Navigating with an animal brain: a neural network for landmark identification and navigation

Navigating in an unknown environment is a task commonly accomplished by most animals. Nevertheless, it is not justified to infer that this capacity needs complex reasoning involving abstract geometrical computations. In this paper, the authors´ aim is to show that such behavior, including switching between goals, can be simulated by simple artificial neural networks (NN) where no complex computation is performed. The authors present a real development and simulations about a Khepera robot and a simulated system named Prometheus. The authors use a novel neural architecture named PerAc (Perception-Action) which is a systematic way to decompose the control of an autonomous robot in perception and action flows. The authors show that action simplifies the interpretation of perception: each action is a choice and conditions entirely the future of the robot.