Soft Finger Research Articles

Sulfur‐ and Nitrogen‐Rich Porous π‐Conjugated COFs as Stable Electrode Materials for Electro‐Ionic Soft Actuators

AbstractAn unprecedented type of electronically conjugated porous covalent organic framework (COF) is synthesized for specific use in the electrode materials of electro‐ionic soft actuators. Because the basic structure contains most of the electron‐rich nucleophilic heteroatoms found in the periodic table with high percentages (13.84% for sulfur, 13.97% for nitrogen, and 19% for oxygen), adding only a small amount of these COFs (4.0 wt%) to poly(3,4‐ethylenedioxythiophene)‐poly(styrenesulfonate) (PEDOT‐PSS) increases the specific capacitance by 380%, resulting in a significant improvement of the actuation performance without any back‐relaxation. In comparison with pure PEDOT‐PSS electrodes, the COFs‐based actuators show a 280% shorter phase delay, 350% faster rise time, 290% higher bending displacement, 320% stronger blocking force, and ultralong durability up to 20 000 cycles of continuous actuation. These impressive improvements in performance enable the realization of a soft touch finger for confidently opening folders and swiping pages on a fragile smartphone device. While the application of these COFs in actuators can contribute to the progress of ionic soft actuators and realization of soft robotics, these COFs can be employed directly or modified for use in numerous fields, such as electro‐ or photocatalysts, sensors, and optoelectronics.

Design of a Tele-operable Soft Actuator

Communication delay between robotic control systems and the robotic actuator is a major issue since the soft robotic motion requires controls to be precise and have quick responses. This is also essential for real time control of robots where the user should be able to send and receive signals with minimum delay to emulate a real time control. Specifically, in soft robotics , the communication delay could become a bigger issue due to the fact that soft robots require mode of pressure exerted into the actuator to make any kind of motion. A delay in such instances could cause the whole system to malfunction and even could cause the actuator to explode in the case of rapid and subtle changes in internally applied pressure. In this research, available IoT (Internet of Things) related systems and models have been studied and suitable IoT system was designed. The main focus of this research was to study the communication delay in IoT embedded hand gesture controlled soft robot finger (actuator). Two separate data processing units have been introduced in the master side and in the slave side to rapid capturing and sending data. By using this approach, processing delay is expected to be minimized. Transmission delay, propagation delay and queuing delay are found to be handled through external factors. These factors could be changed by using various kinds of servers and service providers. Soft elastomer actuator was designed and developed using standard rapid prototyping and soft lithography techniques generally used in soft robotics. Testing, data collecting and analyzing the system are carried out at various locations in Sri lanka and finally the Round Trip Delay Time (RTD) for each case was compared.

Bone-Inspired Bending Soft Robot.

Bending soft robots must be structured and predictable to be used in applications such as a grasping hand. We developed soft robot fingers with embedded bones to improve the performance of a puppetry robot with haptic feedback. The manufacturing process for bone-inspired soft robots is described, and two mathematical models are reported: one to predict the stiffness and natural frequency of the robot finger and the other for trajectory planning. Experiments using different prototypes were used to set model parameters. The first model, which had a fourth-order lumped mass-spring-damper configuration, was able to predict the natural frequency of the soft robot with a maximum error of 18%. The model and the experimental data demonstrated that bone-inspired soft robots have higher natural frequency, lower phase shift, better controllability, and higher stiffness compared with traditional fiber-reinforced bending soft robots. We also showed that the dynamic performance of a bending soft robot is independent of whether water or air is used for the media and independent of the media pressure. Results from the second model showed that the path of a bone-inspired soft robot is a function of the relative lengths of the bone segments, which means that the model can be used to direct the design of the robot to achieve the desired trajectory. This model was able to correctly predict the trajectory path of the robot.

Soft Humanoid Hands with Large Grasping Force Enabled by Flexible Hybrid Pneumatic Actuators

Soft grippers and actuators have attracted increasing attention due to safer and more adaptable human-machine and environment-machine interactions than their rigid counterparts. In this study we present a novel soft humanoid hand that is capable of robustly grasping a variety of objects with different weights, sizes, shapes, textures, and stiffnesses. The soft hand fingers are made of flexible hybrid pneumatic actuators (FHPAs) designed based on a modular approach. A theoretical model is proposed to evaluate the bending deformation, grasping force, and loading capacity of the FHPAs, and the effects of various design parameters on the performance of the FHPA are investigated for optimizing the soft hands. This new FHPA achieves a balance of required flexibility and necessary stiffness, and the resulting soft humanoid hand has the merits of fast response, large grasping force, low cost, light weight, and ease of fabrication and repair, which shows promise for a variety of applications such as fruit picking, product packaging, and manipulation of fragile objects.

