Robotic end-effectors have been developed for various applications. Most of them are driven by electric or pneumatic actuator/actuators, which usually make the end-effector bulky and vulnerable due to the external cables and air tubes. In this study, we propose a novel passive robotic gripper with a locking mechanism that does not require any actuators. Locking and unlocking of the gripper fingers are performed through contact with external environment, such as ground, table, and conveyor. To facilitate gripper design, modeling of the deformed finger shape was conducted, and experimental validation was performed. A robotic gripper with eight such passive fingers were fabricated using 3D printer. Experiments were conducted to investigate the grasping capacities in terms of object size and weight. We found that the larger the object, the greater the weight capacity of the gripper, which increased significantly when the object exceeded a certain size. In addition, experiments on grasping various food products were carried out and results suggested that the proposed gripper could grasp objects with complex shapes and soft fragile properties, but damages were caused on very fragile objects due to the rigid structure of the gripper.

Soft robots have long been attractive to robotic engineers due to their remarkable dexterity; however, reports that standardize soft actuators into modularized off-shelf devices akin to rigid robots are still rare, and the mechanical efficiency of existing designs is still limited. This work identifies origami folding to enable the design of LEGO-like modularized soft actuators with high mechanical efficiency in terms of payload capability and workspace. Herein, three modularized origami actuators that can generate translational, bending, and twisting motion are designed, prototyped, and tested. The translational actuator can contract to 40% of its original length, and the twisting and bending actuators can exert 31° and 52° angular motions, respectively. The translational actuator can exert a blocked force of about 821 times self-weight. The motion of origami soft actuators is accurately modeled using rigid body kinematics, and complex systems built by them are captured by homogeneous transformation. Finally, the modularized design and efficient kinematic model are verified on a manipulator and a reconfigurable letter. Benefiting from the unprecedented modularity and mechanical efficiency, these LEGO-like origami actuators are promising for practical applications like food handling and healthcare.

Edge computing has become an alternative low-latency provision of cloud computing thanks to its close-proximity to the users, and the geo-distribution nature of edge servers enables the utilization green energy from the environment on-site. To pursue the goal of low-carbon edge computing, it is desirable to minimize the operational expenditure by scheduling the computing resource and green energy according to the spatially and temporally varying user demands. In this article, inspired by the successful application of deep reinforcement learning (DRL) in diverse domains, we propose a DRL-based edge computing management strategy which continuously explores the states and adaptively makes decisions on service management and energy scheduling, towards long-term cost minimization. Different from model-based solutions, our proposal is a model-free method, without any assumption on statistical knowledge as a priori, and therefore is practical in implementation. To speedup the agent training procedure, we further design a prioritized replay memory by utilizing the model-based solution as a guideline to set the transition priority. Extensive experiment results based on real-world traces validate that our proposed DRL-based strategy can make considerably progress compared to the one-shot greedy strategy, and it can learn the system dynamically to manage the edge computing services at runtime.

Soft actuators are compliant structures that are generally made of elastomers and generates large deformation. The behavior of these structures cannot be estimated accurately using infinitesimal strain theories. The objective of soft robotics applications is the controlled large deformation of these structures. In this article, we propose an analytical model for a pneu-net soft actuator. The model is based on the Euler–Bernoulli finite strain hyperelastic thin cantilever beam theory. The deformation of the air chambers is modeled using finite strain membrane theory. The analytical model is developed for two different states of the actuator: 1) free space; and 2) when the actuator was subjected to tip contact. The proposed theoretical model predicts the deformation and force characteristics of the actuator for the grasping state. The theoretical formulation of the developed model is different from previously developed infinitesimal strain models for the actuator, as it considers the axial stretch and forces applied to the actuator. In addition, it can be theoretically implemented on similar structured actuators for various applications. The theoretically calculated deformation and force characteristics of different actuators are compared with the finite element (FE) model and experimental characteristics. The results suggest that the proposed model can predict the actuator deformation and force characteristics as accurately as the FE model, but the computation time of the proposed model is less than 1% that of the FE model. The proposed model is further implemented on a three-finger gripper to predict the air pressure required for a stable grasp of different objects and is validated experimentally.

