Unmanned aerial vehicle (UAV) research requires the integration of cutting-edge technology into existing autopilot frameworks. This process can be arduous, requiring extensive resources, time, and detailed knowledge of the existing system. ROSplane is a lean, open-source fixed-wing autonomy stack built by researchers for researchers. It is designed to accelerate research by providing clearly defined interfaces with an easily modifiable framework. Powered by ROS 2, ROSplane allows for rapid integration of low or high-level control, path planning, or estimation algorithms. A focus on lean, easily understood code and extensive documentation lowers the barrier to entry for researchers. Recent developments to ROSplane improve its capacity to accelerate UAV research, including the transition from ROS 1 to ROS 2, enhanced estimation and control algorithms, increased modularity, and an improved aerodynamic modeling pipeline. This aerodynamic modeling pipeline significantly reduces the effort of transitioning from simulation to real-world testing without requiring expensive system identification or computational fluid dynamics tools. ROSplane's architecture reduces the effort required to integrate new research tools and methods, expediting hardware experimentation.

ROSflight 2.0: Lean ROS 2-Based Autopilot for Unmanned Aerial V ehicles Abstract-- ROSflight is a lean, open-source autopilot ecosystem for unmanned aerial vehicles (UA Vs). Designed by researchers for researchers, it is built to lower the barrier to entry to UA V research and accelerate the transition from simulation to hardware experiments by maintaining a lean (not full-featured), well-documented, and modular codebase. This publication builds on previous treatments and describes significant additions to the architecture that improve the modularity and usability of ROSflight, including the transition from ROS 1 to ROS 2, supported hardware, low-level actuator mixing, and the simulation environment. We believe that these changes improve the usability of ROSflight and enable ROSflight to accelerate research in areas like advanced-air mobility. Hardware results are provided, showing that ROSflight is able to control a multirotor over a serial connection at 400 Hz while closing all control loops on the companion computer . In recent years, interest in unmanned aerial vehicles (UA Vs) has increased significantly. Technological advances have enabled numerous applications of UA Vs, including package delivery, photography, search-and-rescue, firefighting, as well as military applications. Advanced air mobility (AAM), a category broadly referring to increasing autonomy in urban areas for civilian use, is also currently an area of high interest.

After years of growth, drone-based delivery is transforming logistics. At its core, real-time 6-DoF drone pose tracking enables precise flight control and accurate drone landing. With the widespread availability of urban 3D maps, the Visual Positioning Service (VPS), a mobile pose estimation system, has been adapted to enhance drone pose tracking during the landing phase, as conventional systems like GPS are unreliable in urban environments due to signal attenuation and multi-path propagation. However, deploying the current VPS on drones faces limitations in both estimation accuracy and efficiency. In this work, we redesign drone-oriented VPS with the event camera and introduce EV-Pose to enable accurate, high-frequency 6-DoF pose tracking for accurate drone landing. EV-Pose introduces a spatio-temporal feature-instructed pose estimation module that extracts a temporal distance field to enable 3D point map matching for pose estimation; and a motion-aware hierarchical fusion and optimization scheme to enhance the above estimation in accuracy and efficiency, by utilizing drone motion in the \textit{early stage} of event filtering and the \textit{later stage} of pose optimization. Evaluation shows that EV-Pose achieves a rotation accuracy of 1.34$\degree$ and a translation accuracy of 6.9$mm$ with a tracking latency of 10.08$ms$, outperforming baselines by $>$50\%, \tmcrevise{thus enabling accurate drone landings.} Demo: https://ev-pose.github.io/

French troops board oil tanker linked to Russian'shadow fleet' French soldiers have boarded an oil tanker believed to be part of Russia's shadow fleet, used to evade sanctions imposed because of the war in Ukraine. The Boracay left Russia last month and was off the coast of Denmark when unidentified drones forced the temporary closure of several airports last week. It has been anchored off western France for a few days. French President Emmanuel Macron said at an EU leaders' summit in Copenhagen on Wednesday that the crew had committed serious offences, but did not elaborate. Kremlin spokesman Dmitry Peskov said Russia had no knowledge of the vessel.

