Objective:Spinal Muscular Atrophy (SMA) is a genetic neuromuscular disease affecting the lower motor neuron, carrying a significant burden on patients' general motor skills and quality of life, characterized by a great variability in phenotypic expression. As new therapeutic options make their appearance on the scene, sensitive clinical tools and outcome measures are needed, especially in adult patients undergoing treatment, in which the expected clinical response is a mild improvement or stabilization of disease progression.Methods:Here, we describe a new functional motor scale specifically designed for evaluating the endurance dimension for the upper and lower limbs in adult SMA patients.Results:The scale was first tested in eight control healthy subjects and then validated in ten adult SMA patients, proving intra- and inter-observer reliability. We also set up an evaluation protocol by using wearable devices including surface EMG and accelerometer.Conclusions:The endurance evaluation should integrate the standard clinical monitoring in the management and follow-up of SMA adult patients.

Neuromuscular Disorders (NMDs) are conditions that affect a high percentage of the population, globally. The assessment of the muscular activity of the limbs is currently performed by means of bulky and costly pieces of equipment or based on the expertise of the operator. AUTOMA 2.0 builds on the results achieved within previous studies, being a wearable sensorized device with high flexibility, able to detect the main force/displacement information of the limbs, in order to define a objective framework related to the specific patient in view of a more correct diagnosis and treatment. A pilot study carried out on healthy patients was successfully performed with a interdisciplinary team to tests the components of AUTOMA 2.0 in view of its employment for NMD patients.

Quality control in industry involves trained operators to manipulate and inspect metallic surfaces in order to identify, and eventually correct, manufacturing defects. These tasks are manually performed, and a poor performance ( e.g. , missing defects) leads to an increase of the costs and prolongation of the manufacturing time cycle. In this work, we propose a multi-agent robotic platform to autonomously perform Industry 4.0 quality control processes of metallic surfaces. The platform consists of three anthropomorphic robots with custom-made end-effectors designed to manipulate, inspect, and eventually correct a metallic frame of a motorcycle. The description of a novel multi-agent platform is followed by the presentation of the developed inspection procedure, in which a linear laser scanner is used to reconstruct the three-dimensional metallic surface of a motorcycle with a resolution of ~0.1 mm. In order to validate the platform, we perform a set of experiments to assess the performance of the robotic platform in a real Industry 4.0 scenario. Results confirmed that such a system guarantees a sub-millimetric precision to identify defects on complex-shaped metallic surfaces and effectively correct them. The proposed robotic platform can be adopted for overcoming the drawbacks of a traditional procedure that relies on visual-tactile manual defects correction ( e.g. , low-repeatability, high-subjectivity) and is scalable to different industrial applications. The proposed approach aims to elevate the role of operators to expert supervisors of the process, limiting the interactions with potentially-dangerous tools/procedures and thus improving the working conditions in an industrial 4.0 scenario. Note to Practitioners —This work was motivated by a crucial need in industry, i.e. to automatize the manufacturing quality control, translating the commonly-used visual-based manual approach performed by operators to an objective robotic one that relies on defect detection by using a linear laser scanner. A novel multi-agent robotic platform, developed by the authors, showed its effectiveness in automatizing complex tasks, in which huge workspace and different tools are required. The aim of the paper was to develop a fully automatized application that covers the entire quality control process, while focusing on one specific phase, i.e. automatic inspection. The integrated software and the infrastructural communication protocols of the entire robotic platform were designed to be flexible in order to realize a new reference for industrial applications, where a multi-agent approach is demanded. The experimental validation focused on a specific use case (a motorcycle frame selected due to its complex structure, i.e. multiple curvatures with variable radii, and large volumes), but the proposed platform and the implemented methodology have to be intended as a general purpose approach, adaptable to any industrial process and mechanical component. The authors, starting by a laboratory development with extensive tests, applied and demonstrated the feasibility of the proposed approach in a real Industry 4.0 scenario.

