Artificial neural network-based kinematics Jacobian solution for serial manipulator passing through singular configurations (original) (raw)
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2010
Abstract: This paper is devoted to the application of Artificial Neural Networks (ANN) to the solution of the Inverse Kinematics (IK) problem for serial robot manipulators, in this study two networks were trained and compared to examine the effect of considering the Jacobian Matrix to the efficiency of the IK solution. Given the desired trajectory of the end effector of the manipulator in a free-of-obstacles workspace, Offline smooth geometric paths in the joint space of the manipulator are obtained.
Neural Network Schemes in Cartesian Space Control of Robot Manipulators
In this paper we are studying the Cartesian space robot manipulator control problem by using Neural Networks (NN). Although NN compensation for model uncertainties has been traditionally carried out by modifying the joint torque/force of the robot, it is also possible to achieve the same objective by using the NN to modify other quantities of the controller. We present and evaluate four different NN controller designs to achieve disturbance rejection for an uncertain system. The design perspectives are dependent on the compensated position by NN. There are four quantities that can be compensated: torque t , force F, control input U and the input trajectory X d. By defining a unified training signal all NN control schemes have the same goal of minimizing the same objective functions. We compare the four schemes in respect to their control performance and the efficiency of the NN designs, which is demonstrated via simulations.
Neural Network Control of Robot Manipulator
—Neural networks are simplified models of the biological nervous system and therefore have drawn their motivation from the kind of computing performed by human brain. A NN is a highly interconnected network of a large number of processing elements called neurons in an architecture inspired by the brain. A neural network can be massively parallel and therefore is said to be massively parallel distributed processing. NN have been successfully applied to problems in the fields of pattern recognition, image processing,forecasting, and optimization. This paper investigates the performance of the neural network design to robot manipulators. Their performances is evaluated in extensive simulations carried out in MATLAB/Simulink by varying the number of neurons and layers in an NN structure.
International Journal of Emerging Research in Management and Technology, 2018
Nowadays the robot technology is advancing rapidly and the use of robots in industries has been increasing. In designing a robot manipulator, kinematicsplays a vital role. The kinematic problem of manipulator control is divided into two types, direct kinematics and inverse kinematics. Robot inverse kinematics, which is important in robot path planning, is a fundamental problem in robotic control. Past solutions for this problem have been through the use of various algebraic or algorithmic procedures, which may be less accurate and time consuming. Artificial neural networks have the ability to approximate highly non-linear functions applied in robot control. The neural network approach deserves examination because of the fundamental properties of computation speed, and they can generalize untrained solutions. In the present work an attempt has been made to evaluate the problemof robot inverse kinematics of Stanford manipulator using artificial neural network approach. Finally two pro...