scholarly journals Flexible, Low-mass Robotic Arm Actuated by Electroactive Polymers and Operated Equivalently to Human Arm and Hand

Robotics 98 ◽  
1998 ◽  
Author(s):  
Y. Bar-Cohen ◽  
T. Xue ◽  
M. Shahinpoor ◽  
J. Simpson ◽  
J. Smith
Keyword(s):  
Electroactive Polymers ◽  
Robotic Arm ◽  
Human Arm ◽  
Low Mass

Flexible low-mass robotic arm actuated by electroactive polymers

1998 ◽  
Author(s):  
Yoseph Bar-Cohen ◽  
T. Xue ◽  
Mohsen Shahinpoor ◽  
Joycelyn S. Harrison ◽  
Joseph G. Smith
Keyword(s):  
Electroactive Polymers ◽  
Robotic Arm ◽  
Low Mass

Low-mass muscle actuators using electroactive polymers (EAP)

1998 ◽  
Author(s):  
Yoseph Bar-Cohen ◽  
T. Xue ◽  
Mohsen Shahinpoor ◽  
Joycelyn S. Harrison ◽  
Joseph G. Smith
Keyword(s):  
Electroactive Polymers ◽  
Low Mass ◽  
Muscle Actuators

Design Human-Robot Collaborative Lifting Task Using Optimization

2021 ◽  
Author(s):  
Asif Arefeen ◽  
Yujiang Xiang
Keyword(s):  
Degrees Of Freedom ◽  
Joint Angle ◽  
Joint Torque ◽  
Robotic Arm ◽  
Inverse Dynamic ◽  
Floating Base ◽  
Grasping Forces ◽  
Design Variables ◽  
Human Arm ◽  
Lifting Task

Abstract In this paper, an optimization-based dynamic modeling method is used for human-robot lifting motion prediction. The three-dimensional (3D) human arm model has 13 degrees of freedom (DOFs) and the 3D robotic arm (Sawyer robotic arm) has 10 DOFs. The human arm and robotic arm are built in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The interactions between human arm and box, and robot and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. The inverse dynamic optimization is used to simulate the lifting motion where the summation of joint torque squares of human arm is minimized subjected to physical and task constraints. The design variables are control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box, and the box grasping forces at each time point. A numerical example is simulated for huma-robot lifting with a 10 Kg box. The human and robotic arms’ joint angle, joint torque, and grasping force profiles are reported. These optimal outputs can be used as references to control the human-robot collaborative lifting task.

scholarly journals Optimal Design of a Bio-Inspired Anthropocentric Shoulder Rehabilitator

2006 ◽  
Vol 3 (3) ◽  
pp. 199-208 ◽  
Author(s):  
S. K. Mustafa ◽  
G. Yang ◽  
S. H. Yeo ◽  
W. Lin
Keyword(s):  
Performance Evaluation ◽  
Shoulder Joint ◽  
Stroke Rehabilitation ◽  
Design Methodology ◽  
Anatomical Structure ◽  
Robotic Arm ◽  
Analysis And Design ◽  
Non Invasive ◽  
Performance Requirements ◽  
Human Arm

This paper presents the design of a bio-inspired anthropocentric 7-DOF wearable robotic arm for the purpose of stroke rehabilitation. The proposed arm rehabilitator synergistically utilizes the human arm structure with non-invasive kinematically under-deterministic cable-driven mechanisms to form a completely deterministic structure. It offers the advantages of being lightweight and having high dexterity. Adopting an anthropocentric design concept also allows it to conform to the human anatomical structure. The focus of this paper is on the analysis and design of the 3-DOF-shoulder module, called the shoulder rehabilitator. The design methodology is divided into three main steps: (1) performance evaluation of the cable-driven shoulder rehabilitator, (2) performance requirements of the shoulder joint based on its physiological characteristics and (3) design optimization of the shoulder rehabilitator based on shoulder joint physiological limitations. The aim is to determine a suitable configuration for the development of a shoulder rehabilitator prototype.

scholarly journals Reduced-complexity representation of the human arm active endpoint stiffness for supervisory control of remote manipulation

2017 ◽  
Vol 37 (1) ◽  
pp. 155-167 ◽  
Author(s):  
Arash Ajoudani ◽  
Cheng Fang ◽  
Nikos Tsagarakis ◽  
Antonio Bicchi
Keyword(s):  
Robotic Arm ◽  
Model Parameters ◽  
Time Model ◽  
Remote Manipulation ◽  
Proposed Model ◽  
Human Arm ◽  
Human Motor ◽  
Reduced Complexity ◽  
Stiffness Profile ◽  
Endpoint Stiffness

In this paper, a reduced-complexity model of the human arm endpoint stiffness is introduced and experimentally evaluated for the teleimpedance control of a compliant robotic arm. The modeling of the human arm endpoint stiffness behavior is inspired by human motor control principles on the predominant use of the arm configuration in directional adjustments of the endpoint stiffness profile, and the synergistic effect of muscular activations, which contributes to a coordinated modification of the task stiffness in all Cartesian directions. Calibration and identification of the model parameters are carried out experimentally, using perturbation-based arm endpoint stiffness measurements in different arm configurations and cocontraction levels of the chosen muscles. Consequently, the real-time model is used for the remote control of a compliant robotic arm while executing a drilling task, a representative example of tool use in environments with constraints and dynamic uncertainties. The results of this study illustrate that the proposed model enables the master to execute the remote task by modulation of the directions of the major axes of the endpoint stiffness ellipsoid and its volume using natural arm configurations and the cocontraction of the involved muscles, respectively.

