The Oceanid? vehicle is self-propelled by gravity and instructed to autonomously glide lateral to a predefined location on the ocean bottom, for a variety of scientific applications. Upon completion of sensor data logging the unit will perform an environmentally friendly ballast shift creating the required propulsion to return to the surface for final recovery onboard.
When traversing the vertical water column, descending, or ascending, a set of complementary sensors may assist and improve the quality of flight control in real time, or sensor data collected- and post-processed for future utilization during re-deployments or if numerous vehicles is to be dropped sequentially. Measuring and logging basic sensor data such as pressure, temperature, and salt content, in the various water layers, may be used to enhance the acoustic communication, and subsequently improve the underwater positioning through acoustic ranging and triangulation.
All vehicles combine an advance inertia measurement unit, pressure- and compass data, and the digital-twin model of the vehicle, to estimate a relative position along the planned trajectory. This project aim at measuring the difference in this estimated dataset to those positions calculated based on triangulation of acoustic range measurements. By collecting the dataset from one vehicle transit, or ideally multiple vehicles deployed to cover all compass headings, and post-process the data-volume, it is expected to be able to establish a semi real time current profile.
This high-density current profile, measured purely based on existing hardware onboard the individual vehicle and collectively processed onboard the support vessel, may ultimately be uploaded to further improve the quality of flight control of new deployments, e.g., positioning accuracy and efficient lateral displacement.
During the period, scaled experiments were performed on board R / V Gunnerus (/ NTNU) with ROV support, where acoustic measurements, sound speed and current measurements have been carried out to establish a data set for further development of processing algorithms, type. position estimation and to finally establish a current profile. The results have already led to the correction of the test plan, given a calendar delay in offshore testing to kv-2/2022, and new detailed sub-tests are being prepared for testing in water in kv-4/2021. A new supplier for acoustic equipment has been selected and already implemented, and a small number of "dummy" vessels have been established to phase in new acoustics and driver electronics.
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Environmental monitoring is also challenging in terms of ideally covering larger area of exploration, and longer-term measurements, compared to a more condensed production focus in an offshore license, i.e. requesting more efficient deployment of large survey spreads with a coarse sensor density. This also requires means of effective underwater communication and control, since an adoption of vessel-of-opportunity is foreseen and hardwiring between sensors physically impractical.
The project proposed addresses all three challenges described above – cost efficient deployment and operation, flexibility and scalability. Complementary to a recently completed R&D-initiative (Oceanid™), funded by the European Commission (through the Horizon 2020 program), it is proposed to further investigate the concept of free-drop nodes. Or drones that “transport” a generic sensor payload laterally away from the surface drop-point, accurately and repeatedly to a predefined location on the seafloor, and upon completing the survey, return safely to the surface. The drones are purely propelled by gravity shift and a patented and environmental neutral salt-slurry ballast.
Our initiative is targeting a variety of applications, e.g. installation inshore or large seismic surveys offshore. The challenge is the underwater communication and navigation, and the development of tools supporting a safe control of all autonomous activity.
Based on the proposal - an efficient and accurate positioning capacity with high update-rate, the digital twin, a viritual representation of the autonomous drop-node, will allow a post-process comparison. One instance could be dedicated to the ocean current influencing the actual trajectory path, and the subtraction between the real- and simulated dataset post-processed to establish a high-density current profile. No additional sensor hardware is required, and the results can immediately be re-used for new deployments to further improve operational efficiency.
