suave_planta

A PDDL-based managing system for SUAVE.

This repo contains the experimental setup used in the paper "Plan your Self-Adaptation! – Efficient Task and Architecture Co-adaptation Planning for Robots".

The remainder of this README explains how to reproduce the experiments.

Docker

The first step is to run the suave_planta docker image.

Run docker image without web interface (with nvidia)(don't forget to install the docker-nvidia-toolkit)

docker run -it --rm --gpus all --runtime=nvidia --name suave_planta -e DISPLAY=$DISPLAY -e QT_X11_NO_MITSHM=1 -e NVIDIA_VISIBLE_DEVICES=all -e NVIDIA_DRIVER_CAPABILITIES=all -v $HOME/suave/results:/home/ubuntu-user/suave/results -v /dev/dri:/dev/dri -v /tmp/.X11-unix:/tmp/.X11-unix -v /etc/localtime:/etc/localtime:ro ghcr.io/kas-lab/suave_planta:main

Without a nvidia GPU:

docker run -it --rm --name suave_planta -e DISPLAY=$DISPLAY -e QT_X11_NO_MITSHM=1 -v $HOME/suave/results:/home/ubuntu-user/suave/results -v /dev/dri:/dev/dri -v /tmp/.X11-unix:/tmp/.X11-unix -v /etc/localtime:/etc/localtime:ro ghcr.io/kas-lab/suave_planta:main

Note: If you want to use the host machine display, run the following command before running the docker container:

xhost +

Development

To develop suave_planta with docker, you will need to fetch the repos you want to develop into your host machine and mount the relevant directories inside the container.

For example:

Mount $HOME/suave/results, $HOME/suave_ws/src/suave, $HOME/suave_ws/src/suave_planta, and $HOME/suave_ws/src/ros2_planning_system

docker run -it --rm --gpus all --runtime=nvidia --name suave_planta -e DISPLAY=$DISPLAY -e QT_X11_NO_MITSHM=1 -e NVIDIA_VISIBLE_DEVICES=all -e NVIDIA_DRIVER_CAPABILITIES=all -v /dev/dri:/dev/dri -v /tmp/.X11-unix:/tmp/.X11-unix -v /etc/localtime:/etc/localtime:ro -v $HOME/suave/results:/home/ubuntu-user/suave/results -v $HOME/suave_ws/src/suave_planta:/home/ubuntu-user/suave_ws/src/suave_planta -v $HOME/suave_ws/src/ros2_planning_system:/home/ubuntu-user/suave_ws/src/plansys2 -v $HOME/suave_ws/src/suave:/home/ubuntu-user/suave_ws/src/suave suave_planta

Run SUAVE with PLANTA

With Runner

SUAVE

You can run it with the launchfile:

Experiment 1:

ros2 launch suave_planta exp1_suave_runner.launch.py

Experiment 2:

ros2 launch suave_planta exp2_suave_runner.launch.py

Experiment 3:

ros2 launch suave_planta exp3_suave_runner.launch.py

Note: with the default configuration and launch files above, the results will be save in the directory ~/suave/results

SUAVE extended

Experiment 1:

ros2 launch suave_planta extended_exp1_suave_runner.launch.py

Experiment 2:

ros2 launch suave_planta extended_exp2_suave_runner.launch.py

Experiment 3:

ros2 launch suave_planta extended_exp3_suave_runner.launch.py

Note: with the default configuration and launch files above, the results will be save in the directory ~/suave/results

Custom runner config

Changing experiments config: Simply create a new configuration file for the runner, for example by modifying the exp3_runner_config.yml file. Then, pass it to the suave_runnernode, for example, by modifying the exp3_suave_runner.launch.py launch file.

