scholarly journals Endpoint stiffness magnitude increases linearly with a stronger power grasp

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
A. Takagi ◽  
G. Xiong ◽  
H. Kambara ◽  
Y. Koike
Keyword(s):  
Power Grasp ◽  
Endpoint Stiffness

scholarly journals Grasping Assisting Algorithm in Tele-Operated Robotic Gripper

2021 ◽  
Vol 11 (6) ◽  
pp. 2640
Author(s):  
Tomer Fine ◽  
Guy Zaidner ◽  
Amir Shapiro
Keyword(s):  
State Of The Art ◽  
Human Subjects ◽  
Quality Measures ◽  
Human Operator ◽  
Physical Presence ◽  
Complete State ◽  
The Future ◽  
Power Grasp ◽  
Grasp Quality

The involvement of Robots and automated machines in different industries has increased drastically in recent years. Part of this revolution is accomplishing tasks previously performed by humans with advanced robots, which would replace the entire human workforce in the future. In some industries the workers are required to complete different operations in hazardous or difficult environments. Operations like these could be replaced with the use of tele-operated systems that have the capability of grasping objects in their surroundings, thus abandoning the need for the physical presence of the human operator at the area while still allowing control. In this research our goal is to create an assisting system that would improve the grasping of a human operator using a tele-operated robotic gripper and arm, while advising the operator but not forcing a solution. For a given set of objects we computed the optimal grasp to be achieved by the gripper, based on two grasp quality measures of our choosing (namely power grasp and precision grasp). We then tested the performance of different human subjects who tried to grasp the different objects with the tele-operated system, while comparing their success to unassisted and assisted grasping. Our goal is to create an assisting algorithm that would compute optimal grasps and might be integrated into a complete, state-of-the-art tele-operated system.

scholarly journals Learning to Control Arm Stiffness Under Static Conditions

2004 ◽  
Vol 92 (6) ◽  
pp. 3344-3350 ◽  
Author(s):  
Mohammad Darainy ◽  
Nicole Malfait ◽  
Paul L. Gribble ◽  
Farzad Towhidkhah ◽  
David J. Ostry
Keyword(s):  
Impedance Control ◽  
Arm Movement ◽  
Realistic Model ◽  
Impedance Change ◽  
Robotic Device ◽  
Lateral Direction ◽  
Maximum Stiffness ◽  
Asymmetric Pattern ◽  
Static Conditions ◽  
Endpoint Stiffness

We used a robotic device to test the idea that impedance control involves a process of learning or adaptation that is acquired over time and permits the voluntary control of the pattern of stiffness at the hand. The tests were conducted in statics. Subjects were trained over the course of 3 successive days to resist the effects of one of three different kinds of mechanical loads: single axis loads acting in the lateral direction, single axis loads acting in the forward/backward direction, and isotropic loads that perturbed the limb in eight directions about a circle. We found that subjects in contact with single axis loads voluntarily modified their hand stiffness orientation such that changes to the direction of maximum stiffness mirrored the direction of applied load. In the case of isotropic loads, a uniform increase in endpoint stiffness was observed. Using a physiologically realistic model of two-joint arm movement, the experimentally determined pattern of impedance change could be replicated by assuming that coactivation of elbow and double joint muscles was independent of coactivation of muscles at the shoulder. Moreover, using this pattern of coactivation control we were able to replicate an asymmetric pattern of rotation of the stiffness ellipse that was observed empirically. These findings are consistent with the idea that arm stiffness is controlled through the use of at least two independent co-contraction commands.

