Took me a while to even model what was being asked.
First, clarify the problem constraints and define the cost model: each drone flight covers up to 10 units, and the person walks empty-handed to retrieve cargo when the drone falls short. Then, model the process as a dynamic programming or greedy problem where you compute the minimum walking distance by considering optimal station choices for drone launches and retrievals.
Pro tip: Demonstrate strong problem-solving by discussing edge cases (e.g., no stations, target less than 10) and analyzing time/space complexity. Also, mention that you'd confirm assumptions with the interviewer before coding.
Ask questions to understand the exact mechanics: Does the person start at position 0? Can stations be used multiple times? Is the drone's range exactly 10 units? What is the cost of walking (distance only)?
Define the state as the current position of the person and the remaining distance to target. The cost is the total walking distance. At each step, the person can either walk to a station, launch a drone (which flies up to 10 units), and if the drone falls short, walk to retrieve it.
Derive a recurrence relation: Let dp[i] be the minimum walking distance to reach distance i. For each station at position p, if p <= i, the person can walk to p (cost p), launch drone to p+10, and if p+10 < i, walk to p+10 to retrieve (cost 10), then continue from p+10. So dp[i] = min over stations p <= i of (p + 10 + dp[i - (p+10)]) if p+10 < i, else p + (i - p) if drone reaches target? Actually, need to carefully model.
Use dynamic programming with memoization or iterative bottom-up. Consider greedy if optimal substructure holds. Analyze time complexity O(n * m) where n is target distance and m is number of stations, and space O(n).
Walk through examples, including edge cases like target=0, no stations, stations beyond target. Verify with brute force for small inputs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.