/aikido

Artificial Intelligence for Kinematics, Dynamics, and Optimization

Primary LanguageC++BSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

AIKIDO - AI for KIDO Build Status codecov Codacy BadgeDOI

⚠️ Warning: AIKIDO is under heavy development. These instructions are primarily for reference by the developers.

AIKIDO is a C++ library, complete with Python bindings, for solving robotic motion planning and decision making problems. This library is tightly integrated with DART for kinematic/dynamics calculations and OMPL for motion planning. AIKIDO optionally integrates with ROS, through the suite of aikido_ros packages, for execution on real robots.

Building from Source

Dependencies

AIKIDO depends on ROS. You should install ROS by adding the ROS repository to your sources.list as follows. We encourage users to install noetic with at least the following packages:

$ sudo apt install ros-noetic-actionlib ros-noetic-geometry-msgs ros-noetic-interactive-markers ros-noetic-roscpp ros-noetic-std-msgs ros-noetic-tf ros-noetic-trajectory-msgs ros-noetic-visualization-msgs

AIKIDO also depends on CMake, Boost, DART (version 6.8.5 or above), OMPL, yaml-cpp, tinyxml2, pr-control-msgs, libmicrohttpd, and the Python development headers (python-dev on Debian systems). DART and AIKIDO both make heavy use of C++14 and require a modern compiler.

On Ubuntu Focal using CMake

You should install the ROS packages as described above to build all the ROS-dependent AIKIDO components (e.g., aikido-control-ros).

Install the other dependencies:

$ sudo add-apt-repository ppa:dartsim/ppa
$ sudo add-apt-repository ppa:personalrobotics/ppa
$ sudo apt-get update
$ sudo apt-get install cmake build-essential libboost-filesystem-dev libdart6-optimizer-nlopt-dev libdart6-utils-dev libdart6-utils-urdf-dev libmicrohttpd-dev libompl-dev libtinyxml2-dev libyaml-cpp-dev pr-control-msgs

Once the dependencies are installed, you can build and install AIKIDO using CMake:

$ mkdir build
$ cd build
$ cmake ..
$ make  # you may want to build AIKIDO using multi-core by executing `make -j4`
$ sudo make install

AIKIDO includes several optional components that depend on ROS. While we suggest building AIKIDO in a Catkin workspace (see below) to enable the ROS components, it is also possible to build those components in a standalone build. To do so, source the setup.bash file in your Catkin workspace before running the above commands, e.g.:

$ . /path/to/my/workspace/setup.bash

On Ubuntu Focal using Catkin

It is also possible to build AIKIDO as a third-party package inside a Catkin workspace. To do so, clone AIKIDO into your Catkin workspace and use the catkin build command like normal.

If you are using the older catkin_make command, then you must build your workspace with catkin_make_isolated. This may dramatically increase your build time, so we strongly recommend that you use catkin build, which is provided by the catkin_tools package, if possible.

On macOS using CMake

Please install Homebrew as described above, then you can easily install the dependencies as follows:

$ cd <aikido_directory>
$ brew bundle

Once the dependencies are installed, you can build and install AIKIDO using CMake:

$ cd <aikido_directory>
$ mkdir build
$ cd build
$ cmake ..
$ make  # you may want to build AIKIDO using multi-core by executing `make -j4`
$ sudo make install

Code Style

Please follow the AIKIDO style guidelines when making a contribution.

License

AIKIDO is licensed under a BSD license. See LICENSE for more information.

Authors

AIKIDO is developed by the Personal Robotics Lab in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. The library was started by Michael Koval (@mkoval) and Pras Velagapudi (@psigen). It has received major contributions from Shushman Choudhury (@Shushman), Ethan Gordon (@egordon), Brian Hou (@brianhou), Aaron Johnson (@aaronjoh), Jennifer King (@jeking), Gilwoo Lee (@gilwoolee), Jeongseok Lee (@jslee02), and Clint Liddick (@ClintLiddick). We also would like to thank Michael Grey (@mxgrey) and Jeongseok Lee (@jslee02) for making changes to DART to better support AIKIDO.