Power grasp force distribution control using artificial neural networks

1992 ◽  
Vol 9 (5) ◽  
pp. 635-661 ◽  
Author(s):  
Mark D. Hanes ◽  
Stanley C. Ahalt ◽  
Khalid Mirza ◽  
David E. Orin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Force Distribution ◽  
Grasp Force ◽  
Artificial Neural ◽  
Power Grasp

Force Control Strategy of Five-Digit Precision Grasping With Aligned and Unaligned Configurations

2022 ◽  
pp. 001872082110409
Author(s):  
Po-Tsun Chen ◽  
Hsiu-Yun Hsu ◽  
You-Hua Su ◽  
Chien-Ju Lin ◽  
Hsiao-Feng Chieh ◽  
...  
Keyword(s):  
Control Strategy ◽  
Force Control ◽  
Task Type ◽  
Applied Force ◽  
Force Variability ◽  
Force Transducers ◽  
Task Requirements ◽  
Power Grasp ◽  
Precision Grasping ◽  
Large Moment

Objective To investigate the digit force control during a five-digit precision grasp in aligned (AG) and unaligned grasping (UG) configurations. Background The effects of various cylindrical handles for tools on power grasp performance have been previously investigated. However, there is little information on force control strategy of precision grasp to fit various grasping configurations. Method Twenty healthy young adults were recruited to perform a lift-hold-lower task. The AG and UG configurations on a cylindrical simulator with force transducers were adjusted for each individual. The applied force and moment, the force variability during holding, and force correlations between thumb and each finger were measured. Result No differences in applied force, force correlation, repeatability, and variability were found between configurations. However, the moments applied in UG were significantly larger than those in AG. Conclusion The force control during precision grasp did not change significantly across AG and UG except for the digit moment. The simulator is controlled efficiently with large moment during UG, which is thus the optimal configuration for precision grasping with a cylindrical handle. Further research should consider the effects of task type and handle design on force control, especially for individuals with hand disorders. Application To design the handle of specific tool, one should consider the appropriate configuration according to the task requirements of precision grasping to reduce the risk of accumulating extra loads on digits with a cylindrical handle.

scholarly journals sEMG-based Endpoint Stiffness Estimation of Human Arm using Gene Expression Programming

2019 ◽  
Vol 1267 ◽  
pp. 012016
Author(s):  
Jiang Zainan ◽  
Yang Fan ◽  
Li Chongyang ◽  
Liu Daxiang ◽  
Wang Chenliang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Human Arm ◽  
Endpoint Stiffness

How does simulation of an observed external body state influence categorisation of an easily graspable object?

2018 ◽  
Vol 72 (6) ◽  
pp. 1466-1477
Author(s):  
Denis Brouillet ◽  
Arthur-Henri Michalland ◽  
Ronan Guerineau ◽  
Mooruth Draushika ◽  
Guillaume Thebault
Keyword(s):  
Response Times ◽  
Second Phase ◽  
Dominant Hand ◽  
Pain Mechanisms ◽  
Hand Grip ◽  
Group 2 ◽  
Two Phases ◽  
Response Group ◽  
Power Grasp ◽  
Motor Programme

Several works have provided evidence of a resonant motor effect while observing a hand interacting with painful stimuli. The aim of this work is to show that participants are sensitive to the observation of an injured hand when they have to categorise an easily graspable object with their own hand. In Experiment 1, participants indicated whether or not photographs of objects (graspable or non-graspable, left or right oriented) could be grasped with their dominant hand, by tapping a key on a keyboard. Target objects were preceded by primes consisting of photographs of hands (injured vs healthy) in a grasping posture (power grasp). Experiment 2 consisted of two phases: In the first phase, participants had to categorise square or circle shapes. After their response (Group 1: tapping a key vs Group 2: constricting a hand grip), photograph of two types of hand (injured vs healthy) was displayed on the computer screen. In the second phase, participants had to indicate whether objects could be easily grasped with their dominant hand. Target objects were preceded by primes (square and circle) as shown in the first phase. Results show that response times were slower when the graspable target objects were right oriented and preceded by the photograph or a geometric shape associated with an injured hand. This response delay was accentuated in the handgrip condition. These results highlight that the view of an injured hand activates motor programme and pain mechanisms associated with participants relative to the consequences of the simulated grasping action.

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.

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