A-Star-Path-Planning-Simulated-On-Coppeliasim-ROS2

Overview

This project focuses on A* Path Planning, a popular pathfinding algorithm used in robotics and artificial intelligence. A* is known for its efficiency and optimality in finding the shortest path from a start point to a goal point on a grid or graph.

The project includes an implementation of the A* algorithm and visualization of its performance on various maps, making it a valuable resource for understanding path planning in a simulated environment.

A* Algorithm

The A* algorithm is a search algorithm used to find the optimal path from a start node to a goal node in a weighted graph. It combines two essential components:

• G-Cost (Actual Cost): The cost to reach the current node from the start node.
• H-Cost (Heuristic Cost): An estimation of the cost from the current node to the goal node.

A* selects nodes to expand based on their total cost, which is the sum of G-Cost and H-Cost. By exploring nodes with lower total cost first, A* efficiently finds the shortest path while considering a heuristic to guide its search.

Flowchart

Prerequisites:

• Download Coppeliasim edu v.4.5.1.

Building the environment:

mkdir ros2_ws && cd ros2_ws
git clone https://github.com/MostafaELFEEL/A-Star-Path-Planning-Simulated-On-Coppeliasim-ROS2.git
colcon build

Running the Simulation:

1. Open CoppeliaSim:

cd ~/<Path_to_Coppeliasim_Folder> && ./coppeliaSim.sh

Make sure to change <Path_to_Coppeliasim_Folder> with Coppeliasim folder path.

2. Execute the Python script for 2D or 3D path planning:

cd && ros2 run path_planning 2dpath_planning

or

cd && ros2 run path_planning 3dpath_planning

Results of 2D path planning: map number 1, 2, and 3. (only 3 maps)

Results of 3D path planning: map number 1, 2, and 4. (There are 7 maps)