Baby Sub: a conceptual learning approach to planning and control of AUVs

This paper describes a novel approach to the development of a learning control system for autonomous underwater vehicles (AUV) which presents the AUV as a “baby”-that is, a system with no a priori knowledge of the world in which it operates, but with behavior acquisition techniques that allow it to build this knowledge from the environment itself. The learning techniques are rooted in a nested hierarchical algorithm molded from processes of early cognitive development in humans. The algorithm extracts data from the environment and by means of correlation, it creates schemata (rules) that are used for control. This system is robust enough to deal with a constantly changing environment because such changes provoke the creation of new schemata using generalization, while still maintaining minimal computational complexity, thanks to the system's multiresolutional nature