Navigation PLANTA

This repository applies PLANTA to a robot self-adaptation scenario based on the paper "Software architecture and task plan co-adaptation for mobile service robots", and compares it with the solution proposed in that paper.

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 get the docker image used for the experiments.

You can either build it locally:

docker build -t navigation_planta .

Or you can download it with:

docker pull ghcr.io/kas-lab/navigation_planta:main

Run with locally built image:

docker run --rm -it -v $HOME/navigation_planta_ws/src/navigation_planta/results:/navigation_pddl_tomasys/results -v /etc/localtime:/etc/localtime:ro navigation_planta:latest python scripts/runner.py

Run with github image:

docker run --rm -it -v $HOME/navigation_planta_ws/src/navigation_planta/results:/navigation_pddl_tomasys/results -v /etc/localtime:/etc/localtime:ro ghcr.io/kas-lab/navigation_planta:main python scripts/runner.py

Note: If you want to change the directory where the results are going to be saved in the host machine, replace HOME/navigation_planta_ws/src/navigation_planta/results with the path of the directory where you want the results to be saved.

Manual installation

Install deps:

pip install networkx
pip install numpy==1.26.4
pip install matplotlib
pip install scipy

Install Unified Planning:

pip install unified-planning

Install fast-downward unified planning engine:

pip install unified-planning[fast-downward]

Build:

colcon build --symlink-install --packages-skip plansys2_downward_planner

Generate random maps and test with fast-downard

In the main folder

python scripts/map_generator.py
ros2 run downward_ros fast-downward.py --alias lama-first pddl/domain.pddl pddl/problem.pddl

Convert OWL ontology to PDDL

Normal version:

export PATH=$HOME/navigation_pddl_tomasys_ws/src/owl_to_pddl:$PATH

OWLToPDDL.sh --owl=owl/navigation.owl --tBox --inDomain=pddl/domain_sas.pddl --outDomain=pddl/domain_sas_created.pddl --aBox --inProblem=pddl/problem.pddl --outProblem=pddl/problem_created.pddl --replace-output --add-num-comparisons -Xmxn16g

ROS version:

ros2 run owl_to_pddl owl_to_pddl.py --ros-args -p owl_file:=owl/navigation.owl -p in_domain_file:=pddl/domain_sas.pddl -p out_domain_file:=pddl/domain_sas_created.pddl -p in_problem_file:=pddl/problem.pddl -p out_problem_file:=pddl/problem_created.pddl

Run fast-downward solver

export PATH=$HOME/navigation_pddl_tomasys_ws/src/downward:$PATH

fast-downward.py  pddl/domain_sas_created.pddl pddl/problem_created.pddl --search "astar(blind())"

ros2 run downward_ros fast-downward.py --alias lama-first pddl/domain_sas_created.pddl pddl/problem_created.pddl

Example of results

Parameters:

self.num_nodes = 30
self.nodes_skip = 0.1 # 10%
self.unconnected_amount = 0.15 # 10%
self.unsafe_amount = 0.25 # 10%
self.dark_amount = 0.25 # 10%

time:

Solution found.
Peak memory: 45500 KB
Remove intermediate file output.sas
search exit code: 0

INFO     Planner time: 0.15s

real	0m0,325s
user	0m0,270s
sys	0m0,054s

Parameters:

self.num_nodes = 300
self.nodes_skip = 0.1 # 10%
self.unconnected_amount = 0.15 # 10%
self.unsafe_amount = 0.25 # 10%
self.dark_amount = 0.25 # 10%

time:

Solution found.
Peak memory: 50856 KB
Remove intermediate file output.sas
search exit code: 0

INFO     Planner time: 8.33s

real	0m8,517s
user	0m8,230s
sys	0m0,287s

Run PRISM

time prism navigate_map_one_path.prism -pf 'R{"time"}min=? [ F stop ]' -exportstrat stdout -const INITIAL_BATTERY=5000,INITIAL_LOCATION=1,TARGET_LOCATION=5,INITIAL_CONFIGURATION=1

time prism test.prism -pf 'R{"energy"}min=? [ F stop ]' -exportstrat stdout -const INITIAL_BATTERY=32560,INITIAL_LOCATION=0,TARGET_LOCATION=7,INITIAL_CONFIGURATION=1

time prism l52_l24.prism -javamaxmem 16g -cuddmaxmem 16g -pf 'R{"energy"}max=? [ F stop ]' -exportstrat stdout -const INITIAL_BATTERY=32560,INITIAL_LOCATION=0,TARGET_LOCATION=16,INITIAL_CONFIGURATION=1

python scripts/run_camara_prism_scenario.py

Run with docker:

docker run --rm -it -v $HOME/navigation_pddl_tomasys_ws/src/navigation_pddl_tomasys/results:/navigation_pddl_tomasys/results pddl_tomasys:latest python scripts/run_camara_prism_scenario.py

docker run --rm -it -v $HOME/navigation_pddl_tomasys_ws/src/navigation_pddl_tomasys/results:/navigation_pddl_tomasys/results  ghcr.io/rezenders/navigation_pddl_tomasys:main python scripts/run_camara_prism_scenario.py

Run grid map evaluation

docker run --rm -it -v $HOME/navigation_pddl_tomasys_ws/src/navigation_pddl_tomasys/results:/navigation_pddl_tomasys/results  ghcr.io/rezenders/navigation_pddl_tomasys:main python scripts/run_grid_map_scenario.py

Map example: