This paper describes the results of dynamic tests performed to study the robustness of a dynamics model of an industrial manipulator. The tests show that the joint friction changes during the robot operation. The variation can be identified in a double exponential law and thus the variation can be predicted. The variation is due to the heat generated by the friction. A model is used to estimate the temperature and related friction variation. Experimental data collected on two robots EFORT ER3A-C60 are presented and discussed. Repetitive tests performed on different days showed that the inertial and friction parameters can be robustly estimated and that the value of the measured joint friction can be used to estimate the unexpected conditions of the joints. Future applications may include sensorless identification of collisions, predictive maintenance programs, or human–robot interaction.

The HANDS dataset has been created for human-robot interaction research, and it is composed of spatially and temporally aligned RGB and Depth frames. It contains 12 static single-hand gestures performed with both the right-hand and the left-hand, and 3 static two-hands gestures for a total of 29 unique classes. Five actors (two females and three males) have been acquired performing the gestures, each of them adopting a different background and light conditions. For each actor, 150 RGB frames and their corresponding 150 Depth frames per gesture have been collected, for a total of 2400 RGB frames and 2400 Depth frames per actor.

The paper presents a second-order friction model for the joints of industrial robot manipulators that takes into account temperature effects. A solution based on a polynomial description of the friction is proposed. The theoretical analysis and the experimental measurements have shown that friction decreases with increasing temperature, which in turn depends on the working cycle of the manipulator. The mathematical model here proposed allows to foresee the friction variation during extensive working cycles and it does not require the use of a transducer for the measurement of the joint internal temperature; therefore it is well suitable for low-cost industrial applications, to improve the control performance or to predict the energy consumption. Experimental tests performed on a commercial six degrees-of-freedom (6 DOF) manipulator show that the model is effective in estimating the joint temperature and the friction torque during the robot operations.

Since cobots are designed to be flexible, they are frequently repositioned to change the production line according to the needs; hence, their working area (user frame) needs to be often calibrated. Therefore, it is important to adopt a fast and intuitive user frame calibration method that allows even non-expert users to perform the procedure effectively, reducing the possible mistakes that may arise in such contexts. The aim of this work was to quantitatively assess the performance of different user frame calibration procedures in terms of accuracy, complexity, and calibration time, to allow a reliable choice of which calibration method to adopt and the number of calibration points to use, given the requirements of the specific application. This has been done by first analyzing the performances of a Rethink Robotics Sawyer robot built-in user frame calibration method (Robot Positioning System, RPS) based on the analysis of a fiducial marker distortion obtained from the image acquired by the wrist camera. This resulted in a quantitative analysis of the limitations of this approach that only computes local calibration planes, highlighting the reduction of performances observed. Hence, the analysis focused on the comparison between two traditional calibration methods involving rigid markers to determine the best number of calibration points to adopt to achieve good repeatability performances. The analysis shows that, among the three methods, the RPS one resulted in very poor repeatability performances (1.42 mm), while the three and five points calibration methods achieve lower values (0.33 mm and 0.12 mm, respectively) which are closer to the reference repeatability (0.08 mm). Moreover, comparing the overall calibration times achieved by the three methods, it is shown that, incrementing the number of calibration points to more than five, it is not suggested since it could lead to a plateau in the performances, while increasing the overall calibration time.

In this paper the novel teleoperation method "Hands-Free" is presented. Hands-Free is a vision-based augmented reality system that allows users to teleoperate a robot end-effector with their hands in real time. The system leverages OpenPose neural network to detect the human operator hand in a given workspace, achieving an average inference time of 0.15 s. The user index position is extracted from the image and converted in real world coordinates to move the robot end-effector in a different workspace.The user hand skeleton is visualized in real-time moving in the actual robot workspace, allowing the user to teleoperate the robot intuitively, regardless of the differences between the user workspace and the robot workspace.Since a set of calibration procedures is involved to convert the index position to the robot end-effector position, we designed three experiments to determine the different errors introduced by conversion. A detailed explanation of the mathematical principles adopted in this work is provided in the paper.Finally, the proposed system has been developed using ROS and is publicly available at the following GitHub repository: https://github.com/Krissy93/hands-free-project.