Behavior Based Adaptive Control for Autonomous Oceanographic Sampling

This paper describes an investigation into the adaptive control of autonomous mobile sensor platforms for providing oceanographic sampling. Mobile sensor platforms provide an ability to rapidly sample oceanographic data of interest for real-time input into ocean environmental models with the goal of reducing the modeling uncertainty by introducing selected sampled data. The major objective of this paper is to describe the autonomy architecture developed to support adaptive sampling. This architecture consists of an open-source distributed autonomy architecture and an approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control in complex environments with multiple constraints. Experimental results are provided for an adaptive ocean thermal gradient tracking application performed by an autonomous surface craft in Monterey Bay. These results highlight not only the suitability of autonomous sensor platforms for providing adaptive sampling of the ocean environment but, also, the suitability of our behavior-based autonomy approach and distributed autonomy architecture for providing a simple, flexible, and scalable method for autonomous sensor platform control. The paper concludes with an overview of future adaptive sampling experiments planned with autonomous underwater sensor platforms using the same methodology.