Alternatively, you can run the suave_runner node directly with the parameters you want. Check some examples below:

PLANTA:

ros2 run suave_runner suave_runner \
  --ros-args \
  -p gui:=False \
  -p experiment_logging:=True \
  -p experiments:='[
    "{\"experiment_launch\": \"ros2 launch suave_planta suave_planta.launch.py\", \
      \"num_runs\": 20, \
      \"adaptation_manager\": \"planta\", \
      \"mission_name\": \"suave\"}"
  ]'

SUAVE extended with PLANTA:

ros2 run suave_runner suave_runner \
  --ros-args \
  -p gui:=False \
  -p experiment_logging:=True \
  -p experiments:='[
    "{\"experiment_launch\": \"ros2 launch suave_planta suave_planta_extended.launch.py\", \
      \"num_runs\": 20, \
      \"adaptation_manager\": \"planta\", \
      \"mission_name\": \"suave_extended\"}"
  ]'

SUAVE with no managing subsystem:

ros2 run suave_runner suave_runner \
  --ros-args \
  -p gui:=False \
  -p experiment_logging:=True \
  -p experiments:='[
    "{\"experiment_launch\": \"ros2 launch suave_none suave_none.launch.py\", \
      \"num_runs\": 1, \
      \"adaptation_manager\": \"none\", \
      \"mission_name\": \"suave\"}"
  ]'

Manually

Run ardusub:

sim_vehicle.py -L RATBeach -v ArduSub  --model=JSON --console

Run the simulation:

ros2 launch suave simulation.launch.py x:=-17.0 y:=2.0

Run PLANTA:

ros2 launch suave_planta suave_planta.launch.py

Run the experimental analysis

To reproduce exactly the analysis done for the paper, run the commands below.

SUAVE

Experiment 1:

ros2 launch suave_planta exp1_analysis.launch.py

Experiment 2:

ros2 launch suave_planta exp2_analysis.launch.py

Experiment 3:

ros2 launch suave_planta exp3_analysis.launch.py

Note: the results are saved in the results folder in this package

SUAVE extended

Experiment 1:

ros2 launch suave_planta extended_exp1_analysis.launch.py

Experiment 2:

ros2 launch suave_planta extended_exp2_analysis.launch.py

Experiment 3:

ros2 launch suave_planta extended_exp3_analysis.launch.py

Note: the results are saved in the results folder in this package

Custom analysis

Changing analysis config: Simply create a new configuration file for the analysis node, for example by modifying the exp3_analysis_config.yml file. Then, pass it to the statistical_analysis node, for example, by modifying the exp3_analysis.launch.py launch file.

OWL to PDDL

If you want to try the OWL to PDDL conversion separetly, check the examples below.

With ROS:

ros2 run owl_to_pddl owl_to_pddl.py --ros-args -p owl_file:=owl/suave.owl -p in_domain_file:=pddl/suave_domain.pddl -p out_domain_file:=pddl/suave_domain_created.pddl -p in_problem_file:=pddl/suave_problem.pddl -p out_problem_file:=pddl/suave_problem_created.pddl

SUAVE extended

ros2 run owl_to_pddl owl_to_pddl.py --ros-args -p owl_file:=owl/suave_extended.owl -p in_domain_file:=pddl/suave_domain_extended.pddl -p out_domain_file:=pddl/suave_domain_extended_created.pddl -p in_problem_file:=pddl/suave_problem_extended.pddl -p out_problem_file:=pddl/suave_problem_extended_created.pddl

Without ROS:

OWLToPDDL.sh --owl=owl/suave.owl --tBox --inDomain=pddl/suave_domain.pddl --outDomain=pddl/suave_domain_test.pddl --aBox --inProblem=pddl/suave_problem.pddl --outProblem=pddl/suave_problem_test.pddl --add-num-comparisons --replace-output

SUAVE extended

OWLToPDDL.sh --owl=owl/suave_extended.owl --tBox --inDomain=pddl/suave_domain_extended.pddl --outDomain=pddl/suave_domain_extended_test.pddl --aBox --inProblem=pddl/suave_problem_extended.pddl --outProblem=pddl/suave_problem_extended_test.pddl --add-num-comparisons --replace-output

Planning with fast downward

If you want to run the planner individually, check the examples below.

ros2 run downward_ros fast-downward.py pddl/suave_domain_created.pddl pddl/suave_problem_created.pddl --search 'astar(blind())'

SUAVE extended:

ros2 run downward_ros fast-downward.py pddl/suave_domain_extended_created.pddl pddl/suave_problem_extended_created.pddl --search 'astar(blind())'