Neural network-based construction of inverse kinematics model for serial redundant manipulators

Solving the inverse kinematics of redundant manipulators is difficult, because knowledge of the manipulators and their evaluation functions is required. To solve this problem, we propose a novel method of enabling a neural network model to learn the inverse kinematics. The method achieves learning i...

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Bibliographic Details
Published in:Artificial life and robotics 2019-12, Vol.24 (4), p.487-493
Main Authors: Takatani, Hideaki, Araki, Nozomu, Sato, Takao, Konishi, Yasuo
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computation by Abstract Devices
Computer Science
Computer simulation
Control
Inverse kinematics
Kinematics
Mathematical models
Mechatronics
Neural networks
Original Article
Robotics
Software reviews
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Summary:Solving the inverse kinematics of redundant manipulators is difficult, because knowledge of the manipulators and their evaluation functions is required. To solve this problem, we propose a novel method of enabling a neural network model to learn the inverse kinematics. The method achieves learning independent of the structure of the evaluation function, by combining multiple neural network models. The method can obtain the neural network models of the inverse kinematics via an automatic calculation process using only training data, which consist of the postures, end-points, and evaluation values. In this paper, the algorithm used by the method and its background is explained, and the effectiveness of the method is validated by a numerical simulation.
ISSN:1433-5298
1614-7456
DOI:10.1007/s10015-019-00552-y