Computationally efficient kinematics for manipulators with spherical wrists base
International Journal of Robotics Research
IEEE Transactions on Systems, Man and Cybernetics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Advances in Engineering Software
Reliability-based approach to the inverse kinematics solution of robots using Elman's networks
Engineering Applications of Artificial Intelligence
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A new evolutionary-based algorithm is proposed to solve the robot manipulator optimal path generation problem. The following scenario is considered: given a learnt joint path describing a robot manipulator simple task in the Cartesian space, an optimal path is calculated when a different initial joint configuration is considered. The optimization problem is formulated as the minimization of both the end-effector pose error and the total joint displacement so as to ensure convergence towards the learnt path and a smooth joint motion. To solve the optimization problem an algorithm based on an evolutionary method called Differential Evolution (DE) is used. DE is a stochastic direct search optimization method based on the evolution of a candidate solution population in an iterative process of mutation, recombination, and selection. Since the algorithm does not require the use of the Jacobian matrix during the kinematic inversion, singularities problems are overcome. Results on the optimal path generation of a six degrees of freedom robot manipulator are also presented.