Path Planning using Hybrid RRT-A* algorithm and Case Study

This repo brings you robust, optimal path planning for non-holonomic mobile robots (TurtleBot3) using RRT-A* and Informed RRT*—tested in both 2D and Gazebo. Follow the steps below to get rolling!

• For more details and project breakdown, please check out my blog: link

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🚦 Step 1: Pre-Requisites

• Ubuntu 18.04
• ROS Melodic
• Gazebo 9.1
• Turtlebot3 Packages

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🧰 Step 2: Check for Python Libraries

Make sure you have these installed (use pip or apt as needed):

import numpy
import matplotlib.patches
import math
import rospy
import time
import heapq
import random
import sys
import pygame

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🛠️ Step 3: Installing TurtleBot3 Packages

mkdir -p planning_ws/src
cd planning_ws
catkin_make
source devel/setup.bash

# Clone TurtleBot3 repositories
git clone https://github.com/ROBOTIS-GIT/turtlebot3.git src/turtlebot3
git clone https://github.com/ROBOTIS-GIT/turtlebot3_msgs.git src/turtlebot3_msgs
git clone https://github.com/ROBOTIS-GIT/turtlebot3_simulations.git src/turtlebot3_simulations

cd ..
catkin_make

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🚀 Step 4: Running the Package

1. Download the package:
   Place project_5 in your planning_ws/src directory.

2. Build the package:

   catkin_make

3. Source your workspace:

   source devel/setup.bash

4. Set the TurtleBot3 model:

   export TURTLEBOT3_MODEL=burger

5. Make the node executable:

   cd src/project_5/nodes
   chmod +x task

6. Launch the node and Gazebo environment:

   roslaunch project_5 proj.launch

7. Configure at runtime:

   • Enter clearance value (recommended: 0.03 to 0.07)
   • Enter RPM value when prompted (suggested: 10)

8. Watch the magic:

   • The node will initialize in a few seconds.
   • TurtleBot3 will autonomously navigate to the goal in about a minute.

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🏁 That’s it!

You’re all set to explore optimal path planning with RRT-A* and Informed RRT* on your TurtleBot3. For troubleshooting or more details, check the code comments and documentation.

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Pro Tip:
Tweak the clearance and RPM values to see how your TurtleBot3 handles different environments and constraints!

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Happy path planning! 🚗💨