Package: gaikpy
Introductiongaikpy 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 theNICO robot, 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the ik in a hybrid manipulation context for the NICO robot ( the measured calculation times in the paper are already outdated, gaikpy is much faster now ).Neuro-Genetic Visuomotor Architecture for Robotic GraspingMatthias Kerzel, Josua Spisak, Erik Strahl, Stefan Wermter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.pdfHistory and creditsThe Neuro Inspired COmpanion ( orNICO) is the robot we developed at theKnowledge Technology groupat theUniversity of Hamburgto fullfill ourresearch needs.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 foundikpy, 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 hasdeveloped a genetic based ik approach in C#, we took parts of this concept and adapted this to our python approach, which you see here with gaikpy.InstallationFrom the pypi repositorycreate a python environmentpython3 -m venv envactivate the environementsource ./env/bin/activateget gaikpy and the depening packagespip install gaikpyrun the demodemo.pyUsing the souce code from githubClone the repository with git clonegit clonehttps://github.com/knowledgetechnologyuhh/gaikpy.gitChange to the directorycd gaikpyCreate a python environmentpython3 -m venv envActivate your environmentsource ./env/bin/activateInstallpython setup.py developorpython setup.py installRun the testpython setup.py test
You should have no errors ( warnings are fine, the included libraries might throw some)Run the demopython demo.pyBuild the docscd gaikpy/docsmake htmlbrowse at gaikpy/docs/build/htmlUsageUse the gaikpy to solve the full pose ik for you.The pip package and the github rep contain both the NICO urdf model, so that you use it instantly.The demo.py program uses the NICO model and calculates the ik for some test values.Take a look at demo.py and adapt it to you usecase or robot:Start with your imports#!/usr/bin/env python
# -*- coding: utf-8 -*-

import gaikpy
from gaikpy import chain
from gaikpy import robot
from gaikpy import chain_definitions
import time
import pickle
import osWe need a URDF, which is the ROS, but as well in other context, model representation of a robot
Here we take take NICO model that is included in the packagenico_path = gaikpy.get_nico_urdf_path()+"/complete_modified_bounds.urdf"We have to define a chain containing the URDF and a list of joint / link combinations with all joints
taking part in he movement. Below is a definiion o the right NICO armrightchain = chain.Chain.from_urdf_file(
            # The path to he URDF
            nico_path , 
            # The chain: We start with a link and the first joint up to the last joint involved
            base_elements=["torso:11", "r_shoulder_z", "right_shoulder:11", 
            "r_shoulder_y", "right_collarbone:11", "r_arm_x", "right_upper_arm:11", "r_elbow_y", 
            "right_lower_arm:11", "r_wrist_z","right_wrist:11", "r_wrist_x", 
            "right_palm:11", "r_ringfingers_x"],
            # Not all the joints listed have to take part in the movement
            # This is a mask blending out joints inbetween the chain 
            # (here we do not want to involve the finger joints) 
            active_joints_mask=[False, True, True, True, True, True, True, False, False, False])For visualization we built he a full robot model
again using the URDFnico_right_chain=chain_definitions.nico_right_chain_active
nico=robot.robot(nico_path,nico_right_chain)We use some poses, where we can be sure, that they are mechanically
reachable (cause we have used forward kinematics to generate the dataset, see this in the examples about generating movement examples)with open(gaikpy.get_nico_data_path()+'/nico_right_20_new.p', 'rb') as f:
            sample_set = pickle.load(f)Go through all the samples in file
This contains pairs of poses in Eukledian and in the joint space
Of cours we use only the Eukledian ones in this examplefor sample in sample_set:

    # joi is the data in joint space for the robot (not used)
    # sfwr is the pose in Euklidan space
    (joi,sfwr)=sample
    print ("Original pose: \n" + str(sfwr))

    # Show a target pointer in the scene, so that we know, which pose NICOs hand shoul take 
    nico.update_target(sfwr)Calculate the IK, get the robots joint valuesik_kor = rightchain.inverse_kinematics(sfwr, method="ga_simple",include_orientation=True,
                        numGenerations=1000,max_iter=100000,dist_acc=0.01,or_acc=0.1,
                        multiproc=True,orientation_weight=-1)

    # Change the joint data to the full joint representation
    joi=rightchain.active_from_full(ik_kor)

    # Get the Eukledian pose using orward kinematics
    fwr=rightchain.forward_kinematics(ik_kor,full_kinematics=False)

    #Let us compare the results
    print ("gaikpy result: \n" + str(fwr))Update the pose on the visualised NICOnico.update_robot_pose(joi)

    #Wait a time to display the result
    time.sleep(0.3)Use other robotsUsing 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 demo.py or the NICO example ./examples/visualise_NICO.py to see how it works.
We will integrate other robots by default in the future.LicenseGNU GENERAL PUBLIC LICENSE Version 3