A Novel Joint Design for Robotic Hands With Humanlike Nonlinear Compliance

Robotic hands are typically too rigid to react against unexpected impacts and disturbances in order to prevent damage. Human hands have great versatility and robustness due, in part, to the passive compliance at the hand joints. In this paper, we present a novel design for joint with passive compliance that is inspired by biomechanical properties of the human hands. The design consists of a compliant material and a set of pulleys that rotate and stretch the material as the joint rotates. We created six different compliant materials, and we optimized the joint design to match the desired humanlike compliance. We present two design features that allow for the tuning of the joint torque profile, namely, a pretension mechanism to increase pretension of the compliant material, and a design of varying pulley configuration. We built a prototype for the new joint by using additive manufacturing to fabricate the design components and built a test-bed with a force sensor and a servo motor. Experimental results show that the joint exhibits a nonlinear, double exponential joint compliance with all six compliant materials. The design feature involving variable pulley configurations is effective in adjusting the slope of joint torque during the joint rotation while the pretension mechanism showed only a limited effect on increasing the torque amplitude. Overall, with its small size, light weight, low friction, and humanlike joint compliance, the presented joint design is ready for implementation in robotic hands.

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Unidirectional variable stiffness hydraulic actuator for load-carrying knee exoskeleton

International Journal of Advanced Robotic Systems ◽

10.1177/1729881416686955 ◽

2017 ◽

Vol 14 (1) ◽

pp. 172988141668695 ◽

Cited By ~ 7

Author(s):

Jun Zhu ◽

Yu Wang ◽

Jinlin Jiang ◽

Bo Sun ◽

Heng Cao

Keyword(s):

High Efficiency ◽

Experimental Testing ◽

Variable Stiffness ◽

Torque Control ◽

Joint Torque ◽

Hydraulic Actuator ◽

Passive Behavior ◽

Compliance Behavior ◽

Load Carrying ◽

Passive Compliance

This article presents the design and experimental testing of a unidirectional variable stiffness hydraulic actuator for load-carrying knee exoskeleton. The proposed actuator is designed for mimicking the high-efficiency passive behavior of biological knee and providing actively assistance in locomotion. The adjustable passive compliance of exoskeletal knee is achieved through a variable ratio lever mechanism with linear elastic element. A compact customized electrohydraulic system is also designed to accommodate application demands. Preliminary experimental results show the prototype has good performances in terms of stiffness regulation and joint torque control. The actuator is also implemented in an exoskeleton knee joint, resulting in anticipant human-like passive compliance behavior.

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Design and Performance of a Motor-Driven Mechanism to Conduct Experiments With the Human Index Finger

Journal of Mechanisms and Robotics ◽

10.1115/1.4028639 ◽

2015 ◽

Vol 7 (3) ◽

Cited By ~ 2

Author(s):

Pei-Hsin Kuo ◽

Jerod Hayes ◽

Ashish D. Deshpande

Keyword(s):

Index Finger ◽

Human Subjects ◽

Joint Stiffness ◽

Viscoelastic Behavior ◽

Neuromuscular Control ◽

External Loading ◽

Human Hand ◽

Robotic Hands ◽

Hand Biomechanics ◽

Human Hands

Passive properties of the human hands, defined by the joint stiffness and damping, play an important role in hand biomechanics and neuromuscular control. Introduction of mechanical element that generates humanlike passive properties in a robotic form may lead to improved grasping and manipulation abilities of the next generation of robotic hands. This paper presents a novel mechanism, which is designed to conduct experiments with the human subjects in order to develop mathematical models of the passive properties at the metacarpophalangeal (MCP) joint. We designed a motor-driven system that integrates with a noninvasive and infrared motion capture system, and can control and record the MCP joint angle, angular velocity, and passive forces of the MCP joint in the index finger. A total of 19 subjects participated in the experiments. The modular and adjustable design was suitable for variant sizes of the human hands. Sample results of the viscoelastic moment, hysteresis loop, and complex module are presented in the paper. We also carried out an error analysis and a statistical test to validate the reliability and repeatability of the mechanism. The results show that the mechanism can precisely collect kinematic and kinetic data during static and dynamic tests, thus allowing us to further understand the insights of passive properties of the human hand joints. The viscoelastic behavior of the MCP joint showed a nonlinear dependency on the frequency. It implies that the elastic and viscous component of the hand joint coordinate to adapt to the external loading based on the applied frequency. The findings derived from the experiments with the mechanism can provide important guidelines for design of humanlike compliance of the robotic hands.

