Generation of Emergent Navigation Behavior in Autonomous Agents Using Artificial Vision

In this work, we deal with the dynamics of the movements of autonomous agents, which are able to move in the environment using their own vision. For this, we apply the Continuous Time Recurrent Artificial Neural Network and the genetic encoding proposed in [1] [2]. However, we use a new sensorial description, which consists in captured images by a virtual camera, evolving an artificial visual cortex. The experiments show that the agents are able to navigate in the environment and to find the exit, in a non-programmed way, using only the visual data passed to the neural network. This has the flexibility to be applied in various environments, without displaying a forced tendency by a possible behavioral modeling as in other techniques.