| Name | ur-analytic-ik JSON |
| Version |
0.0.7
JSON |
| download |
| home_page | None |
| Summary | C++ implementation with Python bindings of analytic forward and inverse kinematics for the Universal Robots. |
| upload_time | 2024-12-09 15:55:45 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
robotics
kinematics
universal-robots
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
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.
pre-built wheels are availabe on PyPI and can be installed with pip:
```bash
pip install ur_analytic_ik
```
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)
```
Development
--------------------
This codebase uses [nanobind]() to provide python bindings for the FK/IK functions.
## building
**python package building**
This is the easiest option. It leverages scikit-build to create a python package and build the bindings. This flow is based on https://github.com/wjakob/nanobind_example
- Create a conda environment for the project: `conda env create -f environment.yaml`
- to create the python package, including the bindings: `pip install .` (this uses scikit-build to build the C++ from the top-level CMakelist.txt)
- you can now import the library in python.
**C++ building**
if you want to build the C++ code without building the bindings or creating a python package:
- make sure you have a C++ compiler available.
- make sure you have the [Eigen]() package available, if not run `apt install libeigen3-dev`.
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
```
- run `cmake -S . -B` & `cmake --build build` from the `src/` dir.
- execute `./build/main`
## testing
run `pytest -v .`
Tests are also automatically executed in github for each commit.
Wheels are built automatically for all PRs, you can check them on [test PyPI]().
## Releasing
- bump the version in the `pyproject.toml` file. We use [semantic versioning](). Use pre-releases if you want to test changes.
- create a new tag, corresponding to the version: `git tag vX.Y.Z-...`
- push the tag `git push --tag`, this will already trigger a build of the wheels on [test PyPI](https://test.pypi.org/project/ur-analytic-ik/)
- once you have verified the wheels work and are built properly, create a new release with the same name as the semantic version for the tag on github. This will trigger an upload to [PyPI](https://pypi.org/project/ur-analytic-ik/).
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.
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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\npre-built wheels are availabe on PyPI and can be installed with pip:\n\n```bash\npip install ur_analytic_ik\n```\n\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\n\n\nDevelopment\n--------------------\n\nThis codebase uses [nanobind]() to provide python bindings for the FK/IK functions.\n\n## building\n**python package building** \n\nThis is the easiest option. It leverages scikit-build to create a python package and build the bindings. This flow is based on https://github.com/wjakob/nanobind_example\n\n- Create a conda environment for the project: `conda env create -f environment.yaml` \n- to create the python package, including the bindings: `pip install .` (this uses scikit-build to build the C++ from the top-level CMakelist.txt)\n- you can now import the library in python.\n\n\n**C++ building**\n\nif you want to build the C++ code without building the bindings or creating a python package:\n\n- make sure you have a C++ compiler available.\n- make sure you have the [Eigen]() package available, if not run `apt install libeigen3-dev`.\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- run `cmake -S . -B` & `cmake --build build` from the `src/` dir. \n- execute `./build/main`\n\n\n## testing\n\nrun `pytest -v .`\n\nTests are also automatically executed in github for each commit.\n\nWheels are built automatically for all PRs, you can check them on [test PyPI]().\n\n\n## Releasing\n\n- bump the version in the `pyproject.toml` file. We use [semantic versioning](). Use pre-releases if you want to test changes.\n- create a new tag, corresponding to the version: `git tag vX.Y.Z-...` \n- push the tag `git push --tag`, this will already trigger a build of the wheels on [test PyPI](https://test.pypi.org/project/ur-analytic-ik/)\n- once you have verified the wheels work and are built properly, create a new release with the same name as the semantic version for the tag on github. This will trigger an upload to [PyPI](https://pypi.org/project/ur-analytic-ik/).\n\n\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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