Can Ukraine restore its pre-war borders? Why are Tomahawk missiles for Ukraine a'red line' for Russia? Is Russia testing NATO with aerial incursions in Europe? Macron, Meloni argue for caution in responding to Russian'provocations' Danish Prime Minister Mette Frederiksen has called on Europe to arm itself to respond to Russia's hybrid warfare, but other major continental leaders have argued for caution against getting trapped in a tit-for-tat cycle of escalation with Moscow. "I hope that everybody recognises now that there is a hybrid war and one day it's Poland, the other day it's Denmark, and next week it will probably be somewhere else that we see sabotage or we see drones flying," Frederiksen told reporters on Wednesday.

Flytrex and Uber Eats will begin testing drone food delivery in select U.S. markets by year-end, potentially reducing delivery times to just minutes for customers.

Accurate visual localization from aerial views is a fundamental problem with applications in mapping, large-area inspection, and search-and-rescue operations. In many scenarios, these systems require high-precision localization while operating with limited resources (e.g., no internet connection or GNSS/GPS support), making large image databases or heavy 3D models impractical. Surprisingly, little attention has been given to leveraging orthographic geodata as an alternative paradigm, which is lightweight and increasingly available through free releases by governmental authorities (e.g., the European Union). To fill this gap, we propose OrthoLoC, the first large-scale dataset comprising 16,425 UAV images from Germany and the United States with multiple modalities. The dataset addresses domain shifts between UAV imagery and geospatial data. Its paired structure enables fair benchmarking of existing solutions by decoupling image retrieval from feature matching, allowing isolated evaluation of localization and calibration performance. Through comprehensive evaluation, we examine the impact of domain shifts, data resolutions, and covisibility on localization accuracy. Finally, we introduce a refinement technique called AdHoP, which can be integrated with any feature matcher, improving matching by up to 95% and reducing translation error by up to 63%. The dataset and code are available at: https://deepscenario.github.io/OrthoLoC.

A core challenge in AI-guided autonomy is enabling agents to navigate realistically and effectively in previously unseen environments based on natural language commands. We propose UAV-VLN, a novel end-to-end Vision-Language Navigation (VLN) framework for Unmanned Aerial Vehicles (UAVs) that seamlessly integrates Large Language Models (LLMs) with visual perception to facilitate human-interactive navigation. Our system interprets free-form natural language instructions, grounds them into visual observations, and plans feasible aerial trajectories in diverse environments. UAV-VLN leverages the common-sense reasoning capabilities of LLMs to parse high-level semantic goals, while a vision model detects and localizes semantically relevant objects in the environment. By fusing these modalities, the UAV can reason about spatial relationships, disambiguate references in human instructions, and plan context-aware behaviors with minimal task-specific supervision. To ensure robust and interpretable decision-making, the framework includes a cross-modal grounding mechanism that aligns linguistic intent with visual context. We evaluate UAV-VLN across diverse indoor and outdoor navigation scenarios, demonstrating its ability to generalize to novel instructions and environments with minimal task-specific training. Our results show significant improvements in instruction-following accuracy and trajectory efficiency, highlighting the potential of LLM-driven vision-language interfaces for safe, intuitive, and generalizable UAV autonomy.

Why is Denmark being targeted with mystery drone flights? Police officers stand guard after all traffic has been closed at Copenhagen Airport due to drone reports on Sept. 22 | Ritzau Scanpix / via REUTERS Copenhagen - Denmark's support for Ukraine, its lack of anti-drone defenses and this week's EU summit in Copenhagen could all explain the unidentified drone sightings over Danish airspace that have been widely blamed on Russia. Drones have been seen across the Scandinavian country, including over military sites, since Sept. 22, prompting brief closures at several airports and a ban on all civilian drone flights until Friday. The drone intrusions were extremely well-executed and also clearly designed to humiliate the authorities, just to show that you can actually fly drones over Danish airports and critical infrastructure, said Rasmus Dahlberg, an expert on hybrid threats. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Danish PM calls for strong answer from EU leaders to Russia's hybrid attacks EU leaders have met in Copenhagen under pressure to boost European defence after a series of Russian incursions into EU airspace, and days after drones targeted Danish airports. Danish Prime Minister Mette Frederiksen told reporters that from a European perspective there is only one country... willing to threaten us and that is Russia, and therefore we need a very strong answer back. The incursions have become most acute for countries on the EU's eastern flank such as Poland and Estonia. A number of member states have already backed plans for a multi-layered drone wall to quickly detect, then track and destroy Russian drones. We meet at a time when Russia have intensified their attacks in Ukraine, where we have seen Russian airspace violations and unwanted drone activity in several European countries, Frederiksen told a news conference after the talks had concluded.