Progettazione e sviluppo di una mano robotica sotto-attuata per robot umanoide bipede

Lo scopo di questo lavoro è la progettazione e lo sviluppo di una mano robotica antropomorfa che possa essere integrata sul robot bipede SABIAN. L'esame dello stato dell'arte verte sulle mani robotiche per robot umanoidi presenti nella letteratura scientifica, con particolare attenzione ai meccanismi di trasmissione. L'obiettivo è quello di progettare una mano facilmente controllabile con un ridotto numero di attuatori che sia in grado di realizzare operazioni di presa anche in ambiente non strutturato e gestualità avanzata. Un'accurata analisi della trasmissione sotto-attuata e l'individuazione di indici globali di performance ha permesso di strutturare una procedura di ottimizzazione volta alla prevenzione dei fenomeni di instabilità della presa. Il dimensionamento del sistema di estensione passivo segue sulla base del Know-How dell'ARTS-lab, che unitamente al controllo, si prefigge lo scopo di replicare la dinamica del dito umano nella fase di chiusura prima della presa. Il lavoro si è concluso con la progettazione della mano e dell'unità di attuazione, puntando l'attenzione all'integrazione dei sensori nella struttura e la realizzazione delle dita

The SmartHand transradial prosthesis

<p>Abstract</p> <p>Background</p> <p>Prosthetic components and control interfaces for upper limb amputees have barely changed in the past 40 years. Many transradial prostheses have been developed in the past, nonetheless most of them would be inappropriate if/when a large bandwidth human-machine interface for control and perception would be available, due to either their limited (or inexistent) sensorization or limited dexterity. <it>SmartHand </it>tackles this issue as is meant to be clinically experimented in amputees employing different neuro-interfaces, in order to investigate their effectiveness. This paper presents the design and on bench evaluation of the SmartHand.</p> <p>Methods</p> <p>SmartHand design was bio-inspired in terms of its physical appearance, kinematics, sensorization, and its multilevel control system. Underactuated fingers and differential mechanisms were designed and exploited in order to fit all mechatronic components in the size and weight of a natural human hand. Its sensory system was designed with the aim of delivering significant afferent information to the user through adequate interfaces.</p> <p>Results</p> <p>SmartHand is a five fingered self-contained robotic hand, with 16 degrees of freedom, actuated by 4 motors. It integrates a bio-inspired sensory system composed of 40 proprioceptive and exteroceptive sensors and a customized embedded controller both employed for implementing automatic grasp control and for potentially delivering sensory feedback to the amputee. It is able to perform everyday grasps, count and independently point the index. The weight (530 g) and speed (closing time: 1.5 seconds) are comparable to actual commercial prostheses. It is able to lift a 10 kg suitcase; slippage tests showed that within particular friction and geometric conditions the hand is able to stably grasp up to 3.6 kg cylindrical objects.</p> <p>Conclusions</p> <p>Due to its unique embedded features and human-size, the SmartHand holds the promise to be experimentally fitted on transradial amputees and employed as a bi-directional instrument for investigating -during realistic experiments- different interfaces, control and feedback strategies in neuro-engineering studies.</p

The SSSA-MyHand: a dexterous lightweight myoelectric hand prosthesis

The replacement of a missing hand by a prosthesis is one of the most fascinating challenges in rehabilitation engineering. State of art prostheses are curtailed by the physical features of the hand, like poor functionality and excessive weight. Here we present a new multi-grasp hand aimed at overcoming such limitations. The SSSA-MyHand builds around a novel transmission mechanism that implements a semi-independent actuation of the abduction/adduction of the thumb and of the flexion/extension of the index, by means of a single actuator. Thus, with only three electric motors the hand is capable to perform most of the grasps and gestures useful in activities of daily living, akin commercial prostheses with up to six actuators, albeit it is as lightweight as conventional 1-Degrees of Freedom prostheses. The hand integrates position and force sensors and an embedded controller that implements automatic grasps and allows inter-operability with different human-machine interfaces. We present the requirements, the design rationale of the first prototype and the evaluation of its performance. The weight (478 g), force (31 N maximum force at the thumb fingertip) and speed of the hand (closing time: <370 ms), make this new design an interesting alternative to clinically available multi-grasp prostheses

Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography. The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal. Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed. The next step included the implementation of hierarchical hidden semi-Markov models to capture transitions between piecewise segments of movements and between different movements. Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier. The effectiveness of the novel algorithms was assessed versus methods commonly used for real-time classification of EMGs in a prosthesis control application. The obtained results show that using hidden semi-Markov models as the top layer, instead of the hidden Markov models, ranks top in all the relevant metrics among the tested combinations. The choice of the presented methodology for the control of prosthetic hand is also supported by the equal or lower computational complexity required, compared to other algorithms, which enables the implementation on low-power microcontrollers, and the ability to adapt to user preferences of executing individual movements during activities of daily living

Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study

Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system. High decoding accuracy of the three gestures was achieved in both offline analysis (85.7%, 84.5%, and 69.7%) and online tests (80% and 82%, tested on two participants only). We found that the decoding performance was maintained even with a subset of channels selected by a greedy algorithm. More importantly, these selected channels were mostly distributed along the central sulcus and clustered in the area of 3 interelectrode squares. Our findings of the reduced and clustered distribution of ECoG channels further supported the feasibility of clinically implementing the ECoG-based BMI system for the control of hand gestures

