Robotic Finger Research Articles

Integration of an Adaptively Reconfigurable Compliant Anatomical Palmar Mechanism with Multi-DOF Robotic Finger Grasping

The primary focus of robotic hand design has been placed on integrating a series of rotational joints at each finger. The paper presents the design of a compliant anatomical palmar mechanism (CAPM) that augments the robotic hand's capabilities by adapting between precision and power grasps. A design of this robotic hand has been developed based on the anatomical inspiration of the human hand to find a direct relationship between palmar deformation and the rotation of the fingers at a shared joint. Palmar deformation has an effect on the orientation and position of the fingertip's location when contacting a target object. Previously determined simulation results of this geometrically complex, large displacement, hyper-elastic structure are compared to initial experimental results to show simultaneous varied conformity of the palm.

Highly Stretchable Additively Manufactured Capacitive Proximity and Tactile Sensors for Soft Robotic Systems

Soft robotics are considered one of the most promising approaches towards fully collaborative robotic devices. Soft robotic systems are intrinsically safe due to their compliant nature. Using additive fabrication for the development of soft capacitive sensors, enables high flexibility and individualization capabilities in terms of design and material properties. In this work, we outline the sensorization of stretchable capacitive sensors for proximity and tactile detection. This sensor will represent an essential component of a soft robotic finger in future work. While the functional principle has already been shown previously, here we explore the design space further and perform more in depth test of the influences of the strain on the tactile measurements. The capacitive pad’s sensitivity is evaluated by applying normal force on each sensor, where the applied force is ranged between 1N and 16N, with resultant capacitive change of the four tactile sensors between 1 pF and 60 pF. The capacitive pad’s proximity sensor is tested using five different materials, with proximity range of 20mm, with change in capacitance between 0.1 pF and 2.4 pF. An essential aspect of soft robotics is that also the sensor may be subject to substantial deformations. We demonstrate that our proposed sensor design can withstand large strains of more than 35% while the sensitivity towards sensing normal forces does not significantly change. This demonstrates that the proposed sensor design will be suitable for various soft robotics applications, where large deformations may occur.

Cocklebur-inspired “branch-seed-spininess” 3D hierarchical structure bionic electronic skin for intelligent perception

Traditional electronic skin (e-skin) with single micro-nano structures has a limited capacity to optimize the comprehensive sensing properties, which greatly hinders the intellectualization process. Herein, cocklebur-inspired bionic e-skin consisting of the “branch-seed-spininess” three-dimensional hierarchical structure, namely the P(VDF-TrFE) fiber-TiO2 pillar-TiO2 thorn structure (FPTS), is proposed. Benefiting from the hierarchical characteristics of the FPTS, the e-skin exhibits a high sensitivity of 0.163 kPa−1 (<20 kPa), wide pressure sensing range of 0–200 kPa, and ultrafast response/relaxation time of < 5.6 ms. In light of these salient features, versatile intelligent perception applications are explored. First, robot fingers are equipped with the FPTS e-skins and realize the traditional Chinese medicine pulse diagnosis of the radial artery in different positions and pressing degrees. Second, a flexible perception array with 100 FPTS e-skin units enables the spatial pressure mapping of flat/curved surfaces and the perception of different Braille characters. Most importantly, by making full use of the FPTS e-skin with a five-layer deep neural network, an intelligent gesture perception system is developed for accurate recognition (average accuracy: 98.33%) of sign language and precise control of the robot hand, which is of great significance for the development of intelligent sign language translation in artificial intelligence.

