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Model the grid as an unweighted graph and use BFS from the start cell to find the shortest path to the destination, since BFS explores level by level and guarantees the minimum number of steps. Track visited cells to avoid cycles and return the distance when the destination is reached, or -1 if the queue is exhausted.
Pro tip: Clarify edge cases upfront—such as start equals destination, start or destination blocked, or empty grid—and mention that BFS is optimal for unweighted grids, while A* could be used if heuristics are available. This shows you think about correctness and performance trade-offs.
Ask about grid size, whether diagonal moves are allowed, if start/destination can be blocked, and if the grid can be empty. Confirm that movement is only in four directions and that each step costs 1.
State that BFS is ideal for unweighted shortest path because it explores all nodes at distance k before distance k+1, guaranteeing the first time you reach the destination is via a shortest path.
Initialize a queue with the start cell and a visited set (or distance matrix). While the queue is not empty, dequeue a cell, check if it's the destination, and enqueue all valid unvisited neighbors (within bounds, not blocked).
If the destination is reached, return the current distance (or the distance stored for that cell). If the queue empties without reaching the destination, return -1.
State that time and space complexity are O(R*C) where R and C are grid dimensions. Mention that A* with a heuristic (e.g., Manhattan distance) could be faster in practice but BFS is simpler and sufficient.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.