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Preprint · 2026

Air-Ground Teaming

When to Ask for Help: Uncertainty-Triggered Aerial Refinement for Off-Road Navigation

Pranay Samineni, Adam Thorpe, John Chubick, Anand Balakrishnan, Christian Ellis, Swarat Chaudhuri, Ufuk Topcu

arXiv preprint arXiv:2611.06702, 2026

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System overview: the ground robot fuses obstacle and semantic maps into a cost map for MPPI planning; when uncertainty is high it asks the drone for the most useful aerial map.
Fig. 2. The UGV consumes LiDAR, RGB-D, and IMU data to build layers of maps (obstacles, semantic traversability) that serve as a basis for a fused costmap for motion planning. During planning time, while the projected MPPI plans stay below an uncertainty threshold, the nominal MPPI plan is used. Otherwise, counterfactual diagnosis is used to request the best aerial map that would resolve the uncertainty.

Abstract

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.

Cite

@misc{samineni2026when,
  title = {{When to Ask for Help: Uncertainty-Triggered Aerial Refinement for Off-Road Navigation}},
  author = {Pranay Samineni and Adam Thorpe and John Chubick and Anand Balakrishnan and Christian Ellis and Swarat Chaudhuri and Ufuk Topcu},
  year = {2026},
  eprint = {2611.06702},
  archivePrefix = {arXiv}
}