This is a lightweight Python wrapper over OpenRave's generated IKFast C++ executables for the xArm 6 robot arm. IKFast "analytically solves robot inverse kinematics equations and generates optimized C++ files" for fast runtime speeds (more about IKFast here).

Note: this package can be easily modified to support other robot arms.

• xarm6_robot.dae - a custom COLLADA file describing the kinematics of the xArm 6 robot arm. Modify this if you change the arm or tool center point (TCP) position. This was converted from xarm6_robot.urdf using ROS collada
• ikfast61.cpp - C++ code at the heart of IKFast, generated by OpenRave using xarm6_robot.dae. No need to modify this.
• ikfast.h - a C++ header file necessary for compiling ikfast61.cpp. No need to modify this.
• ikfast_wrapper.cpp - a C++ wrapper around ikfast61.cpp. Includes forward kinematics in addition to the inverse kinematics provided by ikfast61.cpp. Modify this to change how FK and IK results are passed to your code.
• ikfastpy.pyx, Kinematics.hpp, setup.py - Cython code to link C++ with Python.
• demo.py - a demo in Python to test FK and IK calls to IKFast.

This implementation requires the following dependencies (tested on Ubuntu 16.04.4 LTS):

• NumPy, Cython. You can quickly install/update these dependencies by running the following:

  pip install --user numpy Cython

1. Checkout this repository and compile the Cython wrapper:

   git clone https://github.com/andyzeng/ikfastpy.git
   cd ikfastpy
   python setup.py build_ext --inplace

2. Run the demo in Python to test FK and IK calls to IKFast:

   python demo.py

Important: ensure all rotation matrices are valid before feeding into IKFast, otherwise no IK solution will be detected. R is a rotation matrix if and only if R is orthogonal, i.e. RR^T = R^TR = I, and det(R) = 1.

Note: IKFast does not return solutions for singularities. In most cases, an approximate IK solution can be found for singularities by slightly perturbing the target end effector pose before re-computing IK solutions.

1. Download and install OpenRave. See these installation instructions for Ubuntu 16.04.

2. Modify the kinematics of the arm or TCP position (link6) by changing xarm6_robot.dae respectively. You can find a description of the OpenRave XML file format here.

You can use ROS collada_urdf to convert from URDF to COLLADA DAE:

rosrun collada_urdf collada_urdf xarm6_robot.urdf xarm6_robot.dae

1. (Optional) Debug the kinematics using OpenRave's viewer:

   openrave xarm6_robot.dae

2. (Optional) Check the links in your file:

   openrave-robot.py xarm6_robot.dae --info links

3. Use OpenRave to re-generate the IKFast C++ code ikfast61.cpp.

   python `openrave-config --python-dir`/openravepy/_openravepy_/ikfast.py --robot=xarm6_robot.dae --iktype=transform6d --baselink=1 --eelink=7 --savefile=ikfast61.cpp --maxcasedepth 1

If you find IKFast useful, please cite OpenRave:

@phdthesis{diankov_thesis,
  author = "Rosen Diankov",
  title = "Automated Construction of Robotic Manipulation Programs",
  school = "Carnegie Mellon University, Robotics Institute",
  month = "August",
  year = "2010",
  number= "CMU-RI-TR-10-29",
  url={http://www.programmingvision.com/rosen_diankov_thesis.pdf},
}

This module was also a part of Visual Pushing and Grasping. If you find it useful in your work, please consider citing:

@inproceedings{zeng2018learning,
  title={Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning},
  author={Zeng, Andy and Song, Shuran and Welker, Stefan and Lee, Johnny and Rodriguez, Alberto and Funkhouser, Thomas},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2018}
}