Anthropomorphic Robotic Finger Research Articles

An Anthropomorphic Robotic Finger With Innate Human-Finger-Like Biomechanical Advantages Part I: Design, Ligamentous Joint, and Extensor Mechanism

Exploring human hand fundamental biomechanical features and exploiting them to robotic hands have been proven to be an effective approach to enhancing artificial hands' performance, especially when interacting with various objects in dynamic unstructured environments. In this article, a bioinspired anthropomorphic robotic finger is first proposed, which embeds human finger musculoskeletal features in the design. Based on this design, three human-finger-like biomechanical advantages are systematically investigated and embodied in the bioinspired robotic finger. This article for the first time derives, presents, and experimentally verifies the mathematical models for the variable stiffness of finger ligamentous joints and self-adaptive morphing mechanism of finger flexible tendon sheaths, and validates and compares the influence of the reticular and linear extensor morphologies on fingertip feasible forces in three-dimensional (3-D) space. In this Part I of the article, two of the biomechanical properties, i.e., joint stiffness generated by the ligamentous joint of the finger, and fingertip feasible force space influenced by the reticular extensor mechanism are systematically investigated through theoretical modeling and experimental verification. Correspondingly, two biomechanical advantages were found, i.e., the ligamentous joint of the finger could provide anisotropic variable joint stiffness, enhancing the adaptivity, dexterity, and stability of fingers; and a reticular extensor mechanism could enlarge the fingertip feasible force space in 3-D space by 30.9% theoretically and 146.4% experimentally on average compared with the linear extensor, contributing to enrich force conditions during interactions. The third biomechanical advantage, i.e., fingertip force–velocity workspace can be augmented through the flexible tendon sheath, and grasping tests for a robotic hand designed with the aforementioned advantages are presented in Part II of this article.

An Anthropomorphic Robotic Finger With Innate Human-Finger-Like Biomechanical Advantages Part II: Flexible Tendon Sheath and Grasping Demonstration

The human hand has a fantastic ability to interact with various objects in the dynamic unstructured environment of our daily activities. We believe that this outstanding performance benefits a lot from the unique biological features of the hand musculoskeletal system. In Part I of this article, a bio-inspired anthropomorphic robotic finger was developed, based on which two human-finger-like biomechanical advantages were elaborately investigated, including the anisotropic variable stiffness associated with the ligamentous joints and the enlarged feasible force space associated with the reticular extensor mechanisms. In Part II, the fingertip force-velocity characteristics resulting from the flexible tendon sheath are studied. It indicates that the fingertip force–velocity workspace can be greatly augmented owing to the self-adaptive morphing of the flexible tendon sheaths, showing the average improvement of 41.2% theoretically and 117.5% experimentally compared with the results of 2 mm, 4 mm, and 6 mm size rigid tendon sheaths. Grasping tests and comparisons are then conducted with four three-fingered robotic hands (one with the robotic finger proposed in Part I, one with hinge joints, one with linear extensors, and one with rigid tendon sheaths) and the human hands of six subjects to handle various objects on flat, rough, and soft surfaces. The results show that the novel bio-inspired design in this research could improve the grasping success rates of the robotic hand. Compared with the grasping test results from the robotic hand with the bio-inspired robotic finger proposed in Part I, the overall grasping performance of a robotic hand with hinge joints, linear extensors, and rigid tendon sheaths decreases by 10%, 6%, and 17%, respectively. The results have also shown that with the embedded biomechanical advantages, even without complex control and sensory systems, the robotic fingers can achieve very comparable performance to human fingers in the grasping demonstrations presented, indicating average 94% of the success rate achieved by the human fingers. Successfully demonstrating 14 of 16 grasp types in the Cutkoskey taxonomy further shows the human-finger-like grasping capability of the proposed robotic fingers.

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.

An Optical-based Fingertip Force Sensor

This work introduces the design and prototype development of a novel optical-based fingertip force sensor, which is integrated in a bio-mimetic finger for robotic and prosthetic manipulation. Given the optical nature of the sensor, the proposed device is free of any electrical and metal components. Accordingly, the design improves manipulation of objects with high electromagnetic compatibility performance, reducing sensor’s susceptibility in the presence of these disturbances. The sensor has inherently high SNR and low-power consumption. The concept of using simplified and low cost optical based fingertip force sensor for bio-mimetic anthropomorphic fingers has not been widely implemented so far. The sensor is based on a cantilever design combined with a couple of fiber optics and a reflective surface, it is integrated on the distal phalange of a novel bio-mimetic and anthropomorphic robotic finger. Sensor design was performed and optimized throughout a 3D printing process and Finite Element Analysis. The sensor’s sensitivity (0.098V/N), resolution (0.01N), accuracy (3% of full scale output (FS)) and hysteresis (9.24% of FS) were characterized through a calibration processes. Various applications like surgical manipulation or handling in harsh environment (i.e. high voltage, high temperature and explosive environments) will benefit from the proposed sensor’s performance, reliability, simplicity and bio-compatibility.

