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aurt-0.0.4
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تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
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تبلیغات ما
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مشاهده بیشترتوضیحات
A robot dynamic parameters calibration toolbox.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | aurt-0.0.4 |
| نام | aurt |
| نسخه کتابخانه | 0.0.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Emil Madsen, Daniella Tola, Claudio Gomes |
| ایمیل نویسنده | ema@ece.au.dk |
| آدرس صفحه اصلی | https://github.com/INTO-CPS-Association/aurt/ |
| آدرس اینترنتی | https://pypi.org/project/aurt/ |
| مجوز | - |
# Aarhus University Robotics Toolbox (AURT) Overview
# Installation
To install the tool, type:
```
pip install aurt
```
or, if plotting and visualization features are needed,
```
pip install aurt[vis]
```
# Command Line Interface
The following shows the different use cases that aurt supports.
In order to improve performance, the model is compiled in different stages,
in a way that allows the user to try alternative joint dynamics models without having to re-create the full model,
which is a computationally demanding procedure.
## Compile Rigid Body Dynamics Model
```
aurt compile-rbd --mdh mdh.csv --out rigid_body_dynamics
```
Reads the Modified Denavit-Hartenberg (MDH) parameters in file `mdh.csv` and outputs rigid-body dynamics model to file `rigid_body_dynamics`.
The generated model does not include the joint dynamics.
To visualize the kinematics of the robot, make sure the `roboticstoolbox-python` is installed, and add the argument `--plot` to the `compile-rbd` command.
<p align="center">
<img src="resources/robot_Plot.png" alt="MDH plot" width="400"/>
</p>
## Compile Robot Dynamics Model
```
aurt compile-rd --model-rbd rigid_body_dynamics --friction-torque-model square --friction-viscous-powers 2 1 4 --out robot_dynamics
```
Reads the rigid-body dynamics model created with the `compile-rbd` command, and generates the robot dynamics model,
taking into account the joint dynamics configuration.
The friction configuration options are:
- `--friction-torque-model TYPE` where `TYPE in {none, square, absolute}` are depicted in the figure below for, respectively, parts (a), (b), and (c).
<p align="center">
<img src="resources/friction_load_models.png" alt="The different possibilities for joint torque-dependent friction models" width="400"/>
</p>
- `--friction-viscous-powers POWERS` where `POWERS` is a set <img src="https://render.githubusercontent.com/render/math?math=S"> of integers having the format `P1 P2 ...` used to define the odd polynomial function
<img src="https://render.githubusercontent.com/render/math?math=\mathrm{f}_v"> in the angular velocity
<img src="https://render.githubusercontent.com/render/math?math=\dot{q}"> of any joint as
<img src="https://render.githubusercontent.com/render/math?math=\mathrm{f}_v(\dot{q}) = \sum_{i\in S}F_{v,\!i}\,b_i">
with <img src="https://render.githubusercontent.com/render/math?math=F_{v,\!i}"> the viscous coefficient of friction corresponding to the integer element
<img src="https://render.githubusercontent.com/render/math?math=i"> of
<img src="https://render.githubusercontent.com/render/math?math=S">,
<img src="https://render.githubusercontent.com/render/math?math=b_i=|\dot{q}|\,\dot{q}^{i-1}"> if
<img src="https://render.githubusercontent.com/render/math?math=i"> is even
and <img src="https://render.githubusercontent.com/render/math?math=b_i = \dot{q}^i"> otherwise.
## Calibrate
```
aurt calibrate --model robot_dynamics --data measured_data.csv --gravity 0 0 -9.81 --out-params calibrated_parameters.csv --out-calibrated-model rd_calibrated --plot
```
Reads; 1) the model produced by the `compile-rd` command, 2) the measured data in `measured_data.csv`, and 3) the gravity components `GX GY GZ` and writes; 1) the values of the calibrated base parameters to `calibrated_parameters.csv` and 2) the calibrated robot dynamics model to `rd_calibrated`.
The gravity vector determines the orientation of the robot base for which the parameters will be calibrated.
For showing the calibration plot, use the argument `--plot`.
The measured data should contain the following fields:
- `timestamp` of type float, representing the number of seconds passed from a given reference point.
- `target_qd_j` of type float, representing the `j`th joint target angular velocity, as computed by the robot controller, where `j` is an integer in `{0, 1, ..., N}`.
- `actual_q_j` of type float, representing the `j`th joint angle, as measured by the robot controller, where `j` is an integer in `{0, 1, ..., N}`.
- `actual_current_j` of type float, representing the `j`th joint current, as measured by the robot controller, where `j` is an integer in `{0, 1, ..., N}`.