A gesture-based telemanipulation control for a robotic arm with biofeedback-based grasp

2017 ◽  
Vol 44 (5) ◽  
pp. 575-587 ◽  
Author(s):  
Yassine Bouteraa ◽  
Ismail Ben Abdallah
Keyword(s):  
Real Time ◽  
Force Feedback ◽  
Emg Biofeedback ◽  
Control Technique ◽  
Robotic Arm ◽  
Effective Control ◽  
Content Type ◽  
Human Arm ◽  
Gesture Control ◽  
Posture Recognition

Purpose The idea is to exploit the natural stability and performance of the human arm during movement, execution and manipulation. The purpose of this paper is to remotely control a handling robot with a low cost but effective solution. Design/methodology/approach The developed approach is based on three different techniques to be able to ensure movement and pattern recognition of the operator’s arm as well as an effective control of the object manipulation task. In the first, the methodology works on the kinect-based gesture recognition of the operator’s arm. However, using only the vision-based approach for hand posture recognition cannot be the suitable solution mainly when the hand is occluded in such situations. The proposed approach supports the vision-based system by an electromyography (EMG)-based biofeedback system for posture recognition. Moreover, the novel approach appends to the vision system-based gesture control and the EMG-based posture recognition a force feedback to inform operator of the real grasping state. Findings The main finding is to have a robust method able to gesture-based control a robot manipulator during movement, manipulation and grasp. The proposed approach uses a real-time gesture control technique based on a kinect camera that can provide the exact position of each joint of the operator’s arm. The developed solution integrates also an EMG biofeedback and a force feedback in its control loop. In addition, the authors propose a high-friendly human-machine-interface (HMI) which allows user to control in real time a robotic arm. Robust trajectory tracking challenge has been solved by the implementation of the sliding mode controller. A fuzzy logic controller has been implemented to manage the grasping task based on the EMG signal. Experimental results have shown a high efficiency of the proposed approach. Research limitations/implications There are some constraints when applying the proposed method, such as the sensibility of the desired trajectory generated by the human arm even in case of random and unwanted movements. This can damage the manipulated object during the teleoperation process. In this case, such operator skills are highly required. Practical implications The developed control approach can be used in all applications, which require real-time human robot cooperation. Originality/value The main advantage of the developed approach is that it benefits at the same time of three various techniques: EMG biofeedback, vision-based system and haptic feedback. In such situation, using only vision-based approaches mainly for the hand postures recognition is not effective. Therefore, the recognition should be based on the biofeedback naturally generated by the muscles responsible of each posture. Moreover, the use of force sensor in closed-loop control scheme without operator intervention is ineffective in the special cases in which the manipulated objects vary in a wide range with different metallic characteristics. Therefore, the use of human-in-the-loop technique can imitate the natural human postures in the grasping task.

Human-Like Posture Correction for Seven-Degree-of-Freedom Robotic Arm

2021 ◽  
pp. 1-16
Author(s):  
Yu-Heng Deng ◽  
Jen-Yuan (James) Chang
Keyword(s):  
Support Vector Machine Model ◽  
Humanoid Robots ◽  
Degree Of Freedom ◽  
Robotic Arm ◽  
Support Vector ◽  
Machine Model ◽  
Human Arm ◽  
Inverse Kinematics Problem ◽  
Point Trajectories ◽  
Point To Point

Abstract Owing to advancements in robotics, researchers have been focusing on integrating humanoid robots into actual environments. Most humanoid robots are equipped with seven-degree-of-freedom (DoF) arms that allow them to be flexible in different scenarios. The controller of a 7-DoF robotic arm must select one solution among the infinite sets of solutions for a given inverse kinematics problem. To date, no suitable approach has been developed for identifying appropriate human-like postures for a robotic arm with an offset wrist configuration. In this paper, we propose a novel algorithm that considers the movement of the human arm to consistently find a suitable human-like posture. First, a one-class support vector machine model is employed to classify human-like postures. Then, the algorithm uses the redundancy characteristic of a 7-DoF robotic arm with a linear regression model to enhance the search of human-like postures. Finally, the proposed algorithm is demonstrated in simulation, where it successfully optimized point-to-point trajectories by modifying only the endpoint posture.

Humanoid motion planning of robotic arm based on human arm action feature and reinforcement learning

Mechatronics ◽  
2021 ◽  
Vol 78 ◽  
pp. 102630
Author(s):  
Aolei Yang ◽  
Yanling Chen ◽  
Wasif Naeem ◽  
Minrui Fei ◽  
Ling Chen
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Robotic Arm ◽  
Human Arm
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