A* Path Planner

Overview

This repository demonstrates path planning using the A* algorithm for a mobile robot navigating through a maze-like environment. The code generates a scenario with walls and obstacles, plans a path from a specified start position to a goal position, and simulates the robot's movement along the planned path.

Usage

1. Open MATLAB.
2. Set the current directory to the location of the script.
3. Run the script Path_Planner_AStar.m.

Script Description

1. Define the scenario with a floor and walls.
2. Create a binary occupancy map from the scenario.
3. Plan a path using the A* algorithm from a specified start position to a goal position.
4. Generate a trajectory for the robot to follow based on the planned path.
5. Load a robot model and create a platform for the robot.
6. Visualize the scenario, planned path, and robot movement in a 3D environment.
7. Simulate the robot's movement along the planned path.

A* Algorithm Description

The A* algorithm used in this script works as follows:

1. Initialization:
   • Start with an initial node representing the starting position.
   • Create an open list to store nodes to be evaluated and a closed list to store nodes that have already been evaluated.
   • Initialize the cost of the starting node as 0 and the heuristic cost (estimated cost to reach the goal from the current node) as the Euclidean distance from the starting node to the goal node.
2. Expansion:
   • Repeat the following steps until either the goal node is reached or there are no more nodes to evaluate:
     • Select the node from the open list with the lowest total cost (sum of the actual cost from the starting node to the current node and the heuristic cost).
     • Remove the selected node from the open list and add it to the closed list.
     • Generate neighboring nodes of the selected node.
     • For each neighboring node:
       • If the neighboring node is not walkable or is in the closed list, skip it.
       • If the neighboring node is not in the open list, compute its actual cost and heuristic cost, and add it to the open list.
       • If the neighboring node is already in the open list, update its cost if the new cost is lower than the previous cost.
3. Backtracking:
   • Once the goal node is reached, trace back the path from the goal node to the starting node by following the parent pointers stored in each node.
   • Construct the optimal path by concatenating the nodes from the starting node to the goal node.
4. Path Output:
   • Output the optimal path generated by the A* algorithm, which represents the sequence of nodes (grid cells) to traverse from the starting position to the goal position.

Customize

• Modify the maze layout, wall positions, and colors by adjusting the variables wallPositions, wallSizes, and wallColor.
• Change the start and goal positions by updating the variables startPosition and goalPosition.
• Adjust the robot's height and trajectory parameters as needed.

Result

Notes

• Ensure that the start position is not inside any obstacles to avoid errors during path planning.

Aknowledgements

• This project was developed with the assistance of MathWorks resources.