Development update 10/26/21
Hi all, we are excited to share some updates to our relaxed_ik library. Apologies for the delay, I have been attending to many unforeseen circumstances over the past few months.
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The original RelaxedIK code was written as somewhat messy “research code” in 2016, and each iteration of the code after that focused on code cleanup and computation speed (e.g., the port to Julia, and subsequently to Rust). This long-term development on a single codebase has been nice for some use cases, e.g., everything is connected well with ROS, everything has maintained compatibility with legacy code in Python and Julia, etc, but this large monolithic structure has also made it difficult to port the RelaxedIK solver to other applications in a lightweight manner. Thus, much of our recent development has focused on improving the portability of RelaxedIK. To this end, we introduce a new repository called relaxed_ik_core which contains just the central kernel of the RelaxedIK runtime without the extra ROS and robot setup baggage. The relaxed_ik_core repo comes prepackaged with a common set of pre-compiled and pre-trained robot models (UR5, Sawyer, etc.), allowing users with one of those robots to completely skip the setup steps. We are also hoping to grow that set of pre-compiled robots in that repo, so feel free to make pull requests with additional robot info files. The lightweight relaxed_ik_core package has allowed us to port the code to other applications and languates, such as the Unity game engine, Mujoco, CoppeliaSim, Python 2 & 3, ROS1, and ongoing development for ROS2. Note that going forward, this current repository (i.e., github/uwgraphics/relaxed_ik) should ONLY be used for robot setup and compilation or to maintain any older legacy setups. For anything else, it is essentially deprecated and further development will shift over to relaxed_ik_core. For additional information, please consult the documentation
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We recently presented a paper at ICRA 2021 on a new method called CollisionIK (video). CollisionIK is a per-instant pose optimization method that can generate configurations that achieve specified pose or motion objectives as best as possible over a sequence of solutions, while also simultaneously avoiding collisions with static or dynamic obstacles in the environment. This is in contrast to RelaxedIK, which could only avoid self-collisions. The current research code for CollisionIK is available on relaxed_ik_core, and I am also working on an improved implementation of collisionIK that will be released in the coming months. The citation for this paper is:
@article{rakita2021collisionik,
title={CollisionIK: A Per-Instant Pose Optimization Method for Generating Robot Motions with Environment Collision Avoidance},
author={Rakita, Daniel and Shi, Haochen and Mutlu, Bilge and Gleicher, Michael},
journal={arXiv preprint arXiv:2102.13187},
year={2021}
}
Please email or post here if any questions come up!
Development update 1/3/20
RelaxedIK has been substantially rewritten in the Rust programming language. Everything is still completely ROS compatible and should serve as a drop-in replacement for older versions of the solver.
The Rust relaxedIK solver is MUCH faster than its python and julia alternatives. Testing on my laptop has indicated that the solver can run at over 3000Hz for single arm robots (tested on ur3, ur5, jaco, sawyer, panda, kuka iiwa, etc) and about 2500Hz for bimanual robots (tested on ABB Yumi and Rainbow Robotics DRC-Hubo+). All of the new code has been pushed to the Development branch, and will be pushed to the main branch after a brief testing phase. It is highly recommended that the development branch be used at this point, as it has many more features and options than the main branch.
git clone -b dev https://github.com/uwgraphics/relaxed_ik.git
If you are working with an older version of relaxedIK, note that you will have to start from a fresh repo and go through the start_here.py procedures again to work with the Rust version of the solver.
If you have any comments or questions on any of this, or if you encounter any bugs in the new rust version of the solver, feel free to post an issue or email me directly at [email protected]
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)
Video of presentation at RSS 2018 (RelaxedIK part starts around 12:00) : https://youtu.be/bih5e9MHc88?t=737
Video explaining relaxedIK https://youtu.be/AhsQFJzB8WQ
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:
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.