A General Learning CO-Evolution Method to Generalize Autonomous Robot Navigation Behavior

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 highperformance reactive behavior for navigation and collisions avoidance. The coevolutive method 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 mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.