A dynamic tactile sensor on photoelastic effect

Certain photoelastic materials exhibit birefringent characteristics at a very low level of strain. This property of material may be suitable for dynamic or wave propagation studies, which can be exploited for designing tactile sensors. This paper presents the design, construction and testing of a novel dynamic sensor based on photoelastic effect, which is capable of detecting object slip as well as providing normal force information. The paper investigates the mechanics of object slip, and develops an approximate model of the sensor. This allows visualization of various parameters involved in the sensor design. The model also explains design improvements necessary to obtain continuous signal during object slip. The developed sensor has been compared with other existing sensors and experimental results from the sensor have been discussed. The sensor is calibrated for normal force which is in addition to the dynamic signal that it provides from the same contact location. The sensor has a simple design and is of a small size allowing it to be incorporated into robotic fingers, and it provides output signals which are largely unaffected by external disturbances

Slippage detection for grasping force control of robotic hand using force sensing resistors

This paper presents the formulation of a nonlinear adaptive backstepping force control in grasping weight-varying objects using robotic hand driven by Pneumatic Artificial Muscle (PAM). The modelling and control problems arise from the high nonlinear PAM dynamics and the inherent hysteresis leading to a lack of robustness in the hand’s performance. The robotic finger and the PAM actuator been mathematically modelled as a nonlinear second order system based on an empirical approach. An adaptive backstepping controller has been designed for force control of the pneumatic hand. The estimator of the system uncertainty is incorporated into the proposed control law and a slip detection strategy is introduced to grasp objects with changing weights. The simulation and experimental results show that the robotic hand can maintain grasping an object and stop further slippage when its weight is increased up to 500 g by detecting the slip signal from the force sensor. The results also have proven that the adaptive backstepping controller is capable to compensate the uncertain coulomb friction force of PAM actuator with maximum hysteresis error 0.18◦

Tactile-based Manipulation of Deformable Objects with Dynamic Center of Mass

International audienceTactile sensing feedback provides feasible solutions to robotic dexterous manipulation tasks. In this paper, we present a novel tactile-based framework for detecting/correcting slips and regulating grasping forces while manipulating de-formable objects with the dynamic center of mass. This framework consists of a tangential force based slip detection method and a deformation prevention approach relying on weight estimation. Moreover, we propose a new strategy for manipulating deformable heavy objects. Objects with different stiffnesses, surface textures, and centers of mass are tested in experiments. Results show that proposed approaches are capable of handling objects with uncertainties in their characteristics, and also robust to external disturbances

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

Improving grasping forces during the manipulation of unknown objects

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMany of the solutions proposed for the object manipulation problem are based on the knowledge of the object features. The approach proposed in this paper intends to provide a simple geometrical approach to securely manipulate an unknown object based only on tactile and kinematic information. The tactile and kinematic data obtained during the manipulation is used to recognize the object shape (at least the local object curvature), allowing to improve the grasping forces when this information is added to the manipulation strategy. The approach has been fully implemented and tested using the Schunk Dexterous Hand (SDH2). Experimental results are shown to illustrate the efficiency of the approach.Peer ReviewedPostprint (author's final draft

Integrated Circuitry to Detect Slippage Inspired by Human Skin and Artificial Retinas

This paper presents a bioinspired integrated tactile coprocessor that is able to generate a warning in the case of slippage via the data provided by a tactile sensor. Some implementations use different layers of piezoresistive and piezoelectric materials to build upon the raw sensor and obtain the static (pressure) as well as the dynamic (slippage) information. In this paper, a simple raw sensor is used, and a circuitry is implemented, which is able to extract the dynamic information from a single piezoresistive layer. The circuitry was inspired by structures found in human skin and retina, as they are biological systems made up of a dense network of receptors. It is largely based on an artificial retina , which is able to detect motion by using relatively simple spatial temporal dynamics. The circuitry was adapted to respond in the bandwidth of microvibrations produced by early slippage, resembling human skin. Experimental measurements from a chip implemented in a 0.35-mum four-metal two-poly standard CMOS process are presented to show both the performance of the building blocks included in each processing node and the operation of the whole system as a detector of early slippage.Ministerio de Economía y Competitividad TEC2006-12376-C02-01Gobierno de España TEC2006- 1572