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:

• mbariwecutils: 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
  • buoyapicpp: C++ Interface to MBARI Power Buoy including Controller examples to run against a physical or simulated buoy.
  • buoyapipy: Python Interface to MBARI Power Buoy including Controller examples to run against a physical or simulated buoy.
• mbariwecgz
  • 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 buoyapicpp and buoyapipy. Please see cpp examples and python examples for example controller implementations.

• mbariwectemplate_cpp
• mbariwectemplate_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 mbariwecgz 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-container-toolkit.

   1. Follow Installation steps for Ubuntu (currently 3 steps)
   2. Skip down to Configuration and run nvidia-ctk to set up the nvidia container runtime

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

1. 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/

1. Build the docker image

If you have an NVIDIA graphics card ./build.bash nvidia_opengl_ubuntu24 ./build.bash mbari_wec Otherwise ./build.bash mbari_wec --no-nvidia

1. Run the container

If you have an NVIDIA graphics card ./run.bash mbari_wec_nvidia Otherwise ./run.bash mbari_wec_no_nvidia --no-nvidia

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

./join.bash <name of image> where the name of the image is one of mbari_wec_nvidia or mbari_wec_no_nvidia

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

Logs from the run will be saved to /logs in the container which is mapped to ~/mbari_wec/docker/logs on the host. These logs will be in the same CSV format as generated by the physical buoy. You may also collect rosbags in the /logs folder in the container. The rosbags collected in the sim will also be the same ROS 2 messages collected on the physical buoy.

Run

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

. ~/mbari_wec_ws/install/setup.bash

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

export SDF_PATH=$GZ_SIM_RESOURCE_PATH

1. 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

Publications

Dizon, Chris, Ryan Coe, Andrew Hamilton, Dominic Forbush, Michael Anderson, Ted Brekken, and Giorgio Bacelli. 2024. "Analysis on Evaluations of Monterey Bay Aquarium Research Institute’s Wave Energy Converter’s Field Data Using WEC-Sim and Gazebo: A Simulation Tool Comparison" Applied Sciences 14, no. 23: 11169. https://doi.org/10.3390/app142311169