On-board Adaptive Informative Sampling for AUVs: a Feasibility Study

摘要

Autonomous underwater vehicles (AUVs) can be used for sampling our lakes and oceans to measure temperature, Chlorophyll abundance, or other physical properties of the water. A commonly proposed method for collecting informative data in robotics is informative sampling or informative path planning. In informative sampling, information-theoretic metrics are used on top of a created model to decide where to deploy sensors or robots. If a model of the environment is already available, this can be done off-line. Alternatively a model can be learned and optimized for on-line, in adaptive informative sampling (AIS). In this paper we report on field trials where we assess the feasibility of running AIS on board a commercial-off-the-shelf AUV. We propose a method of quantifying AIS field performance by using bathymetric data. We show that it is possible to create a Gaussian Process model on board a small commercial-off-the-shelf AUV, and to use this model to determine waypoints for sampling. Our field results show similar performance curves between full and half duration runs, resulting in good models of bathymetry and Chlorophyll fields.