Visualize Pathfinding Algorithms

Name: Visualize Pathfinding Algorithms
Author: Kitmul

Visualize A*, Dijkstra, BFS, and DFS pathfinding algorithms step by step on an interactive grid.

Visualize pathfinding algorithms step by step on an interactive grid. Draw walls by clicking and dragging, generate random mazes, then run A*, Dijkstra, BFS, or DFS to watch the algorithm explore the grid in real time. Compare visited nodes, path length, and execution time across algorithms. Includes pause, speed control, and maze generation. Everything runs in your browser with zero server calls.

Speed

Start

End

Wall

Visited

Path

Click and drag to draw walls. Click Run to start the algorithm. Use Generate Maze for random obstacles.

Your data stays in your browser

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Tutorial

How to Use

Set Up the Grid

Click and drag on the grid to draw walls. Use Generate Maze for automatic obstacle placement. The green cell is Start and red is End.

Choose an Algorithm

Select A*, Dijkstra, BFS, or DFS from the algorithm tabs. Each explores the grid differently and produces different results.

Run and Compare

Press Run to watch the algorithm explore. Compare nodes visited, path length, and execution time across algorithms to understand their trade-offs.

Guide

Complete Guide to Pathfinding Algorithms

Why Pathfinding Matters

Pathfinding algorithms are fundamental to computer science and engineering. They power GPS navigation, game AI, network routing, and robotics. Understanding how different algorithms explore a graph and the trade-offs between optimality, speed, and memory usage is essential for any software engineer.

Breadth-First vs Depth-First Search

BFS explores all neighbors at the current distance before moving farther, guaranteeing shortest paths on unweighted graphs. DFS follows one path as deep as possible before backtracking. BFS uses more memory (a queue of frontiers) while DFS uses less (a stack). BFS has O(V+E) time complexity.

Dijkstra and Weighted Graphs

Dijkstra extends BFS to weighted graphs by always expanding the node with the smallest known distance. On unweighted grids it behaves identically to BFS. Its time complexity is O((V+E) log V) with a priority queue. It always finds optimal paths but explores more nodes than necessary.

A* and Heuristic Search

A* combines Dijkstra's guaranteed optimality with a heuristic estimate of remaining distance. By prioritizing nodes that appear closer to the goal, A* typically explores far fewer nodes than Dijkstra while still finding the shortest path. The heuristic must be admissible (never overestimate) to guarantee optimality.

Sources

Examples

Worked Examples

Example: BFS on Open Grid

Given: a 10x10 grid with no walls, start at (0,0), end at (9,9).

Step 1: BFS expands from (0,0) visiting all cells at distance 1, then distance 2, etc.

Step 2: Each ring of expansion covers cells at Manhattan distance d from start.

Step 3: BFS reaches (9,9) at distance 18 (9 right + 9 down).

Result: BFS finds optimal path of length 19 cells (including start). Visits approximately 100 cells on an open grid.

Example: A* vs Dijkstra Efficiency

Given: a 25x40 grid with scattered walls, start at (12,5), end at (12,34).

Step 1: Dijkstra explores equally in all directions from start, visiting many cells above and below.

Step 2: A* prioritizes cells closer to the goal (lower Manhattan distance), focusing exploration rightward.

Step 3: Both find the same optimal path, but A* visits 40-60% fewer nodes.

Result: A* finds the same shortest path as Dijkstra while visiting significantly fewer nodes due to its heuristic guidance.

Use Cases

Learn Algorithm Differences

“Run BFS and DFS on the same grid to see how breadth-first explores evenly in all directions while depth-first dives deep along one path. This builds intuition for when each approach is appropriate.”

Understand A* Heuristics

“Compare A* against Dijkstra on an open grid. A* uses Manhattan distance heuristic to explore fewer nodes while finding the same optimal path, demonstrating why heuristics matter for performance.”

Prepare for Technical Interviews

“Graph traversal and shortest path questions are common in coding interviews. Visualizing the algorithms builds the mental model needed to implement them from scratch and reason about edge cases.”

Frequently Asked Questions

?What is the difference between A* and Dijkstra?

Both find shortest paths, but A* uses a heuristic to estimate distance to the goal, exploring fewer nodes. Dijkstra explores all directions equally and is optimal but slower without a heuristic.

?Does BFS always find the shortest path?

Yes, on unweighted grids BFS always finds the shortest path because it explores nodes in order of distance from the start. DFS does not guarantee shortest paths.

?Why does DFS visit fewer nodes sometimes?

DFS explores one direction deeply before backtracking. If the goal happens to be in that direction, it finds it quickly. But the path found may be much longer than optimal.

?What heuristic does A* use here?

Manhattan distance (sum of horizontal and vertical distances to the goal). This is admissible for 4-directional grid movement, guaranteeing optimal paths.

?How is the maze generated?

Using recursive backtracking, a classic maze generation algorithm that creates a perfect maze with exactly one path between any two cells, then clears space around start and end.

?Is my data private when using this tool?

Completely. All algorithms run in your browser. No data is sent to any server.

?Is this tool free to use?

Yes. Fully free with no sign-up, no limits, and no ads.

?Can I use diagonal movement?

The current implementation uses 4-directional movement (up, down, left, right). This matches the Manhattan distance heuristic used by A* for guaranteed optimal paths.

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Boost Your Capabilities

Visualize Pathfinding Algorithms

How to Use

Set Up the Grid

Choose an Algorithm

Run and Compare

Complete Guide to Pathfinding Algorithms

Why Pathfinding Matters

Breadth-First vs Depth-First Search

Dijkstra and Weighted Graphs

A* and Heuristic Search

Worked Examples

Example: BFS on Open Grid

Example: A* vs Dijkstra Efficiency

Use Cases

Learn Algorithm Differences

Understand A* Heuristics

Prepare for Technical Interviews

Frequently Asked Questions

?What is the difference between A* and Dijkstra?

?Does BFS always find the shortest path?

?Why does DFS visit fewer nodes sometimes?

?What heuristic does A* use here?

?How is the maze generated?

?Is my data private when using this tool?

?Is this tool free to use?

?Can I use diagonal movement?

How do you like this tool?

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