Information-driven robotic sampling in the coastal ocean

Multi-vehicle adaptive 3D mapping for targeted ocean sampling

Expanding spatial presentation from two-dimensional profile transects to three-dimensional ocean mapping is key for a better understanding of ocean processes. Phytoplankton distributions can be highly patchy and the accurate identification of these patches with the context, variability, and uncertainty of measurements on relevant scales is difficult to achieve. Traditional sampling methods, such as plankton nets, water samplers and in-situ vertical sensors, provide a snapshot and often miss the fine-scale horizontal and temporal variability. Here, we show how two autonomous underwater vehicles measured, adapted to, and reported real-time chlorophyll a measurements, giving insights into the spatiotemporal distribution of phytoplankton biomass and patchiness. To gain the maximum available information within their sensing scope, the vehicles moved in an adaptive fashion, looking for the regions of the highest predicted chlorophyll a concentration, the greatest uncertainty, and the least possibility of collision with other underwater vehicles and ships. The vehicles collaborated by exchanging data with each other and operators via satellite, using a common segmentation of the area to maximize information exchange over the limited bandwidth of the satellite. Importantly, the use of multiple autonomous underwater vehicles reporting real-time data combined with targeted sampling can provide better match with sampling towards understanding of plankton patchiness and ocean processes.

Impact of flow field resolution on produced water transport in Lagrangian and Eulerian models

During offshore petroleum production, large volumes of produced water are continuously discharged. The environmental impact from such discharges is typically assessed with numerical models, which simulate the transport and dilution of the produced water plume in order to predict environmental concentrations of its chemical constituents. In this study we investigate the effects of model resolution (800 m and 4 km) on produced water dispersion. We also compare two different types of models, a Lagrangian particle model, and an Eulerian grid-based ocean model to assess the Eulerian consistency of the Lagrangian model. We consider a point source off the coast of mid-Norway, during two different seasons (winter and spring). In general, the two models are in reasonable agreement. We find a substantial difference in tracer distribution and concentrations between the two resolutions, and to a lesser extent between seasons; in particular, the 800 m model shows lower concentrations along the coast.

A Novel Paradigm for Underwater Monitoring Using Mobile Sensor Networks

This paper presents a novel autonomous environmental monitoring methodology based on collaboration and collective decision-making among robotic agents in a heterogeneous swarm developed within the project subCULTron, tested in a realistic marine environment. The swarm serves as an underwater mobile sensor network for exploration and monitoring of large areas. Different robotic units enable outlier and fault detection, verification of measurements and recognition of environmental anomalies, and relocation of the swarm throughout the environment. The motion capabilities of the robots and the reconfigurability of the swarm are exploited to collect data and verify suspected anomalies, or detect potential sensor faults among the swarm agents. The proposed methodology was tested in an experimental setup in the field in two marine testbeds: the Lagoon of Venice, Italy, and Biograd an Moru, Croatia. Achieved experimental results described in this paper validate and show the potential of the proposed approach.