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A QP Artificial Neural Network inverse kinematic solution for accurate robot path control

  • Şahin Yıldırım
  • İkbal Eski
Article

Abstract

In recent decades, Artificial Neural Networks (ANNs) have become the focus of considerable attention in many disciplines, including robot control, where they can be used to solve nonlinear control problems. One of these ANNs applications is that of the inverse kinematic problem, which is important in robot path planning. In this paper, a neural network is employed to analyse of inverse kinematics of PUMA 560 type robot. The neural network is designed to find exact kinematics of the robot. The neural network is a feedforward neural network (FNN). The FNN is trained with different types of learning algorithm for designing exact inverse model of the robot. The Unimation PUMA 560 is a robot with six degrees of freedom and rotational joints. Inverse neural network model of the robot is trained with different learning algorithms for finding exact model of the robot. From the simulation results, the proposed neural network has superior performance for modelling complex robot’s kinematics.

Key Words

Artificial Neural Network Robot Path Control PUMA Robot 

Nomenclature

ai

The distance fromZ i toZ i+1 measured alongX i

αi

The angle betweenZ i toZ i+1 measured aboutX i

di

The distance fromX i-1 toX i measured alongZ i

θi

The angle betweenX i-1 toX i measured aboutX i

px, py, pz

End-effector coordinate ofx, y, z frame

60T

Transform matrices from have to joint 6

ci

Cos θ i

si

Sin θ i

c23

c2c3-s2s3

s23

c2s3+s2C3

c12

c1c2-s1s2

s12

c1s2+s1c2

sϕ

s123

cε

c123

η

Learning rate

α

Momentum term

Δwij(t)

The weight matrices fromi.layer toj. layer variations after update

E(t)

Error variations for weights

δ(t)

Derivation of error in weights

\(\bar \delta (t)\)

Exponential average of past value of δ

K

Constant value for learning rate

φ

Correction factor of learning rate

θ

Coefficient of the exponential average value

μ

Maximum growth factor

Wij

Weight matrices fromi.layer toj.layer

N

Training numbers

nI

Number of neurons in the input layer

nH

Number of neurons in the hidden layer

nO

Number of neurons in the output layer

zj(t)

The output vector of the hidden layer

AF

Activation function

References

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Copyright information

© The Korean Society of Mechanical Engineers (KSME) 2006

Authors and Affiliations

  1. 1.Faculty of Engineering, Mechanical Engineering DepartmentErciyes UniversityKayseriTurkey

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