The use of drones in agriculture is becoming more and more popular. The paper presents a novel approach to distinguish between different field's plowing techniques by means of an RGB-D sensor. The presented system can be easily integrated in commercially available Unmanned Aerial Vehicles (UAVs). In order to successfully classify the plowing techniques, two different measurement algorithms have been developed. Experimental tests show that the proposed methodology is able to provide a good classification of the field's plowing depths.

This paper introduces an intelligent system able to perform quality control assessment in an industrial production line. Deep learning techniques are employed and proved successful in a real application for the inspection of welding defects on an assembly line of fuel injectors. Starting from state-of-the-art deep architectures and using the transfer learning technique, it is possible to train a network with about 7 million parameters using a reduced number of injector's images, obtaining an accuracy of 97.22%. The system is also configured in order to exploit new data, collected during operation, to extend the existing dataset and to improve further its performance. The developed system shows that deep neural networks can successfully perform quality inspection tasks that are usually demanded to humans.

The present work, which describes the mechatronic design and development of a novel rehabilitation robotic exoskeleton hand, aims to present a solution for neuromusculoskeletal rehabilitation. It presents a full range of motion for all hand phalanges and was specifically designed to carry out position and force-position control for passive and active rehabilitation routines. System integration and preliminary clinical tests are also presented.

The use of radio frequency identification (RFID) technology for the traceability of products throughout the production chain, warehouse management, and the retail network is spreading in the last years, especially in those industries in line with the concept of Industry 4.0. The last decade has seen the development of increasingly precise and high-performance methods for the localization of goods. This work proposes a reliable 2-D localization methodology that is faster and provides a competitive accuracy, concerning the state-of-the-art techniques. The proposed method leverages a phase-distance model and exploits the synthetic aperture approach and unwrapping techniques for facing phase ambiguity and multipath phenomena. Trilateration applied on consecutive phase readings allows finding hyperbolae as the localization solution space. Analytic calculus is used to compute intersections among the conics that estimate the tag position. An algorithm computes intersections quality to select the best estimation. Experimental tests are conducted to assess the quality of the proposed strategy. A mobile robot equipped with a reader antenna localizes in 2-D the tags placed in an indoor scenario and reconstructs the map of the environment through a simultaneous localization and mapping (SLAM) algorithm. Note to Practitioners —A localization technology based on passive ultrahigh-frequency (UHF) radio frequency identification (RFID) is an enabling technology for intelligent warehouses, logistics, and retails. For this reason, this work presents a novel method to estimate the tag location with high accuracy. A reader antenna is mounted on an autonomous mobile robot that can move in an indoor or outdoor environment due to a simultaneous localization and mapping (SLAM) algorithm. The motion of the antenna generates a synthetic aperture. The system receives the phase measurements from the RFID tags and generates a distance model through phase unwrapping. In such a way, the possible locations of the tags in the environment are generated, creating conics. The trilateration step is performed analytically, intersecting the obtained conics. The resulting estimations are very accurate and not computation expensive. Therefore, the proposed approach can be employed in any application where localizing objects is fundamental even when reduced computational power is available, e.g., in warehouses where the products are at known heights, or where the items are placed on a fixed infrastructure, such as high-shelves logistics, to produce the inventory of the tagged objects within each shelf.

This paper presents a particle swarm optimization (PSO) in synthetic aperture radar (SAR) methods to enable real-time 3D localization of UHF-RFID tags. The reader antennas are moved through a moving agent (e.g., unmanned aerial vehicle, unmanned ground vehicle, robot) and they collect several phase samples by resembling a synthetic array. Thanks to the PSO approach the 3D matching function of the SAR method can be calculated in a reduced number of points by keeping an acceptable localization error. A numerical analysis demonstrates the method applicability through a comparison with conventional SAR methods based on the exhaustive search (3D dense grid) of the maximum point. Localization performance is investigated when an agent is equipped with a single antenna moving along a 3D trajectory or with two reader antennas at different height running a planar trajectory. Then, an experimental campaign in indoor scenario with an RFID-equipped unmanned ground vehicle shows the method effectiveness in performing real-time 3D positioning with centimeter-order localization error.