Toward adaptive robotic sampling of phytoplankton in the coastal ocean.

Currents, wind, bathymetry, and freshwater runoff are some of the factors that make coastal waters heterogeneous, patchy, and scientifically interesting-where it is challenging to resolve the spatiotemporal variation within the water column. We present methods and results from field experiments using an autonomous underwater vehicle (AUV) with embedded algorithms that focus sampling on features in three dimensions. This was achieved by combining Gaussian process (GP) modeling with onboard robotic autonomy, allowing volumetric measurements to be made at fine scales. Special focus was given to the patchiness of phytoplankton biomass, measured as chlorophyll a (Chla), an important factor for understanding biogeochemical processes, such as primary productivity, in the coastal ocean. During multiple field tests in Runde, Norway, the method was successfully used to identify, map, and track the subsurface chlorophyll a maxima (SCM). Results show that the algorithm was able to estimate the SCM volumetrically, enabling the AUV to track the maximum concentration depth within the volume. These data were subsequently verified and supplemented with remote sensing, time series from a buoy and ship-based measurements from a fast repetition rate fluorometer (FRRf), particle imaging systems, as well as discrete water samples, covering both the large and small scales of the microbial community shaped by coastal dynamics. By bringing together diverse methods from statistics, autonomous control, imaging, and oceanography, the work offers an interdisciplinary perspective in robotic observation of our changing oceans.

Integrated Monitoring of Mola mola Behaviour in Space and Time.

Over the last decade, ocean sunfish movements have been monitored worldwide using various satellite tracking methods. This study reports the near-real time monitoring of fine-scale (< 10 m) behaviour of sunfish. The study was conducted in southern Portugal in May 2014 and involved satellite tags and underwater and surface robotic vehicles to measure both the movements and the contextual environment of the fish. A total of four individuals were tracked using custom-made GPS satellite tags providing geolocation estimates of fine-scale resolution. These accurate positions further informed sunfish areas of restricted search (ARS), which were directly correlated to steep thermal frontal zones. Simultaneously, and for two different occasions, an Autonomous Underwater Vehicle (AUV) video-recorded the path of the tracked fish and detected buoyant particles in the water column. Importantly, the densities of these particles were also directly correlated to steep thermal gradients. Thus, both sunfish foraging behaviour (ARS) and possibly prey densities, were found to be influenced by analogous environmental conditions. In addition, the dynamic structure of the water transited by the tracked individuals was described by a Lagrangian modelling approach. The model informed the distribution of zooplankton in the region, both horizontally and in the water column, and the resultant simulated densities positively correlated with sunfish ARS behaviour estimator (rs = 0.184, p<0.001). The model also revealed that tracked fish opportunistically displace with respect to subsurface current flow. Thus, we show how physical forcing and current structure provide a rationale for a predator's fine-scale behaviour observed over a two weeks in May 2014.

Robotics in remote and hostile environments.

In our continuing quest for knowledge, robots are powerful tools for accessing environments too dangerous or too remote for human exploration. Early systems functioned under close human supervision, effectively limited to executing preprogrammed tasks. However, as exploration moves to regions where communication is ineffective or unviable, robots will need to carry out complex tasks without human supervision. To enable such capabilities, robots are being enhanced by advances ranging from new sensor development to automated mission planning software, distributed robotic control, and more efficient power systems. As robotics technology becomes simultaneously more capable and economically viable, individual robots operated at large expense by teams of experts are increasingly supplemented by teams of robots used cooperatively under minimal human supervision.