Development of Anthropomorphic Robot Finger for Violin Fingering

This paper proposes a robot hand for a violin-playing robot and introduces a newly developed robot finger. The proposed robot hand acts as the left hand of the violin-playing robot system. The violin fingering plays an important role in determining the tone or sound when the violin is being played. Among the diverse types of violin fingering playing, it is not possible to produce vibrato with simple position control. Therefore, we newly designed a three-axis load cell for force control, which is mounted at the end of the robot finger. Noise is calculated through an analysis of the resistance difference across the strain gauge attached to the proposed three-axis load cell. In order to ensure the stability of the three-axis load cell by analyzing the stress distribution, the strain generated in the load cell is also verified through a finite element analysis. A sound rating quality system previously developed by the authors is used to compare and analyze the sound quality of the fourth-octave C-note played by a human violinist and the proposed robot finger.

非伸縮分岐腱により把持形態が適応的に変化する3指ロボットハンドの開発

Human can pinch and grasp an object using his finger or fingertip with multiple contact points. The multiple-point contact increases stability while the object is grasped and controlled. We developed the anthropomorphic robotic finger with non-elastic branching tendon mechanism in order to realise the reaching motion with less actuators in our previous work. However, it was difficult to accomplish the grasping with multipoint contact. Thus, we develop a new robot hand system equipped branching tendons which realizes multiple-point contact while it grasps the object using with the whole fingers or finger-tips. In this paper, we introduce the developed robotic hand and show the reason why the robotic hand can realize the multiple-point contact.

A Study about Sound Quality for Violin Playing Robot

Abstract This paper introduces a violin playing robot that imitates the playing technique of human. A violinist learns how to play through an endless practice. A bowing velocity, bowing force, and sound point are important factors in determining the sound quality. Thus, in this paper, the sound quality has been analysed in the variable speed using the violin playing robot, where an industrial vertical multi-joint robot arm is used. Fast Fourier transform is used to convert the played sound using a 32-bit microcontroller, and then the result is compared to the natural frequency of the G string. This paper also studies the robotic hand for violin fingering. The robotic finger by constructing a 3-axis load cell at the end of finger. Based on the amplified strain gauge value, it was able to apply an appropriate force on the string when playing the violin. The robot hand that is similar to the size of a hand of adult male made a form of Anthropomorphic-dexterous. So this has a total of 12 degrees of freedom(DOF). A control method works based on Wire driven. And then, the end of the hand equipped with a 3-axis load cell to measure the force of fingertip at real-time. Lastly, this paper describes the mechanism and experimental results of anthropomorphic robotic finger, which is developed to facilitate the performance of violin playing robot. In order to present the feasibility of accurate control, a 3-axis load cell is developed and mounted at the end of finger.

Design of the drive mechanism for an anthropomorphic robotic finger

Mechanisms may be used in the design of robotic fingers, in order to obtain drive systems which can be used for gripping or manipulating objects. In this work, a design methodology for a drive mechanism of an “anthropomorphic finger” is presented. The finger was previously developed by computational tools (CAD) at the UMNG-Colombia. The proposed methodology takes into account the mathematical relationships between the mechanical elements of the drive mechanism to carry out a dimensional synthesis, taking into account that the mechanism must fulfi ll the anthropometric characteristics of a human hand based on ten numerical positions (which were obtained from a previous UMNG work). Numerical simulation results and CAD modeling for the drive mechanism are presented. Finally, a discussion about results is included.

Kinematic Model of Anthropomorphic Robotics Finger Mechanisms

Research on Kinematic Model of Anthropomorphic robotics Finger mechanisms is being carried out to accommodate a variety of tasks such as grasping and manipulation of objects in the field of industrial applications, service robots, and rehabilitation robots. The first step in realizing a fully functional of anthropomorphic robotics Finger mechanisms is kinematic modeling. In this paper, a Kinematic Model of Anthropomorphic robotics Finger mechanism is proposed based on the biological equivalent of human hand where each links interconnect at the metacarpophalangeal (MCP), proximal interphalangeal (PIP) and distal interphalangeal (DIP) joints respectively. The Kinematic modeling was carried out using Denavit Hartenburg (DH) algorithm for the proposed of Kinematic Model of Anthropomorphic robotics Finger mechanisms. Index Terms— Anthropomorphic robot Finger, Modeling, Robotics, Simulation I. Introduction Among the vast applications of robotics, robotic assistance in human daily life and has been the major factor that contributes to its development. The focus on the anthropomorphism robotic limbs is currently undergoing a very rapid development. The creation of a multifingered anthropomorphic robotic hand is a challenge that demands innovative integration of mechanical, electronics, control and embedded software designs.

