This repository contains a complete simulation and visualization system for Unmanned Surface Vessels, with a focus on intelligent navigation, obstacle avoidance, and control.

• Advanced USV dynamics simulation with realistic water environment
• Multiple control algorithms (PID, LQR, MPC)
• Global and local path planning
• Obstacle detection and avoidance with COLREG compliance
• Real-time 3D visualization
• Extensible architecture for adding new components

• Python 3.8 or later
• Dependencies listed in requirements.txt
• Modern web browser for visualization interface

1. Clone this repository:

   git clone https://github.com/chenxingqiang/Unmanned-Surface-Vessels.git
   cd Unmanned-Surface-Vessels
   

2. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

Use the provided shell script:

./run_usv.sh

Or for more options:

./run_usv.sh --mode obstacles --port 9000 --browser

• --mode or -m: Choose between standard or obstacles (with advanced obstacle avoidance)
• --port or -p: Specify the port to run the web server (default: 9000)
• --debug or -d: Run in debug mode with additional logging
• --browser or -b: Automatically open the visualization in your default web browser

You can also use the Python script directly:

python run_usv.py --mode obstacles --port 9000 --browser

The USV system consists of the following main components:

1. Simulation: Models the USV dynamics and environment
2. Control: Implements various control algorithms
3. Navigation: Handles path planning and obstacle avoidance
4. Visualization: Provides a web-based 3D interface

The system has been thoroughly tested under various scenarios to evaluate performance. Below are key test results and their analysis:

This test compares different obstacle avoidance strategies. The results demonstrate:

• Effective COLREG-compliant obstacle avoidance in dynamic environments
• Comparison of path deviations with different obstacle densities
• Safety distance maintenance while preserving efficient navigation

The waypoint navigation tests show:

• Accurate tracking of predefined waypoints
• Smooth transitions between waypoints
• Performance comparison of different path planning algorithms

PID controller performance specifically tuned for waypoint navigation demonstrates:

• Minimal overshoot at waypoint transitions
• Rapid convergence to desired heading
• Stability in maintaining course between waypoints

Testing at increased speeds reveals:

• Control system stability at higher velocities
• Compensation for increased momentum during turns
• Error bounds at various speed settings

Station keeping tests evaluate the USV's ability to maintain position:

• Position hold accuracy under various conditions
• Energy efficiency of station keeping algorithms
• Drift compensation in the presence of disturbances

These tests analyze the system's response to environmental disturbances:

• Wind and current compensation effectiveness
• Recovery time after significant disturbances
• Comparative performance of different control strategies under disturbances

The system can be configured through YAML files located in the config/ directory. The main configuration file is config/default_config.yaml.

The modular architecture makes it easy to extend the system with new components:

• Add new control algorithms in usv_system/control/
• Implement new obstacle avoidance strategies in usv_system/obstacle_avoidance/
• Create new environmental models in usv_system/models/

This project is licensed under the MIT License - see the LICENSE file for details.

This project was developed as part of research on autonomous navigation systems for marine vessels.

For questions or feedback, please open an issue in this repository.