New reporting about over a dozen unidentified drones that were allowed to fly over Langley Air Force Base has prompted fresh calls for change to a threat that experts say will only become more prevalent. For more than two weeks in December 2023, the mystery drones traipsed into restricted airspace over the installation, home to key national security facilities and the F-22 Raptor stealth fighters. Experts say the incident is likely one of many that U.S. authorities are underprepared to tackle in an evolving threat environment. Lack of a standard protocol for such incursions left Langley officials unsure of what to do – other than allow the 20-foot-long drones to hover near their classified facilities. The Pentagon has said little about the incidents other than to confirm they occurred after a Wall Street Journal report this month.

Aerial robotics for transporting suspended payloads as the form of freely-floating manipulator are growing great interest in recent years. However, the prior information of the payload, such as the mass, is always hard to obtain accurately in practice. The force/torque caused by payload and residual dynamics will introduce unmodeled perturbations to the system, which negatively affects the closed-loop performance. Different from estimation-like methods, this paper proposes Neural Predictor, a learning-based approach to model force/torque caused by payload and residual dynamics as a dynamical system. It results a hybrid model including both the first-principles dynamics and the learned dynamics. This hybrid model is then integrated into a MPC framework to improve closed-loop performance. Effectiveness of proposed framework is verified extensively in both numerical simulations and real-world flight experiments. The results indicate that our approach can capture force/torque caused by payload and residual dynamics accurately, respond quickly to the changes of them and improve the closed-loop performance significantly. In particular, Neural Predictor outperforms a state-of-the-art learning-based estimator and has reduced the force and torque estimation errors by up to 66.15% and 33.33% while using less samples.

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.

Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in dynamic, data-rich environments for applications such as environmental monitoring and surveillance. These scenarios demand efficient data processing while maintaining privacy and security, making Federated Learning (FL) a promising solution. FL allows UAVs to collaboratively train global models without sharing raw data, but challenges arise due to the non-Independent and Identically Distributed (non-IID) nature of the data collected by UAVs. In this study, we show an integration of the state-of-the-art FL methods to UAV Swarm application and invetigate the performance of multiple aggregation methods (namely FedAvg, FedProx, FedOpt, and MOON) with a particular focus on tackling non-IID on a variety of datasets, specifically MNIST for baseline performance, CIFAR10 for natural object classification, EuroSAT for environment monitoring, and CelebA for surveillance. These algorithms were selected to cover improved techniques on both client-side updates and global aggregation. Results show that while all algorithms perform comparably on IID data, their performance deteriorates significantly under non-IID conditions. FedProx demonstrated the most stable overall performance, emphasising the importance of regularising local updates in non-IID environments to mitigate drastic deviations in local models.

Ukraine has struck a manufacturer of military explosives deep inside Russian territory overnight, as well as storage infrastructure at a military airfield in the Lipetsk region, Kyiv's General Staff has said in a statement. For their part, Russian air defence units downed 110 Ukrainian drones over the country, Russia's Ministry of Defence said Sunday, including one over the Moscow region, 43 over the border region of Kursk, and 27 over the southwestern Lipetsk region. The Russian SHOT Telegram channel reported that drones attempted to strike the Ya. The explosives plant, one of the largest manufacturers of its kind used by Russian forces in the war that Moscow launched against Ukraine in February 2022, is subject to sanctions by the United States and the European Union. Such large-scale aerial attacks are still relatively rare on Russia. Kyiv's General Staff said in a post on Telegram the Sverdlov factory had been making chemical components for artillery ammunition and aerial bombs, adding that it was still assessing the damage from its attack.

Ukraine launched a series of drones targeting Moscow and western Russia, according to regional officials. Russian air defence units downed 110 Ukrainian drones over Russia, the Ministry of Defence said, including one over the Moscow region, 43 over the border region of Kursk, and 27 over the southwestern Lipetsk region. Russia's air defence units destroyed at least one drone flying towards the capital, Moscow Mayor Sergei Sobyanin said on the Telegram messaging app, while drone debris sparked several short-lived fires in Lipetsk, the regional governor said on the app. No injuries or significant damage were reported from the attacks. Four firefighters suffered minor shrapnel wounds in a Ukrainian drone attack in an industrial zone in the city of Dzerzhinsk in Russia's Nizhny Novgorod region, the regional governor said.

DJI has filed a lawsuit against the US Department of Defense over its addition to the Pentagon list that designates it as a "Chinese military company." In its filing, shared by The Verge, the company said it's challenging the designation because it's "neither owned nor controlled by the Chinese military." It described itself as the "largest privately owned seller of consumer and commercial drones," mostly used by first responders, fire and police departments, businesses and hobbyists. The company claimed that because the Pentagon has officially proclaimed it as a national security threat, it has suffered "ongoing financial and reputational harm." It also said that it has lost business from both US and internal customers, which terminated contracts and refused to enter new ones, and it has been banned from signing contracts with multiple federal government agencies.

WIRED loved 2023's DJI Air 3 (9/10, WIRED Recommends). The midrange consumer drone was easy and safe to fly and compact enough to carry almost anywhere, but I found the most appealing feature to be its innovative dual-camera setup. CMOS sensor), it expanded my creative options for aerial photos and video. I could shoot wide vistas one moment, then switch to the telephoto lens to get closer to a particular feature of the landscape or compress it against the background for more dramatic framing. The new DJI Air 3S takes the concept a step further by increasing the sensor size of the wide-angle camera to a full inch, which improves dynamic range and low-light image quality.

The Israeli military, which released the video, said it shows the last moments of Mr. Sinwar, an architect of the Oct. 7 attacks, before he was killed in Rafah on Wednesday. While the room, the man's clothing and the arm injury broadly match other visual evidence of Mr. Sinwar's death, The Times could not independently verify his identity in the video. In modern warfare, militaries use drones to scope out enemy positions, and often release propaganda videos showing enemy soldiers being killed. On the battlefields in Ukraine, both sides in that conflict have released a steady stream of drone footage showing panicked soldiers moments before their deaths. But here the drone footage shows a solitary figure remarkably close-up. In a war often seen from far away, in large explosions or wide vistas of broken buildings, the moment is remarkably personal.

Existing multi-agent deep reinforcement learning (MADRL) methods for multi-UAV navigation face challenges in generalization, particularly when applied to unseen complex environments. To address these limitations, we propose a Dual-Transformer Encoder-based Proximal Policy Optimization (DTPPO) method. DTPPO enhances multi-UAV collaboration through a Spatial Transformer, which models inter-agent dynamics, and a Temporal Transformer, which captures temporal dependencies to improve generalization across diverse environments. This architecture allows UAVs to navigate new, unseen environments without retraining. Extensive simulations demonstrate that DTPPO outperforms current MADRL methods in terms of transferability, obstacle avoidance, and navigation efficiency across environments with varying obstacle densities. The results confirm DTPPO's effectiveness as a robust solution for multi-UAV navigation in both known and unseen scenarios.