Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Analysis of Mechanisms and Robot Manipulators
Analysis of Mechanisms and Robot Manipulators
Design improvements for a linear hybrid step micro-actuator
Microsystem Technologies - Special issue: Colloquium on Micro Production, Aachen, Germany, 2-3 March 2005
Advances in Engineering Software
Advances in Engineering Software
Techniques for Generating the Goal-Directed Motion of Articulated Structures
IEEE Computer Graphics and Applications
FPGA Implementation of Genetic Algorithm for UAV Real-Time Path Planning
Journal of Intelligent and Robotic Systems
Advances in Engineering Software
Reliability-based approach to the inverse kinematics solution of robots using Elman's networks
Engineering Applications of Artificial Intelligence
Inverse kinematics in robotics using neural networks
Information Sciences: an International Journal
Robotics and Computer-Integrated Manufacturing
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Performance assessment of foraging algorithms vs. evolutionary algorithms
Information Sciences: an International Journal
Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning
Robotics and Computer-Integrated Manufacturing
Information Sciences: an International Journal
Similarity-based training set acquisition for continuous handwriting recognition
Information Sciences: an International Journal
Information Sciences: an International Journal
Integration of particle swarm optimization and genetic algorithm for dynamic clustering
Information Sciences: an International Journal
Grey-prediction self-organizing fuzzy controller for robotic motion control
Information Sciences: an International Journal
Multi-stage genetic programming: A new strategy to nonlinear system modeling
Information Sciences: an International Journal
Three-dimensional neural net for learning visuomotor coordination of a robot arm
IEEE Transactions on Neural Networks
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
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The solution of the inverse kinematics problem is fundamental in robot control. Many traditional inverse kinematics problem solutions, such as the geometric, iterative, and algebraic approaches, are inadequate for redundant robots. Recently, much attention has been focused on a neural-network-based inverse kinematics problem solution in robotics. However, the precision of the result obtained from a neural network requires improvement for certain sensitive tasks. In this paper, neural network and genetic algorithms are used together to solve the inverse kinematics problem of a six-joint Stanford robotic manipulator to minimize the error at the end effector. The proposed hybrid approach combines the characteristics of neural networks and evolutionary techniques to obtain more precise solutions. Three Elman neural networks were trained using separate training sets because one of the sets yields better results than the other two. The floating-point portions of each network were placed in the initial population of the genetic algorithm with the floating-point portions from randomly generated solutions. The end-effector position error was defined as the fitness function, and the genetic algorithm was implemented. Using this approach, the floating-point portion of the neural-network result was improved by up to ten significant digits using a genetic algorithm, and the error was reduced to micrometer levels. These results were compared with those from studies in the literature and found to be significantly better.