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The perception of slip is one of the distinctive abilities of human tactile sensing. The sense of touch allows recognizing a wide set of properties of a grasped object, such as shape, weight and dimension. Based on such properties, the applied force can be accordingly regulated avoiding slip of the grasped object. Despite the great importance of tactile sensing for humans, mechatronic hands (robotic manipulators, prosthetic hands etc.) are rarely endowed with tactile feedback. The necessity to grasp objects relying on robust slip prevention algorithms is not yet corresponded in existing artificial manipulators, which are relegated to structured environments then. Numerous approaches regarding the problem of slip detection and correction have been developed especially in the last decade, resorting to a number of sensor typologies. However, no impact on the industrial market has been achieved. This paper reviews the sensors and methods so far proposed for slip prevention in artificial tactile perception, starting from more classical techniques until the latest solutions tested on robotic systems. The strengths and weaknesses of each described technique are discussed, also in relation to the sensing technologies employed. The result is a summary exploring the whole state of art and providing a perspective towards the future research directions in the sector.

The gripper is the far end of a robotic arm. It is responsible for the contacts between the robot itself and all the items present in a work space, or even in a social space. Therefore, to provide grippers with intelligent behaviors is fundamental, especially when the robot has to interact with human beings. As shown in this article, we built an instrumented pneumatic gripper prototype that relies on different sensors’ information. Thanks to such information, the gripper prototype was able to detect the position of a given object in order to grasp it, to safely keep it between its fingers and to avoid slipping in the case of any object movement, even very small. The gripper performance was evaluated by means of a generic grasping algorithm for robotic grippers, implemented in the form of a state machine. Several slip tests were carried out on the pneumatic gripper, which showed a very fast response time and high reliability. Objects of various size, shape and hardness were employed to reproduce different grasping scenarios. We demonstrate that, through the use of force, torque, center of pressure and proximity information, the behavior of the developed pneumatic gripper prototype outperforms the one of the traditional pneumatic gripping devices.

Tactile sensing is fundamental for the human hand to achieve high dexterity. Most prosthetic hands are still devoid of tactile sensors, implying that the user cannot perceive external stimulation nor react in a fine manner. As a consequence, unforeseen events, e.g., slippage, are difficult to manage. This article proposes an algorithm to perform slippage detection with tactile sensors integrated into prosthetic hands. The algorithm is based on the filtering of the tactile sensor output; rectification and envelope follow the filtering. A binary signal, relating to slippage, is finally computed. An electrical circuit has been designed and built to elaborate the tactile signals. These have been embedded in a bioinspired fingertip mounted on a prosthetic hand, which has been interfaced with a robotic arm to assess the algorithm capability to identify slippage. Eight different surfaces have been employed, while three sliding velocities have been tested with a random interaction force between the fingertip and the test surfaces. Finally, experiments in a closed-loop configuration have been conducted to demonstrate the algorithm effectiveness in dynamic manipulation. Results proved the adequacy of the algorithm in terms of slippage detection and short latency between onset of slippage, actual detection and hand reaction.

Mechanical systems are typically composed of a number of contacting surfaces that move against each other. Such surfaces are subject to friction forces. These dissipate part of the actuation energy and cause an undesired effect on the overall system functioning. Therefore, a suitable model of friction is needed to elide its action. The choice of such a model is not always straightforward, as it is influenced by the system properties and dynamics. In this paper, we show the identification of different friction models and evaluate their prediction capability on an experimental dataset. Despite being state-of-the-art models, some modifications were introduced to improve their performance. A pneumatic gripper was used to collect the data for the models evaluation. Two experimental setups were mounted to execute the experiments: information from two pressure sensors, a load cell and a position sensor was employed for the identification. During the experiments, the gripper was actuated at different constant velocities. Results indicate that all the identified models offer a proper prediction of the real friction force.

Logistic optimization is a strategic element in many industrial processes, given that an optimized logistics makes the processes more efficient. A relevant case, in which the optimization of logistics can be decisive, is the maintenance in a Wind Farm where it can lead directly to a saving of energy cost. Wind farm maintenance presents, in fact, significant logistical challenges. They are usually distributed throughout the territory and also located at considerable distances from each other, they are generally found in places far from uninhabited centers and sometimes difficult to reach and finally spare parts are rarely available on the site of the plant itself. In this paper, we will study the problem concerning the optimization of maintenance logistics of wind plants based on the use of specific vehicle routing optimization algorithms. In particular a pickup-and-delivery algorithm with time-window is adopted to satisfy the maintenance requests of these plants, reducing their management costs. The solution was applied to a case study in a renewable energy power plant. Results time reduction and simplification and optimization obtained in the real case are discussed to evaluate the effectiveness and efficiency of the adopted approach.

The use of AI methods is currently evolving tasks done in the past by image analysts. During the last years, technology had helped to jump into fully automatic methods for monitoring and surveillance tasks, such as object detection, change detection and many more. In this work we want to show some of the AI-based models which RHEA Group has been working on which can be applied to the maritime domain, such as ship detection and super-resolution of satellite data. Each of these models can be further extended and specialized into specific monitoring and surveillance tasks, from the detection of ghost ships, measure environmental damage or monitoring of critical infrastructure near harbors or protected areas. In this paper, we illustrate some examples of the status of our research activities and the developments of these prototype applications.