A Model-Based Sensor Fusion Approach for Force and Shape Estimation in Soft Robotics

In this paper, we address the challenge of sensor fusion in Soft Robotics for estimating forces and deformations. In the context of intrinsic sensing, we propose the use of a soft capacitive sensor to find a contact's location, and the use of pneumatic sensing to estimate the force intensity and the deformation. Using a FEM-based numerical approach, we integrate both sensing streams and model two Soft Robotics devices we have conceived. These devices are a Soft Pad and a Soft Finger. We show in an evaluation that external forces on the Soft Pad can be estimated and that the shape of the Soft Finger can be reconstructed.

Quasi-static analysis of planar sliding using friction patches

Flat objects lying on a surface are hard to grasp, but could be manipulated by sliding along the surface in a non-prehensile manner. This strategy is commonly employed by humans as pre-manipulation, for example to bring a cell phone to the edge of a table to pick it up. To endow robots with a similar capability, we introduce a mathematical model of planar sliding by means of a soft finger. The model reveals various aspects of interaction through frictional contacts, which can be used for planning and control. Specifically, using a quasi-static analysis we are able to derive a hybrid dynamical system to predict the motion of the object and the interaction forces. The conditions for which the object sticks to the friction patch, pivots, or completely slides against it are obtained. It is possible to find fixed points of the system and the path taken by the object to reach such configurations. Theoretical as well as comprehensive experimental results are presented.

Soft Robotic Finger Embedded with Visual Sensor for Bending Perception

SUMMARYThe various vision-based tactile sensors have been developed for robotic perception in recent years. In this paper, the novel soft robotic finger embedded with the visual sensor is proposed for perception. It consists of a colored soft inner chamber, an outer structure, and an endoscope camera. The bending perception algorithm based on image preprocessing and deep learning is proposed. The boundary of color regions and the position of marker dots are extracted from the inner chamber image and label image, respectively. Then the convolutional neural network with multi-task learning is trained to obtain bending states of the finger. Finally, the experiments are implemented to verify the effectiveness of the proposed method.

Design and analysis of a flexible robotic hand with soft fingers and a changeable palm

This study describes the design of a novel flexible robotic hand that can adapt its configurations to different grasping demands. Firstly, a mathematical model, based on the Yeoh strain energy function and virtual work principle, is established to investigate deformation properties of the designed soft finger. To achieve a flexible grasping capability, a changeable palm is presented with its variable configurations in terms of target objects with different sizes and shapes. A kinematic model of the flexible robotic hand is established, and then the numerical simulations based on the Monte-Carlo method and Matlab is applied to analyse the workspace of the hand and address the parameter optimisation problem of the rigid-flexible coupled system. Furthermore, an optimised grasping strategy on the basis of the principle of optimal efficiency is proposed to obtain an optimal grasping pose for the target object. Finally, a prototype is developed and tested in a laboratory to demonstrate the feasibility and effectiveness of our proposed hand. The results of practical experiments show that the robotic hand cannot only stably grasp objects with different sizes and shapes but also flexibly manipulate soft and fragile ones.

Soft Robotic Gripper Driven by Flexible Shafts for Simultaneous Grasping and In-Hand Cap Manipulation

Performing a successful robotic grasping to uncertain objects in unstructured environments is challenging. This study presents a new compliant soft robotic gripper for objects handling and cap manipulation through the coordination of three soft fingers and in-hand manipulation. The experiments are conducted to validate that the soft robotic gripper can successfully realize simultaneous grasping and capping manipulations with only one flexible shaft actuation for every single soft finger. Note to Practitioners —Uncertain object manipulation tasks pose significant challenges to a robotic gripper while grasping and capping unknown objects without damaging them. The existing rigid grippers have experienced flexible manipulation through multiple degrees of freedom (DoFs) by complex mechanical structures, and the soft gripper can realize stiffness-compliant manipulation differently. The proposed novel robotic in-hand manipulation can execute grasping and cap manipulation by a single flexible shaft to simultaneously achieve bending and rotational movements. The relationship between stretching force and finger’s curvature can enable a custom design for user-specific applications.

