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http://dx.doi.org/10.5391/JKIIS.2007.17.7.862

Neural Learning-Based Inverse Kinematics of a Robotic Finger  

Kim, Byoung-Ho (Bio-mimetic Control & Robotics Lab., Div. of Electrical and Mechatronics Eng., Kyungsung Univ.)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.17, no.7, 2007 , pp. 862-868 More about this Journal
Abstract
The planar motion of the index finger in general human hands is usually implemented by the actuation of three joints. This task requires a technique to determine the joint combination for each fingertip position which is well-known as the inverse kinematics problem in robotics. Especially, it is an essential work for grasping and manipulation tasks by robotic and humanoid fingers. In this paper, an intelligent neural learning scheme for solving such inverse kinematics is presented. Specifically, a multi-layered neural network is utilized for effective inverse kinematics, where a dynamic neural learning algorithm is employed for fast learning. Also, a bio-mimetic feature of general human fingers is incorporated to the learning scheme. The usefulness of the proposed approach is verified by simulations.
Keywords
inverse kinematics; neural learning scheme; robotic finger;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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