The unknown initial direction is the part that trips people up.
Treat the problem as an online graph exploration with unknown topology, using a systematic search like DFS with backtracking to guarantee coverage. Maintain a relative map of visited cells and walls, and use the robot's orientation to navigate back to unexplored frontiers. Stop immediately when isTarget() returns true.
Pro tip: In an interview, explicitly discuss the trade-offs between DFS and BFS: DFS uses less memory but may revisit cells, while BFS finds the target in fewest moves but requires more memory. Choose based on likely target distance and grid size.
Ask about grid size, whether the target is guaranteed reachable, and if the robot's starting position/orientation is known. Confirm that move() returns success/failure and that isTarget() can be called anytime.
Use a relative coordinate system to track visited cells and walls. Implement DFS with backtracking: at each cell, try all four directions, moving to unvisited open cells and returning to the previous cell when stuck.
Write helper functions to turn to a desired absolute direction (e.g., using turnLeft/turnRight) and to move forward if the cell is open. Update the robot's orientation and position after each action.
After each move, call isTarget(); if true, stop immediately. Ensure the algorithm terminates when all reachable cells are explored (if target not found).
Explain time complexity O(V+E) for DFS, where V is number of open cells and E is adjacent open pairs. Mention potential optimizations like BFS for shortest path or heuristic search if target location is hinted.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.