Neural Networks’ Based Inverse Kinematics Solution for Serial Robot Manipulators Passing Through Singularities

Hasan, Ali T.; Al-Assadi, Hayder M. A. Ali; Isa, Ahmad Azlan Mat

doi:10.5772/14977

Cited by 5 publications

(2 citation statements)

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“…This problem usually involves a set of nonlinear coupled, algebraic equations. The inverse kinematics problem is especially complex and time-consuming for redundant types of robotic manipulators [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Intelligent techniques have been one popular subject in recent years in robotics as one way to make control systems able to act more intelligently and with a high degree of autonomy. Artificial neural networks have been widely applied in robotics for the extreme flexibility that comes from their learning ability and functionapproximation capability in nonlinear systems [2]. Many papers have been published about the neural-networkbased inverse kinematics solution for robotic manipulators [7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of the Various Training Algorithms and Network Topologies in a Neural-Network-Based Inverse Kinematics Solution for Robots

Sari

2014

International Journal of Advanced Robotic Systems

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Recently, artificial neural networks have been used to solve the inverse kinematics problem of redundant robotic manipulators, where traditional solutions are inadequate. The training algorithm and network topology affect the performance of the neural network. There are several training algorithms used in the training of neural networks. In this study, the effect of various learning algorithms on the learning performance of the neural networks on the inverse kinematics model learning of a seven-joint redundant robotic manipulator is investigated. After the implementation of various training algorithms, the Levenberg-Marquardth (LM) algorithm is found to be significantly more efficient compared to other training algorithms. The effect of the various network types, activation functions and number of neurons in the hidden layer on the learning performance of the neural network is then investigated using the LM algorithm. Among different network topologies, the best results are obtained for the feedforward network model with logistic sigmoid-activation function (logsig) and 41 neurons in the hidden layer. The results are presented with graphics and tables.

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Section: Introductionmentioning

confidence: 99%