A python visualization of the A* path finding algorithm. It allows you to pick your start and end location and view the process of finding the shortest path.
A* is an informed search algorithm, or a best-first search, meaning that it is formulated in terms of weighted graphs: starting from a specific starting node of a graph, it aims to find a path to the given goal node having the smallest cost (least distance travelled, shortest time, etc.). It does this by maintaining a tree of paths originating at the start node and extending those paths one edge at a time until the goal node is reached.
At each iteration of its main loop, A* needs to determine which of its paths to extend. It does so based on the cost of the path and an estimate of the cost required to extend the path all the way to the goal. Specifically, A* selects the path that minimizes
f(n)=g(n)+h(n)
where n is the next node on the path, g(n) is the cost of the path from the start node to n, and h(n) is a heuristic function that estimates the cost of the cheapest path from n to the goal.
The A* algorithm is widely used in various domains for pathfinding and optimization problems. It has applications in robotics, video games, route planning, logistics, and artificial intelligence. In robotics, A* helps robots navigate obstacles and find optimal paths. In video games, it enables NPCs to navigate game environments intelligently. Route planning applications use A* to find the shortest or fastest routes between locations. Logistics industries utilize A* for vehicle routing and scheduling. A* is also employed in AI systems, such as natural language processing and machine learning, to optimize decision-making processes. Its versatility and efficiency make it a valuable algorithm in many real-world scenarios.
To deploy this project install Pygame
pip install pygame