🚤 ASV-Simulation

A ROS Noetic + Gazebo 11 simulation for an Autonomous Surface Vessel (ASV) with real-time perception, state control, and C++ A* path planning. Built as a rapid showcase project for generalist robotics roles.

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🎥 Demo Videos

🔴 Red Object Detection (Stop when centered)
Watch a short demonstration of the system detecting a red object in simulation and commanding the robot to stop when it is centered in view.
👉 Watch on YouTube

🛑 Detection + Avoidance + Resume
Watch a demonstration of the autonomous system detecting a red object, executing an avoidance maneuver when it's too close, and resuming patrol when safe.
👉 Watch on YouTube

🧭 A* Path Planning Visualization
See the A* path planning module in action as it computes an optimal path through a grid-based environment and publishes the result to RViz.
👉 Watch on YouTube

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📌 Project Goals

• Build a functional ASV simulator using ROS Noetic and Gazebo 11
• Integrate red-object detection using OpenCV
• Implement real-time state changes (approach, avoid, patrol)
• Add a C++ A* path planner with ROS visualization
• Showcase ability to learn and apply robotics skills rapidly

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⚙️ Features

• ✅ TurtleBot3-based simulation in Gazebo
• ✅ Real-time red object detection using OpenCV + ROS camera feed
• ✅ Behavior state machine with patrol, stop, and avoidance
• ✅ C++ implementation of A* path planning
• ✅ Visualization of path in RViz using nav_msgs/Path
• ✅ Unit testing with Google Test
• 🔜 Full control loop integration with Gazebo vessel model
• 🔜 Single-command launch automation

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🗂️ Directory Structure

ASV-Simulation/
├── scripts/           # Python ROS nodes (e.g., red object detector)
├── src/               # C++ ROS nodes (e.g., astar_planner)
├── test/              # Unit tests using Google Test
├── launch/            # Launch files
├── models/            # Custom Gazebo models
├── worlds/            # Custom Gazebo world file(s)
├── urdf/              # Optional robot description
├── CMakeLists.txt     # ROS build script
├── package.xml        # ROS package metadata
└── README.md

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🚀 How to Run

✅ Prerequisites

• Ubuntu 20.04
• ROS Noetic
• Gazebo 11
• Dependencies:
  • cv_bridge
  • rqt_image_view
  • turtlebot3, turtlebot3_gazebo

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1️⃣ Build the Workspace

cd ~/catkin_ws/src
git clone https://github.com/yourusername/ASV-Simulation.git asv_simulation
cd ~/catkin_ws
catkin_make
source devel/setup.bash

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2️⃣ Launch the Gazebo World

export TURTLEBOT3_MODEL=burger
roslaunch turtlebot3_gazebo turtlebot3_world.launch

Ensure a red object is visible to the robot. You can place one manually or modify the .world file.

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3️⃣ Start Red Buoy Detector

rosrun asv_simulation red_buoy_detector.py

This will:

• Subscribe to /image_raw
• Convert the feed with cv_bridge
• Detect red objects using HSV masking
• Show OpenCV output window labeled "Red Detection"

Use this to verify detection:

rqt_image_view /image_raw

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4️⃣ Run the A* Path Planner

rosrun asv_simulation astar_planner

This node:

• Computes a path from (0, 0) to (9, 9) in a hardcoded grid
• Publishes the path as nav_msgs/Path to /asv_path
• Can be visualized in RViz (fixed frame = map, display type = Path)

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🧪 Testing

Unit tests are provided using Google Test.

To run them:

catkin_make run_tests

Included Tests:

• ✅ Path exists from (0,0) to (9,9)
• ✅ Path is trivial if start == goal
• ✅ Path is empty if start point is blocked

These confirm algorithm correctness and boundary behavior.

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🔍 Implementation Highlights

Red Buoy Detection

• Implemented in scripts/red_buoy_detector.py
• Uses cv_bridge and OpenCV to extract red regions from HSV color space
• Triggers state transitions based on object proximity and centering

A* Path Planning

• Implemented in src/astar.cpp and astar_planner.cpp
• Uses classic A* with Euclidean heuristic
• Publishes nav_msgs/Path for visualization
• Separated into astar.h/cpp for testability and modularity

Testing

• Google Test framework in test/test_astar.cpp
• Validates planning logic and rejection of invalid conditions

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✍️ Author

Jeff Lacey
Software Engineer – iOS, Robotics, Autonomous Systems
GitHub: github.com/jeffla

This project was created as a 7-day fast-track demonstration. Feedback welcome!