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Autonomous drone racing (ADR) systems have recently achieved champion-level performance, yet remain highly specific to drone racing. While end-to-end vision-based methods promise broader applicability, no system to date simultaneously achieves full sim-to-real transfer, onboard execution, and champion-level performance. In this work, we present SkyDreamer, to the best of our knowledge, the first end-to-end vision-based ADR policy that maps directly from pixel-level representations to motor commands. SkyDreamer builds on informed Dreamer, a model-based reinforcement learning approach where the world model decodes to privileged information only available during training. By extending this concept to end-to-end vision-based ADR, the world model effectively functions as an implicit state and parameter estimator, greatly improving interpretability. SkyDreamer runs fully onboard without external aid, resolves visual ambiguities by tracking progress using the state decoded from the world model's hidden state, and requires no extrinsic camera calibration, enabling rapid deployment across different drones without retraining. Real-world experiments show that SkyDreamer achieves robust, high-speed flight, executing tight maneuvers such as an inverted loop, a split-S and a ladder, reaching speeds of up to 21 m/s and accelerations of up to 6 g. It further demonstrates a non-trivial visual sim-to-real transfer by operating on poor-quality segmentation masks, and exhibits robustness to battery depletion by accurately estimating the maximum attainable motor RPM and adjusting its flight path in real-time. These results highlight SkyDreamer's adaptability to important aspects of the reality gap, bringing robustness while still achieving extremely high-speed, agile flight.

EU foreign policy chief Kaja Kallas has said a new anti-drone system should be fully operational by the end of 2027, as part of a drive to toughen defences against Russia and be fully prepared for possible conflict by 2030. Drones are already redefining warfare. Having drone defences is no longer optional for anyone, Kallas said, referring to Russia's ongoing war in Ukraine and fears that Moscow may attack the EU. The European Commission's defence roadmap also proposes strengthening the EU's eastern borders and building air and space shields. Several EU nations have faced Russian incursions into their airspace and US President Donald Trump has urged the bloc to do more to defend itself.

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? EU discusses'drone wall' to protect airspace from Russian violations The European Commission is in discussions to adopt a new counter-drone initiative to protect European Union airspace from Russian violations, as it seeks to strengthen border security with its own advanced drone technology after a string of drone incursions were reported in a host of EU and NATO member countries over the past month. The proposal, which was included in a defence policy "roadmap" presented on Thursday, will aim for the new anti-drone capabilities to reach initial capacity by the end of next year and become fully operational by the end of 2027, according to a draft of the document.

BRUSSELS - NATO and the EU on Wednesday sought ways to boost anti-drone defenses, as Europe scrambles to counter the threat from Russia after a series of air incursions. High-profile incidents in Poland and Estonia have set off a flurry of activity from European officials to plug gaps in the continent's defenses. NATO has launched a new mission and beefed up forces on its eastern border, but it is playing catch-up as it tries to tap Ukraine's experience and get to grips with the drone threat from Moscow. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Unmanned Aerial Vehicles (UAVs) are increasingly being utilized in various private and commercial applications, e.g., traffic control, parcel delivery, and Search and Rescue (SAR) missions. Machine Learning (ML) methods used in UAV-Assisted Sensor Networks (UASNETs) and, especially, in Deep Reinforcement Learning (DRL) face challenges such as complex and lengthy model training, gaps between simulation and reality, and low sampling efficiency, which conflict with the urgency of emergencies, such as SAR missions. In this paper, an In-Context Learning (ICL)-Data Collection Scheduling (ICLDC) system is proposed as an alternative to DRL in emergencies. The UAV collects sensory data and transmits it to a Large Language Model (LLM), which creates a task description in natural language. From this description, the UAV receives a data collection schedule that must be executed. A verifier ensures safe UAV operations by evaluating the schedules generated by the LLM and overriding unsafe schedules based on predefined rules. The system continuously adapts by incorporating feedback into the task descriptions and using this for future decisions. This method is tested against jailbreaking attacks, where the task description is manipulated to undermine network performance, highlighting the vulnerability of LLMs to such attacks. The proposed ICLDC significantly reduces cumulative packet loss compared to both the DQN and Maximum Channel Gain baselines. ICLDC presents a promising direction for intelligent scheduling and control in UASNETs.

In this letter, we introduce Geometric Model Predictive Path Integral (GMPPI), a sampling-based controller capable of tracking agile trajectories while avoiding obstacles. In each iteration, GMPPI generates a large number of candidate rollout trajectories and then averages them to create a nominal control to be followed by the Unmanned Aerial Vehicle (UAV). We propose using geometric SE(3) control to generate part of the rollout trajectories, significantly increasing precision in agile flight. Furthermore, we introduce varying rollout simulation time step length and dynamic cost and noise parameters, vastly improving tracking performance of smooth and low-speed trajectories over an existing Model Predictive Path Integral (MPPI) implementation. Finally, we propose an integration of GMPPI with a stereo depth camera, enabling online obstacle avoidance at high speeds, a crucial step towards autonomous UAV flights in complex environments. The proposed controller can track simulated agile reference trajectories with position error similar to the geometric SE(3) controller. However, the same configuration of the proposed controller can avoid obstacles in a simulated forest environment at speeds of up to 13m/s, surpassing the performance of a state-of-the-art obstacle-aware planner. In real-world experiments, GMPPI retains the capability to track agile trajectories and avoids obstacles at speeds of up to 10m/s.

Ukrainian authorities are growing frustrated with a surge in foreign components being found in Russian drones, with a senior diplomat calling on allies to tighten sanctions controls as Moscow scales up military production. Vladyslav Vlasiuk, Ukrainian President Volodymyr Zelenskyy's special envoy on sanctions, said the European Union's sanctions regime is showing cracks as enforcement is carried out by member states rather than the bloc as a whole -- even as the Kremlin expands large-scale aerial attacks. We would like the European Union to step up exports control for European companies," Vlasiuk said in an interview in Kyiv. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

We present See, Point, Fly (SPF), a training-free aerial vision-and-language navigation (AVLN) framework built atop vision-language models (VLMs). SPF is capable of navigating to any goal based on any type of free-form instructions in any kind of environment. In contrast to existing VLM-based approaches that treat action prediction as a text generation task, our key insight is to consider action prediction for AVLN as a 2D spatial grounding task. SPF harnesses VLMs to decompose vague language instructions into iterative annotation of 2D waypoints on the input image. Along with the predicted traveling distance, SPF transforms predicted 2D waypoints into 3D displacement vectors as action commands for UAVs. Moreover, SPF also adaptively adjusts the traveling distance to facilitate more efficient navigation. Notably, SPF performs navigation in a closed-loop control manner, enabling UAVs to follow dynamic targets in dynamic environments. SPF sets a new state of the art in DRL simulation benchmark, outperforming the previous best method by an absolute margin of 63%. In extensive real-world evaluations, SPF outperforms strong baselines by a large margin. We also conduct comprehensive ablation studies to highlight the effectiveness of our design choice. Lastly, SPF shows remarkable generalization to different VLMs. Project page: https://spf-web.pages.dev

Army Secretary Dan Driscoll says the Army is developing small drones based on Ukraine lessons, envisioning every infantryman having a drone for future missions.