Fingerprint-inspired surface texture for the enhanced tip pinch performance of a soft robotic hand in lubricated conditions

The core capabilities of soft grippers/soft robotic hands are grasping and manipulation. At present, most related research often improves the grasping and manipulation performance by structural design. When soft grippers rely on compressive force and friction to achieve grasping, the influence of the surface microstructure is also significant. Three types of fingerprint-inspired textures with relatively regular patterns were prepared on a silicone rubber surface via mold casting by imitating the three basic shapes of fingerprint patterns (i.e., whorls, loops, and arches). Tribological experiments and tip pinch tests were performed using fingerprint-like silicone rubber films rubbing against glass in dry and lubricated conditions to examine their performance. In addition to the textured surface, a smooth silicone rubber surface was used as a control. The results indicated that the coefficient of friction (COF) of the smooth surface was much higher than that of films with fingerprint-like textures in dry and water-lubricated conditions. The surface with fingerprint-inspired textures achieved a higher COF in oil-lubricated conditions. Adding the fingerprint-like films to the soft robotic fingers improved the tip pinch gripping performance of the soft robotic hand in lubricated conditions. This study demonstrated that the surface texture design provided an effective method for regulating the grasping capability of humanoid robotic hands.

Development and evaluation of a robust soft robotic gripper for apple harvesting

Fruit harvesting is facing challenges due to the labour shortage, which has been more severe since the rapid pandemic. Robotic harvesting has been attempted in autonomous fruit harvesting tasks, such as apple harvesting. However, current apple harvesting robots show limited harvesting performance in the orchard environment due to the inefficiency of the robotic grippers. This research presents a fruit harvesting method that includes a novel soft robotic gripper and a detachment strategy to achieve apple harvesting in the natural orchard. The soft robotic gripper includes four tapered soft robotic fingers (SRF) and one multi-mode suction cup. The SRF is customised to avoid interference with obstacles during grasping, and its compliance and force exertion are comprehensively evaluated with FEA and experiments. The multi-mode suction cup can provide suction adhesion force, show active extrusion/withdrawal, and present passive compliance mode. The simultaneously twist-pulling motion is finally proposed and implemented to detach the apples from the trees. The proposed robotic gripper is compact, compliant with apple grasping and generates a large grasping force. Our proposed method is finally validated in a natural orchard and achieves a detachment, damage and harvesting rate of 75.6%, 4.55%, and 70.77%, respectively.

A soft supernumerary hand for rehabilitation in sub-acute stroke: a pilot study

In patients with subacute stroke, task specific training (TST) has been shown to accelerate functional recovery of the upper limb. However, many patients do not have sufficient active extension of the fingers to perform this treatment. In these patients, here we propose a new rehabilitation technique in which TST is performed through a soft robotic hand (SoftHand-X). In short, the extension of the robotic fingers is controlled by the patient through his residual, albeit minimal, active extension of the fingers or wrist, while the patient was required to relax the muscles to achieve full flexion of the robotic fingers. TST with SoftHand-X was attempted in 27 subacute stroke patients unable to perform TST due to insufficient active extension of the fingers. Four patients (14.8%) were able to perform the proposed treatment (10 daily sessions of 60 min each). They reported an excellent level of participation. After the treatment, both clinical score of spasticity and its electromyographic correlate (stretch reflex) decreased. In subacute stroke patients, TST using SoftHand-X is a well-accepted treatment, resulting in a decrease of spasticity. At present, it can be applied only in a small proportion of the patients who cannot perform conventional TST, though extensions are possible.

One-shot additive manufacturing of robotic finger with embedded sensing and actuation

A main challenge in the additive manufacturing (AM) field is the possibility to create structures with embedded actuators and sensors: addressing this requirement would lead to a reduction of manual assembly tasks and product cost, pushing AM technologies into a new dimension for the fabrication of assembly-free smart objects. The main novelty of the present paper is the one shot fabrication of a 3D printed soft finger with an embedded shape memory alloy (SMA) actuator and two different 3D printed sensors (strain gauge and capacitive force sensor). 3D printed structures, fabricated with the proposed approach, can be immediately activated after their removal from the build plate, providing real-time feedback because of the embedded sensing units. Three different materials from two nozzles were extruded to fabricate the passive elements and sensing units of the proposed bioinspired robotic finger and a custom-made Cartesian pick and place robot (CPPR) was employed to integrate the SMA spring actuator into the 3D printed robotic finger during the fabrication processes. Another novelty of the present paper is the direct integration of SMA actuators during the 3D printing process. The low melting thermoplastic polycaprolactone (PCL) was extruded: its printing temperature of 70 °C is lower than the SMA austenitic start temperature, preventing the SMA activation during the manufacturing process. Two different sensors based on the piezoresistive principle and capacitive principle were studied, 3D printed and characterized, showing respectively a sensitivity ratio of change in resistance to finger bending angle to be 674.8 frac{Omega }{^circ mathrm{Angle}} and a capacitance to force ratio of 0.53 frac{mathrm{pF}}{mathrm{N}}. The proposed manufacturing approach paves the way for significant advancement of AM technologies in the field of smart structures with embedded actuators to provide real-time feedback, offering several advantages, especially in the soft robotics domain.