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Plantar flexor moment arm and muscle volume predict torque-generating capacity in young men

Journal of Applied Physiology ◽

10.1152/japplphysiol.01140.2013 ◽

2014 ◽

Vol 116 (5) ◽

pp. 538-544 ◽

Cited By ~ 37

Author(s):

Josh R. Baxter ◽

Stephen J. Piazza

Keyword(s):

Plantar Flexion ◽

Young Men ◽

Knee Extensor ◽

Muscle Volume ◽

Joint Torque ◽

Muscle Size ◽

Plantar Flexor ◽

Moment Arm ◽

Joint Rotation ◽

Moment Arms

Muscle volume is known to correlate with maximal joint torque in humans, but the role of muscle moment arm in determining maximal torque is less clear. Moderate correlations have been reported between maximal isometric knee extensor torque and knee extensor moment arm, but no such observations have been made for the ankle joint. It has been suggested that smaller muscle moment arms may enhance force generation at high rates of joint rotation, but this has not yet been observed for ankle muscles in vivo. The purpose of the present study was to correlate plantar flexor moment arm and plantar flexor muscle volume with maximal plantar flexor torque measured at different rates of plantar flexion. Magnetic resonance imaging was used to quantify the plantar flexor moment arm and muscle volume of the posterior compartment in 20 healthy young men. Maximal plantar flexor torque was measured isometrically and at three plantar flexion speeds using an isokinetic dynamometer. Plantar flexor torque was significantly correlated with muscle volume (0.222 < R2 < 0.322) and with muscle moment arm at each speed (0.323 < R2 < 0.494). While muscle volume was strongly correlated with body mass and stature, moment arm was not. The slope of the torque-moment arm regression line decreased as the rate of joint rotation increased, indicating that subjects with small moment arms experienced smaller reductions in torque at high speeds. The findings of this study suggest that plantar flexor moment arm is a determinant of joint strength that is at least as important as muscle size.

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Transforming Human Hand Motion for Telemanipulation

Presence Teleoperators & Virtual Environments ◽

10.1162/pres.1992.1.1.63 ◽

1992 ◽

Vol 1 (1) ◽

pp. 63-79 ◽

Cited By ~ 34

Author(s):

Thomas H. Speeter

Keyword(s):

Force Feedback ◽

Robotic Manipulation ◽

Human Hand ◽

Robotic Hand ◽

Robotic Hands ◽

Automated Control ◽

Functional Transformation ◽

Hand Motion ◽

Computationally Expensive ◽

Human Hands

Manipulation by teleoperation (telemanipulation) offers an apparently straightforward and less computationally expensive route toward dextrous robotic manipulation than automated control of multifingered robotic hands. The functional transformation of human hand motions into equivalent robotic hand motions, however, presents both conceptual and analytical problems. This paper reviews and proposes algorithmic methods for transforming the actions of human hands into equivalent actions of slave multifingered robotic hands. Forward positional transformation is considered only, the design of master devices, feedforward dynamics, and force feedback are not considered although their importance for successful telemanipulation is understood. Linear, nonlinear, and functional mappings are discussed along with performance and computational considerations.

