Humanoid Finger Research Articles

Design of a Multi-Mode Mechanical Finger Based on Linkage and Tendon Fusion Transmission.

Today, most humanoid mechanical fingers use an underactuated mechanism driven by linkages or tendons, with only a single and fixed grasping trajectory. This paper proposes a new multi-mode humanoid finger mechanism based on linkage and tendon fusion transmission, which is embedded with an adjustable-length tendon mechanism to achieve three types of grasping mode. The structural parameters of the mechanism are optimized according to the kinematic and static models. Furthermore, a discussion was conducted on how to set the speed ratio of the linkage driving motor and the tendon driving motor to adjust the length and tension of the tendon, in order to achieve the switching of the shape-adaptive, coupled-adaptive, and variable coupling-adaptive grasping modes. Finally, the multi-mode functionality of the proposed finger mechanism was verified through multiple grasping experiments.

A tactile force/Angle perception method for flexible humanoid finger joints

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Design and Simulation Experiment of Rigid-Flexible Soft Humanoid Finger

This paper is based on the “Fast Pneumatic Mesh Driver” (FPN) used to couple a silicone rubber soft body with a rigid skeleton. A rigid-flexible coupling soft-body human-like finger design scheme is proposed to solve the problem of low load on the soft-body gripping hand. The second-order Yeoh model is used to establish the statics model of the soft humanoid finger, and the ABAQUS simulation analysis software is used for correction and comparison to verify the feasibility of the soft humanoid finger bending. The thickness of the driver cavity and the confining strain layer were determined by finite element simulation. The mold casting process is used to complete the preparation of human-like fingers and design a pneumatic control system for experiments combined with 3D printing technology. The experimental results show that the proposed rigid-flexible coupling soft body imitating the human finger structure can realize the corresponding actions, such as the multi-joint bending and side swinging, of human fingers. Compared with the traditional pure soft-body finger, the fingertip output force is significantly improved. The optimal design and simulation analysis of the human gripper and the feasibility of the application have practical guiding significance.

Design, Modeling and Control of an Enhanced Soft Pneumatic Network Actuator

Inspired by nature, soft-bodied pneumatic network actuators (PNAs) composed of compliant materials have been successfully applied in the fields of industry and daily life because of large-amplitude motion and long life span. However, compliant materials simultaneously limit the output force, challenge the dynamic modeling and impede corresponding control. In this paper, we investigate the design, modeling and control of an enhanced PNA. First, an enhanced structure is proposed to improve the output force of PNAs with features of simplification of fabrication, lightweight and compliant material retentivity. Second, a dynamic model of the enhanced PNA is constructed based on the Euler–Lagrange (EL) method. Finally, an adaptive robust controller is addressed for PNAs in presence of system uncertainties without knowledge of its bounds in prior. Experiment results show that the output force of the enhanced PNA is four times greater than the actuator without enhanced structures, which affords to theoretical estimation. Moreover, the proposed controller is utilized and compared with previous works in humanoid finger experiments to illustrate the effectiveness.

Soft robots with self-powered configurational sensing

Abstract In recent years, soft robots have been evolved at a rapid pace. Soft robots possess infinite degrees of freedom because of the stretchability, which also poses a great challenge for sensing and controlling. Triboelectric nanogenerator (TENG) holds a promising potential as a self-powered sensor for soft robots due to its high sensitivity, flexibility and simplicity. In this work, a sensorized pneumatic soft actuator (PSA) is designed by integrating TENG based self-powered sensors inside the chambers. The sensors give feedback on the dynamic and static configurations of the PSA. The output voltage of a TENG has a good linear relationship with the local bending angle. In addition, the voltage output of all TENGs connected in parallel is linearly related to the total bending angle of the PSA. Applications of the sensorized PSA in a universal soft gripper and humanoid fingers are demonstrated. The self-powered sensors can give corresponding feedback when the soft gripper catches cups with different sizes/weights or when the humanoid fingers make different gestures. The sensorized PSA pave the way for precise control and safely interaction.

Humanoid Robot Actuation through Precise Chemical Sensing Signals

AbstractAs the need for assistive robots increases in aging societies, various assistive robot systems including humanoid robots have been developed. Humanoid robotic hands are one of the most useful parts to assist humans efficiently. While sensing pressure or temperature from the robotic hands is extensively studied, sensing chemicals is less widely studied despite the significant importance. Here, a unique platform of smartly moving humanoid fingers actuated by chemical sensing is reported. The sensor is printed with disposable and biocompatible cellulose conductive ink materials. Rf intensity change of the sensor with NH4+ membrane depends on NH4+ ion concentration where R² is 0.9576. The smart bending motion of a finger is accomplished by logically programed actuation through detecting the change of interested ion concentration from 0.01 to 1 m at the ion‐selective membrane electrode (ISME) sensor with resulted bending angles from 10° to 67° accordingly. The overall signal‐to‐noise ratio is over 10. This sensing robot concept may be expanded to applications of microrobots which receive external stimulus, judge, and execute the actuation to carry out programed tasks.

