| Name | ur-analytic-ik JSON |
| Version |
0.0.5
JSON |
| download |
| home_page | |
| Summary | C++ implementation with Python bindings of analytic forward and inverse kinematics for the Universal Robots. |
| upload_time | 2024-03-16 22:28:57 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | |
| keywords |
robotics
kinematics
universal-robots
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
UR Analytic IK
================
C++ implementation with Python bindings of analytic forward and inverse kinematics for the Universal Robots based on [Alternative Inverse Kinematic Solution of the UR5 Robotic Arm](https://link.springer.com/chapter/10.1007/978-3-030-90033-5_22).
> This project is still very experimental, the API will likely still change.
Installation
------------
> Don't forget to activate your venv or conda environment.
Clone this repository, then
```bash
cd ur-analytic-ik
pip install .
```
Usage
-----
Afterwards, you should be able to issue the FK and IK functions like this:
```python
import numpy as np
from ur_analytic_ik import ur5e
eef_pose = np.identity(4)
X = np.array([-1.0, 0.0, 0.0])
Y = np.array([0.0, 1.0, 0.0])
Z = np.array([0.0, 0.0, -1.0])
top_down_orientation = np.column_stack([X, Y, Z])
translation = np.array([-0.2, -0.2, 0.2])
eef_pose[:3, :3] = top_down_orientation
eef_pose[:3, 3] = translation
solutions = ur5e.inverse_kinematics(eef_pose)
```
More examples:
```python
import numpy as np
from ur_analytic_ik import ur3e
joints = np.zeros(6)
eef_pose = np.identity(4)
eef_pose[2, 3] = 0.4
tcp_transform = np.identity(4)
tcp_transform[2, 3] = 0.1
ur3e.forward_kinematics(0, 0, 0, 0, 0, 0)
ur3e.forward_kinematics(*joints)
tcp_pose = ur3e.forward_kinematics_with_tcp(*joints, tcp_transform)
joint_solutions = ur3e.inverse_kinematics(eef_pose)
joint_solutions = ur3e.inverse_kinematics_closest(eef_pose, *joints)
joint_solutions = ur3e.inverse_kinematics_with_tcp(eef_pose, tcp_transform)
```
Testing
-------
In the root directory of this repo, to run the tests:
```
pytest -v
```
Development
--------------------
Some linux users have eigen installed at /usr/include/eigen3 instead of /usr/include/Eigen. Symlink it:
```
sudo ln -sf /usr/include/eigen3/Eigen /usr/include/Eigen
sudo ln -sf /usr/include/eigen3/unsupported /usr/include/unsupported
```
**Releasing:**
Similar to how I release my pure Python projects e.g. [`airo-models`](https://github.com/airo-ugent/airo-models).
One additional step is needed: manually create a release on Github.
Welcome Improvements
--------------------
### Python API
Adding an IK function that returns the closest solution and accepts a TCP transform.
Reducing the amount of separate IK functions, e.g. replacing:
```python
ur3e.inverse_kinematics_with_tcp(eef_pose)
# with
ur3e.inverse_kinematics(eef_pose, tcp=tcp_transform)
```
The same holds for functions ending with `_closest()`.
### Performance
Currently IK runs at about 10 μs / EEF pose on my laptop.
However, before I implemented the filtering of the solutions, it was closer to 3 μs.
Part of this is because I adapted the bindings in `ur_analytic_ik_ext.cpp` to return vectors with the solutions.
### Code Quality
* Adding more technical documentation.
* `ur_analytic_ik_ext.cpp` should be made much more readable.
* Reducing some duplication e.g. when defining the IK/FK functions and bindings for the different robots.
Raw data
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"description": "UR Analytic IK\n================\nC++ implementation with Python bindings of analytic forward and inverse kinematics for the Universal Robots based on [Alternative Inverse Kinematic Solution of the UR5 Robotic Arm](https://link.springer.com/chapter/10.1007/978-3-030-90033-5_22).\n\n> This project is still very experimental, the API will likely still change.\n\nInstallation\n------------\n\n> Don't forget to activate your venv or conda environment.\n\nClone this repository, then\n```bash\ncd ur-analytic-ik\npip install .\n```\n\n\nUsage\n-----\nAfterwards, you should be able to issue the FK and IK functions like this:\n\n```python\nimport numpy as np\nfrom ur_analytic_ik import ur5e\n\neef_pose = np.identity(4)\nX = np.array([-1.0, 0.0, 0.0])\nY = np.array([0.0, 1.0, 0.0])\nZ = np.array([0.0, 0.0, -1.0])\ntop_down_orientation = np.column_stack([X, Y, Z])\ntranslation = np.array([-0.2, -0.2, 0.2])\n\neef_pose[:3, :3] = top_down_orientation\neef_pose[:3, 3] = translation\n\nsolutions = ur5e.inverse_kinematics(eef_pose)\n```\n\nMore examples:\n```python\nimport numpy as np\nfrom ur_analytic_ik import ur3e\n\njoints = np.zeros(6)\neef_pose = np.identity(4)\neef_pose[2, 3] = 0.4\ntcp_transform = np.identity(4)\ntcp_transform[2, 3] = 0.1\n\nur3e.forward_kinematics(0, 0, 0, 0, 0, 0)\nur3e.forward_kinematics(*joints)\ntcp_pose = ur3e.forward_kinematics_with_tcp(*joints, tcp_transform)\n\njoint_solutions = ur3e.inverse_kinematics(eef_pose)\njoint_solutions = ur3e.inverse_kinematics_closest(eef_pose, *joints)\njoint_solutions = ur3e.inverse_kinematics_with_tcp(eef_pose, tcp_transform)\n```\n\n\nTesting\n-------\nIn the root directory of this repo, to run the tests:\n```\npytest -v\n```\n\nDevelopment\n--------------------\nSome linux users have eigen installed at /usr/include/eigen3 instead of /usr/include/Eigen. Symlink it:\n```\nsudo ln -sf /usr/include/eigen3/Eigen /usr/include/Eigen\nsudo ln -sf /usr/include/eigen3/unsupported /usr/include/unsupported\n```\n\n**Releasing:**\nSimilar to how I release my pure Python projects e.g. [`airo-models`](https://github.com/airo-ugent/airo-models).\nOne additional step is needed: manually create a release on Github.\n\nWelcome Improvements\n--------------------\n\n### Python API\nAdding an IK function that returns the closest solution and accepts a TCP transform.\n\nReducing the amount of separate IK functions, e.g. replacing:\n```python\nur3e.inverse_kinematics_with_tcp(eef_pose)\n# with\nur3e.inverse_kinematics(eef_pose, tcp=tcp_transform)\n```\nThe same holds for functions ending with `_closest()`.\n\n### Performance\nCurrently IK runs at about 10 \u03bcs / EEF pose on my laptop.\nHowever, before I implemented the filtering of the solutions, it was closer to 3 \u03bcs.\nPart of this is because I adapted the bindings in `ur_analytic_ik_ext.cpp` to return vectors with the solutions.\n\n### Code Quality\n* Adding more technical documentation.\n* `ur_analytic_ik_ext.cpp` should be made much more readable.\n* Reducing some duplication e.g. when defining the IK/FK functions and bindings for the different robots.\n",
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