## Predict
```
aurt predict --model rd_calibrated --data measured_data.csv --gravity 0 0 -9.81 --out predicted_output.csv
```
Reads; 1) the model produced by the `calibrate` command,
2) the measured data in `measured_data.csv`, and
3) the gravity components `GX GY GZ`,
and writes the predicted output to `predicted_output.csv`.
The prediction fields are:
- `timestamp` of type float, referring to the time of the measured data, as in [Calibrate](#predict).
- `predicted_current_j` of type float, representing the `j`th joint current, as predicted by the robot model, where `j` is an integer in `{0, 1, ..., N}`.
## Calibrate and Validate
```
aurt calibrate-validate --model robot_dynamics --data measured_data.csv --gravity 0 0 -9.81 --calibration-data-rel FRACTION --out-params calibrated_parameters.csv --out-calibrated-model rd_calibrated --out-prediction predicted_output.csv --plot
```
Simultaneously calibrates and validates the robot dynamics model using the dataset `measured_data.csv`.
The command implements the functionalities of the commands `calibrate` and `predict`.
The data of `measured_data.csv` is separated into two consecutive parts 1) calibration data and 2) validation data.
The calibration data has a duration of 0.1 < `FRACTION` < 0.9 times the duration of `measured_data.csv` while the remaining part of the data is used for validation.
# Contributing
## Development environment
To setup the development environment:
1. Open terminal in the current folder.
2. Install all packages for development: `pip install -e .[vis]`.
3. Unpack the datasets (see [Dataset management](#dataset-management))
4. To run all non live tests, open a command prompt or powershell in the repository root, and run `python build.py --run-tests all-non-live`.
If you are using Linux, use `python3` instead of `python`.
_NOTE: Run tests before commits. If they don't pass, fix them before committing._
## Publishing this package on pypi
1. Update version in `setup.py`
2. Make sure all tests, except the live ones, are passing.
3. Delete folders `dist` `build` if they exist.
4. Activate virtual environment.
5. Install twine and wheel: `pip install twine wheel`
6. Create a source distribution: `python setup.py sdist`
7. Create the binary distribution: `python setup.py bdist_wheel`
8. Upload distribution to PyPI: `python -m twine upload dist/*`
9. When asked for username and password, use the token and password created with your PyPI account.
## Dataset management
### Small dataset (< 100MB compressed)
If the data is small, then:
- Each round of experiments should be placed in a folder with an informative name, inside the Dataset folder.
- There should be a readme file in there explaining the steps to reproduce the experiment, parameters, etc...
- The csv files should be 7ziped and committed. Do not commit the csv file.
- There should be tests that use the data there.
### Large Datasets (>= 100MB compressed)
If the data is large, then:
- A "lite" version of the dataset should be in the dataset folder (following the same guidelines as before)
- This is important to run the tests.
- the larger version should be placed in the shared drive (see below).
There is a shared drive for large datasets.
The shared drive **Nat_robot-datasets** has been created with **Emil Madsen** as owner.
| **Shared Drive** | **Owner** | **E-mail** | **Department** |
| ------------------ | ---------------------- | ------------------------------------- | ----------------------------------------- |
| Nat_robot-datasets | au504769 (Emil Madsen) | [ema@ece.au.dk](mailto:ema@ece.au.dk) | Electrical and Computer Engineering (ECE) |
**Read/write access is assigned to:**
| **Username** | **Name** | **E-mail** | **Department** |
| ------------ | ------------------------------ | --------------------------------------------------------- | ----------------------------------------- |
| au602135 | Cláudio Ângelo Gonçalves Gomes | [claudio.gomes@ece.au.dk](mailto:claudio.gomes@ece.au.dk) | Electrical and Computer Engineering (ECE) |
| au522101 | Christian Møldrup Legaard | [cml@ece.au.dk](mailto:cml@ece.au.dk) | Electrical and Computer Engineering (ECE) |
| au513437 | Daniella Tola | [dt@ece.au.dk](mailto:dt@ece.au.dk) | Electrical and Computer Engineering (ECE) |
For more information on access, self-service and management of files: https://medarbejdere.au.dk/en/administration/it/guides/datastorage/data-storage/
نیازمندی
| مقدار | نام |
|---|---|
| >=1 | numpy |
| >=1 | sympy |
| >=1 | pandas |
| >=0.24 | scikit-learn |
| - | CacheMan |
| - | roboticstoolbox-python |
| >=1 | matplotlib |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl aurt-0.0.4:
pip install aurt-0.0.4.whl
نصب پکیج tar.gz aurt-0.0.4:
pip install aurt-0.0.4.tar.gz