Adaptive Sampling for Marine Robotics

• Main Author: Fossum, Trygve Olav
• Format: Dissertation
• Language: English

Maintaining a healthy ocean is of the utmost importance. Having only a limited set of resources available to study this vast domain requires research and science to focus on more efficient data collection. Determining when and where to sample is, in this regard, a crucial question. The introduction of robotic elements into ocean observation practices have augmented traditional ship-based sampling and provided efficient and reliable sensing platforms for autonomous sampling of oceanographic data, enabling measurements on scales logistically impossible using traditional techniques. However, robotic sampling still relies on deterministic pre-programmed sensing schemes, consisting of sequential waypoints scripted with mission planning tools. In this case, all relevant information is implemented into the mission a priori. This is problematic, since prior knowledge of oceanographic conditions is usually poor leading to substantial uncertainty; consequently, plans for sampling the oceans are suboptimal at best. Alternatively, the platform can be programmed to adjust the sampling plan online during the mission, capitalizing on both prior and current (in-situ) information. In this setting, sampling happens sequentially over time, according to a conditional plan which changes online during the mission in response to observed data. This type of autonomous sampling scheme is typically referred to as adaptive sampling or data-driven sampling. Adaptive/data-driven strategies can operate on an a posterior knowledge base and react to current conditions. The impact of this is twofold: i) enabling the sensor platform to divert from the mission if favorable circumstances materialize (opportunistic behavior), and ii) increasing the prospect of retrieving pursued information more effectively. The latter aspect is often considered the most noteworthy, especially for resource intensive environmental sensing applications, having the potential to reduce time and cost. This thesis presents different methods and applications in adaptive sampling for marine robotics, focusing on exploration of the upper ocean using single platform applications. The coastal ocean and the upper water column are characterized by substantial heterogeneity and spatio-temporal variation. Sampling can therefore benefit from access to synoptic marine data sources such as ocean models and remote sensing, but due to computational limitations and accuracy, these information sources must be used in combination w