scholarly journals Underactuated robotic hand for a fully automatic dishwasher based on grasp stability analysis*

2021 ◽  
pp. 1-15
Author(s):  
Jun Kinugawa ◽  
Hiroki Suzuki ◽  
Junya Terayama ◽  
Kazuhiro Kosuge
Keyword(s):  
Stability Analysis ◽  
Robotic Hand ◽  
Grasp Stability ◽  
Fully Automatic

Modeling and Grasp Stability Analysis for Object Manipulation by Soft Rolling Fingertips

2014 ◽  
Vol 11 (03) ◽  
pp. 1450020 ◽  
Author(s):  
John Fasoulas ◽  
Michael Sfakiotakis
Keyword(s):  
Stability Analysis ◽  
Dynamic Model ◽  
Object Manipulation ◽  
Kinematics And Dynamics ◽  
Control Law ◽  
Two Dimensional ◽  
Orientation Control ◽  
Grasp Stability ◽  
Different Types ◽  
General Dynamic

This paper presents a general dynamic model that describes the two-dimensional grasp by two robotic fingers with soft fingertips. We derive the system's kinematics and dynamics by incorporating rolling constraints that depend on the deformation and on the rolling distance characteristics of the fingertips' material. We analyze the grasp stability at equilibrium, and conclude that the rolling properties of the fingertips can play an important role in grasp stability, especially when the width of the grasped object is small compared to the radius of the tips. Subsequently, a controller, which is based on the fingertips' rolling properties, is proposed for stable grasping concurrent with object orientation control. We evaluate the dynamic model under the proposed control law by simulations and experiments that make use of two different types of soft fingertip materials, through which it is confirmed that the dynamic model can successfully capture the effect of the fingertips' deformation and their rolling distance characteristics. Finally, we use the dynamic model to demonstrate by simulations the significance of the fingertips' rolling properties in grasping thin objects.

Grasp stability analysis for elastic fingertips by using potential energy

2014 ◽  
Author(s):  
Tokuo Tsuji ◽  
Kosei Baba ◽  
Kenji Tahara ◽  
Kensuke Harada ◽  
Ken'ichi Morooka ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Potential Energy ◽  
Grasp Stability

1A1-B32 Grasp Stability Analysis of Two Objects in Three Dimensions

2006 ◽  
Vol 2006 (0) ◽  
pp. _1A1-B32_1-_1A1-B32_4
Author(s):  
Takayoshi YAMADA ◽  
Tomoya YAMAMOTO ◽  
Yasuyuki FUNAHASHI
Keyword(s):  
Stability Analysis ◽  
Three Dimensions ◽  
Grasp Stability

263 Grasp Stability Analysis of An Object in Two Dimensions

2011 ◽  
Vol 2011.60 (0) ◽  
pp. _263-1_-_263-2_
Author(s):  
Takayoshi YAMADA ◽  
Shuichi YAMANAKA ◽  
Manabu YAMADA ◽  
Hidehiko YAMAMOTO
Keyword(s):  
Stability Analysis ◽  
Two Dimensions ◽  
Grasp Stability

scholarly journals Bioinspired Fingertip for Anthropomorphic Robotic Hands

2014 ◽  
Vol 11 (1-2) ◽  
pp. 25-38 ◽  
Author(s):  
Marco Controzzi ◽  
Marco D'Alonzo ◽  
Carlo Peccia ◽  
Calogero Maria Oddo ◽  
Maria Chiara Carrozza ◽  
...  
Keyword(s):  
Mechanical Response ◽  
Polymeric Materials ◽  
Fe Model ◽  
Robotic Hand ◽  
Compression Tests ◽  
Robotic Hands ◽  
Experimental Conditions ◽  
Grasp Stability ◽  
Significant Step ◽  
Human Finger

Background: An artificial fingertip with mechanical features and appearance similar to the human fingertip could represent a significant step forward towards the development of the next generation artificial hands. However, so far, a fingertip showing a good trade-off among mechanical features, appearance and anthropomorphism, along with its 3D computational model, is still missing.Objective: To explore and develop an artificial fingertip demonstrating a mechanical response similar to the human fingertip, in order to improve the grasp stability of robotic hands.Methods: Taking inspiration from the multi-layered structure of the human finger, novel artificial fingertips, composed of a rigid core and covered by layers of polymeric materials with different degrees of stiffness and topped by a hard nail were developed. An accurate 3D finite element (FE) model was also developed in order to simulate and evaluate the internal mechanical behavior of the prototypes under external indentations. The mechanical response of the prototypes was assessed and compared with that of the human fingertip and the FE model results, under different experimental conditions. Finally, the artificial fingertips were integrated into an anthropomorphic robotic hand and evaluated in grip tests, in order to compare the grasp stability with respect to conventional stiff (metal) fingertips.Results: The developed prototypes demonstrated a response to compression tests similar to the human finger and the FE model showed a discrete accuracy (mean error 7%). Finally, an increased ability (by 96%) in stably holding objects during precision grips with respect to conventional stiff fingers was demonstrated.Conclusion: Multi-layered biomimetic fingertips can improve grasp stability and cosmetic appearance of anthropomorphic robot hands.

scholarly journals Development and Grasp Stability Estimation of Sensorized Soft Robotic Hand

2021 ◽  
Vol 8 ◽  
Author(s):  
P. M. Khin ◽  
Jin H. Low ◽  
Marcelo H. Ang ◽  
Chen H. Yeow
Keyword(s):  
State Estimation ◽  
Dynamic Change ◽  
Training Data ◽  
Estimation Accuracy ◽  
Robotic Hand ◽  
Stability Estimation ◽  
Pneumatic Pressure ◽  
Grasp Stability ◽  
Lstm Network ◽  
The Given

This paper introduces the development of an anthropomorphic soft robotic hand integrated with multiple flexible force sensors in the fingers. By leveraging on the integrated force sensing mechanism, grip state estimation networks have been developed. The robotic hand was tasked to hold the given object on the table for 1.5 s and lift it up within 1 s. The object manipulation experiment of grasping and lifting the given objects were conducted with various pneumatic pressure (50, 80, and 120 kPa). Learning networks were developed to estimate occurrence of object instability and slippage due to acceleration of the robot or insufficient grasp strength. Hence the grip state estimation network can potentially feedback object stability status to the pneumatic control system. This would allow the pneumatic system to use suitable pneumatic pressure to efficiently handle different objects, i.e., lower pneumatic pressure (50 kPa) for lightweight objects which do not require high grasping strength. The learning process of the soft hand is made challenging by curating a diverse selection of daily objects, some of which displays dynamic change in shape upon grasping. To address the cost of collecting extensive training datasets, we adopted one-shot learning (OSL) technique with a long short-term memory (LSTM) recurrent neural network. OSL aims to allow the networks to learn based on limited training data. It also promotes the scalability of the network to accommodate more grasping objects in the future. Three types of LSTM-based networks have been developed and their performance has been evaluated in this study. Among the three LSTM networks, triplet network achieved overall stability estimation accuracy at 89.96%, followed by LSTM network with 88.00% and Siamese LSTM network with 85.16%.

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