Physics-Informed Graph Neural Network (PIGNN) for BlueROV2 Dynamics

This project implements a heterogeneous Physics-Informed Graph Neural Network to model the 4-DOF dynamics of a BlueROV2 Heavy underwater vehicle. It extends the PINC codebase by replacing the feedforward DNN with a GNN whose graph topology encodes the physical subsystem structure of the vehicle.

Architecture

The heterogeneous graph has four node types representing distinct physical subsystems:

Node Type	Count	Feature Dim	Description
Hull	1	9	4-DOF rigid-body state
Thruster	8	8	One per T200 thruster (Heavy config)
Hydrodynamic	1	4	Drag and added-mass effects
Buoyancy	1	6	Restoring forces (gravity / buoyancy)

Three directed edge types encode force interactions:

Edge Type	Count	Feature Dim	Physical Meaning
Thruster → Hull	8	7	Force/torque from each thruster
Hydrodynamic → Hull	1	8	Drag + added-mass coupling
Buoyancy → Hull	1	4	Restoring force (primarily heave)

Physics-Informed Loss

The model uses the Fossen equation (4-DOF):

M·ν̇ + C(ν)·ν + D(ν)·ν + g(η) = τ

as a soft constraint. The total loss combines:

• Data loss — 1-step-ahead MSE
• Physics-residual loss — penalises deviations from the Fossen ODE
• Initial-condition loss — self-consistency at t=0
• Rollout loss — multi-step prediction error

Hard constraints are encoded via the graph topology itself (only thrusters introduce external forces; all edges target the hull).

Project Structure

pignn_project/
├── data/
│   ├── create_data.py        # Trajectory generation via simulator
│   └── data_utility.py       # Dataset class & input generators
├── models/
│   ├── graph_builder.py      # Heterogeneous graph construction
│   ├── pignn.py              # PIGNN model (core architecture)
│   └── model_utility.py      # Loss functions, training loop
├── training/
│   └── train_pignn.py        # Main training script
├── scripts/
│   └── evaluate_model.py     # Rollout evaluation & plotting
├── src/
│   ├── parameters.py         # BlueROV2 physical parameters
│   ├── bluerov.py            # NumPy simulator (Numba-accelerated)
│   └── bluerov_torch.py      # PyTorch differentiable simulator
├── tests/
│   └── test_pignn.py         # Comprehensive test suite
├── requirements.txt
└── README.md

Installation

# Clone and enter the project
cd pignn_project

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Note: PyTorch Geometric requires separate installation. See https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html

Usage

1. Generate Data

python data/create_data.py

This creates training_set/, dev_set/, test_set_interp/, and test_set_extrap/ directories containing .pt files.

2. Train the Model

python training/train_pignn.py

Monitor with TensorBoard:

tensorboard --logdir runs

3. Evaluate

python scripts/evaluate_model.py \
    --model_path models_saved/pignn_bluerov2_direct_best_dev_epoch_100 \
    --dataset dev_set \
    --n_trajs 5

4. Run Tests

pytest tests/ -v

Governing Equations

The 4-DOF Fossen model (surge, sway, heave, yaw):

• State: η = [x, y, z, ψ], ν = [u, v, w, r]
• Representation: ψ → (cos ψ, sin ψ) for continuity → 9-dim state
• Roll and pitch are excluded (BlueROV2 is passively stable in these DOFs)

References

• Fossen, T.I. (2011). Handbook of Marine Craft Hydrodynamics and Motion Control
• PINC repository: https://github.com/eivacom/pinc-xyz-yaw
• ConFIG gradient method: https://github.com/tum-pbs/ConFIG

License

GPL-3.0 (following the upstream PINC repository)