A* Visualizer .core

Interactive Grid Search with Cost Estimation

Speed: 500ms

Iterations

Open Set

Closed Set

Obstacle

Heuristic (h)

0.00

Actual Cost (g)

0.00

Total (f = g + h)

0.00

Step: 1 / 0

0% Complete

A_STAR_SEARCH.PY

def a_star(start, goal):
    open_set = {start}
    came_from = {}
    g_score = {start: 0}
    f_score = {start: h(start, goal)}

    while open_set:
        current = min(open_set, key=f_score.get)
        if current == goal:
            return reconstruct_path(current)

        open_set.remove(current)
        closed_set.add(current)
        for neighbor in neighbors(current):
            tentative_g = g_score[current] + cost(current, neighbor)
            if tentative_g < g_score[neighbor]:
                came_from[neighbor] = current
                g_score[neighbor] = tentative_g
                f_score[neighbor] = g_score[neighbor] + h(neighbor, goal)

Logic Execution ProfileOptimized

Pro-Tip: Heuristics

The Manhattan Distance is preferred for grid-based pathfinding where diagonal movement is restricted. For 3D spaces, Euclidean distance ensures the heuristic is admissible and consistent.

Algorithm Status

Select start and end nodes to begin.