Application of Neural Networks in Robot Forward Kinematics

Abstract

In 1955, Denavit and Hartenberg [1] developed a 4x4 homogeneous transformation matrix to describe robot kinematics relationships. For an articulated coordinate arm system, robot kinematics transformations can be expressed as the deterministic relationships between the end-effector position and the joint angles. Recently, the concepts of neural networks have been applied to approximate the robot kinematics. [2, 3, 4] This study has applied backpropagation augmented with an orthogonal array to reduce data input to approximate the robot kinematics. Simulations were applied on an articulated manipulator like the Cincinnati T3-756 robot. The simulation results show the performance comparison between neural networks with random sampling and neural networks which structure the sampling with orthogonal arrays.

Department(s)

Engineering Management and Systems Engineering

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 The Authors, All rights reserved.

Publication Date

01 Dec 1994

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