Design of Hybrid Fully Actuated and Self-Adaptive Mechanism for Anthropomorphic Robotic Finger

Abstract Prior research on robotic hands predominantly focused on high degrees-of-freedom of fully actuated fingers to replicate a natural human hand or on creative designs of underactuated fingers to make a self-adaptive motion. However, in most cases, fully actuated fingers encounter difficulty in grasping unstructured objects, while underactuated fingers experience problems in performing precise grasping motions. To deal with any possible scenarios, this study presents a novel design of an anthropomorphic robotic finger that combines both advantages—fully actuated and self-adaptive (FASA) modes—at once. Actuated by tendons, the FASA finger can grasp objects adaptively and achieve accurate angle positioning with the same mechanical design. Based on the kinetostatic analysis, the guideline for selecting a torsion spring is proposed to fulfill the functions of the FASA finger and attain the optimal design of torsional stiffness, which manifests itself in a series of tests on different configurations of torsion spring. Likewise, the kinematic analysis for the fully actuated mode is given proof that two joints can move independently by controlling two motors. Ultimately, experimental results reflected the capability of the FASA finger to perform not only independent precision angle motion but also self-adaptive grasping motion without any change in mechanical structure.

Design and Experimental Research of Robot Finger Sliding Tactile Sensor Based on FBG.

Aiming at the problem of flexible sliding tactile sensing for the actual grasp of intelligent robot fingers, a double-layer sliding tactile sensor based on fiber Bragg grating (FBG) for robot fingers is proposed in this paper. Firstly, the optimal embedding depth range of FBG in the elastic matrix of polydimethylsiloxane (PDMS) was determined through finite element analysis and static detection experiments of finger tactile sensing. Secondly, the sensor structure is optimized and designed through the simulation and dynamic experiments of sliding sensing to determine the final array structure. Thirdly, the sensing array is actually pasted on the surface of the robot finger and the sensing characteristics testing platform is built to test and analyze the basic performance of the sliding tactile sensor. Then, the sensor array is actually attached to the finger surface of the robot and the sensing characteristics testing platform is built to experiment and analyze the basic performance of the sliding tactile sensor. Finally, a sliding tactile sensing experiment of robot finger grasping is conducted. The experimental results show that the sliding tactile sensor designed in this paper has good repeatability and creep resistance, with sensitivities of 12.4 pm/N, 11.6 pm/N, and 14.5 pm/N, respectively, and the overall deviation is controlled within 5 pm. Meanwhile, it can effectively sense the signals of the robot fingers during static contact and sliding. The sensor has a high degree of fit with the robot finger structure, and has certain application value for the perception of sliding tactile signals in the object grasping of intelligent robot objects.