Aerial drones are systems that have been largely employed in a number of civil applications, from surveillance and environmental monitoring to day-to-day service. In view of this, the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC) held in Abu Dhabi (UAE) was a competition that aimed to push forward the technological limits of the actual aerial devices. We present the design and development of drone prototypes to meet one of the goals of Challenge 1, namely autonomously identifying and attacking randomly-positioned colored targets. This paper reports the main hardware/software design choices to accomplish the highest flexibility and usability of our aerial platforms, namely the strategies to control the overall dynamics and vision for the eventual interaction with targets. We achieved the fourth place among 22 international teams, since we successfully dispatched all the targets, which were dispersed throughout an area of 6000 m2 , in 365 s. Although focused on specific goals, the methodologies developed in view of the competition are suitable for further improvements towards other application fields.

This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

Digital techniques are a strategic tool to design new commercial products, reducing time and waste. This is particularly relevant for shoe manufacturing and, in particular, for high-heeled shoes, for which a trade-off between comfort and attractiveness is difficult to achieve. This paper offers a new set of tools to design high-heeled shoes that exploits the synergies between modeling and experiments, aiming at predicting the comfort of such products, improving the manufacturing process by optimizing the design step. As a case study, two actual commercial 11-cm-heel shoe models, differentiated by the openness of the front side, were used to deploy the digital design procedure. A finite-element model was implemented by combining the outcomes from reverse engineering techniques, to reconstruct the foot and shoe topologies, and the experimental characterization of the materials used for the final shoe products. Pressure maps on the toes and the footbed were used as benchmarks for a comparison with experiments, made with commercial sensorized insoles. Non-uniform pressures for both shoe models were observed, with highest values for the closed-shaped specimen that presented peaks of ≈ 160 kPa on the footbed and ≈ 140 kPa on the external toes. The here presented digital approach has the potential to improve the design process that will not require the traditional fabrication of countless handicraft prototypes, saving time and the associated prototyping costs. Finally, although this work focused on a niche of the shoe market, this approach may be extended to other products, which customization has a key role in the manufacturing process.

Generalization ability in tactile sensing for robotic manipulation is a prerequisite to effectively perform tasks in ever-changing environments. In particular, performing dynamic tactile perception is currently beyond the ability of robotic devices. A biomimetic approach to achieve this dexterity is to develop machines combining compliant robotic manipulators with neuroinspired architectures displaying computational adaptation. Here we demonstrate the feasibility of this approach for dynamic touch tasks experimented by integrating our sensing apparatus in a 6 degrees of freedom robotic arm via a soft wrist. We embodied in the system a model of spike-based neuromorphic encoding of tactile stimuli, emulating the discrimination properties of cuneate nucleus neurons based on pathways with differential delay lines. These strategies allowed the system to correctly perform a dynamic touch protocol of edge orientation recognition (ridges from 0 to 40°, with a step of 5°). Crucially, the task was robust to contact noise and was performed with high performance irrespectively of sensing conditions (sensing forces and velocities). These results are a step forward toward the development of robotic arms able to physically interact in real-world environments with tactile sensing.

Neuromuscular conditions are characterized by muscular weakness, for which the subjective clinical phenotype is normally characterized by means of neurological examination. However, this assessment is dramatically affected by inter-observer variability, possibly impacting on the patients' diagnosis. To help solving this issue, an interoperable HW/SW solution was implemented and preliminarily tested in a clinical setting, representing a useful tool for collecting data in a safe, secure and reliable way, and providing the clinician with a valid tool for objectivizing the parameters acquired during the neurological exam. Thanks to the data elaboration part, the solution can undoubtedly represent a valid tool to support the diagnostic path and can potentially be applied to several conditions, not only in the neuromuscular domain but also in the whole neurological field.