Research and Design on Modular Humanoid Finger with SMA Actuator

Research and Design on Modular Humanoid Finger with SMA Actuator

A cortically-inspired model for inverse kinematics computation of a humanoid finger with mechanically coupled joints

The human hand’s versatility allows for robust and flexible grasping. To obtain such efficiency, many robotic hands include human biomechanical features such as fingers having their two last joints mechanically coupled. Although such coupling enables human-like grasping, controlling the inverse kinematics of such mechanical systems is challenging. Here we propose a cortical model for fine motor control of a humanoid finger, having its two last joints coupled, that learns the inverse kinematics of the effector. This neural model functionally mimics the population vector coding as well as sensorimotor prediction processes of the brain’s motor/premotor and parietal regions, respectively. After learning, this neural architecture could both overtly (actual execution) and covertly (mental execution or motor imagery) perform accurate, robust and flexible finger movements while reproducing the main human finger kinematic states. This work contributes to developing neuro-mimetic controllers for dexterous humanoid robotic/prosthetic upper-extremities, and has the potential to promote human–robot interactions.

Temperature prediction with humanoid finger sensor based on Fourier’s law

BioTac is a humanoid finger sensor that can perceive multiple physical properties such as temperature, contact force, surface features, micro-vibration, etc. However measuring temperature with BioTac has a large delay due to its conducting medium, which is unfavorable for intelligent perception. So a method of temperature prediction based on Fourier’s Law is proposed. Firstly, a multi-functional calibration device for BioTac is built. Secondly, the pressure signal of BioTac is calibrated for the next temperature calibration. Thirdly, the relational expression between the temperature signal from BioTac and the actual temperature is given using the Least Square method. Then the calculated temperature according to the relational expression is compensated by thermal flux, which is the predicted temperature. Finally, the proposed method is verified using the calibration device. The results show that the proposed method can greatly reduce the time of measuring temperature, and the prediction accuracy meets the demand of application.

Artificial Sweat for Humanoid Finger

To achieve favorable Frictional Tactile Sensation (FTS) for robot and humanoid fingers, this report investigated the effects of human finger sweat on Friction Coefficient (FC) and verified the effectiveness of artificial sweat on FTS for a humanoid finger. The results show that the model sweat (salt and urea water faked real sweat) increases the FC of the real finger sliding on the high hygroscopic and rough surface (paper), whereas on the low hygroscopic and smooth surface (PMMA), the sweat forms a fluid film and decreases FC, restricting severe finger adhesion. Further, the film formation and capillary adhesion force of sweat were discussed. The experimental results with the artificial sweats (ethanol and water) and humanoid finger (silicone rubber skin with tactile sensors) verifies the effectiveness. The artificial sweat restricts severe adhesion (stick-slip vibration), and enhances cognitive capability of FTS.

An Adaptive Neural Network Learning-based Solution for the Inverse Kinematics of Humanoid Fingers

This paper presents an adaptive neural network learning-based solution for the inverse kinematics of humanoid fingers. For the purpose, we specify an effective finger model by considering the interphalangeal joint coordination inherent in human fingers. In order to find a proper joint combination for any fingertip trajectory, we propose an adaptive learning scheme by using a multi-layered neural network. It is interesting to use an adaptive learning rate algorithm that leads the neural network to get the inverse kinematic solution quickly. The usefulness of the proposed approach is verified by exemplary simulations for the general motion of humanoid fingers.

A Computational Design Method for a Shape Memory Alloy Wire Actuated Compliant Finger

This paper presents a computational method to design a compliant finger for robotic manipulations. As traditional mechanical fingers require bulky electromagnetic motors and numerous relative moving parts to achieve dexterous motion, we propose a class of fingers; the manipulation of which relies on finger deflections. These compliant fingers are actuated by shape memory alloy (SMA) wires that exhibit high work-density, frictionless, and quiet operations. The combination of compliant members with embedded SMA wires makes the finger more compact and lightweight. Various SMA wire layouts are investigated to reduce their response time while maintaining sufficient output force. The mathematical models of finger deflection caused by SMA contraction are then derived along with experimental validations. As finger shapes are essential to the range of deflected motion and output force, we find its optimal initial shapes through the use of a shape parametrization technique. We further illustrate our method by designing a humanoid finger that is capable of three-dimensional manipulation. Since compliant fingers can be fabricated monolithically, we expect the proposed design method to be utilized for applications of various scales.

뉴럴 러닝 기반 로봇 손가락의 역기구학

일반적으로 인간손에 있는 검지 손가락의 평면운동은 3개의 관절운동에 의해 이루어진다. 이러한 운동을 위해서는 기본적으로 역기구학 문제를 풀어야 하는데, 이것은 로봇 손을 이용한 파지나 조작행위에 있어서 필수적이다. 따라서 본 논문에서는 이러한 로봇 손가락의 역기구학 문제를 지능적으로 해결할 수 있는 뉴럴 러닝에 기반한 방법을 제안하고자 한다. 제안된 방법은 뉴럴 러닝에 있어서 동적인 학습율을 적용함으로써 보다 빠른 학습이 가능하고, 생체모방에 근거한 인간 손가락의 운동특성을 고려하는 것이 특징이다. 제안된 방법의 유용성을 입증하기 위하여 시뮬레이션을 수행한다. The planar motion of the index finger in general human hands is usually implemented by the actuation of three joints. This task requires a technique to determine the joint combination for each fingertip position which is well-known as the inverse kinematics problem in robotics. Especially, it is an essential work for grasping and manipulation tasks by robotic and humanoid fingers. In this paper, an intelligent neural learning scheme for solving such inverse kinematics is presented. Specifically, a multi-layered neural network is utilized for effective inverse kinematics, where a dynamic neural learning algorithm is employed for fast learning. Also, a bio-mimetic feature of general human fingers is incorporated to the learning scheme. The usefulness of the proposed approach is verified by simulations.