Learning Environmental Field Exploration with Computationally Constrained Underwater Robots: Gaussian Processes Meet Stochastic Optimal Control.

Autonomous exploration of environmental fields is one of the most promising tasks to be performed by fleets of mobile underwater robots. The goal is to maximize the information gain during the exploration process by integrating an information-metric into the path-planning and control step. Therefore, the system maintains an internal belief representation of the environmental field which incorporates previously collected measurements from the real field.

Embedded Spherical Localization for Micro Underwater Vehicles Based on Attenuation of Electro-Magnetic Carrier Signals.

Self-localization is one of the most challenging problems for deploying micro autonomous underwater vehicles ( μ AUV) in confined underwater environments. This paper extends a recently-developed self-localization method that is based on the attenuation of electro-magnetic waves, to the μ AUV domain. We demonstrate a compact, low-cost architecture that is able to perform all signal processing steps present in the original method.