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.

In the high-stakes domain of search-and-rescue missions, the deployment of Unmanned Aerial Vehicles (UAVs) has become increasingly pivotal. These missions require seamless, real-time communication among diverse roles within response teams, particularly between Remote Operators (ROs) and On-Site Operators (OSOs). Traditionally, ROs and OSOs have relied on radio communication to exchange critical information, such as the geolocation of victims, hazardous areas, and points of interest. However, radio communication lacks information visualization, suffers from noise, and requires mental effort to interpret information, leading to miscommunications and misunderstandings. To address these challenges, this paper presents VizCom-AR, an Augmented Reality system designed to facilitate visual communication between ROs and OSOs and their situational awareness during UAV-driven search-and-rescue missions. Our experiments, focus group sessions with police officers, and field study showed that VizCom-AR enhances spatial awareness of both ROs and OSOs, facilitate geolocation information exchange, and effectively complement existing communication tools in UAV-driven emergency response missions. Overall, VizCom-AR offers a fundamental framework for designing Augmented Reality systems for large scale UAV-driven rescue missions.

In this paper, we address the vision-based autonomous landing problem in complex urban environments using deep neural networks for semantic segmentation and risk assessment. We propose employing the SegFormer, a state-of-the-art visual transformer network, for the semantic segmentation of complex, unstructured urban environments. This approach yields valuable information that can be utilized in smart autonomous landing missions, particularly in emergency landing scenarios resulting from system failures or human errors. The assessment is done in real-time flight, when images of an RGB camera at the Unmanned Aerial Vehicle (UAV) are segmented with the SegFormer into the most common classes found in urban environments. These classes are then mapped into a level of risk, considering in general, potential material damage, damaging the drone itself and endanger people. The proposed strategy is validated through several case studies, demonstrating the huge potential of semantic segmentation-based strategies to determining the safest landing areas for autonomous emergency landing, which we believe will help unleash the full potential of UAVs on civil applications within urban areas.

Autonomous aerial vehicles can provide efficient and effective solutions for radio frequency (RF) source tracking and localizing problems with applications ranging from wildlife conservation to search and rescue operations. Existing lightweight, low-cost, bearing measurements-based methods with a single antenna-receiver sensor system configurations necessitate in situ rotations, leading to substantial measurement acquisition times restricting searchable areas and number of measurements. We propose a GyroCopter for the task. Our approach plans the trajectory of a multi-rotor unmanned aerial vehicle (UAV) whilst utilizing UAV flight dynamics to execute a constant gyration motion to derive "pseudo-bearing" measurements to track RF sources. The gyration-based pseudo-bearing approach: i) significantly reduces the limitations associated with in situ rotation bearing; while ii) capitalizing on the simplicity, affordability, and lightweight nature of signal strength measurement acquisition hardware to estimate bearings. This method distinguishes itself from other pseudo-bearing approaches by eliminating the need for additional hardware to maintain simplicity, lightweightness and cost-effectiveness. To validate our approach, we derived the optimal rotation speed and conducted extensive simulations and field missions with our GyroCopter to track and localize multiple RF sources. The results confirm the effectiveness of our method, highlighting its potential as a practical and rapid solution for RF source localization tasks.