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mpc-trajectory-generator

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A trajectory generator utilizing the A-star algorithm in conjunction with a Nonlinear Model Predictive Control solver to obtain a smooth trajectory that satisfies vehicle constraints.

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최종 버전 다운로드 (.zip)

- default.yaml
- jconf.yaml
- jconf_0.yaml
- jconf_1.yaml
- jconf_2.yaml
- jconf_3.yaml
- jconf_4.yaml
- jconf_5.yaml
- README.md
- smooth_velocity.yaml
- william_config.yaml
- example_image.png
- paper.pdf
- README.md
- environment.yml
- - __init__.py
  - mpc_generator.py
- - __init__.py
  - config.py
  - draw_graph.py
  - README.md
- - __init__.py
  - graphs.py
  - visibility.py
- eval_diff_configs.py
- gen_res_diff_maps.py
- gen_runtime_plots.py
- main.py
- path_generator.py
- README.md
.gitignore
LICENSE
README.md

// repository documentation

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# mpc-trajectory-generator
Project in the course SSY226 Design project in systems, control and mechatronics given the autumn of 2020 at Chalmers University of Technology. We've implemented an algorithm that generates a smooth, collision free trajectory for a differential drive robot.

![Example](https://raw.githubusercontent.com/wljungbergh/mpc-trajectory-generator/master/docs/example_image.png "Example")
## Dependencies

### OpEn
The NMPC formulation is solved using open source implementation of PANOC, namely [OpEn](https://alphaville.github.io/optimization-engine/). Follow the [installation instructions](https://alphaville.github.io/optimization-engine/docs/installation) before proceeding. 
### Python depecencies
To install all neccessary requirements, easiest way is to create an environment using conda by navigating to the root of this repo and run 
   ```
   conda env create -f env/environment.yml
   ```

Activate the environment using
   ```
   conda activate ssy226
   ```
   
If the environment file is changed, simply update an existing environment by running (at root of repo)
   ```
   conda env update -f env/environment.yml --prune
   ```

## Algorithm 
The algorithm is explained in detail in the accompinying (unpublished) [paper](docs/paper.pdf). In summary, the algorithm models the environment as polygons and find the shortest path using A*, implemented in the [extremitypathfinder](https://github.com/MrMinimal64/extremitypathfinder) python package. This path is then followed by an NMPC controller to generate a smooth trajectory for a differential drive robot.

The algorithm can be run by running [main.py](src/main.py), make sure to set `build=True` if the NPMC solver hasn't been built before. By changing `g = graphs.get_graph(complexity=11)` to a different complexity, different predefined scenarios can be run. Different configuration exist in [configs](configs) and which to use is specified by changing the filename `config_fn = 'default.yaml'` to something different. **Note**, some of the config parameters require a rebuild of the NMPC solver to take effect.

New scenarios can be added to [graphs.py](src/visibility/graphs.py) and tested by changing selected scenario in [main.py](src/main.py) as described above. Scenarios can easily be created by using the GUI implemented in [draw_graph.py](src/utils/draw_graph.py) and appedning the output to [graphs.py](src/visibility/graphs.py).

## Contribution
Feel free to open Issues and/or Pull requestes for bug reports/improvements.