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原文摘要

Heterogeneous air-ground teams can use aerial sensing to address the limited range and viewpoint of ground-vehicle sensors, but deciding when the UGV needs assistance and what information the UAV should collect remains a challenge. This paper presents a planner-conditioned air-ground assistance method that evaluates how uncertain map information affects the UGV's current MPPI rollouts. When navigation remains degraded, we score the same rollouts under resolved map hypotheses, select the map layer with the largest useful improvement, request the corresponding aerial measurement, fuse the returned information, and replan. Unlike approaches driven by fixed support roles, task structure, or uncertainty alone, our method conditions assistance on whether resolving uncertainty improves predicted ground navigation. Across 180 simulation trials on three off-road routes, the method achieves 98.9% goal attainment while reducing mean UAV flight time by 87.7% relative to continuous greedy aerial mapping. Aggregated across the routes, each second of estimated UAV flight time saves 2.94 s of UGV navigation time. Field experiments spanning approximately 2 km of UGV operation demonstrate uncertainty resolution and successful navigation recovery.