In this paper, we propose a simple analytical model for pneu-net soft actuator. The model is based on Euler– Bernoulli finite strain hyperelastic thin cantilever beam theory. The deformation of the air chambers is modelled using infinitesimal strain membrane theory. The proposed theoretical model estimates the deformation and force characteristics of the actuator. The developed model accounts the axial stretch and forces applied to the actuator. The theoretical deformation and force characteristics of different actuators are compared with finite element (FE) model and experimental characteristics. The theoretically estimated deformation and force of the actuator are similar to the FE model, but the theoretical model computation time is less than 1% of the FE model.

This paper presents a new approach for active tactile sensation that utilizes soft morphological computation. This work is inspired by human finger's wet-induced wrinkles, which appear after a long time soaking in water, and has been indicated as an efficient means for enhancement of gripping in wet environment. We created a tactile sensing system that is an integration of actuation (pneumatic actuator) and sensing elements (strain gauges). This device can change its morphology so that the posture of embedded sensing elements can vary, then generate different responses depending on the sensing tasks. As a result, this device can actively select its sensing functions depending on different sensing tasks. In this paper, the sensing device is both sensitive to indentation contact and sliding action on its surface by using only one type of strain gauge. This preliminary work is an example of soft morphological control in sensing, and expected to open a new trend in development of tactile sensing system.

This paper describes an active tactile sensing system that selects sensing modalities based on specific sensing tasks, by changing its morphology, called Wrin'Tac. This paper was inspired by the human finger wet-induced wrinkle, which is usually observed when one soaks in warm water for a period, and has been indicated as an efficient transformation for enhancement of gripping stability in a wet environment. We proposed a device which is an integration of actuation (pneumatic actuator) and sensing elements (strain gauges) inside a thin, multilayered substrate. Under pressurization, the morphology of the substrate surface (both geometrical and mechanical characteristics) change with appearance of wrinkles. Especially, by formation of wrinkles, this device can change its shape so that the posture of embedded sensing elements (strain gauges) can vary and generate different responses depending on external load conditions. As a result, this device can actively select its sensing functions depending on specific sensing tasks. First, we created a model to investigate the dynamic changes in a strain gauges' mechanical response under formation of wrinkles. Then, a prototype of this sensing device and its fabrication process were proposed to accomplish sensing tasks under vertical indentation and horizontal sliding action on its surface by using one type of strain gauge. This paper is an example of soft morphological control in tactile sensing, and is expected to open a new avenue to development of tactile sensing systems.

A novel three axis force sensor, based on magnetic flux measurements, was used in the fingers of a gripper. The force sensor uses three Hall Effect sensors orthogonally placed at the base of a hemisphere made of silicon rubber. A neodymium permanent magnet was inside the hemisphere. When a force was applied to the perimeter of hemisphere, it compressed the hemisphere displacing the magnet. This displacement caused change in the magnetic field around the Hall-effect sensors. By analysing these changes, we calculated the force in three directions using a lookup table. This sensor can be used in robot grippers to manipulate objects dexterously with tactile feedback. The cheap construction, robustness and reliability are few advantages of this sensor for it to be used in industrial applications. The sensor design, simulation and its characterization are presented in this work. Furthermore, as an application, a peg in a hole experiment was carried out to present the ability of the sensors to be used in robot grippers for manipulation tasks.

Materials and textures identification is a desired ability for robots. Developing such systems require tactile sensors that have enough sensitivity and spatial resolution, and the computational intelligence to meaningfully interpret sensor data. This paper introduces a texture classification algorithm utilizing support vector machine (SVM) classifier. Data taken from a novel three axis tactile sensor that utilize magnetic flux measurements for transduction was used to obtain the three dimensional tactile data. Frobenius norm calculated from the covariance matrix of the above data and the mean values of the three dimensional sensor data were used as features. Palpation velocity and small vertical load variances had minimum influence on the proposed algorithm. We have compared this algorithm with two other classification methods. They are: classify using the feature spatial period that is calculated from principal frequencies of the textures/material, and classify using neural network classifier with special properties of each material's tactile signals as features. For eight classes of material, the proposed algorithm performed faster and more accurately than the comparators when the scanning velocity and the vertical load varied.

