Sensors for Robotic Hands: A Survey of State of the Art

Recent decades have seen significant progress in the field of artificial hands. Most of the surveys, which try to capture the latest developments in this field, focused on actuation and control systems of these devices. In this paper, our goal is to provide a comprehensive survey of the sensors for artificial hands. In order to present the evolution of the field, we cover five year periods starting at the turn of the millennium. At each period, we present the robot hands with a focus on their sensor systems dividing them into categories, such as prosthetics, research devices, and industrial end-effectors.We also cover the sensors developed for robot hand usage in each era. Finally, the period between 2010 and 2015 introduces the reader to the state of the art and also hints to the future directions in the sensor development for artificial hands

ANN Control Based on Patterns Recognition for A Robotic Hand Under Different Load Conditions

في هذا البحث, الشبكة العصبية الاصطناعية (ANN) قد تم تدريبها على انماط نسب المركبات العمودية الى الافقية لقوى التماس عند وقت حدوث الانزلاق, لتكون قادرة على تمييز الانزلاق تحت انواع مختلفة من الأحمال (الحمل الستاتيكي والحمل الديناميكي), ومن ثم توليد اشارة راجعة تستخدم كمشغل لمحرك اليد الصناعية. هذه العملية اجريت بدون الحاجة لأي معلومات حول خواص الجسم الممسوك, مثل الوزن, تركيب السطح, الشكل, معامل الاحتكاك و نوع الحمل المؤثر على الجسم الممسوك. لتحقيق ذلك , تم اقتراح تصميم جديد لرأس الاصبع من اجل كشف الانزلاق في اتجاهات متعددة بين الجسم الممسوك ورؤس الاصابع الاصطناعية. هذا التصميم يتألف من اصبعين مع نظام تشغيل يتضمن اجزاء مرنة (نوابض انضغاطية). هذه النوابض تعمل كمعوض لقوة المسك عند وقت حدوث الانزلاق حتى في وضعية التوقف لمحرك اليد. نسب مركبات قوى التماس يمكن حسابها بواسطة حساسات قوى تقليدية (FlexiForce sensor) بعد معالجة بيانات القوى باستخدام برنامج Matlab/Simulink ومن خلال علاقات رياضية معينة التي تم اشتقاقها لوصف الآلية الميكانيكية للإصبع الاصطناعي.In this paper, the Artificial Neural Network (ANN) is trained on the patterns of the normal component to tangential component ratios at the time of slippage occurrence, so that it can be able to distinguish the slippage occurrence under different type of load (quasi-static and dynamic loads), and then generates a feedback signal used as an input signal to run the actuator. This process is executed without the need for any information about the characteristics of the grasped object, such as weight, surface texture, shape, coefficient of the friction and the type of the load exerted on the grasped object. For fulfillment this approach, a new fingertip design has been proposed in order to detect the slippage in multi-direction between the grasped object and the artificial fingertips. This design is composed of two under-actuated fingers with an actuation system which includes flexible parts (compressive springs). These springs operate as a compensator for the grasping force at the time of slippage occurrence in spite of the actuator is in stopped situation. The contact force component ratios can be calculated via a conventional sensor (Flexiforce sensor) after processed the force data using Matlab/Simulink program through a specific mathematical model which is derived according to the mechanism of the artificial finger

Methods and Sensors for Slip Detection in Robotics: A Survey

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

Tactile Sensors for Friction Estimation and Incipient Slip Detection - Toward Dexterous Robotic Manipulation:A Review

Humans can handle and manipulate objects with ease; however, human dexterity has yet to be matched by artificial systems. Receptors in our fingers and hands provide essential tactile information to the motor control system during dexterous manipulation such that the grip force is scaled to the tangential forces according to the coefficient of friction. Likewise, tactile sensing will become essential for robotic and prosthetic gripping performance as applications move toward unstructured environments. However, most existing research ignores the need to sense the frictional properties of the sensor-object interface, which (along with contact forces and torques) is essential for finding the minimum grip force required to securely grasp an object. Here, we review this problem by surveying the field of tactile sensing from the perspective that sensors should: 1) detect gross slip (to adjust the grip force); 2) detect incipient slip (dependent on the frictional properties of the sensor-object interface and the geometries and mechanics of the sensor and the object) as an indication of grip security; or 3) measure friction on contact with an object and/or following a gross or incipient slip event while manipulating an object. Recommendations are made to help focus future sensor design efforts toward a generalizable and practical solution to sense, and hence control grip security. Specifically, we propose that the sensor mechanics should encourage incipient slip, by allowing parts of the sensor to slip while other parts remain stuck, and that instrumentation should measure displacement and deformation to complement conventional force, pressure, and vibration tactile sensing

Human-Machine Interfaces using Distributed Sensing and Stimulation Systems

As the technology moves towards more natural human-machine interfaces (e.g. bionic limbs, teleoperation, virtual reality), it is necessary to develop a sensory feedback system in order to foster embodiment and achieve better immersion in the control system. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing a wide bandwidth of information. To provide this type of feedback, it is necessary to develop a distributed sensing system that could extract a wide range of information during the interaction between the robot and the environment. In addition, a distributed feedback interface is needed to deliver such information to the user. This thesis proposes the development of a distributed sensing system (e-skin) to acquire tactile sensation, a first integration of distributed sensing system on a robotic hand, the development of a sensory feedback system that compromises the distributed sensing system and a distributed stimulation system, and finally the implementation of deep learning methods for the classification of tactile data. It\u2019s core focus addresses the development and testing of a sensory feedback system, based on the latest distributed sensing and stimulation techniques. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives, and the used methodology and contributions; as well as six studies that tackled the development of human-machine interfaces

