Sistema per l’identificazione di difetti su una superficie di almeno una porzione di una scocca e relativo metodo

La presente invenzione riguarda un sistema e un metodo per l’identificazione, la classificazione e la successiva rimozione di difetti di fabbricazione presenti sui componenti di veicoli

An end-to-end framework for few-shot learning of novel grasp strategies

Althoguh there exist an extensive literature on robotic grasping, many problems are still to be solved, especially when dealing with the interaction of robots with unstructured environments, where it is important to provide a method to learn how to generalize grasping and manipulation skills to unknown classes of objects. To address this goal, in this work I proposed a novel method for 2D vision driven grasping of objects which allows few shot learning of new human-inspired grasp primitives, relying on state-of-the-art human grasp taxonomy. This goal was achieved by means of a Gated Graph Neural Network (GGNN) and a planning module. The former allowed the embedding of the human example in the form of a Knowledge Graph that encodes the information about the relationships between grasps in a given taxonomy. The latter allowed the detection of an object in the RGB camera field of view and planned a grasp based on the extracted 2D information and the output of the GGNN. The pipeline consists of the following three modules: Classification Module (the GGNN), which selects the grasp to be performed, Planning Module, which synthesizes a grasp strategy, Control Module, which physically implements the strategy. The model was tested on a Franka manipulator endowed with an anthropomorphic soft underactuated end effector. The framework showed promising results with a 69% success rate in grasping execution and it was able to successfully integrate new strategies while retaining previously learned information, outperforming an ideal model provided with samples from all the classes at training time

A GaN-HEMT Active Drain-Pumped Mixer for S-Band FMCW Radar Front-End Applications

This paper reports for the first time a drain-pumped (DP) mixer using Gallium Nitride (GaN) HEMT technology. Specifically, it describes a method aimed to predict the optimum bias conditions for active DP-mixers, leading to high conversion gain (CG) and linearity, along with the efficient use of the local oscillator drive level. A mixer prototype was designed and fabricated according to the discussed design principles; it exhibited a CG and an input third-order intercept point (IIP3) of +10dB and +11dBm, respectively, with a local oscillator power level of 20 dBm at about 3.7 GHz. In terms of gain and linearity, both figures exceed the documented limitations for the class of mixers considered in this work. To the authors’ best knowledge, this is the first DP mixer operating in the S-band. The prototype was also tested in a radar-like setup operating in the S-band frequency-modulated continuous-wave (FMCW) mode. Measurements carried out in the radar setup resulted in +39.7dB and +34.7dB of IF signal-to-noise-ratio (SNR) for the DP and the resistive mixers, respectively. For comparison purposes, a resistive mixer was designed and fabricated using the same GaN HEMT technology; a detailed comparison between the two topologies is discussed in the paper, thus further highlighting the capability of the DP-mixer for system applications

Learning With Few Examples the Semantic Description of Novel Human-Inspired Grasp Strategies From RGB Data

Data-driven approaches and human inspiration are fundamental to endow robotic manipulators with advanced autonomous grasping capabilities. However, to capitalize upon these two pillars, several aspects need to be considered, which include the number of human examples used for training; the need for having in advance all the required information for classification (hardly feasible in unstructured environments); the trade-off between the task performance and the processing cost. In this letter, we propose a RGB-based pipeline that can identify the object to be grasped and guide the actual execution of the grasping primitive selected through a combination of Convolutional and Gated Graph Neural Networks. We consider a set of human-inspired grasp strategies, which are afforded by the geometrical properties of the objects and identified from a human grasping taxonomy, and propose to learn new grasping skills with only a few examples. We test our framework with a manipulator endowed with an under-actuated soft robotic hand. Even though we use only 2D information to reduce the footprint of the network, we achieve 90% of successful identifications of the most appropriate human-inspired grasping strategy over ten different classes, of which three were few-shot learned, outperforming an ideal model trained with all the classes, in sample-scarce conditions

Sistema per l’identificazione di difetti su una superficie di almeno una porzione di una scocca e relativo metodo

La presente invenzione riguarda un sistema e un metodo per l’identificazione, la classificazione e la successiva rimozione di difetti di fabbricazione presenti sui componenti di veicoli