A new approach to 6-DOF impedance control is proposed, where the end-effector orientation displacement is derived from the rotation matrix expressing the mutual orientation between the compliant frame and the desired frame. An alternative Euler angles-based description is proposed which mitigates the effects of representation singularities. Then, a class of angle/axis representations are considered to derive the dynamic equation for the rotational part of a 6-DOF impedance at the end effector, using an energy-based argument. The unit quaternion representation is selected to further analyze the properties of the rotational impedance. The resulting impedance controllers are designed according to an inverse dynamics strategy with contact force and moment measurements, where an inner loop acting on the end-effector position and orientation error is adopted to confer robustness to unmodeled dynamics and external disturbances. Experiments on an industrial robot were carried out, and the results of case studies are discussed.

This article investigates the problem of controlling the speed of robots in collaborative workcells for automated manufacturing. The solution is tailored to robotic cells for cooperative assembly of aircraft fuselage panels, where only structural elements are present and robots and humans can share the same workspace, but no physical contact is allowed, unless it happens at zero robot speed. The proposed approach addresses the problem of satisfying the minimal set of requirements of an industrial human–robot collaboration (HRC) task: precision and reliability of human detection and tracking in the shared workspace; correct robot task execution with minimum cycle time while assuring safety for human operators. These requirements are often conflicting with each other. The former does not only concern with safety only but also with the need of avoiding unnecessary robot stops or slowdowns in case of false-positive human detection. The latter, according to the current regulations, concerns with the need of computing the minimum protective separation distance between the human operator and the robots by adjusting their speed when dangerous situations happen. This article proposes a novel fuzzy inference approach to control robot speed enforcing safety while maximizing the level of productivity of the robot minimizing cycle time as well. The approach is supported by a sensor fusion algorithm that merges the images acquired from different depth sensors with those obtained from a thermal camera, by using a machine learning approach. The methodology is experimentally validated in two experiments: the first one at a lab-scale and the second one performed on a full-scale robotic workcell for cooperative assembly of aeronautical structural parts. Note to Practitioners —This article discusses a way to handle human safety specifications versus production requirements in collaborative robotized assembly systems. State-of-the-art (SoA) approaches cover only a few aspects of both human detection and robot speed scaling. The present research work proposes a complete pipeline that starts from a robust human tracking algorithm and scales the robot speed in real time. An innovative multimodal perception system composed of two depth cameras and a thermal camera monitors the collaborative workspace. The speed scaling algorithm is optimized to take on different human behaviors during less risky situations or more dangerous ones to guarantee both operator safety and minimum production time with the aim of better profitability and efficiency for collaborative workstations. The algorithm estimates the operator intention for real-time computation of the minimum protective distance according to the current safety regulations. The robot speed is smoothly changed for the psychological advantages of operators, both in the case of single and multiple workers. The result is a complete system, easily implementable on a standard industrial workcell.

The goal of this paper is to provide a critical review of the well-known resolved-acceleration technique for the tracking control problem of robot manipulators in the task space. Various control schemes are surveyed and classified according to the type of end-effector orientation error; namely, those based on Euler angles feedback, quaternion feedback, and angle/axis feedback. In addition to the assessed schemes in the literature, an alternative Euler angles feedback scheme is proposed which shows an advantage in terms of avoidance of representation singularities. An insight into the features of each scheme is given, with special concern to the stability properties of those schemes leading to nonlinear closed-loop dynamic equations. A comparison is carried out in terms of computational burden. Experiments on an industrial robot with open control architecture have been carried out, and the tracking performance of the resolved-acceleration control schemes in a case study involving the occurrence of a representation singularity is evaluated. The pros and cons of each scheme are evidenced in a final discussion focused on practical implementation issues.

In this paper, we introduce the problem of cross-modal visuo-tactile object recognition with robotic active exploration. With this term, we mean that the robot observes a set of objects with visual perception, and later on, it is able to recognize such objects only with tactile exploration, without having touched any object before. Using a machine learning terminology, in our application, we have a visual training set and a tactile test set, or vice versa. To tackle this problem, we propose an approach constituted by four steps: finding a visuo-tactile common representation, defining a suitable set of features, transferring the features across the domains, and classifying the objects. We show the results of our approach using a set of 15 objects, collecting 40 visual examples and five tactile examples for each object. The proposed approach achieves an accuracy of 94.7%, which is comparable with the accuracy of the monomodal case, i.e., when using visual data both as training set and test set. Moreover, it performs well compared to the human ability, which we have roughly estimated carrying out an experiment with ten participants.