This paper presents a vision-only autonomous flight system for small UAVs operating in controlled indoor environments. The system combines semantic segmentation with monocular depth estimation to enable obstacle avoidance, scene exploration, and autonomous safe landing operations without requiring GPS or expensive sensors such as LiDAR. A key innovation is an adaptive scale factor algorithm that converts non-metric monocular depth predictions into accurate metric distance measurements by leveraging semantic ground plane detection and camera intrinsic parameters, achieving a mean distance error of 14.4 cm. The approach uses a knowledge distillation framework where a color-based Support Vector Machine (SVM) teacher generates training data for a lightweight U-Net student network (1.6M parameters) capable of real-time semantic segmentation. For more complex environments, the SVM teacher can be replaced with a state-of-the-art segmentation model. Testing was conducted in a controlled 5x4 meter laboratory environment with eight cardboard obstacles simulating urban structures. Extensive validation across 30 flight tests in a real-world environment and 100 flight tests in a digital-twin environment demonstrates that the combined segmentation and depth approach increases the distance traveled during surveillance and reduces mission time while maintaining 100% success rates. The system is further optimized through end-to-end learning, where a compact student neural network learns complete flight policies from demonstration data generated by our best-performing method, achieving an 87.5% autonomous mission success rate. This work advances practical vision-based drone navigation in structured environments, demonstrating solutions for metric depth estimation and computational efficiency challenges that enable deployment on resource-constrained platforms.

BBC at scene of'brazen' Louvre jewel theft A manhunt is under way for a gang of thieves who carried out a broad daylight raid on Paris's Louvre Museum, and stole jewels described as priceless. The gang appear to have used a mechanical ladder to reach a first-floor window, before breaking into display cases and escaping on motorbikes. The BBC's Hugh Schofield is outside the museum where the extraordinary, daring and brazen robbery took place. Drone footage shows blaze destroying the historic Bernaga Monastery in Italy. Could a Corrie cameo be on the cards for Daniel O'Donnell?

British soldiers will be granted new powers to shoot down drones threatening military bases. The plans, to be unveiled by Defence Secretary John Healey in a speech on Monday, are intended to allow troops to take faster, more decisive action. Four British airbases used by US forces reported mystery drone sightings last year, while drones have disrupted airspace across Europe a number of times in recent months. The new powers will only apply to military sites, but could be extended to civilian locations such as airports. Healey is set to announce the introduction of a kinetic option, first reported by the Daily Telegraph, that would enable British troops or Ministry of Defence (MoD) police to shoot drones posing a threat to a military site in the UK.

Unmanned Aerial Vehicles (UAVs) or drones, are increasingly used in search and rescue missions to detect human presence. Existing systems primarily leverage vision-based methods which are prone to fail under low-visibility or occlusion. Drone-based audio perception offers promise but suffers from extreme ego-noise that masks sounds indicating human presence. Existing datasets are either limited in diversity or synthetic, lacking real acoustic interactions, and there are no standardized setups for drone audition. To this end, we present DroneAudioset (The dataset is publicly available at https://huggingface.co/datasets/ahlab-drone-project/DroneAudioSet/ under the MIT license), a comprehensive drone audition dataset featuring 23.5 hours of annotated recordings, covering a wide range of signal-to-noise ratios (SNRs) from -57.2 dB to -2.5 dB, across various drone types, throttles, microphone configurations as well as environments. The dataset enables development and systematic evaluation of noise suppression and classification methods for human-presence detection under challenging conditions, while also informing practical design considerations for drone audition systems, such as microphone placement trade-offs, and development of drone noise-aware audio processing. This dataset is an important step towards enabling design and deployment of drone-audition systems.

This article presents a fully autonomous aerial masonry construction framework using heterogeneous unmanned aerial vehicles (UAVs), supported by experimental validation. Two specialized UAVs were developed for the task: (i) a brick-carrier UAV equipped with a ball-joint actuation mechanism for precise brick manipulation, and (ii) an adhesion UAV integrating a servo-controlled valve and extruder nozzle for accurate adhesion application. The proposed framework employs a reactive mission planning unit that combines a dependency graph of the construction layout with a conflict graph to manage simultaneous task execution, while hierarchical state machines ensure robust operation and safe transitions during task execution. Dynamic task allocation allows real-time adaptation to environmental feedback, while minimum-jerk trajectory generation ensures smooth and precise UAV motion during brick pickup and placement. Additionally, the brick-carrier UAV employs an onboard vision system that estimates brick poses in real time using ArUco markers and a least-squares optimization filter, enabling accurate alignment during construction. To the best of the authors' knowledge, this work represents the first experimental demonstration of fully autonomous aerial masonry construction using heterogeneous UAVs, where one UAV precisely places the bricks while another autonomously applies adhesion material between them. The experimental results supported by the video showcase the effectiveness of the proposed framework and demonstrate its potential to serve as a foundation for future developments in autonomous aerial robotic construction.

John Healey, the British defense secretary, tours a new military drone production facility in Swindon, U.K., on Sept. 15. Healey is reportedly set to authorize new powers to shoot down drones amid a rise in incursions. British troops will be given new powers to shoot down drones threatening U.K. military bases, the Telegraph reported on Sunday, citing an upcoming announcement on Monday from John Healey, the British defense secretary. Healey is expected to unveil his vision on how to protect Britain's most critical military bases in response to a growing threat posed by Russia, the newspaper said. Although the new powers will initially apply only for military sites, the British government was not ruling out working to extend those powers to other important sites like airports, the Telegraph said, citing a source.

President Trump says U.S. strike on fentanyl-carrying submarine prevented 25,000 American overdose deaths, with survivors from Ecuador and Colombia captured.

Ukrainian President Volodymyr Zelenskyy proposes exchanging thousands of Ukrainian drones for American Tomahawk missiles in a deal with President Donald Trump.

U.S. military seized survivors after a drone strike on alleged drug smuggling boat in the Caribbean, marking first known survivors in President Donald Trump's anti-cartel campaign.

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? Russian war correspondent Ivan Zuyev has been killed by a Ukrainian drone strike while on assignment on the front line of the war in southern Ukraine's Zaporizhia region, his publication, state news agency RIA said. Zuyev's colleague, Yuri Voitkevich, was seriously wounded in the attack.