Every team hands in their Docker image and compressed simulation_files
folder on a USB-drive.
- Generate a random world for the task that looks like the real field:
rosrun virtual_maize_field generate_world.py fre22_task_<task_name>_fast
- Open the world and write down the number of the plants that fell down at the start of the simulation.
- Open the
generated.world
file and remove the plant numbers that were written down. - Copy all relevant simulation files to the competition environment:
cd ~/competition_environment
python3 scripts/copy_simulation_files.py
- For every team, load the Docker file and simulation files:
cd ~/competition_environment
./scripts/load_files.sh -i <path_to_robot_workspace.tgz> -s <path_to_simulation_files.zip> -n <team_name>
For every team, repeat the following procedure:
- Prepare the teams run:
cd ~/competition_environment
./scripts/prepare_run.sh -n <team_name>
- Start the simulation:
cd ~/competition_environment/task_<task_name>_cuda
./start_simulation.sh
- Start the simulation for half a second to have the robot stand on the ground.
- Indicate the start of the simulation to person responsable for the DLG stream.
- Run the simulation, use 3D mouse to follow the robot.
- After 3 minutes, the simulation automatically stops. For task mapping, click on the
show map
button to create a map of the mapped objects. - Possible discussion with the jury about the results.
- Shut down the simulation by
Ctrl+C
. - Stop Docker:
docker-compose down
- Clear up the workspace and copy the results to the teams folder:
cd ~/competition_environment
./scripts/finish_run.sh -n <team_name>
All the results of the run are saved in the competition_files
folder in the directory ~/competition_environment/competition_files/<team_name>/results
. This includes the generated map (pred_map.csv
and mapping_results.png
) and the stats.csv
file containing the number of destroyed plants, distances etc.