The AWAS project targets a key challenge in ocean observation using autonomous systems, which is how to make in situ water sampling and real-time analysis of such samples as an autonomous process onboard small conventional vessels and autonomous surface vessels (ASVs).

The scope includes both bringing water samples back for onshore laboratory analysis, miniaturized autonomous onboard laboratory where machine learning will be used to address bio-optic data analytics, and adaptive techniques to collect samples at optimal locations and times to maximize information.

This implies taking water samples and either bring them to shore for analysis, or to analyse them in an
autonomous miniature lab onboard the marine vessel. All of this shall be done autonomously and demonstrated in an application that involves detection and tracking of harmful algal blooms in an integrated observation system that will collect and use data from meso-scale to microscale. In the scenario we envision, water sampling is triggered by guidance from a drone or satellite that overfly coastal waters where images show a surface expression consistent with conditions which are flagged as adversarial for ocean health including for aquaculture and the coastal environment. In this way, water sampling becomes adaptive and provides significantly increased real-time information value.

For validation of the methods, we plan to use ASVs and fish-farm workboats for environmental monitoring, including phytoplankton bloom dynamics as a case of high relevance for the society in general, and Harmful Algal Blooms (HABs) and salmon lice larvae that are particularly relevant for the aquaculture industry.

The project takes an inter-disciplinary approach to bring researchers, having expertise on enabling technology in AI/autonomy, together with researchers in chemistry and marine biology, having expertise in ocean bio-optics and water sampling, as well as industry. It is expected to not only lead to fundamental knowledge, but also to
innovations that can be exploited by industry and government agencies. NTNU's project partners are NIVA, Maritime Robotics and Moen Marin.

Key personnell

• Stephen Grant, Dept. Biology, postdoc
• Matias Haugum, Dept. Engr. Cybernetics, PhD candidate
• Nicholas Sanchez, Dept. Chemistry, researcher
• Prof. Tor Arne Johansen, Dept. Engr. Cybernetics, project manager
• Prof. Geir Johnsen, Dept. Biology
• Prof. Murat van Ardelan, Dept. Chemistry
• Glaucia Fragoso, Dept. Biology
• Prof. Annette Stahl, Dept. Engr. Cybernetics
• Prof. Jo Arve Alfredsen, Dept. Engr. Cybernetics