A new adaptive learning algorithm for robot manipulator control

Abstract

This paper is devoted to the development and implementation of neural network technology to solve the inverse kinematics problems for serial robot manipulators, given the desired Cartesian path 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. The proposed technique does not require any prior knowledge of the kinematics model of the system being controlled; the main idea of this approach is the use of an artificial neural network to learn the robot system characteristics rather than having to specify an explicit robot system model. Since one of the most important problems in using artificial neural networks is the choice of the appropriate network configuration, two different configurations were compared; they were trained to learn the desired set of joint angles positions from a given set of end effector positions. The generality and efficiency of the proposed algorithm are demonstrated through simulations of a general six-degrees-of-freedom serial robot manipulator.

An Improved Adaptive Kinematics Jacobian Trajectory Tracking of a Serial Robot Passing Through Singular Configurations

“…A major drawback was that it only remembers the most recent data points introduced, the researchers have recommended neural networks so that it would remember the trajectories as it traversed them. Studying the trajectory tracking of a serial manipulator by using ANNs has two problems, one of these is the selection of the appropriate type of network and the other is the generating of suitable training data set (Funahashi, 1998;Hasan et al, 2007). Researchers have applied different methods for gathering training data, while some of them have used the kinematics equations (Karilk & Aydin, 2000;Bingual et al, 2005), others have used the network inversion method (Kuroe et al, 1994); Köker, 2005), while the cubic trajectory planning was also used (Köker et al, 2004), a simulation program has also been used for this purpose (Driscoll, 2000).…”

Development of a real-time-position prediction algorithm for under-actuated robot manipulator by using of artificial neural network

“…Therefore, ANNs have been intensively used for solving regression and classification problems in many fields. A number of realistic approaches have been proposed and justified for applications to robotic systems [20][21][22][23][24][25].…”

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

“…Intelligent control has been introduced as a new direction making control systems able to attribute more intelligence and high degree of autonomy. Artificial Neural Networks (ANN) have been widely used for their extreme flexibility due to the learning ability and the capability of non linear function approximation, a number of realistic control approaches have been proposed and justified for applications to robotic systems (D'Souza et al,2001;Ogawa et al, 2005;Köker, 2005;Hasan et al, 2007;Al-Assadi et al, 2007), this fact leads to expect ANN to be an excellent tool for solving the IK problem for serial manipulators overcoming the problems arising. Studying the IK of a serial manipulator by using ANNs has two problems, one of these is the selection of the appropriate type of network and the other is the generating of suitable training data set (Funahashi, 1998;Hasan et al, 2007).…”