We have proposed a Beam Bundle Model for modeling of a human fingertip during pushing and sliding action with friction, especially stick-to-slip transition, to overcome mentioned issues. In order to construct its three-dimensional non-homogeneous structure, we took sequence of magnetic resonant images, which bring consecutive cross-sectional layers of the human fingertip with distribution of skin, tissue, bone, and nail. Simulation results show a twofold aspect. Firstly, it can generate not only normal force distribution caused by pushing, but also response of friction force during sliding. Secondly, and more interestingly, the model dynamically produces localized displacement phenomenon on the contact area during stick-to-slip phase, which indicates how slippage erodes the contact area before the total slippage of the fingertip occurs. Finally, we investigated role of sliding mechanism acting on human fingertips' contact area in stable lifting of an object, in order to show the potential of the model in studying tactile mechanism of human and apply to robotic systems.

In modern clinical practices, accurate orientation for needle-like tools inserting into soft tissues is cumbersome, mainly due to the tissue's non-linear deformation and the complicated combination of forces between the tissue and the tool. In this paper, the interaction between tissue deformation and friction forces has been discussed. We consider the relative velocity and contact length as the main factors of friction force during tissue deforming. An available friction model has been built for dynamic needle insertion simulation based on Finite Element (FE) framework. A Local Constraint Method (LCM) is proposed to calculate the tissue deformation and apply the friction forces to the tissue frame for avoiding remeshing. In our approach a series of equivalent constraints and forces are generated by decomposing them inside Local Regions (LRs) to nodal points. Simulations based on this method to realize the dynamic needle insertion have been conducted for validation.

Rheological object, such as clay, various food products, biological organs and tissues, has both elastic and plastic properties. Modeling of such objects has not been studied intensively comparing with elastic objects. Previous rheological models were mostly based on linear constitutive law of stress and strain, which is limited to small deformation. In this paper, finite element (FE) model of rheological objects based on linear Cauchy strain tensor was summarized at first. Nonlinear FE model formulated with Green strain tensor was then presented for simulating large deformation and deformation with rotation motion. Instead of constant and symmetrical connection matrices in the linear model, nonlinear model yields non-constant and unsymmetrical connection matrices, which vary with displacements of nodal points. Simulation results with both linear and nonlinear FE models were presented to show the differences. We found that the nonlinear model formulated with Green strain tensor demonstrates more natural simulation behaviors.

Traditional Japanese sweets have long history and intense cultural background. In order to automate the manufacture process and also hand down the skillful techniques of sweets making, it is necessary to investigate the mathematical models of such objects and estimate their physical properties. This paper introduced a five-element physical model to describe the deformation behaviors of such materials. Using finite element (FE) method, a 2D/3D FE dynamic model was formulated to simulate arbitrary shaped objects. An approach for estimating physical parameters was then proposed based on FE simulation and nonlinear optimization. To capture both forces and deformation behaviors, two sets of parameters were identified and employed to simulate real Japanese sweets. A series of experimental results validated the FE model and property estimation method.

There are many kinds of deformable objects in our living life. Some of them exhibit rheological behaviors when they are subject to external force, such as human tissues, human organs, and food. If we want to simulate or control such behaviors, we have to know the physical parameters of the object in advance. In this paper, we propose an approach to identify these parameters based on 2D finite element (FE) simulation and measurement of deformation and force. At first, 2D FE model used to simulate rheological deformation was described. Then, identification method was presented according to the analysis of simulation results. Identification results for simulation were also given. Finally, this method was applied to a object made of clay. Deformation and force were measured by camera and tactile sensor respectively. The identification results show the validity and effectiveness of this method.

There are many kinds of deformable objects in our living life. Some of them such as human tissues, human organs, and food exhibit rheological behaviors when they are subject to external force. In surgery simulation and food engineering, we need to simulate or control such behaviors. In this paper, four-element model associated with finite element (FE) method was employed to model rheological deformation. This model can reach a good approximation of rheological force response when the object experience a standard strain input. An identification approach for estimating physical parameters of rheological deformation was presented based on 2D FE simulation and nonlinear optimization. This identification method aimed at minimizing the difference of force response between the simulation and experiment by using nonlinear least square method. Finally, experiments and identification results were given and both modeling and identification method were validated by comparing the results of simulation and experiments.