In order to improve the availability of wind turbines and to avoid catastrophic consequences, the detection of faults in their earlier occurrence is fundamental. This paper proposes the development of a fault diagnosis scheme relying on identified fuzzy models. The fuzzy theory is exploited since it allows approximating uncertain models and managing noisy data. These fuzzy models, in the form of Takagi-Sugeno prototypes, represent the residual generators used for fault detection and isolation (FDI). A wind turbine benchmark is used to validate the achieved performances of the designed FDI scheme. Finally, extensive comparisons with different fault diagnosis methods highlight the features of the suggested solution.

This paper presents a mobile manipulation platform designed for autonomous depalletizing tasks. The proposed solution integrates machine vision, control and mechanical components to increase flexibility and ease of deployment in industrial environments such as warehouses. A collaborative robot mounted on a mobile base is proposed, equipped with a simple manipulation tool and a 3D in-hand vision system that detects parcel boxes on a pallet, and that pulls them one by one on the mobile base for transportation. The robot setup allows to avoid the cumbersome implementation of pick-and-place operations, since it does not require lifting the boxes. The 3D vision system is used to provide an initial estimation of the pose of the boxes on the top layer of the pallet, and to accurately detect the separation between the boxes for manipulation. Force measurement provided by the robot together with admittance control are exploited to verify the correct execution of the manipulation task. The proposed system was implemented and tested in a simplified laboratory scenario and the results of experimental trials are reported.

Percutaneous nephrolithotomy (PCNL) is considered the gold standard for the treatment of patients with renal stones larger than 20 mm in diameter. The success and treatment outcomes of the surgery are very well known to be highly dependent on the precision and accuracy of the puncture step, since it must allow to reach the stone with a precise and direct path. Thus, performing the renal access during PCNL is the most crucial and challenging step of the procedure with the steepest learning curve. In this letter, we propose an innovative solution, based on an AR application combined with a robotic system, that can assist both an expert surgeon in improving the performance of the surgical operation and a novel surgeon in strongly reducing his/her learning curve. The proposed system is validated on a setup including a KUKA LWR 4+ robot and the Microsoft HoloLens as augmented reality headset, through experiments performed by a sample of 11 users.

The paper addresses the problem of the generation of collision-free trajectories for a robotic manipulator, operating in a scenario in which obstacles may be moving at non-negligible velocities. In particular, the paper aims to present a trajectory generation solution that is fully executable in real-time and that can reactively adapt to both dynamic changes of the environment and fast reconfiguration of the robotic task. The proposed motion planner extends the method based on a dynamical system to cope with the peculiar kinematics of surgical robots for laparoscopic operations, the mechanical constraint being enforced by the fixed point of insertion into the abdomen of the patient the most challenging aspect. The paper includes a validation of the trajectory generator in both simulated and experimental scenarios.

For improving the safety and the reliability of wind turbine installations, the earliest and fastest fault detection and isolation is highly\ud required, since it could be used also for accommodation purpose. Modern wind turbines consist of several important subsystems,\ud which can be affected by malfunctions regarding actuators, sensors, and components. From the turbine control point–of–view they\ud are extremely important since provide the actuation signals, the main functions, as well as the measurements. In this paper, a fault\ud diagnosis scheme based on the identification of fuzzy models is described, in order to detect and isolated these faults in the most\ud efficient way, in order also to improve the energy cost, the production rate, and reduce the operation and maintenance operations.\ud Fuzzy systems are proposed here since the model under investigation is nonlinear, whilst the wind speed measurement is uncertain\ud since it depends on the rotor plane wind turbulence effects. These fuzzy models are described as Takagi–Sugeno prototypes, whose\ud parameters are estimated from the wind turbine measurements. The fault diagnosis methodology is thus developed using these\ud fuzzy models, which are exploited as residual generators. The wind turbine simulator is finally employed for the validation of the\ud obtained performances