Concept
A* Search is an informed graph traversal algorithm that evaluates nodes using f(n) = g(n) + h(n), where g(n) is the exact path cost from the start node and h(n) is an admissible heuristic estimate to the goal.
Computer Science DSA Lab
A* Pathfinding & Heuristic Search Visualizer for Interactive DSA Practice
Paint custom walls and weighted terrain (mud, water), drag start and target pins, test Manhattan/Euclidean heuristics, and benchmark A* search efficiency against Dijkstra in real time. Learn the concept, operation flow, complexity, and real-world use cases through a focused OpenLabs interactive visualizer.
DSA Visualizer
Informed Search & Heuristic Routing Engine
Time: O((V + E) log V) with priority queue, Space: O(V) storing open and closed sets
Step 1
Place Start & Target
Step 2
Paint Obstacles & Weighted Terrain
Step 3
Select Heuristic
Step 4
Visualize Open/Closed Sets
Step 5
Trace Optimal Path
Operation flow
A* maintains an open set priority queue ordered by f(n). By factoring in both historical travel cost and heuristic distance, it directs exploration toward the goal while guaranteeing the shortest path when h(n) is admissible.
Complexity
Time: O((V + E) log V) with priority queue, Space: O(V) storing open and closed sets
Visualization
Watch each operation update the structure or algorithm state step by step.
Learn by visualizing
Understand A* Pathfinding & Heuristic Search through step-by-step interaction
A* maintains an open set priority queue ordered by f(n). By factoring in both historical travel cost and heuristic distance, it directs exploration toward the goal while guaranteeing the shortest path when h(n) is admissible. The lab makes every state change visible, helping students connect DSA theory with practical algorithm behavior.
Understand the governing formula f(n) = g(n) + h(n) and the role of heuristic functions.
Evaluate admissible heuristics: Manhattan (4-directional), Euclidean (straight-line), and Octile (diagonal).
Observe how weighted terrain (mud, swamp) alters path routing compared to uniform grids.
Analyze the difference between Dijkstra (h=0) and Greedy Best-First (f=h) in concave dead-end traps.
Benchmark search efficiency: measure nodes visited, path cost, and compute duration in real time.
Where this concept is used
- Video game NPC navigation and tactical pathfinding
- Autonomous vehicle and robotics motion planning
- Logistics delivery routing and supply chain navigation
- Network routing with quality-of-service (QoS) path metrics
- Procedural puzzle and maze solving in computer vision
How the interactive lab works
Open the A* Pathfinding & Heuristic Search lab, run the available operation controls, and watch the visual state update immediately. Use the animation to trace the operation order, compare complexity, and verify your understanding.
A* Pathfinding & Heuristic Search FAQs
What makes A* different from Dijkstra's algorithm?
Dijkstra's algorithm expands uniformly in all directions (h = 0). A* uses a heuristic h(n) to focus exploration toward the target, typically visiting 60% to 80% fewer nodes while still guaranteeing an optimal path.
What is an admissible heuristic?
A heuristic h(n) is admissible if it never overestimates the actual cost to reach the goal. If h(n) is admissible and consistent, A* is mathematically guaranteed to return the optimal (shortest) path.
Why does Greedy Best-First Search get stuck in dead ends?
Greedy Best-First only considers h(n) (distance to target) and ignores g(n) (cost traveled). When facing a concave wall (U-shape), it rushes inside toward the goal and must exhaust the entire chamber before backtracking.
How does weighted terrain affect A* pathfinding?
Each grid cell has a movement cost. Normal cells cost 1, mud costs 5, and water costs 10. A* will take a geometrically longer path around high-cost terrain if the total cumulative path cost is lower.
Ready to practice A* Pathfinding & Heuristic Search?
Launch the visualizer, trace each step, and build confidence with data structures and algorithms through hands-on learning.