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Design and Analysis of a Novel Six-Component F/T Sensor based on CPM for Passive Compliant Assembly

Measurement Science Review ◽

10.2478/msr-2013-0038 ◽

2013 ◽

Vol 13 (5) ◽

pp. 253-264 ◽

Cited By ~ 9

Author(s):

Qiaokang Liang ◽

Dan Zhang ◽

Yaonan Wang ◽

Yunjian Ge

Keyword(s):

Structural Design ◽

Parallel Mechanism ◽

High Performance ◽

High Sensitivity ◽

Force Sensor ◽

Element Analysis ◽

Compliant Assembly ◽

Compliant Parallel Mechanism ◽

Passive Compliance ◽

Component Force

Abstract This paper presents the design and analysis of a six-component Force/Torque (F/T) sensor whose design is based on the mechanism of the Compliant Parallel Mechanism (CPM). The force sensor is used to measure forces along the x-, y-, and z-axis (Fx, Fy and Fz) and moments about the x-, y-, and z-axis (Mx, My and Mz) simultaneously and to provide passive compliance during parts handling and assembly. Particularly, the structural design, the details of the measuring principle and the kinematics are presented. Afterwards, based on the Design of Experiments (DOE) approach provided by the software ANSYS®, a Finite Element Analysis (FEA) is performed. This analysis is performed with the objective of achieving both high sensitivity and isotropy of the sensor. The results of FEA show that the proposed sensor possesses high performance and robustness.

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End-Effector Force and Joint Torque Estimation of a 7-DoF Robotic Manipulator Using Deep Learning

Electronics ◽

10.3390/electronics10232963 ◽

2021 ◽

Vol 10 (23) ◽

pp. 2963

Author(s):

Stanko Kružić ◽

Josip Musić ◽

Roman Kamnik ◽

Vladan Papić

Keyword(s):

Mean Squared Error ◽

Force Sensor ◽

Robotic Manipulator ◽

Joint Torque ◽

Estimation Methods ◽

Robot Dynamics ◽

Force Estimation ◽

End Effector ◽

Squared Error ◽

Torque Estimation

When a mobile robotic manipulator interacts with other robots, people, or the environment in general, the end-effector forces need to be measured to assess if a task has been completed successfully. Traditionally used force or torque estimation methods are usually based on observers, which require knowledge of the robot dynamics. Contrary to this, our approach involves two methods based on deep neural networks: robot end-effector force estimation and joint torque estimation. These methods require no knowledge of robot dynamics and are computationally effective but require a force sensor under the robot base. Several different architectures were considered for the tasks, and the best ones were identified among those tested. First, the data for training the networks were obtained in simulation. The trained networks showed reasonably good performance, especially using the LSTM architecture (with a root mean squared error (RMSE) of 0.1533 N for end-effector force estimation and 0.5115 Nm for joint torque estimation). Afterward, data were collected on a real Franka Emika Panda robot and then used to train the same networks for joint torque estimation. The obtained results are slightly worse than in simulation (0.5115 Nm vs. 0.6189 Nm, according to the RMSE metric) but still reasonably good, showing the validity of the proposed approach.

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Survey of Available Sensor Technology for Robotic Hands

Volume 1: 25th Design Automation Conference ◽

10.1115/detc99/dac-8656 ◽

1999 ◽

Author(s):

Amit V. Upasani ◽

Chetan Kapoor ◽

Delbert Tesar

Keyword(s):

Contact Force ◽

Force Sensor ◽

Sensor Technology ◽

Robotic Hands ◽

Different Types ◽

Major Attention

Abstract This paper documents a survey of sensor technology for use in robotic hands. This is achieved by studying sensors used in the UTAH/MIT Hand, the Belgrade/USC hand, the Stanford/JPL hand and the Hirzinger Hand. Additionally, off-the-shelf available sensors as well as those under development in research labs are also evaluated. Different types of sensing covered in this report are position, torque, touch/contact force, proximity, vision, and temperature. Different options available for each type of sensing are also outlined. Major attention was given to the contact force sensor, as it is crucial for the grasping task.

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