Giskard(py)

Giskard is an open source motion planning framework for ROS, which uses constraint and optimization based task space control to generate trajectories for the whole body of mobile manipulators.

Installation instructions for Ubuntu 20.04 + Noetic

ROS Workspace

source /opt/ros/noetic/setup.bash           # source ROS
mkdir -p ~/giskardpy_ws/src                 # create directory for workspace
cd ~/giskardpy_ws                           # go to workspace directory
catkin init                                 # init workspace, you might have to pip install catkin-tools
cd src                                      # go to source directory of workspace
wstool init                                 # init rosinstall
wstool merge https://raw.githubusercontent.com/SemRoCo/giskardpy/master/rosinstall/noetic.rosinstall
                                            # update rosinstall file
wstool update                               # pull source repositories
rosdep install --ignore-src --from-paths .  # install dependencies available through apt
pip3 install -r giskardpy/requirements.txt  # install python deps
cd ..                                       # go to workspace directory
catkin build                                # build packages
source ~/giskardpy_ws/devel/setup.bash      # source new overlay

Custom Bullet Bindings

Giskard uses Adrian Röfer's bullet bindings instead of the official ones, as they are much faster for our use case. Install them like this:

mkdir -p ~/libs && cd ~/libs                # choose a place where you want to build pybullet
git clone https://github.com/SemRoCo/bullet3.git
cd bullet3                                  # be sure to be in the bullet3 folder
./build_better_pybullet.sh                  # this script will also clone and build pybind11 into libs
source ~/.bashrc                            # the script adds a python path modification to your bashrc

To test your installation do:

$ python3
Python 3.8.10 (default, Nov 14 2022, 12:59:47) 
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import betterpybullet
>>>

If it doesn't work, make sure that your $PYTHONPATH includes something like /path/to/your/bullet3/build_cmake/better_python:/path/to/your/bullet3/examples/pybullet.

Alternative QP solvers

Giskard supports multiple QP solvers and will automatically use the fasted installed solver.

• qpalm: Default solver, because it is the easiest to install and still reasonably fast.
• qpSWIFT: Fastest open source solver in most cases. Install instructions: https://github.com/qpSWIFT/qpSWIFT.
• gurobi: Commercial solver. Slightly slower than qpSWIFT on most robots. Outperforms qpSWFIT on systems with a lot of dof and/or a large prediction horizon.
  • sudo pip3 install gurobipy
  • You can apply for a free academic license or buy one here: https://www.gurobi.com/academia/academic-program-and-licenses/
  • If you have vpn access to or are in the local network of the IAI of the University of Bremen, follow these instructions: https://ai.uni-bremen.de/wiki/intern/adm/gurobi

Tutorials

https://github.com/SemRoCo/giskardpy/wiki

How to cite

Stelter, Simon, Georg Bartels, and Michael Beetz. "An open-source motion planning framework for mobile manipulators using constraint-based task space control with linear MPC." 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022.