Publication: Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior
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2002-02
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Springer
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Abstract
In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.
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Keywords
Robot navigation problem, Generalized behavior, Competitive coevolution, Learning examples-based, Evolutionary strategies
Bibliographic citation
Journal of Intelligent and Robotic Systems, 2002, vol. 33, n. 2, p. 139-166