Neuromuscular Diseases require a careful study of the genotype-phenotype association in order to be properly diagnosed and to undertake a personalized, efficient treatment planning. To this extent, the merging of genetic, instrumental, muscular, physiotherapy, cardiovascular data is pivotal to define the profile of each single patient and to help the clinicians in defining treatment plans, monitoring the pathological course and, finally, in the patients' care. In this document, we briefly describe the in-progress development of a multimodal approach addressing this topic, focusing on the solutions composing the relevant platform as a whole, adding some brief cues on the future steps to complete the implementation of such solution and providing short hints about its possible future use.

Human management of robots in many specific industrial activities has long been imperative, due to the elevated levels of complexity involved, which can only be overcome through long and wasteful preprogrammed activities. The shared control approach is one of the most emergent procedures that can compensate and optimally couple human smartness with the high precision and productivity characteristic to mechatronic systems. To explore and to exploit this approach in the industrial field, an innovative shared control algorithm was elaborated, designed and validated in a specific case study.

The neuro-robotics paradigm is a design approach, mainly aimed at the fusion of neuroscience and robotic competences and methods to design better robots that can act and interact closely with humans, in several application fields: rehabilitation and personal assistance, prosthetics, urban services, surgery, diagnostics, and environment monitoring.

The paper presents an unusual design paradigm for the development of advanced operating rooms and its implementation. The aim of this approach is moving forward the state of art in operating room design and management. First, the used paradigm to implement the mentioned design methodology is described comparing the operating room context environment with a music perspective. This approach has inspired the development of independent systems which are linked together thanks to a communication common framework called OPERA. Secondly, the OPERA system consisting of heterogeneous set of novel robotic devices and innovative information technology techniques are described. The OPERA hardware and software subsystems modules perform their peculiar activities as a whole like orchestral musicians performing an opera. In particular robotic subsystems (developed by The BioRobotics Institute of Scuola Superiore Sant'Anna, Pisa, Italy), surgical workflow model (designed by EndoCAS, University of Pisa, Pisa, Italy) and a system able to control and synchronize the activities of each single subsystem during the surgical process (developed by I+ s.r.l., Florence, Italy) are presented here.

This paper presents the design and experimental testing of the robotic elbow exoskeleton NEUROBOTICS Elbow Exoskeleton (NEUROExos). The design of NEUROExos focused on three solutions that enable its use for poststroke physical rehabilitation. First, double-shelled links allow an ergonomic physical human-robot interface and, consequently, a comfortable interaction. Second, a four-degree-of-freedom passive mechanism, embedded in the link, allows the user's elbow and robot axes to be constantly aligned during movement. The robot axis can passively rotate on the frontal and horizontal planes 30° and 40°, respectively, and translate on the horizontal plane 30 mm. Finally, a variable impedance antagonistic actuation system allows NEUROExos to be controlled with two alternative strategies: independent control of the joint position and stiffness, for robot-in-charge rehabilitation mode, and near-zero impedance torque control, for patient-in-charge rehabilitation mode. In robot-in-charge mode, the passive joint stiffness can be changed in the range of 24-56 N·m/rad. In patient-in-charge mode, NEUROExos output impedance ranges from 1 N·m/rad, for 0.3 Hz motion, to 10 N·m/rad, for 3.2 Hz motion.

Felipe A. W. Belo Interdepartmental Center “E. Piaggio,” Universitá di Pisa, Pisa, Italy e-mail: felipebelo@gmail.com Andreas Birk Jacobs University, Bremen, Germany e-mail: a.birk@jacobs-university.de Christopher Brunskill Surrey Space Centre, University of Surrey, Surrey, United Kingdom e-mail: C.Brunskill@surrey.ac.uk Frank Kirchner DFKI, University of Bremen, Bremen, Germany e-mail: frank.kirchner@dfki.de Vaios Lappas Surrey Space Centre, University of Surrey, Surrey, United Kingdom e-mail: V.Lappas@surrey.ac.uk C. David Remy Autonomous Systems Lab, Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland e-mail: cremy@ethz.ch Stefano Roccella Scuola Superiore Sant’Anna, Pisa, Italy e-mail: s.roccella@sssup.it Claudio Rossi Centre for Automation and Robotics UPM–CSIC, Universidad Politecnica de Madrid, Madrid, Spain e-mail: claudio.rossi@upm.es Antti Tikanmäki University of Oulu, Oulu, Finland e-mail: antti.tikanmaki@ee.oulu.fi Gianfranco Visentin European Space Technology Centre, European Space Agency, Noordwijk, The Netherlands e-mail: Gianfranco.Visentin@esa.int