Learning tactile skills through curious exploration

We present curiosity-driven, autonomous acquisition of tactile exploratory skills on a biomimetic robot finger equipped with an array of microelectromechanical touch sensors. Instead of building tailored algorithms for solving a specific tactile task, we employ a more general curiosity-driven reinforcement learning approach that autonomously learns a set of motor skills in absence of an explicit teacher signal. In this approach, the acquisition of skills is driven by the information content of the sensory input signals relative to a learner that aims at representing sensory inputs using fewer and fewer computational resources. We show that, from initially random exploration of its environment, the robotic system autonomously develops a small set of basic motor skills that lead to different kinds of tactile input. Next, the system learns how to exploit the learned motor skills to solve supervised texture classification tasks. Our approach demonstrates the feasibility of autonomous acquisition of tactile skills on physical robotic platforms through curiosity-driven reinforcement learning, overcomes typical difficulties of engineered solutions for active tactile exploration and underactuated control, and provides a basis for studying developmental learning through intrinsic motivation in robots

PARLOMA – A Novel Human-Robot Interaction System for Deaf-blind Remote Communication

Deaf-blindness forces people to live in isolation. Up to now there is no existing technological solution enabling two (or many) Deaf-blind persons to communicate remotely among them in tactile Sign Language (t-SL). When resorting to t-SL, Deaf-blind persons can communicate only with persons physically present in the same place, because they are required to reciprocally explore their hands to exchange messages. We present a preliminary version of PARLOMA, a novel system to enable remote communication between Deaf-blind persons. It is composed of a low-cost depth sensor as the only input device, paired with a robotic hand as output device. Essentially, any user can perform handshapes in front of the depth sensor. The system is able to recognize a set of handshapes that are sent over the web and reproduced by an anthropomorphic robotic hand. PARLOMA can work as a “telephone” for Deaf-blind people. Hence, it will dramatically improve life quality of Deaf-blind persons. PARLOMA has been designed in strict collaboration with the main Italian Deaf-blind associations, in order to include end-users in the design phase

instrumented platform for assessment of isometric hand muscles contractions

Measurement of forces exerted by a human hand while performing common gestures is a highly valuable task for assessment of neurorehabilitation and neurological disorders, but also, for control of movement that could be directly transferred to assistive devices. Even though accurate and selective multi-joint measurement of hand forces is desirable in both clinical and research applications there is no commercially available device able to perform such measurements. Moreover, the custom-made systems used in research commonly impose limitations, such as availability of only single, predefined hand aperture. Furthermore, there is no consensus on design requirements for custom made measurement systems that would enable comparison of results obtained during research or clinical hand function studies. In an attempt to provide a possible solution for a device capable of multi-joint hand forces measurement and disseminate it to the research community, this paper presents the mechanical and electronic design of an instrumented platform for assessment of isometric hand muscles contractions. Some of the key features related to the developed system are: flexibility in placing the hand/fingers, fast and easy hand fitting, adjustability to different lengths, circumferences and postures of the digits, and the possibility to register individual bidirectional forces from the digits and the wrist. The accuracy of isometric force measurements was evaluated in a controlled test with the reference high accuracy force gauge device during which the developed system showed high linearity (R 2 = 0.9999). As the more realistic test, the device was evaluated when force was applied to individual sensors but also during the intramuscular electromyography (iEMG) study. The data gathered during the iEMG measurements was thoroughly assessed to obtain three appropriate metrics; the first estimating crosstalk between individual force sensors; the second evaluating agreement between measured forces and forces estimated through iEMG; and the third providing qualitative evaluation of hand force in respect to activations of individual muscle units. The results of these analyses performed on multiple joint forces show agreement with previously published results, but with the difference that in that case, the measurement was performed with a single degree of freedom device. (Less

Online prediction of robot to human handover events using vibrations

One of the main issues for a robotic passer is to detect the onset of a handover, in order to avoid the object from being released when the human partner is not ready or if some impact occurs. This paper presents the methodology for a robotic passer, that is potentially able to estimate the interaction forces by the receiver on the object, thus to achieve fluent and safe handovers. The proposed system uses a vibrator that energizes the object and an accelerometer that monitors vibration propagation through the object during the handover. We focused on the machine-learning technique to classify between four states during object handover. A neural network was trained for these four states and tested online. In experimental trials an accuracy of 85.2% and 93.9% were obtained respectively for four classes and two classes of actions by a neural network classifier

Neural feedback strategies to improve grasping coordination in neuromusculoskeletal prostheses

Conventional prosthetic arms suffer from poor controllability and lack of sensory feedback. Owing to the absence of tactile sensory information, prosthetic users must rely on incidental visual and auditory cues. In this study, we investigated the effect of providing tactile perception on motor coordination during routine grasping and grasping under uncertainty. Three transhumeral amputees were implanted with an osseointegrated percutaneous implant system for direct skeletal attachment and bidirectional communication with implanted neuromuscular electrodes. This neuromusculoskeletal prosthesis is a novel concept of artificial limb replacement that allows to extract control signals from electrodes implanted on viable muscle tissue, and to stimulate severed afferent nerve fibers to provide somatosensory feedback. Subjects received tactile feedback using three biologically inspired stimulation paradigms while performing a pick and lift test. The grasped object was instrumented to record grasping and lifting forces and its weight was either constant or unexpectedly changed in between trials. The results were also compared to the no-feedback control condition. Our findings confirm, in line with the neuroscientific literature, that somatosensory feedback is necessary for motor coordination during grasping. Our results also indicate that feedback is more relevant under uncertainty, and its effectiveness can be influenced by the selected neuromodulation paradigm and arguably also the prior experience of the prosthesis user