Metadata-Version: 2.1
Name: gaikpy
Version: 0.3.1
Summary: Calculates and visualises forward and (full-pose) inverse kinematic realised with a genetic algorithm (ga) for URDF models
Home-page: https://github.com/knowledgetechnologyuhh/gaikpy
Author: Erik Strahl
Author-email: strahl@informatik.uni-hamburg.de
License: UNKNOWN
Description: # Introduction
        
        ![Nico using ik](https://github.com/knowledgetechnologyuhh/gaikpy/blob/main/nico_ik.jpg?raw=true "NICO ik")
        
        gaikpy is a pure python approach to solve the inverse kinematics for every URDF modelled robot.
        
        gaikpy solves the inverse kinematics for every URDF based robot model using a genetic algorithm approach. No pretraining is needed. gaikpy is completely realised in python. gaikpy has already integrated the [NICO robot](https://www.inf.uni-hamburg.de/en/inst/ab/wtm/research/neurobotics/nico.html), but you can easily extend this with every robot with a URDF model at hand. Just take the URDF and define your chain ( containing the joints you want to use) , calculate the ik and maybe visualize it. 
        
        If you use the library in an academic context, you can cite it by citing the paper below. We analysed the ik in a hybrid manipulation context for the NICO robot ( the measured calculation times in the paper are already outdated, gaikpy is much faster now ).
        
        Neuro-Genetic Visuomotor Architecture for Robotic Grasping   
        Matthias Kerzel, Josua Spisak, Erik Strahl, Stefan Wermter
        Artificial Neural Networks and Machine Learning – ICANN 2020, pages 533-545 - 2020.
        
        https://www2.informatik.uni-hamburg.de/wtm/publications/2020/KSSW20/ICANN_2020__Neuro_Genetic_Visuomotor_Framework_Preprint.pdf
        
        # History and credits
        
        The Neuro Inspired COmpanion ( or [NICO](https://www.inf.uni-hamburg.de/en/inst/ab/wtm/research/neurobotics/nico.html) ) is the robot we developed at the [Knowledge Technology group](https://www.inf.uni-hamburg.de/en/inst/ab/wtm/about.html) at the [University of Hamburg](https://www.uni-hamburg.de/en.html) to fullfill our [research needs](https://www2.informatik.uni-hamburg.de/wtm/publications/2017/KSMNHW17/NICO_RO-MAN_2017-PREPRINT-2017.pdf). 
        
        Our philosophy is to have a full python based interface for the NICO ( besides our ROS based interface ), so we searched first for an existing python based ik library. We found [ikpy](https://github.com/Phylliade/ikpy), which is a great software, but was at least at time we started not able to solve the full-pose ik ( but only the position ) for our NICO robot. 
        
        As we favour the idea of bioinspired algoritms in our group and one of our former students has [developed a genetic based ik approach in C#](https://ieeexplore.ieee.org/document/7866587), we took parts of this concept and adapted this to our python approach, which you see here with gaikpy.
        
        
        ![Nico using ik](https://github.com/knowledgetechnologyuhh/gaikpy/blob/main/nico_opt.gif?raw=true "NICO ik")
        
        # Installation
        
        + Clone the repository with git clone 
        + Change to the directory - "cd gaikpy"
        + Create a python environment - "python3 -m venv env"
        + Source your environment - "source ./env/bin/activate"
        + Install - "python setup.py develop" or "python setup.py install"
        + Run the test - "python setup.py test"
        You should have no errors ( warnings are fine, the included libraries might throw some)
        
        # Build the docs
        
        + cd gaikpy/docs
        + make html
        + browse at gaikpy/docs/build/html
        
        # Usage
        
        Use the gaikpy to solve the full pose ik for you. 
        See the example at ./examples/visualise_NICO.py . It will solve the ik of the NICO robot and visualise the results on the screen. Extend the example for your own needs.
        
        # Use other robots
        
        Using other robots is very easy, if you have a URDF model of the robot at hand. Just take the URDF model and list the joints and links of your active chain until the end effector. Just adapt the NICO example ./examples/visualise_NICO.py o see how it works.
        We will integrate other robots by efault in the future. 
        
        # License
        
        GNU GENERAL PUBLIC LICENSE Version 3
Platform: Posix; Linux; MacOS X; Windows
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