Negshell casting: 3D-printed structured and sacrificial cores for soft robot fabrication.

Soft robot fabrication by casting liquid elastomer often requires multiple steps of casting or skillful manual labor. We present a novel soft robotic fabrication technique: negshell casting (negative-space eggshell casting), that reduces the steps required for fabrication by introducing 3D-printed thin-walled cores for use in casting that are meant to be left in place instead of being removed later in the fabrication process. Negshell casting consists of two types of cores: sacrificial cores (negshell cores) and structural cores. Negshell cores are designed to be broken into small pieces that have little effect on the mechanical structure of the soft robot, and can be used for creating fluidic channels and bellows for actuation. Structural cores, on the other hand, are not meant to be broken, and are for increasing the stiffness of soft robotic structures, such as endoskeletons. We describe the design and fabrication concepts for both types of cores and report the mechanical characterization of the cores embedded in silicone rubber specimens. We also present an example use-case of negshell casting for a single joint soft robotic finger, along with an experiment to demonstrate how negshell casting concepts can aid in force transmission. Finally, we present real-world usage of negshell casting in a 6 degree-of-freedom three-finger soft robotic gripper, and a demonstration of the gripper in a robotic pick-and-place task. A companion website with further details about fabrication (as well as an introduction to molding and casting for those who are unfamiliar with the terms), engineering file downloads, and experimental data is provided at https://negshell.github.io/.

Topology optimization of a cable-driven soft robotic gripper

Improving the functionality of soft continuum manipulators to expand their application space has always been an important development direction for soft robotics. It remains very challenging to calculate the deformations of soft materials and predict the basic structure of soft fingers under complex objective functions and constraints. This work develops a cable-driven soft robotic gripper with multi-input and multi-output using topology optimization considering geometric nonlinearity, which not only performs adaptive grasping but also enables finer manipulations such as rotating or panning the target. A scheme that can describe adaptive grasping behavior is proposed, which converts the contact between the clamping surface and the object into a boundary condition to circumvent complex contact nonlinearities. An additive hyperelasticity technique is used to overcome numerical instabilities, and the finite element analysis is performed in ANSYS. Numerical simulations and experimental results are performed to demonstrate the effectiveness of the optimization algorithm and to illustrate the application potential of the proposed gripper.

3D printed, modularized rigid-flexible integrated soft finger actuators for anthropomorphic hands

Owing to the integrated muscular, ligamentous and skeletal structures and coupled degrees of freedom (DoFs), it is a long-term challenge in the field of robotics to design an anthropomorphic hand that mimics the biological structures and dexterous motions of human hands. In this paper, we present pneumatical, multi-material 3D-printed, modularized rigid-flexible integrated soft finger actuators (RFiSFAs) that can be directly assembled to an anthropomorphic hand. First, we introduce the mechanism of the RFiSFA with a pneumatic bellow chamber and a joint structure, and investigate the influence of the chamber material and the bellow number on the flexion angles and output forces performances of the RFiSFA. Next, we design and fabricate a 2-DoF flexion finger with two serial RFiSFAs and a 3-DoF thumb with two serial RFiSFAs and two parallel RFiSFAs. Then, we perform tests to characterize the motion and force performance of the fingers and thumb. Finally, we integrate and assemble an 11-DoF anthropomorphic hand with four flexion fingers and one thumb, and experimental results demonstrate the capability of the hand in grasping objects with different dimensions, shapes and textures.

Dietary management of food protein–induced enterocolitis syndrome during the coronavirus disease 2019 pandemic

As communities struggle to adapt to life under the threat of the global pandemic, COVID-19, those living with Food Protein-Induced Enterocolitis Syndrome (FPIES) must adapt with additional difficulties. Social distancing and shelter-in-place strategies have been implemented, resulting in fewer supermarkets trips, stockpile-purchasing behaviors in up to 74.5% of those surveyed(1), and shortages of staple food items all with potential impact on the availability of foods for those on limited diets. Concern about allergic reactions make exploring alternative or new ingredients undesirable or untenable. Remaining safe at home is important to avoid trips to the emergency department where families may be exposed to the COVID-19 virus and medical attention can be limited due to the burden on global health systems. Parents of children with FPIES are also understandably concerned about meeting their child’s nutritional needs during these times of sheltering-in-place. Now more than ever, advice on what foods to serve and when to serve them is critically important.

