Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.201707035

Soft Robotic Grippers.

The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the “class of bald men,” or the “class of numbers which are much greater than 10,” or the “class of adaptive systems,” etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between 0 and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to...

Fuzzy sets and systems

A highly versatile soft gripper that can handle an unprecedented range of object types is developed based on a new design of dielectric elastomer actuators employing an interdigitated electrode geometry, simultaneously maximizing both electroadhesion and electrostatic actuation while incorporating self-sensing. The multifunctionality of the actuator leads to a highly integrated, lightweight, fast, soft gripper with simplified structure and control.

/pdf/versatile-soft-grippers-with-intrinsic-electroadhesion-based-10fu4b42iq.pdf

Versatile Soft Grippers with Intrinsic Electroadhesion Based on Multifunctional Polymer Actuators

Design of compliant mechanisms using continuum topology optimization: A review

Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission

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 controller for adaptive compliant robotic gripper

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.

Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties

It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult t@? analyze with conventional analytical methods. Here, a novel design of an adaptive neuro fuzzy inference system (ANFIS) for estimation contact forces of a new adaptive gripper is presented. Since the conventional analytical methods is a very challenging task, fuzzy logic based systems are considered as potential candidates for such an application. The main points of this paper are in explanation of kinetostatic analyzing of the new gripper structure using rigid body model with added compliance in every single joint. The experimental results can be used as training data for ANFIS network for estimation of gripping forces. An adaptive neuro-fuzzy network is used to approximate correlation between contact point locations and contact forces magnitudes. The simulation results presented in this paper show the effectiveness of the developed method. This system is capable to find any change in ratio of positions of the gripper contacts and magnitudes of the contact forces and thus indicates state of both finger phalanges.

Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces

Purpose – Passively compliant underactuated mechanisms are one way to obtain the gripper which could accommodate to any irregular and sensitive grasping object. The purpose of the underactuation is to use less active inputs than the number of degrees of freedom of the gripper mechanism to drive the open and close motion of the gripper. Another purpose of underaction is to reduce the number of control variables. Design/methodology/approach – The underactuation can morph shapes of the gripper to accommodate different objects. As a result, the underactuated grippers require less complex control algorithms. The fully compliant mechanism has multiple degrees of freedom and can be considered as an underactuated mechanism. Findings – This paper presents a new design of the adaptive underactuated compliant gripper with distributed compliance. The optimal topology of the gripper structure was obtained by optimality criteria method using mathematical programming technique. Afterwards, the obtained model was improve...

Development of a new type of passively adaptive compliant gripper

Adaptive grippers should be able to detect and recognize grasping objects. To be able to do it control algorithm need to be established to control gripper tasks. Since the gripper movements are highly nonlinear systems it is desirable to avoid using of conventional control strategies for robotic manipulators. Instead of the conventional control strategies more advances algorithms can be used. In this study several soft computing methods are analyzed for robotic gripper applications. The gripper structure is fully compliant with embedded sensors. The sensors could be used for grasping shape detection. As soft computing methods, extreme learning machine (ELM) and support vector regression (SVR) were established. Also other soft computing methods are analyzed like fuzzy, neuro-fuzzy and artificial neural network approach. The results show the highest accuracy with ELM approach than other soft computing methods. Controlling input displacement of a new adaptive compliant gripper.This design of the gripper with embedded sensors as part of its structure.To build an effective prediction model of input displacement of gripper.The impact of the variation in the input parameters.

Nenad D. Pavlović

Papers

Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties

Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces

Development of a new type of passively adaptive compliant gripper

Adaptive control algorithm of flexible robotic gripper by extreme learning machine