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Article

Design and Evaluation of Anthropomorphic Robotic Hand for Object Grasping and Shape Recognition

1
Department of Multimedia Engineering, Kaunas University of Technology, 51423 Kaunas, Lithuania
2
Department of Software Engineering, Kaunas University of Technology, 51423 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Received: 13 November 2020 / Revised: 19 December 2020 / Accepted: 21 December 2020 / Published: 22 December 2020
(This article belongs to the Special Issue Selected Papers from ICCSA 2020)
We developed an anthropomorphic multi-finger artificial hand for a fine-scale object grasping task, sensing the grasped object’s shape. The robotic hand was created using the 3D printer and has the servo bed for stand-alone finger movement. The data containing the robotic fingers’ angular position are acquired using the Leap Motion device, and a hybrid Support Vector Machine (SVM) classifier is used for object shape identification. We trained the designed robotic hand on a few monotonous convex-shaped items similar to everyday objects (ball, cylinder, and rectangular box) using supervised learning techniques. We achieve the mean accuracy of object shape recognition of 94.4%. View Full-Text
Keywords: robot manipulator; shape recognition; supervised learning; object grasping; 3D printing robot manipulator; shape recognition; supervised learning; object grasping; 3D printing
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MDPI and ACS Style

Devaraja, R.R.; Maskeliūnas, R.; Damaševičius, R. Design and Evaluation of Anthropomorphic Robotic Hand for Object Grasping and Shape Recognition. Computers 2021, 10, 1. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010001

AMA Style

Devaraja RR, Maskeliūnas R, Damaševičius R. Design and Evaluation of Anthropomorphic Robotic Hand for Object Grasping and Shape Recognition. Computers. 2021; 10(1):1. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010001

Chicago/Turabian Style

Devaraja, Rahul R., Rytis Maskeliūnas, and Robertas Damaševičius. 2021. "Design and Evaluation of Anthropomorphic Robotic Hand for Object Grasping and Shape Recognition" Computers 10, no. 1: 1. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10010001

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