IKpy Quickstart

Requirements

First, you need to install IKPy (see installations instructions). You also need a URDF file.
By default, we use the files provided in the resources folder of the IKPy repo.

Import the IKPy module :



In [2]:

    
import ikpy
import numpy as np
import ikpy.utils.plot as plot_utils

The basic element of IKPy is the kinematic Chain. To create a chain from an URDF file :



In [3]:

    
my_chain = ikpy.chain.Chain.from_urdf_file("../resources/poppy_ergo.URDF")

Note : as mentioned before, here we use a file in the resource folder.

Inverse kinematics

In Inverse Kinematics, you want your kinematic chain to reach a 3D position in space.

To have a more general representation of position, IKPy works with homogeneous coordinates. It is a 4x4 matrix storing both position and orientation. Prepare your desired position as a 4x4 matrix. Here we only consider position, not orientation of the chain.



In [6]:

    
target_position = [ 0.1, -0.2, 0.1]



In [7]:

    
print("The angles of each joints are : ", my_chain.inverse_kinematics(target_position))









    



The angles of each joints are :  [ 0.          0.77375218 -1.26611491  0.32302884 -0.75260506  1.03033723
  0.        ]

You can check that the Inverse Kinematics is correct by comparing with the original position vector :



In [9]:

    
real_frame = my_chain.forward_kinematics(my_chain.inverse_kinematics(target_position))
print("Computed position vector : %s, original position vector : %s" % (real_frame[:3, 3], target_frame[:3, 3]))









    



Computed position vector : [ 0.0999999  -0.1999999   0.09999988], original position vector : [ 0.1 -0.2  0.1]

Plotting

And finally plot the result :

(If the code below doesn't work, comment the %maplotlib widget line, and uncomment the %matplotlib inline line)



In [12]:

    
# Optional: support for 3D plotting in the NB
# If there is a matplotlib error, uncomment the next line, and comment the line below it.
# %matplotlib inline
%matplotlib widget
import matplotlib.pyplot as plt
fig, ax = plot_utils.init_3d_figure()
my_chain.plot(my_chain.inverse_kinematics(target_position), ax, target=target_vector)
plt.xlim(-0.1, 0.1)
plt.ylim(-0.1, 0.1)









    





 
 










    Out[12]:





(-0.1, 0.1)

You're done! Go to the tutorials to understand the general concepts of the library.



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