Design and Development ofa Mirror Effect Control Prosthetic Hand with Force Sensing

Some of the already available prosthetic hands in the market are operated in open loop, without any feedback and expensive. This system counters those by having the prosthetic hand printed using 3D printer and consist of a feedback sensor to make it a closed loop system. The system generally consists of two sections, mainly Finger Input and Prosthetic Output. The two sections communicate wirelessly for data transferring. The main purpose of the system is to control the prosthetic hand wirelessly using the Mirror Glove by performing a mirror effect that will translate movement from the glove onto the prosthetic hand. The Mirror Glove monitors the movements/bending of each fingers using force sensitive sensor. The prosthetic hand also has a sensor known as force sensitive resistor. The sensors will feedback the pressure on the prosthetic hand during object grasping, allowing the prosthetic hand to grasp delicate object without damaging it. Overall, the system will imitate the flex and relaxing of fingers inside the Mirror Glove and wirelessly control distant prosthetic hand to imitate the human hand

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

Modélisations et stratégie de prise pour la manipulation d'objets déformables

Dexterous manipulation is an important issue in robotics research in which few works have tackled deformable object manipulation. New applications in surgery, food industry or in service robotics require mastering the grasping and manipulation of deformable objects. This thesis focuses on deformable object manipulation by anthropomorphic mechanical graspers such as multi-fingered articulated hands. This task requires a great expertise in mechanical modeling and control: interaction modeling, tactile and vision perception, force / position control of finger movements to ensure stable grasping. The work presented in this thesis focuses on modeling the grasping of deformable objects. To this end, we used a discretization by non-linear mass-spring systems to model deformable bodies in large displacements and deformations while having a low computational cost. To predict the interaction forces between robot hand and deformable object, we proposed an original approach based on a visco-elasto-plastic rheological model to evaluate tangential contact forces and describe the transition between the sticking and slipping modes. The contact forces are evaluated at nodes as function of the relative movements between the fingertips and the surface mesh facets of the manipulated object. Another contribution of this thesis is the use of this model in the planning of 3D deformable object manipulation tasks. This planning consists in determining the optimal configuration of the hand for grasping the objects as well as the paths to track and the efforts to be applied by the fingers to control the deformation of the object while ensuring the stability of the operation. The experimental validation of this work has been carried out on two robotic platforms: a Barrett hand embedded on a Adept S1700D ® manipulator and a Shadow hand embedded on a Kuka LWR4+® manipulator.La manipulation dextre est un sujet important dans la recherche en robotique et dans lequel peu de travaux ont abordé la manipulation d'objets déformables. De nouvelles applications en chirurgie, en industrie agroalimentaire ou encore dans les services à la personne nécessitent la maîtrise de la saisie et la manipulation d'objets déformables. Cette thèse s’intéresse à la manipulation d’objets déformables par des préhenseurs mécaniques anthropomorphiques tels que des mains articulées à plusieurs doigts. Cette tâche requière une grande expertise en modélisation mécanique et en commande : modélisation des interactions, perception tactile et par vision, contrôle des mouvements des doigts en position et en force pour assurer la stabilité de la saisie. Les travaux présentés dans cette thèse se focalisent sur la modélisation de la saisie d'objets déformables. Pour cela, nous avons utilisé une discrétisation par des systèmes masses-ressorts non-linéaires pour modéliser des corps déformables en grands déplacements et déformations tout en ayant un coût calculatoire faible. Afin de prédire les forces d’interactions entre main robotique et objet déformable, nous avons proposé une approche originale basée sur un modèle rhéologique visco-élasto-plastique pour évaluer les forces tangentielles de contact et décrire la transition entre les modes d’adhérence et de glissement. Les forces de contact sont évaluées aux points nodaux en fonction des mouvements relatifs entre les bouts des doigts et les facettes du maillage de la surface de l’objet manipulé. Une autre contribution de cette thèse consiste à utiliser de cette modélisation dans la planification des tâches de manipulation d’objets déformables 3D. Cette planification consiste à déterminer la configuration optimale de la main pour la saisie de l’objet ainsi que les trajectoires à suivre et les efforts à appliquer par les doigts pour contrôler la déformation de l’objet tout en assurant la stabilité de l’opération. La validation expérimentale de ces travaux a été réalisée sur deux plateformes robotiques : une main Barrett embarquée sur un bras manipulateur Adept S1700D et une main Shadow embarquée sur un bras manipulateur Kuka LWR4+