A* Algorithm Implementation in MATLAB for Optimal Path Finding

In this article, we provide a comprehensive exploration of the A* algorithm and its MATLAB implementation, demonstrating how to utilize it for optimal path finding. The A* algorithm is a heuristic-based search algorithm widely employed in solving practical problems such as path planning, game AI, and robotic navigation. It determines the next optimal move by evaluating both the actual cost from the start node and the estimated cost to the target node using a heuristic function. Our discussion covers essential implementation aspects including: - Data structures: Implementation of open and closed lists using priority queues for efficient node retrieval - Heuristic functions: Detailed explanation of Manhattan distance, Euclidean distance, and diagonal distance calculations - Cost evaluation: How the algorithm combines g(n) [actual cost] and h(n) [heuristic cost] using f(n) = g(n) + h(n) We provide complete MATLAB code examples demonstrating: - Node class implementation with properties for coordinates, costs, and parent references - Main algorithm loop with neighbor expansion and path reconstruction - Visualization functions for displaying the search process and final optimal path The code implementation includes key functions such as: - `aStarSearch`: Main algorithm function handling the search process - `calculateHeuristic`: Customizable heuristic function for different distance metrics - `reconstructPath`: Backtracking function to extract the optimal path from goal to start Through studying this A* algorithm implementation, you will gain deeper insights into heuristic search methods and path planning techniques, enabling you to apply these concepts effectively in real-world applications ranging from autonomous vehicle navigation to game development and AI systems.