Requirements:

Python >= 3.8

cv2 (OpenCV 4.5.5)

numpy

math

random

matplotlib

rospy

actionlib

MoveBase

warnings

Normal CV2 Visualization Instructions:

1) Run as python3 tg_rrt_star.py
2) Give the start and goal (x,y,theta) coordinates when prompted.
3) Give step size as 5 for good visualization. Give no. of steps of 30 as 2, i.e., (-60, -30, 0, 30, 60) is actionset.
4) Choose the algorithm you want to run
5) Wait for algorithm to finish. (C-RRT* may take a while at times but rest should run quite fast).

ROS/Gazebo Instructions:

  1) Copy the contents of the provided package into the catkin workspace.

  2) catkin_make or catkin build and source devel/setup.bash

  3) You will need ros-noetic-navigation. (Installation by sudo apt-get install ros-noetic-navigation)

  4) For start and goal location of (150, 50, 0) --> (375, 225, 0)
  export Turtlebot3 Burger
  roslaunch tgrrt_star turtlebot.launch x_pos:=1.5 y_pos:=4.5 z_pos:=0
  roslaunch turtlebot3_navigation turtlebot3_navigation.launch map_file:=$HOME/catkin_ws/src/tgrrt_star/map/map.yaml
  In catkin_ws/src/tgrrt_star/scripts, run python3 tgrrt_star.py

  5) Basically start x and start y multiplied by 3/100 should give you the coordinates x_pos and y_pos to be entered in the roslaunch tgrrt_star  turtlebot.launch arguments.

Results:

RRT*

TG-RRT* (Incenter based)

It can be concluded from the results that TG-RRT* explores lesser area and also takes lesser time and number of iterations to find an optimal path.