Path planning based on a recurrent neural network for an evolutionary robot

Abstract

To investigate path planning for mobile robots based on a recurrent neural network and evolutionary algorithms,a recurrent neural controller was trained via an improved evolutionary algorithm.An algorithm for path planning was developed based on a recurrent neural network for an evolutionary robot.A combination of Gaussian and Cauchy mutations was used to ensure larger mutation steps and escape from local minima.Crossover and mutation probabilities were adjusted automatically according to variations in the diversity of the population and the fitness of individuals.A detailed process to apply the algorithm was presented.The algorithm was compared with the standard feed-forward network-based method of path planning.Experimental results indicated that the recurrent neural controller has high adaptability to dynamic environments.