A Biomimetic Soft‐Rigid Hybrid Finger with Autonomous Lateral Stiffness Enhancement

The application of soft robotics improves the compliance of robotic fingers, which, however, usually loses sufficient stiffness. Here, a biomimetic soft‐rigid hybrid (BSH) finger that can achieve both inherent compliance and autonomous lateral stiffness enhancement is proposed. Driven by pneumatic artificial muscles, the proposed two‐joined BSH finger can produce two flexion degrees of freedom (DOFs) and one lateral DOF motion. Imitating the human finger anatomy, the BSH finger is designed with elliptical bony ridges and eccentrically arranged ligaments. By leveraging the contact interference of the ridges and tensioning of the ligaments, the lateral stiffness of the BSH finger can be autonomously enhanced through finger flexion. During its natural stage, the lateral stiffness is relatively low to ensure mobility and compliance, while in its flexion stage, its lateral stiffness can increase to resist lateral deflection and high payload. To further investigate the advantages of the design, four BSH fingers are assembled into a robotic hand prototype with three grasping types including enveloping grasp, precise pinch, and power grasp. Experimental results demonstrate that the robotic hand prototype is capable of grasping various objects with a wide range of diameters, lengths, thicknesses, and weights, which will verify the effectiveness of the design methodology.

Double-Acting Soft Actuator for Soft Robotic Hand: A Bellow Pumping and Contraction Approach.

When compressing a soft bellow, the bellow will contract and pump out the fluid inside the bellow. Utilizing this property, we propose a novel actuation method called compressing bellow actuation (CBA), which can output fluidic power and tendon-driven force simultaneously. Based on the CBA method, a double-acting soft actuator (DASA) combining fluidic elastomer actuator (FEA) and tendon-driven metacarpophalangeal (MCP) joint is proposed for robotic finger design. The proposed DASA exhibits both compliance and adaptiveness of FEAs, and controllability and large output force of the tendon-driven methods. The fluid in the bellow can be either air or water or even integration of the two, thus constituting three different actuation modes. Mathematical modeling of the relationship between bellow compression displacement and DASA’s bending angle is developed. Furthermore, experimental characterizations of DASA’s bending angle and blocking force are conducted at different actuation modes. The double-acting method can availably promote the bending angle of an FEA by up to 155%, and the blocking force by up to 132% when the FEA is water-filled. A soft robotic hand with a forearm prototype based on the DASA fingers is fabricated for the demonstration of finger motion and gripping applications.

An Educational Test Rig for Kinesthetic Learning of Mechanisms for Underactuated Robotic Hands

Teaching robotics requires interdisciplinary skills and a good creativity, providing instructions and hands-on experiences, exploiting different kinds of learning. Two kinds of learning methods are commonly used: the ‘visual learning’ and the ‘auditory learning’, recognizable by the preference of an approach for images, rather than for texts, or oral explanations. A third possible learning style is the ‘kinesthetic learning’, based on tactile activities, which is generally least exploited, both by teachers in the classroom and by students during individual study. In this perspective, the use of educational test rigs is a good practice and adds an opportunity to share a passion for robotics. The paper focuses on the realization and application of an educational test rig aimed at explaining how a differential mechanism works and how it can be applied to robotic underactuated soft grippers to move multiple robotic fingers independently of each other using just a single actuator. The differential test bench was realized by 3D printing and mounted with the help of students in high school seminaries oriented to encourage students towards robotic or mechatronic studies. This activity was very thrilling for the students and helped them to approach robotics in a natural way, exploiting kinesthetic learning as it is demonstrated by test results.

Proprioception and Exteroception of a Soft Robotic Finger Using Neuromorphic Vision-Based Sensing.

Equipping soft robotic grippers with sensing and perception capabilities faces significant challenges due to their high compliance and flexibility, limiting their ability to successfully interact with the environment. In this work, we propose a sensorized soft robotic finger with embedded marker pattern that integrates a high-speed neuromorphic event-based camera to enable finger proprioception and exteroception. A learning-based approach involving a convolutional neural network is developed to process event-based heat maps and achieve specific sensing tasks. The feasibility of the sensing approach for proprioception is demonstrated by showing its ability to predict the two-dimensional deformation of three points located on the finger structure, whereas the exteroception capability is assessed in a slip detection task that can classify slip heat maps at a temporal resolution of 2 ms. Our results show that our proposed approach can enable complete sensorization of the finger for both proprioception and exteroception using a single camera without negatively affecting the finger compliance. Using such sensorized finger in robotic grippers may provide safe, adaptive, and precise grasping for handling a wide category of objects.

