Artificial Intelligence for Kinematics, Dynamics, and Optimization
C++ Python CMake M4 Shell Makefile C
Latest commit 6a94d4b Jan 31, 2017 @jslee02 jslee02 committed on GitHub Merge pull request #133 from personalrobotics/fix_vdc_constructor
fix missing argument to VanDerCorput constructor

README.md

AIKIDO - AI for KIDO Build Status Coverage Status

⚠️ 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.

Dependencies

AIKIDO depends on CMake, Boost, DART (version 6.1 or above), OMPL, and the Python development headers (python-dev on Debian systems). DART and AIKIDO both make heavy use of C++11 and require a modern compiler.

Installation (Standalone)

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

$ mkdir build
$ cd build
$ cmake ..
$ make
$ sudo make install

Installation (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.

License

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

Authors

Aikido is developed by the Personal Robotics Lab in the Robotics Institute at Carnegie Mellon University. The library was started by Michael Koval (@mkoval) and Pras Velagapudi (@psigen). It has received major contributions from Shushman Choudhury (@Shushman), Aaron Johnson (@aaronjoh), Jennifer King (@jeking), Gilwoo Lee (@lgw903), and Clint Liddick (@ClintLiddick). We also would like to thank Michael Grey (@mxgrey) and J.S. Lee (@jslee02) for making changes to DART to better support Aikido.