README

# Lab Automation Framework Template and Demo This package serves as an installable template to adapt the automation framework consisting of [LabScheduler](https://gitlab.com/OpenLabAutomation/lab-automation-packages/lab-scheduler), [Laborchestrator](https://gitlab.com/OpenLabAutomation/lab-automation-packages/laborchestrator), [PythonLab](https://gitlab.com/OpenLabAutomation/lab-automation-packages/pythonLab), [PlatformStatusDataBase](https://gitlab.com/OpenLabAutomation/lab-automation-packages/platform_status_db) and some SiLA-Servers to a new robotic lab. It also contains out of the box running demo examples.

## Target Audience People who have a robotic arm with several devices they can access via SiLA (or at least python) and look for a framework do describe, orchestrate and schedule workflows on these devices. Some programming skills are necessary.

## Content - Install scripts to install necessary software packages (database, scheduler, pythonLab, orchestrator) - Template for custom robotic lab setup and interface for customizable funtionalities

## Installation 1. Download this package (alternatively fork the repository): ```bash

``` 2. Use your favourite tool to create and activate a new python environment with python 3.11 or higher. For example with pyvenv on linux: ```bash

python -m venv labautomation source labautomation/bin/activate

``` 3. Install all necessary packages:

  • to install all mandatory dependencies run:

```bash

pip install -r requirements.txt -e .

```

  • to also install the example sila servers (necessary for the demo examples to run) add:

```bash

pip install -r requirements_servers.txt

```

  • to also install requirements for stronger scheduling algorithms add:

```bash

pip install -r requirements_mip_cp.txt

``` 4. Install and set up the database

Installation: Run ```bash

``` Setup: Run and follow the instructions to create an admin login to django. On windows you will have execute the steps manually.

```bash

bash scripts/init_db.sh

```

Fill the database: Run ```bash

python scripts/add_lab_setup_to_db.py

``` This adds the lab setup as described in platform_config.yml to the database. Rerun this script after you customized the config file.

## Startup Call from different console tabs

Option 1: In gnome terminal you could start all four o the following services in different tabs by running

```bash

bash scripts/run_services.sh

```

Option 2: Otherwise use individual commands (remember activating the virtual environment):

  • to start the scheduler:

```bash

labscheduler

``` - to start the django database view (optional). If you changed directory, adapt the path: ```bash

run_db_server

``` - to start the orchestrator: ```bash

laborchestrator

``` - to start demo servers: ```bash

start_sila_servers

```

## Usage You can access the GUI for different components: - database of present labware at [http://127.0.0.1:8000/job_logs/present_labware/](http://127.0.0.1:8000/job_logs/present_labware/) - orchestrator at [http://127.0.0.1:8050/](http://127.0.0.1:8050/) - the human interaction sila server: [http://127.0.0.1:8054/](http://127.0.0.1:8054/) - view and manual control of the robotic arm: [http://127.0.0.1:8055/](http://127.0.0.1:8055/)

To see how the example servers are controlled from the orchestrator, go to the the orchestrator GUI and load and start one of the example processes. For a detailed explanation on the GUI usage see our [Demo Guide](docs/quickstart.md) - GreeterTest: Sends a Hello-World to the sila2-example-server - MoverTest: You can view the robots movements in the roboter GUI - HumanTest: you will have to finish the tasks in the human interaction GUI - InterestingExample: A more complex workflow with runtime decisions based on human interaction

## Trouble shooting Future versions of used packages might contain breaking changes. This is the output of pip list in a freshly installed working virtual environment:

<details> <summary>Show full output of pip list</summary>

```text Package Version Editable project location ————————- ———– —————————————————– absl-py 2.3.1 annotated-types 0.7.0 asgiref 3.9.1 blinker 1.9.0 cachelib 0.13.0 certifi 2025.8.3 charset-normalizer 3.4.3 click 8.2.1 dash 3.0.4 dash-bootstrap-components 2.0.4 dash-extensions 2.0.4 dash_interactive_graphviz 0.3.0 dataclass-wizard 0.35.1 Django 5.2.6 EditorConfig 0.17.1 Flask 3.0.3 Flask-Caching 2.3.1 genericroboticarm 1.3.3 graphviz 0.21 grpcio 1.71.0 grpcio-tools 1.71.0 human_server 1.1.6 idna 3.10 ifaddr 0.2.0 immutabledict 4.2.1 importlib_metadata 8.7.0 itsdangerous 2.2.0 Jinja2 3.1.6 jsbeautifier 1.15.4 lab_adaption 0.1.0 /home/stefan/tmp/adaption-template laborchestrator 0.2.7 labscheduler 0.2.47 lxml 6.0.1 markdown-it-py 4.0.0 MarkupSafe 3.0.2 mdurl 0.1.2 more-itertools 10.8.0 narwhals 2.3.0 nest-asyncio 1.6.0 networkx 3.5 numpy 2.3.2 ortools 9.9.3963 packaging 25.0 pandas 2.3.2 pillow 11.3.0 pip 24.0 platform_status_db 0.2.0 /home/stefan/tmp/adaption-template/platform_status_db plotly 6.3.0 protobuf 5.29.4 pydantic 2.11.7 pydantic_core 2.33.2 Pygments 2.19.2 PySCIPOpt 5.6.0 pyserial 3.5 python-dateutil 2.9.0.post0 pythonlab 0.2.2 pytz 2025.2 PyYAML 6.0.2 requests 2.32.5 retrying 1.4.2 rich 14.1.0 setuptools 80.9.0 shellingham 1.5.4 sila2 0.12.2 sila2_example_server 0.0.0 simplejson 3.20.1 six 1.17.0 sqlparse 0.5.3 typer 0.17.3 typing_extensions 4.15.0 typing-inspection 0.4.1 tzdata 2025.2 urllib3 2.5.0 Werkzeug 3.0.6 zeroconf 0.147.0 zipp 3.23.0

```

</details>

## Support For support, feel free to contact stefanmaak@freenet.de

## Authors and acknowledgment Maintained by Stefan Maak(Uppsala University) and Mark Dörr of the working group of Prof. Bornscheuer in Uni Greifswald.

## License MIT Licence.

## Project status The project combines packages with version control and unit-tests. It therefore is and should stay installable and usable, but is (probably) not totally free of bugs. It is also under active development.