and compare it with the real behaviour. According to the methodology used, the results show that the most suitable structure of the selected tested fingers from the point of view of wrapping the finger around the object is a structure without internal filling. Designers can simply use the new evaluation method to compare their designed finger variants and select the most suitable one according to the ability to wrap around the gripped object. They can also use graphs from this work’s results and determine the finger’s dimensions without internal filling according to the required forces and deflection.

In this paper, a new method for the calibration of robotic cell components is presented and demonstrated by identification of an industrial robotic manipulator’s base and end-effector frames in a workplace. It is based on a mathematical approach using a Jacobian matrix. In addition, using the presented method, identification of other kinematic parameters of a robot is possible. The Universal Robot UR3 was later chosen to prove the working principle in both simulations and experiment, with a simple repeatable low-cost solution for such a task—image analysis to detect tag markers. The results showing the accuracy of the system are included and discussed.

Human–robot interaction is becoming an integral part of practice. There is a greater emphasis on safety in workplaces where a robot may bump into a worker. In practice, there are solutions that control the robot based on the potential energy in a collision or a robot re-planning the straight-line trajectory. However, a sensor system must be designed to detect obstacles across the human–robot shared workspace. So far, there is no procedure that engineers can follow in practice to deploy sensors ideally. We come up with the idea of classifying the space as an importance index, which determines what part of the workspace sensors should sense to ensure ideal obstacle sensing. Then, the ideal camera positions can be automatically found according to this classified map. Based on the experiment, the coverage of the important volume by the calculated camera position in the workspace was found to be on average 37% greater compared to a camera placed intuitively by test subjects. Using two cameras at the workplace, the calculated positions were 27% more effective than the subjects’ camera positions. Furthermore, for three cameras, the calculated positions were 13% better than the subjects’ camera positions, with a total coverage of more than 99% of the classified map.