Vision-based heading prediction is useful for UAV--UGV cooperation, but accurate prediction alone does not guarantee that every predicted heading should be issued directly as a control command. This paper investigates the decision problem of when and how a fixed vision-based heading predictor should be trusted for command issuance.
arXiv:2606. 14219v1 Announce Type: cross Abstract: Agentic AI can support unmanned aerial vehicle (UAV) autonomy by providing high-level recovery reasoning when local waypoint- or setpoint-based execution encounters blocked passages, repeated no-progress behavior, or mission-level ambiguity.
The paper introduces RACO, a reliability‑aware adaptive coarse‑to‑fine navigation framework for inspection‑oriented UAV vision‑language navigation. It treats the coarse goal as a runtime hypothesis, using object‑level anchors to correct localization before and at the transition to the fine stage, and applies scale‑adaptive terminal refinement for near‑miss cases. RACO is evaluated on the new LG‑UVI inspection setting and outperforms the HETT baseline by 9.53 and 7.98 percentage points on validation‑unseen and test‑unseen, respectively, while improving inspection‑region arrival and reducing false verification risk.
arXiv:2608. 07557v1 Announce Type: cross Abstract: Vision-Language Navigation for Unmanned Aerial Vehicles (UAV-VLN) requires rapid and reactive control in complex 3D environments.
UAV vision-language navigation (UAV-VLN) is commonly evaluated as goal reaching, but inspection-oriented deployment requires the agent to stop within a valid inspection region and avoid falsely confir...
arXiv:2608. 09467v1 Announce Type: cross Abstract: Unmanned aerial vehicle vision-language navigation (UAV-VLN) requires agents to translate visual observations and language instructions into reliable flight actions in complex environments.
The paper introduces a prediction-informed hierarchical control framework for autonomous UAV landing on a USV in high sea states. It uses a pretrained model to explicitly predict USV attitude, enabling the identification of suitable landing windows and optimal touchdown timing. A conventional low‑level flight controller then maintains UAV stability while following the motion commands.
arXiv:2606. 06836v1 Announce Type: cross Abstract: Language-guided UAV agents must execute long-horizon semantic instructions while producing smooth, physically feasible continuous flight commands, yet existing Vision-Language Navigation (VLN) benchmarks typically use discrete or coarse actions and existing UAV Vision-Language-Action (VLA) tasks focus on short, atomic maneuvers.
The paper introduces VLN on the Fly, an onboard vision‑language navigation stack for aerial robots that separates grounding, planning, and control into inspectable stages. A quantized vision‑language model grounds instructions to a coarse image cell, depth estimation lifts this to a 3D goal, a fast B‑spline planner generates a feasible trajectory, and a pretrained reinforcement learning policy translates the trajectory into motor commands. In controlled indoor flights, the stack achieved the target in 13 of 15 trials with a mean goal error of 5.72 cm and 39.3% GPU utilization, and successfully tracked collision‑free trajectories in cluttered environments.
arXiv:2607. 21400v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions.
The paper presents a two-step method to correct machine‑learning based perception for safety in autonomous systems. First, it uses offline computation to characterize uncertainties from the ML module via preimages of perception contracts. Then, at runtime, a risk heuristic selects specific states from these uncertain estimates to guide control decisions, reducing safety violations in adaptive cruise control scenarios while adding minimal delay.
Uncertainty estimation for Vision-Language-Navigation (VLN) models is a critical task since it can help identify ambiguous and unreliable predictions, enabling agents to make safer navigation decision...
arXiv:2607. 23870v1 Announce Type: cross Abstract: Smart-city airspace is transforming Uncrewed Aerial Vehicles (UAVs) from passive sensing platforms into cyber-physical decision makers that must follow operational rules under degraded observations and ambiguous language.