Adaptive Underwater Robotic Sampling of Dispersal Dynamics in the Coastal Ocean

Abstract

To get a better understanding of the highly nonlinear processes driving the ocean, efficient and informative sampling is critical. By combining robotic sampling with ocean models we are able to choose informative sampling sites and adaptively change our path based on measurements. We present models exploiting prior information from ocean models as well as real-time information from in situ measurements. The method uses compact Gaussian process modeling and objective functions to locate informative sampling sites. Our aim is to get a better understanding of ocean processes and improve real-time monitoring of dispersal dynamics. The case study focuses on a fjord located in Norway containing a seafill for mine tailings. Transportation of the deposited particles are studied, and the sampling method is tested in the area. The results from these sea trials are presented.