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Classic BFS, four directions, nothing tricky.
Model the grid as an unweighted graph and use BFS to find the shortest path from start to end. Track visited cells to avoid cycles and return the distance when the end is reached, or -1 if the queue is exhausted.
Pro tip: Mention that BFS is optimal for unweighted grids, but if the grid were weighted (e.g., different terrain costs), you'd switch to Dijkstra's algorithm. Also, discuss early termination when the end is found to save time.
Confirm the grid dimensions, movement allowed (4-directional or 8-directional), and whether diagonal moves are permitted. Ask if the start and end are guaranteed to be open cells.
Explain that BFS is ideal because it explores level by level, guaranteeing the shortest path in an unweighted graph. Mention that DFS would not guarantee the shortest path.
Initialize a queue with the start cell and a visited set. While the queue is not empty, dequeue a cell, check if it's the end, and enqueue all valid unvisited neighbors (within bounds, not walls). Track distance by storing (cell, distance) or using level-order traversal.
Consider cases where start equals end (return 0), start or end is a wall (return -1), or the grid is empty. Also, discuss memory optimization for large grids (e.g., using a 2D boolean array for visited).
State that time complexity is O(R*C) where R and C are grid dimensions, as each cell is visited at most once. Space complexity is O(R*C) for the queue and visited set in the worst case.
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