This paper proposes an integrated framework for coordinating multiple unmanned aerial vehicles (UAVs) in a distributed fashion to persistently enclose and track a moving target without external localization systems. It is assumed that the UAV can obtain self-displacement and the target's relative position using vision-based methods within its local frame. Additionally, UAVs can measure relative distances and communicate with each other, e.g. by ultrawideband (UWB) sensors. Due to the absence of a global coordinate system, measurements from neighbors cannot be directly utilized for collaborative estimation of the target state. To address this, a recursive least squares estimator (RLSE) for estimating the relative positions between UAVs is integrated into a distributed Kalman filter (DKF), enabling a persistent estimation of the target state. When the UAV loses direct measurements of the target due to environmental occlusion, measurements from neighbors will be aligned into the UAV's local frame to provide indirect measurements. Furthermore, simultaneously ensuring the convergence of the estimators and maintaining effective target tracking is a significant challenge. To tackle this problem, a consensus-based formation controller with bounded inputs is developed by integrating a coupled oscillator-based circular formation design. Theoretical analysis shows that the proposed framework ensures asymptotic tracking of a target with constant velocity. For a target with varying velocity, the tracking error converges to a bounded region related to the target's maximum acceleration. Simulations and experiments validate the effectiveness of the proposed algorithm.

Deep Reinforcement learning (DRL) is used to enable autonomous navigation in unknown environments. Most research assume perfect sensor data, but real-world environments may contain natural and artificial sensor noise and denial. Here, we present a benchmark of both well-used and emerging DRL algorithms in a navigation task with configurable sensor denial effects. In particular, we are interested in comparing how different DRL methods (e.g. model-free PPO vs. model-based DreamerV3) are affected by sensor denial. We show that DreamerV3 outperforms other methods in the visual end-to-end navigation task with a dynamic goal - and other methods are not able to learn this. Furthermore, DreamerV3 generally outperforms other methods in sensor-denied environments. In order to improve robustness, we use adversarial training and demonstrate an improved performance in denied environments, although this generally comes with a performance cost on the vanilla environments. We anticipate this benchmark of different DRL methods and the usage of adversarial training to be a starting point for the development of more elaborate navigation strategies that are capable of dealing with uncertain and denied sensor readings.

With the rapid advancements in wireless communication fields, including low-altitude economies, 6G, and Wi-Fi, the scale of wireless networks continues to expand, accompanied by increasing service quality demands. Traditional deep reinforcement learning (DRL)-based optimization models can improve network performance by solving non-convex optimization problems intelligently. However, they heavily rely on online deployment and often require extensive initial training. Online DRL optimization models typically make accurate decisions based on current channel state distributions. When these distributions change, their generalization capability diminishes, which hinders the responsiveness essential for real-time and high-reliability wireless communication networks. Furthermore, different users have varying quality of service (QoS) requirements across diverse scenarios, and conventional online DRL methods struggle to accommodate this variability. Consequently, exploring flexible and customized AI strategies is critical. We propose a wireless network intent (WNI)-guided trajectory generation model based on a generative diffusion model (GDM). This model can be generated and fine-tuned in real time to achieve the objective and meet the constraints of target intent networks, significantly reducing state information exposure during wireless communication. Moreover, The WNI-guided optimization trajectory generation can be customized to address differentiated QoS requirements, enhancing the overall quality of communication in future intelligent networks. Extensive simulation results demonstrate that our approach achieves greater stability in spectral efficiency variations and outperforms traditional DRL optimization models in dynamic communication systems.

Aerial robots are a well-established solution for exploration, monitoring, and inspection, thanks to their superior maneuverability and agility. However, in many environments of interest, they risk crashing and sustaining damage following collisions. Traditional methods focus on avoiding obstacles entirely to prevent damage, but these approaches can be limiting, particularly in complex environments where collisions may be unavoidable, or on weight and compute-constrained platforms. This paper presents a novel approach to enhance the robustness and autonomy of drones in such scenarios by developing a path recovery and adjustment method for a high-speed collision-resistant drone equipped with binary contact sensors. The proposed system employs an estimator that explicitly models collisions, using pre-collision velocities and rates to predict post-collision dynamics, thereby improving the drone's state estimation accuracy. Additionally, we introduce a vector-field-based path representation which guarantees convergence to the path. Post-collision, the contact point is incorporated into the vector field as a repulsive potential, enabling the drone to avoid obstacles while naturally converging to the original path. The effectiveness of this method is validated through Monte Carlo simulations and demonstrated on a physical prototype, showing successful path following and adjustment through collisions as well as recovery from collisions at speeds up to 3.7 m / s.