Robotic arms can perform grasping actions thanks to their “dexteorus” part, i.e. the gripper. Among the various categories, nowadays pneumatic grippers became the most employed in industry, as they have low cost and little bulkiness. Despite their simplicity, controlling the force applied by these grippers is not straightforward due to the dependence of such a force on the air pressure in the gripper chambers. As a result, it is still tricky to implement closed-loop force control for pneumatic grippers. This paper intends to deliver a control scheme relying on the force measurement to control pneumatic grippers. The force might be measured through a commercial sensor (e.g. a load cell) and fed back to close the control loop. This includes a calibration which maps the force-pressure relation taking into account both desired force and length of the gripper fingers. The control scheme exploits two different pressure regulators to precisely adjust the air pressure inside the gripper chambers (i.e. opening and closing chambers). To this aim, a quadratic programming algorithm is employed. The control scheme performance revealed to be good: results will be shown in terms of gripper response to sinusoidal and step inputs, along with the pressure-force characterization.

Some (tetrahydroimidazo[1,5-a]pyridin-3-yl)phenols were reacted with boron trifluoride diethyl etherate and the resulting BF2-functionalized compounds were fully characterized both in solution using 1H, 13C, 11B, 19F NMR spectroscopy and in the solid state (infrared, fluorescence, X-ray). When excited with UV light these boron difluoride derivatives show in dichloromethane solution an intense fluorescence emission in the UV region, with λmax of emission varying from 357 to 390 nm. Good absolute quantum yields are recorded for most of the compounds. In the solid state, they are characterized by a strong blue emission (388–435 nm), with high absolute quantum yields (up to 0.68) and x,y color coordinates (CIE 1931) close to those expected for standard blue. Time-Dependent Density Functional Theory (TD-DFT) calculations were used to define the nature of the electronic transitions and excited states involved in the fluorescence process.

This paper proposes the use of an instrumented object for the study of the human grasping strategies. The proposed object is able to measure the grasping forces by means of three Force Sensitive Resistor (FSR) sensors and triaxial acceleration through an accelerometer. The object orientation angles (roll and pitch) can be estimated from the accelerometer output in quasi-static condition, whereas slippage events can be detected through the Power Spectrum Density (PSD) computation of the acceleration on at least one of the three axes. An experimental session on 7 healthy subjects has been performed; each subject used the instrumented object to perform 8 tripod grasp trials. All the sensory information, i.e. applied forces, object orientation and slippage, have been analyzed in order to evaluate the grasping strategies of the different subjects.

One of the crucial actions to be performed during a grasping task is to avoid slippage. The human hand can rapidly correct applied forces and prevent a grasped object from falling, thanks to its advanced tactile sensing. The same capability is hard to reproduce in artificial systems, such as robotic or prosthetic hands, where sensory motor coordination for force and slippage control is very limited. In this paper, a novel algorithm for slippage detection is presented. Based on fast, easy-to-perform processing, the proposed algorithm generates an ON/OFF signal relating to the presence/absence of slippage. The method can be applied either on the raw output of a force sensor or to its calibrated force signal, and yields comparable results if applied to both normal or tangential components. A biomimetic fingertip that integrates piezoresistive MEMS sensors was employed for evaluating the method performance. Each sensor had four units, thus providing 16 mono-axial signals for the analysis. A mechatronic platform was used to produce relative movement between the finger and the test surfaces (tactile stimuli). Three surfaces with submillimetric periods were adopted for the method evaluation, and 10 experimental trials were performed per each surface. Results are illustrated in terms of slippage events detection and of latency between the slippage itself and its onset.

This paper intends to provide a critical review of the literature on the technological issues on control and sensorization of hand prostheses interfacing with the Peripheral Nervous System (i.e., PNS), and their experimental validation on amputees. The study opens with an in-depth analysis of control solutions and sensorization features of research and commercially available prosthetic hands. Pros and cons of adopted technologies, signal processing techniques and motion control solutions are investigated. Special emphasis is then dedicated to the recent studies on the restoration of tactile perception in amputees through neural interfaces. The paper finally proposes a number of suggestions for designing the prosthetic system able to re-establish a bidirectional communication with the PNS and foster the prosthesis natural control.

The success of grasping and manipulation tasks of commercial prosthetic hands is mainly related to amputee visual feedback since they are not provided either with tactile sensors or with sophisticated control. As a consequence, slippage and object falls often occur. This article wants to address the specific issue of enhancing grasping and manipulation capabilities of existing prosthetic hands, by changing the control strategy. For this purpose, it proposes a multilevel control based on two distinct levels consisting of (1) a policy search learning algorithm combined with central pattern generators in the higher level and (2) a parallel force/position control managing slippage events in the lower level. The control has been tested on an anthropomorphic robotic hand with prosthetic features (the IH2 hand) equipped with force sensors. Bi-digital and tri-digital grasping tasks with and without slip information have been carried out. The KUKA-LWR has been employed to perturb the grasp stability inducing controlled slip events. The acquired data demonstrate that the proposed control has the potential to adapt to changes in the environment and guarantees grasp stability, by avoiding object fall thanks to prompt slippage event detection.

Cloud and Earth Observation (EO) based services offer the European Research community a wealth of powerful tools. However, for many researchers these tools are currently out of reach. It is difficult to find and select suitable services. Establishing agreements with cloud and EO service providers and ensuring legal and technical compliance requires specialist skills and takes an inordinate amount of time. Equally, service providers find it difficult to reach and meet the needs of the research community in technical, financial and legal areas. The Open Clouds for Research Environments consortium (OCRE) will change this, by putting in place an easy adoption route. In the autumn of 2019, OCRE will run a pan-European tender and establish framework agreements with service providers who meet the requirements of the research community. 10.000 European research and education institutes will be able to directly consume these offerings via the European Open Science Cloud service catalogue, through ready-to-use agreements. They will not have to run a tender of their own. In addition, to stimulate usage, OCRE will make available 9.5 million euro in service credits (vouchers), through adoption funds from the European Commission. OCRE is a pioneer project without precedence, with potentially high impact in the future EO market activities and evolution of service offering, with the objective to burst the usage of EO commercial services by the research environment.