Mirna Issa

Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult @?@? control using conventional techniques. Here, a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling input displacement of a new adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Since the conventional control strategy is a very challenging task, fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS controller, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.

Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties

Highlights? Adaptive neuro-fuzzy estimation of conductive silicone rubber properties. ? Adaptive neuro-fuzzy network to approximate correlation between measured features of the material. ? Adaptive neuro-fuzzy network to predict the conductive silicone rubber future behavior for stress changing. ? A new constitutive model of the conductive silicone rubber. ? A new type of stress prediction model based on artificial neural network. Conductive silicone rubber has great advantages for tactile sensing applications. The electrical behavior of the elastomeric material is rate-dependent and exhibit hysteresis upon cyclic loading. Several constitutive models were developed for mechanical simulation of this material upon loading and unloading. One of the successful approaches to model the time-dependent behavior of elastomers is Bergstrom-Boyce model. An adaptive neuro-fuzzy inference system (ANFIS) model will be established in this study to predict the stress-strain changing of conductive silicone rubber during compression tests. Various compression tests were performed on the produced specimens. An ANFIS is used to approximate correlation between measured features of the material and to predict its unknown future behavior for stress changing. ANFIS has unlimited approximation power to match any nonlinear functions well and to predict a chaotic time series.

Intelligent rotational direction control of passive robotic joint with embedded sensors

Passive compliant joints with springs and dampers ensure a smooth contact with the surroundings, especially if robots are in contact with humans, but the passive compliant joints cannot determine precisely the position of the members of the joint or direction of the collision force. In this paper was proposed the structure of a passive compliant robotic joint with conductive silicone rubber elements as internal embedded sensors. The sensors can operate as absorbers of excessive external collision force instead of springs and dampers and can be used for some measurements. Therefore, this joint presents one type of safe robotic mechanisms with an internally measuring system. The sensors were made by press-curing from carbon-black filled silicone rubber which is an electro active material. Various compression tests of the sensors were done. The main task of this study is to investigate the application of a control algorithm for detecting the direction of the robotic joint angular rotation when subjected to an external collision force. Soft computing methodology, adaptive neuro fuzzy inference strategy (ANFIS), was used for the controller development. The simulation results presented in this paper show the effectiveness of the developed method.

Application of the TRIZ creativity enhancement approach to the design of a passively adaptive compliant robotic gripper

Purpose – The essence of the conceptual design is getting the innovative projects or ideas to ensure the products with best performance. It has been proved that the theory of inventive problem solution (TRIZ) is a systematic methodology for innovation. The purpose of this paper is to illustrate the design of an adaptive robotic gripper as an engineering example to show the significance and approaches of applying TRIZ in getting the creative conceptual design ideas.Design/methodology/approach – Gripping and holding of objects are key tasks for robotic manipulators. The development of universal grippers able to pick up unfamiliar objects of widely varying shapes and surfaces is a very challenging task. The requirement for new adaptive grippers is the ability to detect and recognize objects in their environments.Findings – The main aim of this work is to show a systematic methodology for innovation as an effective procedure to enhance the capability of developing innovative products and to overcome the main ...

Design of compliant robotic joint with embedded‐sensing elements of conductive silicone rubber

Purpose – The purpose of this paper is to propose a new methodological framework within which a compliant robotic joint can be studied.Design/methodology/approach – A new method is presented for detecting the direction of the robotic joint rotation when subjected to an external collision force.Findings – The behaviour of the silicone rubber shows strong non‐linearity, therefore, the sensor‐elements cannot be used for accurate measurements.Originality/value – A new type of safe robotic mechanisms with an internal measuring system is proposed in this paper.

Contact positions estimation of sensing structure using adaptive neuro-fuzzy inference system

Purpose – Tactile sensing is the process of determining physical properties and events through contact with objects in the world. The purpose of this paper is to establish a novel design of an adaptive neuro-fuzzy inference system (ANFIS) for estimation of contact position of a new tactile sensing structure. Design/methodology/approach – The major task is to investigate implementations of carbon-black-filled silicone rubber for tactile sensation; the silicone rubber is electrically conductive and its resistance changes by loading or unloading strains. Findings – The sensor-elements for the tactile sensing structure were made by press-curing from carbon-black-filled silicone rubber. The experimental results can be used as training and checking data for the ANFIS network. Originality/value – This system is capable to find any change of contact positions and thus indicates state of the current contact location of the tactile sensing structure. The behavior of the use silicone rubber shows strong non-linearit...

Electrical properties estimation of conductive silicone rubber for tactile sensing structure

Purpose – The aim of this paper is to investigate implementations of carbon‐black filled silicone rubber for tactile sensation.Design/methodology/approach – The sensor‐elements for this tactile sensing structure were made by press‐curing from carbon‐black filled silicone rubber.Findings – The behaviour of the silicone rubber shows strong non‐linearity, therefore, the sensor cannot be used for accurate measurements. The greatest advantage of this material lies in its high elasticity.Originality/value – A new method for artificial tactile sensing skin for robotic applications.

Passively Adaptive Compliant Gripper

Gripping and holding of objects are key tasks for robotic manipulators. The development of universal grippers able to pick up unfamiliar objects of widely varying shapes and surfaces is a very challenging task. Passively compliant underactuated mechanisms are one way to obtain the gripper which could accommodate to any irregular and sensitive grasping objects. The purpose of the underactuation is to use the power of one actuator to drive the open and close motion of the gripper. The underactuation can morph shapes of the gripper to accommodate to different objects. As a result, they require less complex control algorithms. The fully compliant mechanism has multiple degrees of freedom and can be considered as an underactuated mechanism. This paper presents a new design of the adaptive underactuated compliant gripper with distributed compliance and embedded sensors in the gripper structure. The adaptive gripper surfaces will have the sensing capability by these embedded sensors. The gripper will be made of a silicone rubber and conductive silicone rubber will be used for the embedded sensors. The main points of this paper are in explanation of the construction and production of the gripper structure and showing the methodology of a new sensing capability of the gripper.

Electrical Performance of Electrically Conductive Silicone Rubber in Dependence on Tensile Load According to Various Parameters

The paper presents the results of many investigations to make clear how the different parameters of tensile load which will possibly control the use of a strain sensor made of the conductive silicone rubber affect the electrical properties of this material. In order to clarify the behavior of this conductive silicone rubber during the cyclic load other tests are implemented on several specimens for each test to compare their behavior. The compliant systems with such sensors do not require mounting, and also they can be miniaturized. The target of these investigations is to use the conductive silicone rubber as a strain sensor by means of changing its electrical properties depending on the tensile load under different conditions.