Soft fiber-reinforced bending finger with three chambers actuated by ECF (electro-conjugate fluid) pumps

Fluidic elastomer actuators (FEAs) have been used for various purposes such as grippers, soft robots, artificial muscles etc. A major challenge for the FEAs is their big and cumbersome power sources, which include valves, regulators, hydraulic pumps and air cylinders. Therefore, we integrated an ECF jet generator (a hydraulic pressure source) into a soft fiber-reinforced bending tube with three chambers. ECF (electro-conjugate fluid) is a kind of functional and dielectric fluid, which can generate a strong and active ECF jet when a high DC voltage is applied to its corresponding anode & cathode in ECF. The ECF jet generator has three ECF pumps with three pairs of electrodes, namely, needle-ring electrodes, which are manufactured by machining. The flexible tube consists of three soft chambers fabricated by replicating the molds. The principle is that one chamber (two chambers) of the soft finger expands as the inner pressure is generated by the ECF pump and two other chambers (one chamber), acting as an ECF container, shrinks, which finally causes the finger to bend. We introduce two types of commonly used ECFs and investigate their impact on the soft fiber-reinforced bending finger. Then, we numerically simulate the pressure generated by the ECF generator and the displacement of the bending finger, which are validated by the experiments. Finally, we research the dynamic performance of the bending finger. The work contributes to the miniaturization of power sources in the field of soft robots with spatial motions and is also helpful for the development of other ECF-based soft robots.

A soft robotic finger with self-powered triboelectric curvature sensor based on multi-material 3D printing

Abstract Significant advances in the area of additive manufacturing have provided a novel fabrication method to develop soft robots with self-powered sensors. In this paper, multi-material 3D printing is used to directly print a soft robotic finger with a triboelectric curvature sensor. The reinforced soft finger with a single-electrode triboelectric curvature sensor (RSF-S-TECS) combines the advantages of multi-material 3D printing and stretchable electrodes to achieve fast prototyping and easy system integration. The triboelectric curvature sensor is located on the top of the finger, where one active layer of the S-TECS is directly printed on the upper surface of the reinforced finger body by multi-material 3D printing, and another active layer is made of PDMS and attached to a stretchable electrode. The S-TECS behavior is evaluated for different surface configurations, applied forces and operational frequency using an automated setup. The integrated S-TECS can measure a finger curvature up to 8.2 m-1 under an ultra-low working frequency of 0.06 Hz, proving the effectiveness of the proposed S-TECS as a self-powered curvature sensor. This work presents a novel design and fabrication method of S-TECS for its potential applications in multi-material 3D printed soft robotics.

Fabrication and study of miniaturized soft pneumatic fingers

In this study, a miniaturized soft pneumatic finger was proposed and successfully fabricated. The finger structure comprised an extensible Ecoflex finger, inextensible polydimethylsiloxane (PDMS) layer, and air channel with three expandable chambers. The extensible Ecoflex finger was molded using a 3D-printed mold and bonded with the inextensible PDMS layer to obtain a finger structure. The inextensible PDMS layer was used to enhance finger stiffness. To study finger properties, inextensible PDMS layers with different thicknesses were bonded with the extensible Ecoflex finger, and various sizes of expandable chambers in the extensible Ecoflex finger were developed. Miniaturized soft pneumatic fingers with a 0.65 mm-thick inextensible PDMS layer can exert 14.90 mN of force and achieve a bend angle of 56° under an applied pressure of 16 kPa inside the air channel. Finally, fingers were integrated as to create a small gripper to grab a salmon egg without causing any damage.

