IEEE Transactions on Systems, Man and Cybernetics
Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Approximation capabilities of multilayer feedforward networks
Neural Networks
Introduction to artificial neural systems
Introduction to artificial neural systems
Neural network design
Robot Analysis and Design: The Mechanics of Serial and Parallel Manipulators
Robot Analysis and Design: The Mechanics of Serial and Parallel Manipulators
A Solution of Inverse Kinematics of Robot Arm Using Network Inversion
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Advances in Engineering Software
Reliability-based approach to the inverse kinematics solution of robots using Elman's networks
Engineering Applications of Artificial Intelligence
Contour Tracking of a Redundant Robot Using Integral Variable Structure Control with Output Feedback
Journal of Intelligent and Robotic Systems
A trajectory tracking application of redundant planar robot arm via support vector machines
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Advances in Artificial Intelligence
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Singularities and uncertainties in arm configurations are the main problems in kinematics robot control resulting from applying robot model, a solution based on using Artificial Neural Network (ANN) is proposed here. The main idea of this approach is the use of an ANN to learn the robot system characteristics rather than having to specify an explicit robot system model. Despite the fact that this is very difficult in practice, training data were recorded experimentally from sensors fixed on each joint for a six Degrees of Freedom (DOF) industrial robot. The network was designed to have one hidden layer, where the input were the Cartesian positions along the X, Y and Z coordinates, the orientation according to the RPY representation and the linear velocity of the end-effector while the output were the angular position and velocities for each joint, In a free-of-obstacles workspace, off-line smooth geometric paths in the joint space of the manipulator are obtained. The resulting network was tested for a new set of data that has never been introduced to the network before these data were recorded in the singular configurations, in order to show the generality and efficiency of the proposed approach, and then testing results were verified experimentally.