Robotic Fingers in Reach-to-Grasp Tasks of Rehabilitation

The REHAROB robotic upper limb rehabilitation system was improved with a custom-designed and developed hand/finger therapy module. The new module extends the scope of the applicable motion therapy from passive to active reach-to-grasp activities of daily living tasks, and the range of treated anatomical joints was also extended to every proximal and distal upper limb anatomical joint. Finger exercising and object grasping are supported with a pair of two degree-of-freedom (DOF) robotic fingers. One of the robotic fingers moves the index/middle/ring fingers together, whereas the other robotic finger moves the thumb. A novel hypothesis was established, analyzed, and tested for setting the orientation of the robotic finger moving the thumb. The robotic thumb is not aligned with the patient's thumb; its orientation is optimized in the patient's hand reference system to maximize the efficiency in the opposite grasping task. While most concurrent systems utilize virtual objects for grasping tasks, the REHAROB system exercises five carefully selected reach-and-grasp type activities of daily living (ADL) with real objects. Actuating the human finger phalanges through custom development finger orthoses is described. An advanced feature of the hand/finger therapy module is the left-right hand side changeover by only alternating the orientation of the robotic fingers and exchanging the finger orthoses.

Reconfigurable Soft Robots by Building Blocks.

Soft robots are of increasing interest as they can cope with challenges that are poorly addressed by conventional rigid-body robots (e.g., limited flexibility). However, due to their flexible nature, the soft robots can be particularly prone to exploit modular designs for enhancing their reconfigurability, that is, a concept which, to date, has not been explored. Therefore, this paper presents a design of soft building blocks that can be disassembled and reconfigured to build different modular configurations of soft robots such as robotic fingers and continuum robots. First, a numerical model is developed for the constitutive building block allowing to understand their behavior versus design parameters, then a shape optimization algorithm is developed to permit the construction of different types of soft robots based on these soft building blocks. To validate the approach, 2D and 3D case studies of bio-inspired designs are demonstrated: first, soft fingers are introduced as a case study for grasping complex and delicate objects. Second, an elephant trunk is used for grasping a flower. Third, a walking legged robot. These case studies prove that the proposed modular building approach makes it easier to build and reconfigure different types of soft robots with multiple complexshapes.

Myoelectric Control of a Biomimetic Robotic Hand Using Deep Learning Artificial Neural Network for Gesture Classification

In this article, an effective control system based on a deep learning (DL) algorithm is designed to administer and actuate an in-house 3-D printed robotic hand using myoelectric sensors. Five servo motors are utilized to actuate the robotic hand’s components with 17 degrees of freedom (DoFs). This research investigated using a minimum number of myoelectric sensors to actuate robotic hands and fingers. A convolutional neural network (CNN) is also employed to recognize hand and finger movements’ patterns, and classify gestures. Experiments are conducted in the presence of one to three sensors on the forearm and verified by following both moving and stationary reference hands. Applying various configurations, it is found that at least three sensors are required to move all fingers as well as perform open and close postures. Considering pure data collected by sensors leads to unintended shakes in the fingers due to the fact that a large amount of input data is translated to motor rotation instantaneously. This challenge is overcome by applying a CNN to classify the hand gestures with high accuracy. The presented design is an inexpensive, simple, and easy-to-use robotic hand both in fabrication and classification, which can be used by everyone, particularly resource-poor amputees. Experimental results confirm the effectiveness of the robotic hand for use in below-elbow amputations, such as wrist disarticulation and transradial amputation.

