Planejamento e rastreamento de trajetorias e controle de posição atraves de algoritmos geneticos e redes neurais artificiais / Planning and tracking of trajectories and position control by genetic algorithms and artificial neural networks

AUTOR(ES)
DATA DE PUBLICAÇÃO

2003

RESUMO

In this work genetic algorithms and artificial neural networks are used for robot arm tasks. Initially, the genetic algorithms are employed to control the trajectory of a robot arm in a limited workspace with an obstacle. Operations like crossover and mutation are presented to manipulate trajectories determined by line segments. Artificial neural networks are tested to control two realtime processes: a XY-Table and an inverted pendulum. For these processes, it is used a simple structured control where the neural network provides a gain to the proportional control, generating a control signal to the processes. The process error is used for training a neural network, without any kind of off-line training, i.e., the training of the neural network is in realtime. Also, a function determines the learning rate of the back-propagation algorithms as a function of the errors of the process control. Since the neural controller have multiple variables, it was not possible to define an optimal controller for the processes. To solve this problem, a genetic algorithm was used to determine the best neural controller in the workspace used, where the number of neurons in the input and hidden layers, constants to configure the neural controller and the network topology are optimized. The results obtained show that artificial intelligent techniques can be applied to robotics reducing the time of task planning, like: trajectory planning, track planning and the project of efficient controllers

ASSUNTO(S)

redes neurais (computação) genetic algorithms trajectories planning robot arm algiritmos geneticos position control neural networks robos robos - sistemas de controle

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