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Track Planner

Basic Knowledge Requirements

Before diving into this code, here's a quick heads-up on what you'll need to be familiar with:

  1. Python Programming: It's important to have a good grasp of Python, especially with concepts like functions, loops, and classes, since the example utilizes these fundamentals.
  2. Asynchronous Programming with asyncio: Familiarity with Python's asyncio for writing concurrent code using the async/await syntax.
  3. farm-ng Filter Service Overview: This overview provides a base understanding of the gRPC service the client you create will connect to.
  4. farm-ng Transforms & Poses Overview: This overview provides insight into coordinate frames, transforms, and poses as they pertain to autonomous systems and autonomous navigation.
  5. farm-ng Tracks & Waypoints Overview: This overview provides insight into compiling poses as waypoints into a Track that can be followed by the Amiga.

The Track Planner Example operates as a standalone Python script, in which a Track proto message is generated using the TrackPlanner class. We use the matplotlib library to visualize the Track we created.

We used this example to generate a virtual strawberry field at the 2024 World Ag. Expo!

To successfully run this example, you must use your local PC, as the example won't work if executed directly from a brain (because of the popup window).

1. Install the farm-ng Brain ADK package​

2. Install the example's dependencies​

tip

It is recommended to also install these dependencies and run the example in the brain ADK virtual environment.

Setup​

Recommended

Create a virtual environment

python3 -m venv venv
source venv/bin/activate

Install​

cd py/examples/track_plotter
pip install -r requirements.txt
sudo apt-get install python3-tk

3. Execute the Python script​

info

Since this example must be run from your local PC, you will need update the service_config.json by modifying the host field with your Amiga brain name.

Please check out Amiga Development 101 for more details.

python main.py --service-config service_config.json

You should now see a matplotlib popup with a plot of your Track.

track

4. Customize the run​

You can use the flags --invert and --save-track to invert and/or save your track. Check out more details by running:

python main.py --help

If saving a track, you will need to specify a path (e.g., /Documents/my_track.json).

For example:

python main.py --service-config service_config.json --save-track /Documents/my_track.json

Methods​

The TrackPlanner class contains several methods for primitive motion planning segments:

  • create_straight_segment: Use this method for creating straight turns.
  • create_ab_segment: Use this method to connect your last position to a know location (ab line).
  • create_turn_segment: Use this method to make the robot turn in place.
  • create_arc_segment: Use this method to create smooth turns such as a U-turn.

5. [Optional] Use your custom track on Autoplot​

To use your custom track on Autoplot, all you have to do is copy it to /mnt/data/tracks inside your robot.

First, let's copy your custom track to your home directory on the robot:

cd <to your custom track> # e.g., cd /Documents/
scp <track-name> farm-ng-user-<username>@<your-brain>: # e.g., scp my_track.json farm-ng-user-jdoe@element-vegetable:

scp will copy the track from your local computer to your robot. The next step is to SSH into your robot and move your track to the correct destination

ssh <your-brain>
sudo mv <track-name> /mnt/data/tracks # e.g., sudo mv my_track.json /mnt/data/tracks
SSH access

Make sure to check Access and Development on the Brain to learn how to SSH into your brain

Congrats you are done!