Fully Passive Robotic Finger for Human-Inspired Adaptive Grasping in Environmental Constraints

This article presents an adaptive finger mechanism for grasping objects in an environmental constraint. The proposed finger is designed such that it can be easily installed to a parallel gripper, and hence, operated using a single linear actuation system. The parallel gripper equipped with the proposed finger mechanism features capabilities of self-adaptation to various object shapes and interaction with environmental constraints. Besides, based on the interaction capability with environments, robotic grasping strategies inspired by human grasping approaches such as adaptive pinching and scooping are achieved. In particular, two types of scooping approaches corresponding to the symmetric and asymmetric configurations of the gripper are introduced. In this regard, Hart’s linkage mechanism coupled with a parallelogram is applied to the proposed finger mechanism to synthesize the vertical translation for finger compliance as well as the unique transition motion between the pinching and scooping poses of the fingertip. These two degrees of freedom are entirely passive and can adapt to unknown objects or environmental constraints. The operating principle, limitations, and design guidelines for the proposed finger mechanism are analyzed through kinematics and statics. The fully prototyped two-finger gripper is evaluated through several demonstration scenarios to verify human-inspired grasping with objects of arbitrary shapes in environmental constraints.

Finger-STS: Combined Proximity and Tactile Sensing for Robotic Manipulation

This paper introduces and develops novel touch sensing technologies that enable robots to better sense and react to to intermittent contact interactions. We present Finger-STS, a robotic finger embodiment of the See-Through-your-Skin (STS) sensor that can capture 1) an “in the hand” visual perspective of an object that is being manipulated and 2) a high resolution tactile imprint of the contact geometry. We demonstrate the value of the sensor on a Bead Maze task. Here the multimodal feedback provided by the Finger-STS is leveraged by a robot to locate a bead visually and to guide it across a wire in response to tactile cues, with no additional sensing or planning required. To achieve this, we introduce a set of relevant visuotactile operations using computer vision-based algorithms. In particular, we sense the proximity of the object relative to the sensor as well as the nature of contact as a high resolution stick/slip vector field tracking the object motion in the finger.

Soft Conductive Hydrogel-Based Electronic Skin for Robot Finger Grasping Manipulation.

Electronic skin with human-like sensory capabilities has been widely applied to artificial intelligence, biomedical engineering, and the prosthetic hand for expanding the sensing ability of robots. Robotic electronic skin (RES) based on conductive hydrogel is developed to collect strain and pressure data for improving the grasping capability of the robot finger. RES is fabricated and assembled by the soft functional materials through a sol–gel process for guaranteeing the overall softness. The strain sensor based on piezoresistive hydrogel (gauge factor ~9.98) is integrated onto the back surface of the robot finger to collect the bending angle of the robot finger. The capacitive pressure sensor based on a hydrogel electrode (sensitivity: 0.105 kPa−1 below 3.61 kPa, and 0.0327 kPa−1 in the range from 4.12 to 15 kPa.) is adhered onto the fingertip to collect the pressure data when touching the objects. A robot-finger-compatible RES with strain and pressure sensing function is designed for finger gesture detection and grasping manipulation. The negative force feedback control framework is built to improve grasping manipulation of the robot finger with RES, which would provide a self-adaptive control method to determine whether the objects are grasped successfully or not. Robot fingers integrated with soft sensors would promote the development of sensing and grasping abilities of the robot finger and interaction with human beings.

ESTIMATION OF THIRD FINGER MOTION USING ENSEMBLE KALMAN FILTER

Post-stroke is a stage a patient undergoes if the patient has had a previous stroke. Stroke is a big and serious problem. As the second most common cause of disability of people at age of over 60 years. For patients having experienced a stroke, rehabilitation is a way to make them able to do activities of daily living as before. Stroke Rehabilitation is a comprehensive medical management and rehabilitation (in medical, emotional, social, and vocational aspects) concerning disabilities caused by stroke through a neuro-rehabilitation approach with the aim of optimizing recovery. The finger prosthetic arm robot is one of the results of the health technology development to help accelerate the rehabilitation process specifically for finger movements. One of the efforts to develop a finger robot is to estimate the movement of the fingers, in this case the finger size used is taken from those of Javanese people in Indonesia as the data to be simulated. In this paper is an estimation of the finger motion,particularly that of the third finger of the right hand, conducted using the Ensemble Kalman Filter (EnKF) method. The simulation results produced the third finger motion estimates with an accuracy of around 92% - 99%.