This is the entrypoint for the wave energy harvesting buoy project.

See documentation here.

And MBARI-WEC in action using Gazebo simulator here:

Simulation Repositories

These are the repositories for the project:

• mbari_wec_utils: ROS 2 messages, interface API, and examples for receiving and sending data to a physical or simulated buoy.
  • buoy_interfaces: ROS 2 messages to recieve and send data to a physical or simulated buoy
  • buoy_api_cpp: C++ Interface to MBARI Power Buoy including Controller examples to run against a physical or simulated buoy.
  • buoy_api_py: Python Interface to MBARI Power Buoy including Controller examples to run against a physical or simulated buoy.
• mbari_wec_gz
  • buoy_description: Buoy model description.
  • buoy_gazebo: Gazebo plugins, worlds and launch files to simulate the buoy.

Interfaces and Examples

There are two GitHub template repositories set up (cpp/python) for a quick start on writing a custom controller utilizing buoy_api_cpp and buoy_api_py. Please see cpp examples and python examples for example controller implementations.

• mbari_wec_template_cpp
• mbari_wec_template_py

Install

On Host System

Requirements

At the moment, MBARI WEC is supported by source installation only. Use Ubuntu Jammy (22.04).

1. Install ROS 2 Humble (preferably binary installation)

   MBARI WEC is tested against cyclonedds rmw implementation (default changed from Galactic to Humble)

   sudo apt install -y ros-humble-rmw-cyclonedds-cpp
   export RMW_IMPLEMENTATION=rmw_cyclonedds_cpp
   

2. Install Gazebo Garden (preferably binary installation)

   Installing the binaries for Gazebo is recommended, but if building Gazebo Garden from source, it is necessary to export the PYTHONPATH for gz-math python bindings when building the mbari_wec_gz repository below:

   export PYTHONPATH=$PYTHONPATH:<path to your gz-sim workspace>/install/lib/python
   

   See gz-math Python Get Started tutorial. This step is needed until PYTHONPATH is automatically exported upstream, tracked in this issue

3. Install necessary tools

   sudo apt install python3-vcstool python3-colcon-common-extensions python3-pip git wget
   

4. Install necessary libraries

   curl -s --compressed "https://hamilton8415.github.io/ppa/KEY.gpg" | gpg --dearmor | sudo tee /etc/apt/trusted.gpg.d/ppa.gpg >/dev/null
   sudo curl -s --compressed -o /etc/apt/sources.list.d/my_list_file.list "https://hamilton8415.github.io/ppa/my_list_file.list"
   sudo apt update
   sudo apt install libfshydrodynamics=1.3.1
   

Build

1. Create a workspace, for example:

   mkdir -p ~/mbari_wec_ws/src
   cd ~/mbari_wec_ws/src
   

2. Clone all source repos with the help of vcstool:

   wget https://raw.githubusercontent.com/osrf/mbari_wec/main/mbari_wec_all.yaml
   vcs import < mbari_wec_all.yaml
   cd ~/mbari_wec_ws
   

3. Set the Gazebo version to Garden. This is needed because we're not using an official ROS + Gazebo combination:

   export GZ_VERSION=garden
   

4. Install ROS dependencies

   sudo pip3 install -U rosdep
   sudo rosdep init
   rosdep update
   rosdep install --from-paths src --ignore-src -r -y -i
   

5. Build and install

   source /opt/ros/humble/setup.bash
   cd ~/mbari_wec_ws
   colcon build
   

Using Docker

Requirements

1. Install Docker using installation instructions.

2. Complete the Linux Postinstall steps to allow you to manage Docker as a non-root user.

3. If you have an NVIDIA graphics card, it can help speed up rendering. Install nvidia-docker.

Use Existing Image on DockerHub

MBARI maintains Docker images for the two most recent releases on their DockerHub:

• mbari/mbari_wec:latest
• mbari/mbari_wec:previous

1. Get run.bash script.

   git clone -b main https://github.com/osrf/mbari_wec.git
   cd ~/mbari_wec/docker/
   

   Or

   wget https://raw.githubusercontent.com/osrf/mbari_wec/main/docker/run.bash
   chmod +x run.bash
   

2. Run the container

   If you have an NVIDIA graphics card

   ./run.bash mbari/mbari_wec:latest
   

   Otherwise

   ./run.bash mbari/mbari_wec:latest --no-nvidia
   

Build from Dockerfile

An alternative to using the images from MBARI's DockerHub would be to build from a Dockerfile. This is convenient if you would like to make any changes.

1. Clone the mbari_wec repository to download the latest Dockerfile.

   git clone -b main https://github.com/osrf/mbari_wec.git
   cd ~/mbari_wec/docker/
   

2. Build the docker image

   If you have an NVIDIA graphics card

   ./build.bash nvidia_opengl_ubuntu22
   ./build.bash mbari_wec
   

   Otherwise

   ./build.bash mbari_wec --no-nvidia
   

3. Run the container

   If you have an NVIDIA graphics card

   ./run.bash mbari_wec
   

   Otherwise

   ./run.bash mbari_wec --no-nvidia
   

4. To have another window running the same docker container, run this command in a new terminal:

   ./join.bash mbari_wec
   

Quick start

Quick start scripts are provided in the home directory:

This sources the compiled workspace:

. setup.bash

This sources the compiled workspace and launches the simulation:

./run_simulation.bash

Run

1. In a new terminal (whether on host machine or in Docker container), source the workspace

   . ~/mbari_wec_ws/install/setup.bash
   

2. Set SDF_PATH to allow robot_state_publisher to parse the robot description from the sdformat model:

   export SDF_PATH=$GZ_SIM_RESOURCE_PATH
   

3. Launch the simulation

   ros2 launch buoy_gazebo mbari_wec.launch.py
   

For maintainers only: To upload to DockerHub

Make sure you have permissions to push to the MBARI organization on DockerHub. This permission is given by the MBARI administrator.

Build the mbari_wec Docker image, as detailed above.

Find the image ID for mbari_wec:

docker images

Tag the image with the destination name:

docker tag <IMAGE ID> mbari/mbari_wec:latest

Push to the mbari/mbari_wec public image.

docker push mbari/mbari_wec:latest

You may have to log in for it to recognize your permissions:

docker login