A* Algorithm: Path Planning and Implementation

The A* algorithm is a widely-used method for path planning that efficiently computes optimal paths for robots or autonomous agents while avoiding obstacles through strategic placement of obstacle coordinates. This approach employs heuristic search techniques to explore potential paths, evaluating nodes using the cost function f(n) = g(n) + h(n), where g(n) represents the actual cost from the start node to current node n, and h(n) denotes the admissible heuristic estimate from n to the goal. Key implementation aspects include maintaining open and closed lists for node management, with priority queues typically used to efficiently retrieve the most promising nodes. The algorithm's flexibility allows applications ranging from robotic navigation to game development, where it can control character movement for enemy chasing or evasion scenarios. Critical functions in A* implementations typically involve: - Heuristic calculation (e.g., Manhattan or Euclidean distance) - Neighbor node generation with collision detection - Path reconstruction through backward traversal from goal to start The algorithm's efficiency stems from its balance between exhaustive Dijkstra-style search and greedy best-first approaches, making it suitable for various real-world applications where optimal path computation is required under obstacle constraints.