Model Predictive Planner for UAV Navigation in Non-Convex Air Corridors

Safe autonomous Uncrewed Aerial Vehicle (UAV) navigation in urban environments requires real-time path planning that avoids obstacles. MaxConvNet is a potential-field planner that leverages properties of Maxwell's equations to generate a path to the goal without local minima. We extend the 2D MaxConvNet magnetic field planner to 3D, using a convolutional autoencoder to predict obstacle-aware potential fields from LiDAR-derived 101^3 voxel grids. Evaluation across 100 randomized closed-loop trials in two distinct Cosys-AirSim urban environments, a dense night-time cityscape and a suburban district shows a 100% path planning success rate on both maps without retraining. In offline path planning, 3DMaxConvNet produces path lengths comparable to A* on unseen maps while reducing runtime from 0.155--0.17s to 0.087--0.089s, or about 1.7--1.95 times faster than A*. Against RRT*(3k), 3DMaxConvNet achieves similar path quality while reducing planning runtime from 17.2--17.5s to about 0.09s, which is roughly 193--201 times faster than RRT*(3k).

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.

UAV target following in cluttered environments requires anticipating target motion. Followers that use only the current target state can lag behind turns or choose blocked corridors. They may also trade safe near-horizon motion for lower tracking error. We propose a future-aware flow planning framework for state-informed UAV target following. Predicted target futures guide clean UAV trajectory generation through residual signals aligned with the planning horizon. Risk-scored repair of the executable prefix is embedded in the sampling loop. On fixed in-distribution (ID) and out-of-distribution (OOD) receding-horizon benchmarks, the planner improves the safety--tracking trade-off. It matches zero measured ID collision rate and achieves the highest ID safe-tracking time fraction. It also gives the lowest OOD macro-average collision rate and final tracking error among the compared methods. It does not dominate every metric: Future-MPC remains smoother and stronger on some threshold-based OOD success metrics under its hand-designed objective. Controlled comparisons show that future conditioning with the adapter improves candidate generation before safety repair. Simulator-facing tests examine interface perturbations, sensing, and controller execution. These results support horizon-aligned future guidance and embedded prefix repair as complementary components for safe UAV target following under the tested simulation conditions.