A bird's-eye view of campus featuring Maseeh Hall, captured by a DJI Mavic 3 drone in late August. Given airspace restrictions, the photographer, an FAA-certified drone pilot, had to get FAA clearance three days ahead of time--and hope the weather forecast would hold--to get this shot.

In May 2022, just months after Russia's full-scale invasion of Ukraine, a disparate group of people from across the globe decided that they wanted to fight back. This turned into the North Atlantic Fella Organization (NAFO), a decentralized online activist network designed to combat pro-Kremlin propaganda, primarily focusing on the platform then known as Twitter. The members, identified by cartoon Shiba Inu avatars, mocked Russian government accounts and used meme warfare to disrupt Moscow's propaganda over the invasion. But in November 2022, Elon Musk took control of the platform, changed its name to X, and effectively allowed Russia's propagandists free rein to do whatever they wanted. While NAFO members continued to push back, posting memes in response to any posts from official Russian accounts, they knew they had to do something else to alter the course of the war.

The Lebanese armed group Hezbollah has claimed responsibility for a drone attack last week on Israeli Prime Minister Benjamin Netanyahu's holiday residence in Caesarea in northern Israel. "The Islamic Resistance claims responsibility for the Caesarea operation and targeting Netanyahu's home," the head of Hezbollah's media office, Mohammad Afif, said at a news conference on Tuesday. One of three drones launched from Lebanon hit Netanyahu's holiday residence on Saturday. His spokesperson said the prime minister was not in the vicinity at the time of the attack and there were no casualties. Afif said that if in the previous attack Netanyahu was not hurt, "the coming days and nights and the [battle]fields are between us."

The swift advancement of unmanned aerial vehicle (UAV) technologies necessitates new standards for developing human-drone interaction (HDI) interfaces. Most interfaces for HDI, especially first-person view (FPV) goggles, limit the operator's ability to obtain information from the environment. This paper presents a novel interface, FlightAR, that integrates augmented reality (AR) overlays of UAV first-person view (FPV) and bottom camera feeds with head-mounted display (HMD) to enhance the pilot's situational awareness. Using FlightAR, the system provides pilots not only with a video stream from several UAV cameras simultaneously, but also the ability to observe their surroundings in real time. User evaluation with NASA-TLX and UEQ surveys showed low physical demand ($\mu=1.8$, $SD = 0.8$) and good performance ($\mu=3.4$, $SD = 0.8$), proving better user assessments in comparison with baseline FPV goggles. Participants also rated the system highly for stimulation ($\mu=2.35$, $SD = 0.9$), novelty ($\mu=2.1$, $SD = 0.9$) and attractiveness ($\mu=1.97$, $SD = 1$), indicating positive user experiences. These results demonstrate the potential of the system to improve UAV piloting experience through enhanced situational awareness and intuitive control. The code is available here: https://github.com/Sautenich/FlightAR

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.