BluRaspberry

Biogeochemistry Lawnmower UUV - Raspberry Pi

Optimal Unmanned underwater vehicles (UUVs) Path Planning

Folder Structure

• final_notebooks Notebooks that you can check out if you interested in the process or reproduce what we did.
• data Dataset are shared here.

One-line Description

We aim to develop a system that determine the real-time optimal path for an UUV to explore biologically active regions across a transect of North Atlantic.

Collaborators

Name	Role
Erin Linebarger	Participant
Joyce Cai	Participant
Kayla Mitchell	Participant
Christian Sarason	Project Facilitator

Planning

• Initial idea: use numerical model outputs to simulate potential scenarios for the UUV
• Ideation jam board: link
• Slack channel: #ohw25_proj_blu_raspberry
• Final presentation: Check out the first 13 mins :) link

Background

Goals

Optimization Function for Path Planning

• Develop an algorithm that dynamically adjusts UUV navigation points to maximize sampling efficiency of biological targets (e.g., algal blooms) in the North Atlantic.
• Incorporate environmental variables such as geostrophic currents, temperature, salinity, oxygen utilization, water age, and seafloor distribution.

Integration with Robotic Control Systems

• Create scripts to connect Python-based optimization functions with ROS2, enabling potential direct deployment on UUV platforms.
• Containerize ROS simulations using Docker for reproducibility and open-source use.
• Ensure compatibility with Gazebo for simulation testing.

Targeted Data Acquisition

• Implement a scripted workflow to extract only the necessary oceanographic model data, avoiding full global downloads.
• Download selected variables from the MERCATOR global ocean reanalysis for a defined region, time range, and depth, saving them locally as netCDF files.

Datasets and Methodology

UUV Control and Simulation

• ROS2
• Gazebo

Simulated Ocean field

• OCIM (Ocean Circulation Inverse Model): A global ocean circulation inverse model that estimates large-scale transport and tracer distributions using observational constraints. We use 2° × 2° horizonal resolution and 24 layer.
• Global Ocean Physics Analysis and Forecast: Dataset provides comprehensive, high-resolution ocean state estimates, based on the NEMO (Nucleus for European Modelling of the Ocean) model, a state-of-the-art ocean general circulation model that simulates three-dimensional ocean dynamics, thermodynamics, and sea-ice interactions, assimilating observations to produce accurate analyses and forecasts of the global ocean state. We use 1/12° resolution in the upper 1000 m with a three-day averaging.

Region and time

• Gulf Stream, North Atlantic (30°–45°N, 70°–60°E)
• 3-day average: July 1–3, 2025

Score function Develop the score function based on different considerations, including

• Efficiency: utilize ocean geostrophic current velocity (u,v)
• Biological insteret: gradient of Apparent Oxygen Utilization (AOU). Larger gradient, higer score. AOU is calculated based on temperature and salinity.

Workflow/Roadmap

Results/Findings

Watch the 1000 m/s vehicle flying in the ocean

Lessons Learned

References

1. DeVries, T., & Deutsch, C. (2014). Large-scale variations in the stoichiometry of marine organic matter respiration. Nature Geoscience, 7(12), 890-894.
2. Global Ocean Physics Analysis and Forecast. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). DOI: 10.48670/moi-00016 (Accessed on 21-08-2025)
3. Garcia, H. E., & Gordon, L. I. (1992). Oxygen solubility in seawater: Better fitting equations. Limnology and oceanography, 37(6), 1307-1312.