ROS-based Path Planning for Turtlebot Robot using Informed RRT* algorithm

Author

Arpit Aggarwal

Introduction to the Project

In this project, the Informed RRT* motion planning algorithm was used on ROS Turtlebot 2 to navigate in a configuration space consisting of static obstacles. The path generated by the Informed RRT* motion planning algorithm was compared with the path generated by the RRT* motion planning algorithm on the basis of the optimal time and optimal path from the beginning point to the objective point. This helped us verify that Informed RRT* algorithm outperforms RRT* algorithm in rate of convergence, final solution cost, and ability to find difficult passages while demonstrating less dependence on the state dimension and range of the motion planning problem.

Results

The results obtained using Informed RRT-star algorithm on a rigid robot:

The results obtained using RRT-star algorithm on a rigid robot:

Software Required

To run the code, you will need to install numpy, rospy, matplotlib and gazebo.

Simulation platforms used

For the simulations, we used the gazebo and turtlebot2 package. The world file is located in the world folder and defines the setup of the gazebo environment.

Instructions for running the code

For running the code, follow the detailed instructions given below. First we create a catkin workspace for our project

mkdir -p ~/catkin_ws/src
cd ~/catkin_ws
catkin_make

After creating your catkin workspace change your directory to src and clone this repository

cd ~/catkin_ws/src
git clone --recursive https://github.com/arp95/turtlebot_rrt.git
cd ../
catkin_make

After cloning the repository lets create an executable for our .py file that contains the code to run our program.

cd ~/catkin_ws/src/turtlebot_rrt/scripts
chmod +x turtlebot_rrt.py
cd ../../../
catkin_make

Once all the above steps have been performed lets source our catkin workspace and then run the program

source ./devel/setup.bash
roslaunch turtlebot_rrt demo.launch x:=4 y:=3 yaw:=0

Above as you can see in the end, we have given the x,y and yaw arguments in the command line. This indicates the initial spawn position of the turtlebot in the gazebo environment. The coordinates are represented by (x,y) and the orientation is given by the yaw argument. Once you run the environment a second terminal will pop up in which you need to enter the following information:

x coordinate for the start node(in meters, same as the one given in the roslaunch command):
y coordinate for the start node(in meters, same as the one given in the roslaunch command):
x-coordinate of the goal node(in meters):
y-coordinate of the goal node(in meters):

After entering all these values in the terminal, the Informed RRT-star algorithm finds the optimum path between the entered start node and goal node.

Credits

The following links were helpful for this project:

1. https://github.com/Mayavan/RRT-star-path-planning-with-turtlebot
2. https://arxiv.org/abs/1404.2334
3. https://github.com/AtsushiSakai/PythonRobotics