A logistics startup is prototyping a fleet of autonomous delivery drones. Each drone must re-plan its route on board whenever a new obstacle or no-fly zone is detected, with a hard latency budget of 100 ms and a memory budget under 2 MB. In practice the drones can lose contact with the cloud for several minutes, so all critical decision-making must run locally on the micro-controller. Which specialized data-science approach is MOST appropriate for this edge-computing scenario?
Greedy nearest-neighbor heuristic executed locally each time a new route is required
On-device reinforcement learning with tabular Q-learning that updates policies during every flight
Formulating the path as a mixed-integer linear program (MILP) solved in the cloud and sent to the drone
Single-source Dijkstra shortest-path computed once in the cloud and uploaded to each drone
A greedy, nearest-neighbor heuristic can be evaluated in O(n²) time and O(n) memory, so it fits comfortably within the tight CPU and RAM limits of a micro-controller. Because it makes a single local choice at each step it can be recomputed quickly every time an obstacle appears, even without cloud access.
Reinforcement-learning (tabular or deep) approaches require far more computation and memory during both training and inference, making them impractical on such constrained hardware. Pre-computing a Dijkstra shortest path in the cloud cannot adapt to in-flight changes; once weights change the algorithm must be rerun, which the drone cannot afford to do online. Mixed-integer linear programming yields high-quality routes but solving a MILP is NP-hard and typically far too slow for real-time use on an embedded device. Therefore, the greedy nearest-neighbor heuristic is the best match for the edge constraints.
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Why is the greedy nearest-neighbor heuristic suitable for this scenario?
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Why are reinforcement-learning approaches unsuitable for the drones?