Asynchronous decoding of finger position and of EMG during precision grip using CM cell activity: Application to robot control

Recent brain-machine interfaces (BMI) have demonstrated the use of intracortical signals for the kinematic control of robotic arms. However, for potential restoration of manual dexterity, two issues remain to be addressed: (1) Can hand and digit movements for dexterous manipulation be controlled in a similar way to arm movements? (2) Can the potentially large signal space for decoding of the many degrees of freedom (dof) of hand and digit movements be minimized? The first question addresses BMI control of dexterous prosthetic devices, while the second addresses the problem of whether few, but identified, neurons might provide adequate decoding. Asynchronous decoding of precision grip finger movement kinematics from identified corticomotoneuronal (CM) cell activity was performed with an artificial neural network (ANN). After training over a given session, the ANNs successfully decoded trial-by-trial movement kinematics. Average accuracy over sessions was in the order of 80% and 50% for data sets of two monkeys respectively. Decoding accuracy increased as a function of (1) number of simultaneously recorded CM cells used for prediction, and (2) size of the sliding input window. Subsequently, a robot digit actuated by pneumatic artificial muscles, fed with the predicted trajectory, mimicked the recorded movement offline. Furthermore, CM cell signals were used for decoding of time-varying hand muscle EMG activity. The performance of EMG prediction tended to increase if CM cells that facilitated this particular muscle (compared to CM cells that facilitated other muscles) were used. These results provide evidence that an anthropomorphic robot finger can be controlled offline by spike trains recorded from identified corticospinal neurons. This represents a step towards neuroprosthetic devices for dexterous hand movements.

Roughness Encoding for Discrimination of Surfaces in Artificial Active-Touch

A 2 × 2 array of four microelectromechanical system (MEMS) tactile microsensors is integrated with readout electronics in the distal phalanx of an anthropomorphic robotic finger. A total of 16 sensing elements are available in a 22.3-mm area (i.e., 72 units/cm ) of the artificial finger, thus achieving a density comparable with human Merkel mechanoreceptors. The MEMS array is covered by a polymeric packaging with biomimetic fingerprints enhancing the sensitivity in roughness encoding. This paper shows the ability of the sensor array to encode roughness for discrimination of surfaces, without requiring dedicated proprioceptive sensors for end-effector velocity. Three fine surfaces with 400-, 440-, and 480- μm spatial periods are quantitatively evaluated. Core experiments consisted in active-touch exploration of surfaces by the finger executing a stereotyped human-like movement. A time-frequency analysis on pairs of tactile array outputs shows a clustering of the fundamental frequency, thus yielding 97.6% worst-case discrimination accuracy with a k -nearest-neighbor (k-NN) classifier. Hence, surfaces differing down to 40 μm are identified in active-touch by both hardware and processing methods based on exteroceptive tactile information. Finally, active-touch results with five textiles (which differ in texture or orientation) are shown as a preliminary qualitative assessment of discrimination in a more realistic tactile-stimulation scenario.

A cortical network for control of voluntary movements in a robot finger

Neurobiological control systems have long been studied as possible inspiration for the construction of robotic controllers. In this paper, a control of voluntary joint movements using a cortical network within constraints from neurophysiology and motor psychophysics is presented. The neural controller is proposed to control desired joint trajectories of an anthropomorphic robot finger. Each joint is driven by an agonist–antagonist actuator pair with muscle-like properties. Dynamical neural network proposes functional roles for pre-central cortical cell types in the computation of a descending command to spinal alpha and gamma motor neurons. Neurons in anterior area 5 are proposed to compute the position of the link in question using corollary discharges from area 4 and feedback from muscles spindles. Neurons in posterior area 5 use the position perception signal and desired position signal to compute a desired joint movement direction. The network reinterprets the “neural population activity” to afford unified control of posture, movement and force production. In addition, it suggests how the brain may set automatic and volitional gating mechanisms to vary the balance of static and dynamic feedback information to guide the movement command and to compensate for external forces. The reliability of the cortical neural controller is demonstrated by experimental results, where control system exhibits key kinematic properties of human movement, dynamic compensation and includes asymmetric bell-shaped velocity profiles.

An Anthropomorphic Robot Finger for Investigating Artificial Tactile Perception

In this paper the design, implementation, and testing of an artificial tactile sensing system incorporating an articulated robot finger are presented. It was our primary aim in this work to set up the hardware and software tools necessary for investigating basic issues in artificial tactile perception. In the first part of the paper the criteria followed in the design of the robot finger and of its motor and sensory com ponents are outlined. The second part of the paper deals with the problem of defining a hierarchical architecture for the control of the ex ploratory finger. In this context, particular attention is de voted to the description of a set of tactile subroutines that are intended to replicate some of the basic sensory motor se quences adopted by humans for tactile exploration. The main features of the high-level planner, which is supposed to super vise the execution of those tactile primitives, are also discussed. Finally, experimental results that demonstrate the capabil ity of the robotic system of executing the previously defined tactile subroutines and of extracting specific object features are presented.

Tactile spatial resolution. II. Neural representation of Bars, edges, and gratings in monkey primary afferents.

Tactile spatial resolution. II. Neural representation of Bars, edges, and gratings in monkey primary afferents.