Self-correction mechanism for path integration in a modular navigation system on the basis of an egocentric spatial map
Classical Computer Science approaches to navigation by autonomous robots continue to make good progress. However, we have only a limited understanding of how navigation is implemented in the neural networks of animals, which still perform very much better in navigational tasks than robots. In this paper we explore the implementation of neural network based navigation in a simple robot. We use a modular navigation system that contains separate representations of visual input and the path integration process. These representations are combined to influence the behavior of a robot. Both representations are encoded within recurrent neuronal networks. The outputs of the representations are vectors of polar values that encode the location of the nearest object, or of a specific place in the environment. The robot manoeuvres in relation to these attended locations, in the context of its egocentric spatial map. During ego-motion towards a goal, the network representation of the goal moves in a counter-movement due to applied motor feedback. The robot's position is continuously compared against its visual input, and mismatches between the visually perceived goal position and its spatial representation are corrected.
self-correction path integration navigation robotics representation recurrent network