Neural network solution for forward kinematics problem of HEXA parallel robot

Dehghani, M.; Ahmadi, Mahdi; Khayatian, Alireza; Eghtesad, Mohammad; Farid, Muhammad

doi:10.1109/acc.2008.4587155

Cited by 37 publications

(26 citation statements)

References 16 publications

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“…A hyper-chaotic neural network [39] has been proposed to find local initial points during mathematical programming to find all the solutions of non-linear FK equations. FK solution of a 6-dof HEXA robot has been presented using NN [40]. A comparison of NN with various numerical methods to solve FK has been provided by [33].…”

Section: Artificial Intelligence Based Methodsmentioning

confidence: 99%

Forward kinematics modelling of a parallel ankle rehabilitation robot using modified fuzzy inference

Jamwal

Xie

Tsoi

et al. 2010

Mechanism and Machine Theory

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Section: Artificial Intelligence Based Methodsmentioning

confidence: 99%

Forward kinematics modelling of a parallel ankle rehabilitation robot using modified fuzzy inference

Jamwal

Xie

Tsoi

et al. 2010

Mechanism and Machine Theory

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“…The linear regression of all joints is more than 0.999 which shows very good quality modeling results. So, in compare to the results of MLP neural network, wave-net model has smaller prediction error for FKP modeling of HEXA robot [18]. In this paper, we proposed to use wavelet based neural networks for FK solution of HEXA robot, which can be elaborated to generate the best estimation of forward kinematics of the robot.…”

Section: A Workpace Analysismentioning

confidence: 97%

“…In [17] it has been shown that a minimum number of three passive joint sensors are needed for solving the FKP analytically. Neural network approach is also used to solve FKP in typical workspace of HEXA robot [18].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Based Neural Network Solution for Forward Kinematics Problem of HEXA Parallel Robot

Dehghani

Ahmadi

Khayatian

et al. 2008

2008 International Conference on Intelligent Engineering Systems

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Forward kinematics problem of parallel robots is very difficult to solve in comparison to the serial manipulators because of the highly nonlinear relations between joint variables and position and orientation of the end effector. This problem is almost impossible to solve analytically. Numerical methods are one of the common solutions for this problem. But, convergency of these methods is the drawback of using them. In this paper, wavelet based neural network (wave-net) approach is used to solve the forward kinematics problem of the HEXA parallel manipulator. This problem is solved in the typical workspace of this robot. Simulation results show the advantages of employing wavelet neural networks in enhancement of convergency and decreasing modeling errors.

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“…Boudreau et al 10 proposed a holographic neural network to model a 3-DOF parallel manipulator. Dehghani et al 11 used a multilayer perceptron to solve the forward kinematics for a parallel robot HEXA. Yee and Lim 12 used a feed-forward neural network to recognize the input-output relationships of a Stewart platform.…”

Section: Introductionmentioning

confidence: 99%

Comparison of numerical and neural network methods for the kinematic modeling of a hybrid structure robot

Kang

Chanal

Dai

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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A combination of parallel and serial mechanisms allows for stiff and dexterous motion of the robotic end-effector but increases the complexity of the kinematic problem. Many of the geometric parameters for such robots are difficult to obtain. This paper presents the numerical and neural network methods to solve the kinematics for such a hybrid robot named Exechon Õ . Both methods avoid the geometric measurement in the real robot. The geometric parameters used in the numerical model are identified by a particle swarm optimization algorithm. At the same time, a radial basis functionbased neural network is trained to approximate the kinematics of the Exechon robot. The resultant models are then compared in terms of modeling accuracy and real-time ability. The presented methods are generic and can be applied to other robots with similar structures.

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Neural network solution for forward kinematics problem of HEXA parallel robot

Cited by 37 publications

References 16 publications

Forward kinematics modelling of a parallel ankle rehabilitation robot using modified fuzzy inference

Forward kinematics modelling of a parallel ankle rehabilitation robot using modified fuzzy inference

Wavelet Based Neural Network Solution for Forward Kinematics Problem of HEXA Parallel Robot

Comparison of numerical and neural network methods for the kinematic modeling of a hybrid structure robot

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