Modeling and analysis of soft robotic fingers using the fin ray effect

Soft grasping of random objects in unstructured environments has been a research topic of predilection both in academia and in industry because of its complexity but great practical relevance. However, accurate modeling of soft hands and fingers has proven a difficult challenge to tackle. Focusing on this issue, this article presents a detailed mathematical modeling and performance analysis of parallel grippers equipped with soft fingers taking advantage of the fin ray effect (FRE). The FRE, based on biomimetic principles, is most commonly found in the design of grasping soft fingers, but despite their popularity, finding a convenient model to assess the grasp capabilities of these fingers is challenging. This article aims at solving this issue by providing an analytic tool to better understand and ultimately design this type of soft fingers. First, a kinetostatic model of a general multi-crossbeam finger is established. This model will allow for a fast yet accurate estimation of the contact forces generated when the fingers grasp an arbitrarily shaped object. The obtained mathematical model will be subsequently validated by numerically to ensure the estimations of the overall grasp strength and individual contact forces are indeed accurate. Physical experiments conducted with 3D-printed fingers of the most common architecture of FRE fingers will also be presented and shown to support the proposed model. Finally, the impact of the relative stiffness between different areas of the fingers will be evaluated to provide insight into further refinement and optimization of these fingers.

Rigid-Soft Interactive Learning for Robust Grasping

Inspired by widely used soft fingers on grasping, we propose a method of rigid-soft interactive learning, aiming at reducing the time of data collection. In this paper, we classify the interaction categories into Rigid-Rigid, Rigid-Soft, Soft-Rigid according to the interaction surface between grippers and target objects. We find experimental evidence that the interaction types between grippers and target objects play an essential role in the learning methods. We use soft, stuffed toys for training, instead of everyday objects, to reduce the integration complexity and computational burden and exploit such rigid-soft interaction by changing the gripper fingers to the soft ones when dealing with rigid, daily-life items such as the Yale-CMU-Berkeley (YCB) objects. With a small data collection of 5K picking attempts in total, our results suggest that such Rigid-Soft and Soft-Rigid interactions are transferable. Moreover, the combination of different grasp types shows better performance on the grasping test. We achieve the best grasping performance at 97.5\% for easy YCB objects and 81.3\% for difficult YCB objects while using a precise grasp with a two-soft-finger gripper to collect training data and power grasp with a four-soft-finger gripper to test.

Real-Time Soft Body 3D Proprioception via Deep Vision-Based Sensing

Soft bodies made from flexible and deformable materials are popular in many robotics applications, but their proprioceptive sensing has been a long-standing challenge. In other words, there has hardly been a method to measure and model the high-dimensional 3D shapes of soft bodies with internal sensors. We propose a framework to measure the high-resolution 3D shapes of soft bodies in real-time with embedded cameras. The cameras capture visual patterns inside a soft body, and a convolutional neural network (CNN) produces a latent code representing the deformation state, which can then be used to reconstruct the body's 3D shape using another neural network. We test the framework on various soft bodies, such as a Baymax-shaped toy, a latex balloon, and some soft robot fingers, and achieve real-time computation ( $\leq$ 2.5 ms/frame) for robust shape estimation with high precision ( $\leq$ 1% relative error) and high resolution. We believe the method could be applied to soft robotics and human-robot interaction for proprioceptive shape sensing. Our code is available at: https://ai4ce.github.io/DeepSoRo/ .

Laser-Induced Graphene for Electrothermally Controlled, Mechanically Guided, 3D Assembly and Human-Soft Actuators Interaction.

Mechanically guided, 3D assembly has attracted broad interests, owing to its compatibility with planar fabrication techniques and applicability to a diversity of geometries and length scales. Its further development requires the capability of on-demand reversible shape reconfigurations, desirable for many emerging applications (e.g., responsive metamaterials, soft robotics). Here, the design, fabrication, and modeling of soft electrothermal actuators based on laser-induced graphene (LIG) are reported and their applications in mechanically guided 3D assembly and human-soft actuators interaction are explored. Over 20 complex 3D architectures are fabricated, including reconfigurable structures that can reshape among three distinct geometries. Also, the structures capable of maintaining 3D shapes at room temperature without the need for any actuation are realized by fabricating LIG actuators at an elevated temperature. Finite element analysis can quantitatively capture key aspects that govern electrothermally controlled shape transformations, thereby providing a reliable tool for rapid design optimization. Furthermore, their applications are explored in human-soft actuators interaction, including elastic metamaterials with human gesture-controlled bandgap behaviors and soft robotic fingers which can measure electrocardiogram from humans in an on-demand fashion. Other demonstrations include artificial muscles, which can lift masses that are about 110 times of their weights and biomimetic frog tongues which can prey insects.