relaxed_ik

RelaxedIK Solver

Welcome to RelaxedIK! This solver implements the methods discussed in our paper RelaxedIK: Real-time Synthesis of Accurate and Feasible Robot Arm Motion (http://www.roboticsproceedings.org/rss14/p43.html)

RelaxedIK is an inverse kinematics (IK) solver designed for robot platforms such that the conversion between Cartesian end-effector pose goals (such as "move the robot's right arm end-effector to position X, while maintaining an end-effector orientation Y") to Joint-Space (i.e., the robot's rotation values for each joint degree-of-freedom at a particular time-point) is done both ACCURATELY and FEASIBLY. By this, we mean that RelaxedIK attempts to find the closest possible solution to the desired end-effector pose goals without exhibiting negative effects such as self-collisions, environment collisions, kinematic-singularities, or joint-space discontinuities.

To start using the solver, please follow the step-by-step instructions in the file start_here.py (in the root directory)

If anything with the solver is not working as expected, or if you have any feedback, feel free to let us know! (email: [email protected], website: http://pages.cs.wisc.edu/~rakita) We are actively supporting and extending this code, so we are interested to hear about how the solver is being used and any positive or negative experiences in using it.

Citation

If you use our solver, please cite our RSS paper RelaxedIK: Real-time Synthesis of Accurate and Feasible Robot Arm Motion http://www.roboticsproceedings.org/rss14/p43.html

@INPROCEEDINGS{Rakita-RSS-18, 
    AUTHOR    = {Daniel Rakita AND Bilge Mutlu AND Michael Gleicher}, 
    TITLE     = {RelaxedIK: Real-time Synthesis of Accurate and Feasible Robot Arm Motion}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2018}, 
    ADDRESS   = {Pittsburgh, Pennsylvania}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2018.XIV.043} 
} 

If you use our solver for a robot teleoperation interface, also consider citing our prior work that shows the effectiveness of RelaxedIK in this setting:

A Motion Retargeting Method for Effective Mimicry-based Teleoperation of Robot Arms https://dl.acm.org/citation.cfm?id=3020254

@inproceedings{rakita2017motion,
  title={A motion retargeting method for effective mimicry-based teleoperation of robot arms},
  author={Rakita, Daniel and Mutlu, Bilge and Gleicher, Michael},
  booktitle={Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction},
  pages={361--370},
  year={2017},
  organization={ACM}
}

An Autonomous Dynamic Camera Method for Effective Remote Teleoperation https://dl.acm.org/citation.cfm?id=3171221.3171279

@inproceedings{rakita2018autonomous,
  title={An autonomous dynamic camera method for effective remote teleoperation},
  author={Rakita, Daniel and Mutlu, Bilge and Gleicher, Michael},
  booktitle={Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction},
  pages={325--333},
  year={2018},
  organization={ACM}
}

Dependencies

kdl urdf parser:

>> sudo apt-get install ros-[your ros distro]-urdfdom-py

>> sudo apt-get install ros-[your ros distro]-kdl-parser-py

>> sudo apt-get install ros-[your ros distro]-kdl-conversions

fcl collision library: https://github.com/BerkeleyAutomation/python-fcl

scikit learn: http://scikit-learn.org/stable/index.html

Tutorial

For full setup and usage details, please refer to start_here.py in the src directory.

Coming Soon

Performance critical code will be moved to C++ using Boost Python to speed up the solver. We are currently testing these features and will push it to the central branch when it is stable.