Dexterous manipulation of unknown objects using virtual contact points

The manipulation of unknown objects is a problem of special interest in robotics since it is not always possible to have exact models of the objects with which the robot interacts. This paper presents a simple strategy to manipulate unknown objects using a robotic hand equipped with tactile sensors. The hand configurations that allow the rotation of an unknown object are computed using only tactile and kinematic information, obtained during the manipulation process and reasoning about the desired and real positions of the fingertips during the manipulation. This is done taking into account that the desired positions of the fingertips are not physically reachable since they are located in the interior of the manipulated object and therefore they are virtual positions with associated virtual contact points. The proposed approach was satisfactorily validated using three fingers of an anthropomorphic robotic hand (Allegro Hand), with the original fingertips replaced by tactile sensors (WTS-FT). In the experimental validation, several everyday objects with different shapes were successfully manipulated, rotating them without the need of knowing their shape or any other physical property.Peer ReviewedPostprint (author's final draft

Distributed Sensing and Stimulation Systems Towards Sense of Touch Restoration in Prosthetics

Modern prostheses aim at restoring the functional and aesthetic characteristics of the lost limb. To foster prosthesis embodiment and functionality, it is necessary to restitute both volitional control and sensory feedback. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing high-fidelity spatial information. To provide this type of feedback in prosthetics, it is necessary to sense tactile information from artificial skin placed on the prosthesis and transmit tactile feedback above the amputation in order to map the interaction between the prosthesis and the environment. This thesis proposes the integration of distributed sensing systems (e-skin) to acquire tactile sensation, and non-invasive multichannel electrotactile feedback and virtual reality to deliver high-bandwidth information to the user. Its core focus addresses the development and testing of close-loop sensory feedback human-machine interface, based on the latest distributed sensing and stimulation techniques for restoring the sense of touch in prosthetics. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives and the used methodology and contributions; as well as three studies distributed over stimulation system level and sensing system level. The first study presents the development of close-loop compensatory tracking system to evaluate the usability and effectiveness of electrotactile sensory feedback in enabling real-time close-loop control in prosthetics. It examines and compares the subject\u2019s adaptive performance and tolerance to random latencies while performing the dynamic control task (i.e. position control) and simultaneously receiving either visual feedback or electrotactile feedback for communicating the momentary tracking error. Moreover, it reported the minimum time delay needed for an abrupt impairment of users\u2019 performance. The experimental results have shown that electrotactile feedback performance is less prone to changes with longer delays. However, visual feedback drops faster than electrotactile with increased time delays. This is a good indication for the effectiveness of electrotactile feedback in enabling close- loop control in prosthetics, since some delays are inevitable. The second study describes the development of a novel non-invasive compact multichannel interface for electrotactile feedback, containing 24 pads electrode matrix, with fully programmable stimulation unit, that investigates the ability of able-bodied human subjects to localize the electrotactile stimulus delivered through the electrode matrix. Furthermore, it designed a novel dual parameter -modulation (interleaved frequency and intensity) and compared it to conventional stimulation (same frequency for all pads). In addition and for the first time, it compared the electrotactile stimulation to mechanical stimulation. More, it exposes the integration of virtual prosthesis with the developed system in order to achieve better user experience and object manipulation through mapping the acquired real-time collected tactile data and feedback it simultaneously to the user. The experimental results demonstrated that the proposed interleaved coding substantially improved the spatial localization compared to same-frequency stimulation. Furthermore, it showed that same-frequency stimulation was equivalent to mechanical stimulation, whereas the performance with dual-parameter modulation was significantly better. The third study presents the realization of a novel, flexible, screen- printed e-skin based on P(VDF-TrFE) piezoelectric polymers, that would cover the fingertips and the palm of the prosthetic hand (particularly the Michelangelo hand by Ottobock) and an assistive sensorized glove for stroke patients. Moreover, it developed a new validation methodology to examine the sensors behavior while being solicited. The characterization results showed compatibility between the expected (modeled) behavior of the electrical response of each sensor to measured mechanical (normal) force at the skin surface, which in turn proved the combination of both fabrication and assembly processes was successful. This paves the way to define a practical, simplified and reproducible characterization protocol for e-skin patches In conclusion, by adopting innovative methodologies in sensing and stimulation systems, this thesis advances the overall development of close-loop sensory feedback human-machine interface used for restoration of sense of touch in prosthetics. Moreover, this research could lead to high-bandwidth high-fidelity transmission of tactile information for modern dexterous prostheses that could ameliorate the end user experience and facilitate it acceptance in the daily life

Haptic Glove and Platform with Gestural Control For Neuromorphic Tactile Sensory Feedback In Medical Telepresence

Advancements in the study of the human sense of touch are fueling the field of haptics. This is paving the way for augmenting sensory perception during object palpation in tele-surgery and reproducing the sensed information through tactile feedback. Here, we present a novel tele-palpation apparatus that enables the user to detect nodules with various distinct stiffness buried in an ad-hoc polymeric phantom. The contact force measured by the platform was encoded using a neuromorphic model and reproduced on the index fingertip of a remote user through a haptic glove embedding a piezoelectric disk. We assessed the effectiveness of this feedback in allowing nodule identification under two experimental conditions of real-time telepresence: In Line of Sight (ILS), where the platform was placed in the visible range of a user; and the more demanding Not In Line of Sight (NILS), with the platform and the user being 50 km apart. We found that the entailed percentage of identification was higher for stiffer inclusions with respect to the softer ones (average of 74% within the duration of the task), in both telepresence conditions evaluated. These promising results call for further exploration of tactile augmentation technology for telepresence in medical interventions