Abstract
In this paper the kinematic design of a 6-dof parallel robotic manipulator is analysed. Firstly, the condition number of the inverse kinematic jacobian is considered as the objective function, measuring the manipulator's dexterity and a genetic algorithm is used to solve the optimization problem. In a second approach, a neural network model of the analytical objective function is developed and subsequently used as the objective function in the genetic algorithm optimization search process. It is shown that the neuro-genetic algorithm can find close to optimal solutions for maximum dexterity, significantly reducing the computational burden. The sensitivity of the condition number in the robot's workspace is analysed and used to guide the designer in choosing the best structural configuration. Finally, a global optimization problem is also addressed.
Highlights
The advantages of manipulators based on parallel design architectures, compared to the serial‐based ones, are well recognized and justified by the increasing number of applications which, nowadays, try to explore their inherent low positioning errors and high dynamic performance [1,2]
In our work we quantify the error of the Neural Networks (NNs)’s approximation through testing and validating data sets, and we present a direct comparison of the optima obtained using as the fitness function, either the NN’s approximation or the analytical function
In this work we are mainly interested in exploring the well‐known capability of NNs to approximate complicated nonlinear functions [27,28], when applied to the objective functions associated with the optimal design of parallel manipulators
Summary
The advantages of manipulators based on parallel design architectures, compared to the serial‐based ones, are well recognized and justified by the increasing number of applications which, nowadays, try to explore their inherent low positioning errors and high dynamic performance [1,2]. Among these applications, references can be made to robot manipulators and robotic end‐ effectors, high speed machine‐tools and robotic high‐ precision tasks, flight simulators and haptic devices [3].
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