Unverified Commit 5adb1e78 authored by stonneau's avatar stonneau Committed by GitHub
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Merge pull request #30 from humanoid-path-planner/master

updating documentation
parents a69dd94e 4fbedc3f
......@@ -4,53 +4,76 @@ Copyright 2015 LAAS-CNRS
Author: Steve Tonneau
##Description
## Description
hpp-rbprm-corba implements python bindings for hpp-rbprm, and presents a few example files.
Please refer to this [link](https://github.com/stonneau/hpp-rbprm) for information on hpp-rbprm.
Please refer to this [link](https://github.com/humanoid-path-planner/hpp-rbprm) for information on hpp-rbprm.
##Installation on ubuntu-14.04 64 bit with ros-indigo
## Installation from binary package repository
To install hpp-rbprm-corba:
1. Add robotpkg to your apt configuration: http://robotpkg.openrobots.org/debian.html
2. `sudo apt update && sudo apt install robotpkg-hpp-rbprm-corba`
3. Then, you will need to export some variables to allow you system to find the executables:
1. Install HPP-RBPRM and its dependencies
- see https://github.com/stonneau/hpp-rbprm
`export PATH=${PATH:+$PATH:}/opt/openrobots/bin:/opt/openrobots/sbin`
2. Install HPP-AFFORDANCE-CORBA along with its dependencies
- see https://github.com/anna-seppala/hpp-affordance-corba
`export MANPATH=${MANPATH:+$MANPATH:}/opt/openrobots/man`
3. Use CMake to install the library. For instance:
`export PYTHONPATH=/opt/openrobots/lib/python2.7/site-packages:$PYTHONPATH`
mkdir $HPP_RBPRM_CORBA_DIR/build
cd $HPP_RBPRM_CORBA_DIR/build
cd cmake ..
make install
`export ROS_PACKAGE_PATH="$ROS_PACKAGE_PATH:/opt/openrobots/share"`
`export DEVEL_HPP_DIR=/opt/openrobots/`
##Documentation
(you may want to add these to your .bashrc file)
## Installation From source on ubuntu-16.04 64 bit with ros-kinetic
1. Follow this instructions : http://humanoid-path-planner.github.io/hpp-doc/download.html (select 'Devellopement" in the list)
2. Once this installation is complete, run `make rbprm`
## Optional: installing viewer and python bindings for dependencies
If you are planning to use the visualization tools used by the Gepetto team, along with python examples, you may need a few extra steps:
1. Install the gepetto-viwer server
`sudo apt install -qqy robotpkg-py27-qt4-gepetto-viewer-corba`
`sudo apt install -qqy robotpkg-py27-qt4-hpp-gepetto-viewer`
2. Install the pinocchio bindings
`sudo apt install -qqy robotpkg-py27-pinocchio`
3. Install the dae extension for osg
`sudo apt install -qqy robotpkg-osg-dae`
## Documentation
Open $DEVEL_DIR/install/share/doc/hpp-rbprm-corba/doxygen-html/index.html in a web brower and you
will have access to the code documentation. If you are using ipython, the documentation of the methods implemented
is also directly available in a python console.
##Example
To see the planner in action, two examples from our IJRR submission with HyQ are available. Examples with HRP-2 are also provided,
though they can only be executed if you have access to HRP-2 model.
## Example
To see the planner in action, one example from our IJRR submission with HyQ is available. Examples with HRP-2 are also provided, though they can only be executed if you have access to HRP-2 model.
- First of all, retrieve and build the HyQ model from its github repository:
https://github.com/iit-DLSLab/hyq-description
- If you installed the planner form binaries, you need to download the scripts as explained here. Otherwise you can find them directly in script/scenarios/demos folder. For the binary proceudre, create a folder and cd in to it, then type
```$ rosrun xacro xacro.py hyq_description/robots/hyq_model.urdf.xacro -o hyq.urdf```
`wget https://raw.githubusercontent.com/humanoid-path-planner/hpp-rbprm-corba/master/script/scenarios/demos/darpa_hyq.py`
`wget https://raw.githubusercontent.com/humanoid-path-planner/hpp-rbprm-corba/master/script/scenarios/demos/darpa_hyq_path.py`
`wget https://raw.githubusercontent.com/humanoid-path-planner/hpp-rbprm-corba/devel/script/scenarios/demos/run.sh`
- Make sure to install hyq.urdf in $HPP_DEVEL_DIR/install/share/hpp-rbprm-corba/
- Make the run.sh script executable:`chmod +x run.sh`
- The planning is decomposed in two phases / scripts. First, a root path is computed (\*_path.py files). Then, the contacts are generated along the computed path (\*_interp.py files). The scripts are located in the folder /scripts/scenarios/demos.
- To see the different steps of the process run
```$ ./run.sh darpa_hyq_path.py```
``$ ./run.sh darpa_hyq.py`
The script include comments explaining the different calls to the library. You can call the different methods a() ... d() to see the different steps of the planning.
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