In the originally published article (ROB 29(4): 601–626, 2012; DOI: 10.1002/rob.20429) the affiliation of Claudio Rossi contained an error. This erratum presents the corrected affiliation.

In a society getting older year by year, Robot technology (RT) is expected to play an important role. In order to achieve this objective, the new generation of personal robots should be capable of a natural communication with humans by expressing human-like emotion. In this sense, the hands play a fundamental role in communication, because they have grasping, sensing and emotional expression ability. This paper presents the recent results of the collaboration between the Takanishi Lab of Waseda University, Tokyo, Japan, and the Arts Lab of Scuola Superiore Sant’Anna, Pisa, Italy, and RoboCasa in a biologically-inspired approach for the development of a new humanoid hand. In particular, the grasping and gestural capabilities of the novel anthropomorphic hand for humanoid robotics RCH-1 (RoboCasa Hand No. 1) are presented.

In 2008, the European Space Agency (ESA) challenged universities to design, develop, and test teleoperated robotic systems for a soil‐sampling mission in a simulated lunar‐crater‐like environment. Eight teams participated and developed a wide range of engineering solutions that addressed the various technical and operational challenges posed by the unfavorable terrain and harsh environment. The robotic concepts developed by the teams are presented and evaluated in this paper. We highlight operational and technical issues that the teams experienced during an intensive 8‐day field campaign, report on design solutions that were adopted to assist in operating a robotic system in a lunar environment, and describe the lesson learned through participation in this field‐testing event. © 2012 Wiley Periodicals, Inc.

A pair of muscles powering the human joint in an antagonistic configuration exemplifies the main difference between standard industrial robots and biological motor systems. Since muscles have a natural stiffness that varies with the muscle activation level, the central nervous system can generate stable equilibrium postures, towards which the arm is attracted, by properly regulating the activation levels of antagonistic muscles [1] The elastic properties of muscles contribute to the finite stiffness/compliance properties of the limb, to the stability of the neuro-musculo-skeletal system in the face of significant feedback delays and even allow for the generation of target movements in absence of sensory feedback, by shifting the equilibrium point [2]. Control theories based on the presence of the EP in biological motor systems [3] suggest that movements are programmed as a shift of equilibrium positions rather than through an explicit computation of forces. Thus, there is no need to solve the “inverse dynamics problem” for calculating the torque required to move the arm on the desired trajectory. The implementation of a given neuroscientific hypothesis on a real mechanical system could provide a tool under the full control of the experimenter, reproducing the main functional features of the human arm and being able to interact with the same physical environment of the human. To this end we developed the NEURARM platform, a bio-mimetic planar robotic arm reproducing key features of the human arm as identified at the level of joints and muscles. In particular:

This paper presents a novel, distributed approach to monitor physical interaction between a user and a wearable robot. We propose to apply a matrix of optoelectronic sensors embedded in a thin and compliant silicone bulk onto the user-robot contact surface. This distributed tactile sensor can measure the pressure distribution on the interaction area without affecting the comfort of the user, and does not require the robot to be specifically designed to house it. Besides the estimation of the interaction force/torque, the distributed approach allows to monitor the pressure on the user’s skin. This information is fundamental to assess the comfort and safety of the users which determine the final acceptability of the robot-mediated rehabilitation. The proposed method is preliminary evaluated on an elbow active orthosis during a repetitive rehabilitation task. Experimental results prove the relevance of this approach for the detection of the user motion intention through a measurement of the interaction force distribution.