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P3 - Asymptotically optimal randomized sampling-based path planning

The main task is to implement ROS modules for asymptotically optimal randomized sampling-based path planning using the OMPL library.

Deadline January 5th, 23:59 PST
Points 5
Label in BRUTE P3
Evaluation Solution is evaluated by a demonstration to an instructor
Resources P3-ompl resource package


Assignment

Implement ROS nodes to solve the asymptotically optimal randomized sampling-based path planning. The provided resource pack supports deployment using the STDR simulator. Moreover, an R-VIZ setup file is provided to visualize the robot knowledge of the environment using the messages published by the individual ROS nodes.


Simulator

The simulator provides and processes the following inputs and outputs, respectively

Message type ROS Topic Description
Outputs Robot pose (nav_msgs/Odometry) /robot0/odom Robot pose provided by a simulator
Robot goal (geometry_msgs/Point) /robot0/goal Robot goal provided by an interactive marker
Inputs Velocity command (geometry_msgs/Twist ) /robot0/cmd_vel Velocities to drive the robot

To run the STDR simulator

  1. launch the ROS node

roslaunch uir_ompl_tools uir_backend_stdr.launch


RVIZ

The provided RVIZ setup file may be used to visualize the following topics

Message type Topic Description
Robot odometry (nav_msgs/Odometry) /robot0/odom Robot position and orientation provided by a simulator
Path (nav_msgs/Path) /robot0/path Path to be followed by the robot

Moreover, an interactive marker is setup in RVIZ to place the robot goal on the map.


Expected Behavior


Evaluation

A demonstration in STDR is evaluated by an instructor during the labs.


Suggested architecture


Path following module

The path following module drives the robot along a given path by publishing velocity commands.

Message type Topic
Inputs Path (nav_msgs/Path) /robot0/path Path to be followed by the robot
Robot odometry (nav_msgs/Odometry) /robot0/odom Robot position and orientation provided by a simulator
Outputs Velocity command (geometry_msgs/Twist ) /robot0/cmd_vel Velocities to drive the robot


Path planning module

The path planning module plans a safe path to the published goal.

Message type Topic
Inputs Robot pose (nav_msgs/Odometry) /robot0/odom Robot pose provided by a simulator
Robot goal (geometry_msgs/Point) /robot0/goal Robot goal provided by an interactive marker
Outputs Path (nav_msgs/Path) /robot0/path Path followed by the robot

The path should be computed using a single-query asymptotically optimal randomized sampling-based planner. This may be achieved using the OMPL library with a variant of the RRT* planner. The Rapid library may used to check for collision in a similar manner to the tasks T3a-sampl and T3b-rrt.


Appendix


Setup and ROS Basics

To run the ROS nodes, it is strongly recommended to use either Ubuntu 16 with ROS Kinetic or Ubuntu 18 with ROS Melodic. Detailed installation instructions may be found on ROS web pages ROS Kinetic, ROS Melodic.

On the lab computers, ROS Kinetic is already preinstalled but two more libraries need to be installed before using it.

pip install rospkg
pip install netifaces
Moreover, it is necessary to source the main ROS setup script, which has to be done in each new terminal instance. Thus, it is recommended to add the following source command in your ~/.bashrc file
echo "source /opt/ros/kinetic/setup.bash" >> ~/.bashrc
or alternatively when running ROS Meloding
echo "source /opt/ros/melodic/setup.bash" >> ~/.bashrc

Finally, on Ubuntu 16 the STDR simulator is installed using

sudo apt-get install ros-kinetic-stdr-*
On Ubuntu 18, STDR has to be compiled from source, which can be found at https://github.com/stdr-simulator-ros-pkg/stdr_simulator.


OMPL Setup

The Open Motion Planning Library (OMPL) implements many state-of-the-art sampling-based motion planning algorithms. The OMPL library may be downloaded at this link. To install OMPL and generate its Python bindings, cd into the downloaded folder and run

sudo -s
./install-ompl-ubuntu.sh --python
Note, this may take several hours.
Moreover, when installing on Ubuntu 17.10 and above, where the default version of Python 3, it may be necessary to modify the lines 130-133 of the installation script
    # the default version of Python in 17.10 and above is version 3
    if [[ $ubuntu_version > 1704 ]]; then
        PYTHONV=3
    fi
as follows
    # the default version of Python in 17.10 and above is version 3
    if [[ $ubuntu_version > 1704 ]]; then
        PYTHONV=2
    fi
since the OMPL bindings should be generated for Python 2, which is used by ROS.


Alternatively, it is possible to get OMPL working on Ubuntu 16.04 by installing using the minimum install script.
The script copies the necessary libraries into the /usr/local/lib directory and bindings to the /usr/local/lib/python2.7.


ROS Basics

On Ubuntu 16 and 18 the ROS workspace is created as follows

cd ~/
mkdir rds_ws
mkdir rds_ws/src
cd rds_ws
catkin_make
 
# source the workspace
cd devel
devel_path=$(pwd)
echo "source $devel_path/setup.bash" >> ~/.bashrc
source setup.bash
 
# Add new (custom) package to the workspace
cd ~/rds_ws/src                 # Go to src folder in your workspace.
ln -s [path to your package]    # Create symlink to your package (or place the whole package directly to src).
cd ~/rds_ws
catkin_make                     # Compile the workspace.
 
rospack list                    # Make sure that your pack is known. (Sometimes this is necessary to refresh ROS. ) 
                                # Now, you should be able to use the package within ROS (rosrun or roslaunch).
 
# useful commands
rostopic list              # all current topics in the ROS system
rostopic info [topic name] # show info about topic - publishers, subscribers
rostopic echo [topic name] # writes msgs data on certain topic to stdout
rostopic hz [topic name]   # prints frequency of msgs on a certain topic
 
rosnode list               # all currently running nodes
rosnode info [node name]   # info about the node: published topics, subscribed topics
 
# useful tools
rqt_graph                  # visualize connection between the nodes (might not be 100% accurate)
rviz                       # spatial visualization of the msgs
rosrun tf view_frames      # creates pdf with a visualization of the whole transformation tree (all the /tf msgs in the system) 
rosbag record              # capture ROS msgs on selected topics (data can be played with selected speed afterwards) 
rosbag play [bagfile]      # play data saved in bagfile

The main study material for ROS is http://wiki.ros.org/ROS/Tutorials. A special attention should be given to ROS message-based communication presented in http://wiki.ros.org/ROS/Tutorials/WritingPublisherSubscriber%28python%29.

courses/b4m36uir/projects/p3-motion.txt · Last modified: 2019/11/25 15:37 by bayerja1