Uncertainty-aware AI improves off-road robot navigation routes
UDAV: Uncertainty-Driven Adaptive VLM Waypoint Planner
RoboticsArtificial Intelligence
Summary
Navigation systems for robots using aerial images can get confused and make mistakes without knowing how confident they are. The authors created a method called UDAV that makes many route guesses, finds the most typical one, and measures how reliable it is. If the route looks uncertain, UDAV rethinks the plan to avoid big errors. Tests show this idea makes navigation more accurate and safer for off-road vehicles guided by drones.
What this means in practice
- •For drone navigation teams: Create more reliable off-road routes by using uncertainty-aware waypoint planning for UAV-guided vehicle navigation.
- •For autonomous vehicle developers: Improve ground robot path planning in challenging environments by incorporating stochastic route predictions with confidence measures.
Authors
Ghazal Farhani, Shabnam Shabani
Abstract
Vision-language models (VLMs) can generate routes directly from aerial imagery for off-road navigation, but their predictions provide no indication of reliability. We present UDAV, an Uncertainty-Driven Adaptive VLM Waypoint Planner for UAV-guided UGV navigation. UDAV draws multiple stochastic trajectory predictions, selects their medoid as a self-consistent nominal route, and estimates predictive uncertainty from their spatial dispersion. When the maximum uncertainty across interior waypoints exceeds a threshold, UDAV invokes a reconsideration stage; otherwise, it returns the medoid directly. We evaluate UDAV on 400 held-out trajectory queries from two UAV flights. Stochastic medoid selection reduces the mean average displacement error (ADE) from 147.4 pixels for a deterministic prediction to 115.9 pixels. The complete planner achieves a mean ADE of 110.4 pixels, a 25.1% reduction relative to deterministic planning, while producing valid trajectories for all queries. UDAV also yields the lowest 90th- and 95th-percentile errors among all evaluated configurations, including a higher-budget K=10 consensus baseline. Relative to the K=5 medoid, UDAV reduces these errors from 225.3 and 326.0 pixels to 199.0 and 290.8 pixels, respectively. These results demonstrate that stochastic VLM predictions provide both a stronger nominal route and an actionable uncertainty signal for selectively mitigating large planning errors.