The United States Department of the Treasury has announced sanctions against Chinese makers of drone engines and parts that President Joe Biden's administration says have directly helped Russia mount long-range attacks in the war in Ukraine. The sanctions, issued on Thursday, target three entities and one individual for their involvement in the development and production of Russia's "Garpiya series" long-range attack drones. "The Garpiya has been deployed by Russia in its brutal war against Ukraine, destroying critical infrastructure and causing mass casualties," the Treasury Department said in a statement announcing the measures. "Designed and developed by People's Republic of China (PRC)-based experts, the Garpiya is produced at PRC-based factories in collaboration with Russian defense firms before transferring the drones to Russia for use against Ukraine." Russia has recently used long-range drone attacks to penetrate Ukraine's air defences, wreaking havoc across the country, including a missile strike in the city of Poltava that killed 55 people and wounded 328.

Many of DJI's drones including its latest consumer products are being held up at the US border, the manufacturer said in a blog post today. It appears to be a customs matter and not related to proposed US legislation to ban DJI products (the Countering CCP Drones Act) currently in US Congress. However, the holdup means that sales of DJI's latest Air 3S drone will be delayed, the company told The Verge. "The U.S. Customs and Border Protection (CBP) has cited the Uyghur Forced Labor Prevention Act (UFLPA), as the reason for the current holdups," the DJI ViewPoints team wrote. "This assertion made against DJI, however, is entirely unfounded and categorically false."

Landmine detection using traditional methods is slow, dangerous and prohibitively expensive. Using deep learning-based object detection algorithms drone videos is promising but has multiple challenges due to the small, soda-can size of recently prevalent surface landmines. The literature currently lacks scientific evaluation of optimal ML models for this problem since most object detection research focuses on analysis of ground video surveillance images. In order to help train comprehensive models and drive research for surface landmine detection, we first create a custom dataset comprising drone images of POM-2 and POM-3 Russian surface landmines. Using this dataset, we train, test and compare 4 different computer vision foundation models YOLOF, DETR, Sparse-RCNN and VFNet. Generally, all 4 detectors do well with YOLOF outperforming other models with a mAP score of 0.89 while DETR, VFNET and Sparse-RCNN mAP scores are all around 0.82 for drone images taken from 10m AGL. YOLOF is also quicker to train consuming 56min of training time on a Nvidia V100 compute cluster. Finally, this research contributes landmine image, video datasets and model Jupyter notebooks at https://github.com/UnVeilX/ to enable future research in surface landmine detection.

A Lancaster man was indicted Wednesday by a federal grand jury on charges stemming from his alleged use of a drone to deliver fentanyl and other narcotics to buyers, one of whom died of an overdose. Christopher Patrick "Crany" Laney, 34, has been charged with one count of distributing fentanyl resulting in death, four counts of operating an unregistered aircraft in furtherance of a felony narcotics crime, one count of possessing methamphetamine with intent to distribute, two counts of possessing fentanyl with intent to distribute, and one count of possessing firearms in furtherance of a drug trafficking crime, according to the grand jury indictment. Federal prosecutors alleged that on several occasions in December 2022 and January 2023, Laney used an unregistered drone to transport fentanyl and other narcotics from his home to a nearby church parking lot, where someone collected the drugs before distributing them to buyers. At least one of those people included a woman who died of an overdose in January 2023. The federal grand indictment also accuses Laney of being in possession of methamphetamine and fentanyl at his home, along with multiple firearms lacking serial numbers -- weapons that are referred to as "ghost guns."

When a sophisticated Chinese spy balloon floated over America in early 2023, lawmakers and the public were outraged at the Biden-Harris administration's passivity and initial inclination to keep it quiet – only acknowledging the balloon after two civilian photographers forced their hand. Now, the Wall Street Journal has broken news on an even more stupendous U.S. national security breach, reporting that drones flew over a sensitive nuclear weapons testing facility for three days last October and then, two months later, flew over Langley Air Force Base in Virginia for 17 straight nights while the Biden White House, and the military officers it promoted, dawdled and argued over what to do about it. The swarms started on Dec. 7, 2023. Drones, some as large as 20 feet long, flew at night over the Air Combat Command headquarters with its squadrons of advanced F-22 Raptor fighters. The blame-casting and responsibility-shirking reveal a dangerous pattern of hesitation and risk-averse decision-making.

Drone video shows flooding in a residential area of Chennai in southern India's Tamil Nadu state, after heavy rains. Schools and government offices are closed and more rainfall is forecast.