knowrob_intro

This repository store the first steps into using a self-made ontology for Knowrob.

We offer below intructions to install this repo and use it in two flavours:

• Using the singularity image ro4714-23-3.sif for the KRR course (contains most required KnowRob dependencies)
• Using a native ROS installation

You can also check this extended tutorial

Using the singularity image ro4714-23-3.sif

Outside of the singularity image:

• Install MongoDB using these instructions.

Inside the singularity (singularity shell -p ro47014-23-3.sif):

• Create a catking workspace (or, if you plan to use knowrob with the robot simulation in the course, use the workspace where you have cloned those repositories).
• In that workspace, clone this knowrob_intro repository and Knowrob:

source /opt/ros/noetic/setup.bash
rosdep update
cd ~/catkin_ws/src
git clone https://github.com/kas-lab/knowrob_intro.git

Now clone knowrob and get its dependencies:

source /opt/ros/noetic/setup.bash
wstool init
wstool merge https://raw.githubusercontent.com/kas-lab/knowrob/master/rosinstall/knowrob-base.rosinstall
wstool update
cd ~/catkin_ws
catkin build

source catkin_ws/devel/setup.bash

Launch

Run the mongodb service from OUTSIDE simgularity

sudo systemctl start mongod.service

Inside the singularity (singularity shell -p ro47014-23-3.simg):

Launch knowrob:

source catkin_ws/devel/setup.bash
roslaunch knowrob knowrob.launch 

In another terminal Launch a Prolog CLI as querying interface:

source catkin_ws/devel/setup.bash
rosrun rosprolog rosprolog_commandline.py 

Querying

Inside the Prolog terminal, first load your ontology:

load_owl('package://knowrob_intro/owl/krr_exercise.owl', [namespace(pap, 'http://www.airlab.org/tiago/pick-and-place#')])

pap is the name space defined for your ontology, you can use any name.

Example of queries here, more information at Knowrob model documentation:

is_class(soma:'Crockery').

True.

Querying from code

Inside the singularity (singularity shell -p ro47014-23-3.simg):

Launch knowrob:

source catkin_ws/devel/setup.bash
roslaunch knowrob knowrob.launch 

Launch a test querying code:

source catkin_ws/devel/setup.bash
rosrun knowrob_intro example_query_owl.py 

Using a native ROS installation

Installation

Install SWI prolog and MongoDB using these instructions.

Create a catking repository, clone this repository and Knowrob:

source /opt/ros/noetic/setup.bash
rosdep update
cd ~/catkin_ws/src
git clone https://github.com/kas-lab/knowrob_intro.git
wstool init
wstool merge https://raw.githubusercontent.com/kas-lab/knowrob/master/rosinstall/knowrob-base.rosinstall
wstool update
rosdep install --ignore-src --from-paths .
cd ~/catkin_ws
catkin build

Launch

Run the mongodb service

sudo systemctl start mongod.service

Launch knowrob:

source catkin_ws/devel/setup.bash
roslaunch knowrob knowrob.launch 

Launch a Prolog CLI as querying interface:

rosrun rosprolog rosprolog_commandline.py 

Querying

Inside the Prolog terminal, first load your ontology:

load_owl('package://knowrob_intro/owl/krr_exercise.owl', [namespace(pap, 'http://www.airlab.org/tiago/pick-and-place#')])

pap is the name space defined for your ontology, you can use any name.

Example of queries here, more information at Knowrob model documentation:

is_class(soma:'Crockery').

True.

Querying from code

Launch knowrob:

roslaunch knowrob knowrob.launch 

Launch a test querying code

rosrun knowrob_intro example_query_owl.py 

Interfacing with the knowledge base

To facilitate a quick introduction to knowrob, we have added a brief description of our understanding in the main commands used. However, this documentation only serves as a starting point. For a better understanding please refer to the official documentation.

Main knowrob documentation on interfacing with the knowledge base

• Knowrob model documentation
• Knowrob language documentation
• Knowrob language terms documentation

Our understanding on interfacing with the knowledge base with examples

• TODO

Some comments:

Knowrob launch file loads the following namespaces:

• dc: http://purl.org/dc/elements/1.1/
• dcterms: http://purl.org/dc/terms/
• eor: http://dublincore.org/2000/03/13/eor#
• foaf: http://xmlns.com/foaf/0.1/
• owl: http://www.w3.org/2002/07/owl#
• rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns#
• rdfs: http://www.w3.org/2000/01/rdf-schema#
• serql: http://www.openrdf.org/schema/serql#
• skos: http://www.w3.org/2004/02/skos/core#
• void: http://rdfs.org/ns/void#
• xsd: http://www.w3.org/2001/XMLSchema#
• qudt: http://data.nasa.gov/qudt/owl/qudt#
• dul: http://www.ontologydesignpatterns.org/ont/dul/DUL.owl#
• io: http://www.ontologydesignpatterns.org/ont/dul/IOLite.owl#
• soma: http://www.ease-crc.org/ont/SOMA.owl#
• knowrob: http://knowrob.org/kb/knowrob.owl#
• test: http://knowrob.org/kb/swrl_test#
• urdf: http://knowrob.org/kb/urdf.owl#

Therefore, make sure your .OWL files loads dul and soma namespaces as well. This is an example of the first lines of our .OWL file:

<rdf:RDF xmlns="http://www.airlab.org/tiago/pick-and-place#"
     xml:base="http://www.airlab.org/tiago/pick-and-place"
     xmlns:dc="http://purl.org/dc/elements/1.1/  
     xmlns:DUL="http://www.ontologydesignpatterns.org/ont/dul/DUL.owl#"    
     xmlns:owl="http://www.w3.org/2002/07/owl#"  
     xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"    
     xmlns:xml="http://www.w3.org/XML/1998/namespace"
     xmlns:xsd="http://www.w3.org/2001/XMLSchema#"
     xmlns:SOMA="http://www.ease-crc.org/ont/SOMA.owl#
     xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#">
    <owl:Ontology rdf:about="http://www.airlab.org/tiago/pick-and-place">
        <owl:imports rdf:resource="http://www.ease-crc.org/ont/SOMA.owl"/>
    </owl:Ontology>

Using prolog modules

The following launch files can be used to test examples on using prolog modules with your python code:

• launch/example_query_prolog.launch: example in which the knowledge about objects is in a prolog module.
• launch/example_situation_calculus.launch: example with planning using situation calculus.
• launch/example_owl_kb_and_prolog_rules.launch: example in which the knowledge about objects is store in an OWL ontology, while the store rules are rules in a prolog module.

To run these examples:

• Start the MongoDB (OUTSIDE the singularity image)

systemctl start mongod.service

• Now within the singularity, source your workspace and launch the example:

singularity shell -p ro47014-22-3.simg
source [path_to_your_catkin_ws]/devel/setup.bash
roslaunch knowrob_intro example_[XXX].launch