Flags flutter as soldiers participate in a military parade to mark the 80th anniversary of the end of World War II, in Beijing in September. The maiden flight of the unmanned Jiutian drone mothership has highlighted its advances in unmanned aerial vehicles capable of unleashing weaponized swarms. China conducted the maiden flight of what is considered to be the world's largest drone mothership, underscoring its advances in unmanned aerial vehicles capable of unleashing weaponized swarms. The unmanned Jiutian completed its first mission in the northwestern province of Shaanxi, the official Xinhua News Agency reported Thursday without elaborating. The aerial vehicle has been likened to an aircraft carrier for its ability to host multiple drones and missiles.

A woman and her relatives look at her home, which was damaged during a night of Russian missile and drone strikes, amid Russia's attack on Ukraine, in Novi Petrivtsi, outside Kyiv, on Saturday. Microchip manufacturers Intel, Advanced Micro Devices (AMD) and Texas Instruments were accused in a series of lawsuits of failing to keep their technology out of Russian-made weapons used to kill and wound civilians in Ukraine. Those companies -- along with a company owned by Warren Buffett's Berkshire Hathaway -- demonstrated willful ignorance" as third parties resold restricted chips to Russia to power drones and missiles in violation of U.S. sanctions, according to one of the five suits, filed Wednesday in state court in Texas. The lawsuits, filed on behalf of dozens of Ukrainian civilians by Mikal Watts and prominent law firm Baker & Hostetler, cite five attacks between 2023 and 2025 that killed dozens of people. One attack allegedly involved Iranian-made drones with components associated with Intel and AMD, while the others involved Russian-made KH-101 cruise missiles and Iskander ballistic missiles.

Abstract--The integration of Unmanned Aerial V ehicles (UA Vs) and Unmanned Ground V ehicles (UGVs) is increasingly central to the development of intelligent autonomous systems for applications such as search and rescue, environmental monitoring, and logistics. However, precise coordination between these platforms in real-time scenarios presents major challenges, particularly when external localization infrastructure such as GPS or GNSS is unavailable or degraded [1]. This paper proposes a vision-based, data-driven framework for real-time UA V-UGV integration, with a focus on robust UGV detection and heading angle prediction for navigation and coordination. The system employs a fine-tuned YOLOv5 model to detect UGVs and extract bounding box features, which are then used by a lightweight artificial neural network (ANN) to estimate the UA V's required heading angle. A VICON motion capture system was used to generate ground-truth data during training, resulting in a dataset of over 13,000 annotated images collected in a controlled lab environment. The trained ANN achieves a mean absolute error of 0.1506 and a root mean squared error of 0.1957, offering accurate heading angle predictions using only monocular camera inputs. Experimental evaluations achieve 95% accuracy in UGV detection. This work contributes a vision-based, infrastructure-independent solution that demonstrates strong potential for deployment in GPS/GNSS-denied environments, supporting reliable multi-agent coordination under realistic dynamic conditions. A demonstration video showcasing the system's real-time performance, including UGV detection, heading angle prediction, and UA V alignment under dynamic conditions, is available at: https://github.com/Kooroshraf/UA HE integration of Unmanned Aerial V ehicles (UA Vs) and Unmanned Ground V ehicles (UGVs) has emerged as a powerful paradigm in multi-agent systems, offering significant advantages for surveillance, search and rescue, precision agriculture, and autonomous logistics [2]. UA Vs provide agility and a wide field of view, while UGVs offer stable ground-level interaction and payload capacity.

This work presents a conceptual study on the application of Multi-Agent Reinforcement Learning (MARL) for decentralized control of unmanned aerial vehicles to relay a critical data package to a known position. For this purpose, a family of deterministic games is introduced, designed for scaling studies for MARL. A robust baseline policy is proposed, which is based on restricting agent motion envelopes and applying Dijkstra's algorithm. Experimental results show that two off-the-shelf MARL algorithms perform competitively with the baseline for a small number of agents, but scalability issues arise as the number of agents increase.

With the rapid development of Unmanned Aerial Vehicles (UAVs) and the increasing complexity of low-altitude security threats, traditional UAV identification methods struggle to extract reliable signal features and meet real-time requirements in complex environments. Recently, deep learning based Radio Frequency Fingerprint Identification (RFFI) approaches have greatly improved recognition accuracy. However, their large model sizes and high computational demands hinder deployment on resource-constrained edge devices. While model pruning offers a general solution for complexity reduction, existing weight, channel, and layer pruning techniques struggle to concurrently optimize compression rate, hardware acceleration, and recognition accuracy. To this end, in this paper, we introduce HSCP, a Hierarchical Spectral Clustering Pruning framework that combines layer pruning with channel pruning to achieve extreme compression, high performance, and efficient inference. In the first stage, HSCP employs spectral clustering guided by Centered Kernel Alignment (CKA) to identify and remove redundant layers. Subsequently, the same strategy is applied to the channel dimension to eliminate a finer redundancy. To ensure robustness, we further employ a noise-robust fine-tuning strategy. Experiments on the UAV-M100 benchmark demonstrate that HSCP outperforms existing channel and layer pruning methods. Specifically, HSCP achieves $86.39\%$ parameter reduction and $84.44\%$ FLOPs reduction on ResNet18 while improving accuracy by $1.49\%$ compared to the unpruned baseline, and maintains superior robustness even in low signal-to-noise ratio environments.

Abstract--Orographic soaring can significantly extend the endurance of micro aerial vehicles (MA Vs), but circling behavior, arising from control conflicts between longitudinal and vertical axes, increases energy consumption and the risk of divergence. We propose a control switching method, named SAOS: Switched Control for Autonomous Orographic Soaring, which mitigates circling behavior by selectively controlling either the horizontal or vertical axis, effectively transforming the system from under-actuated to fully actuated during soaring. Additionally, the angle of attack is incorporated into the INDI controller to improve force estimation. Simulations with randomized initial positions and wind tunnel experiments on two MA Vs demonstrate that the SAOS improves position convergence, reduces throttle usage, and mitigates roll oscillations caused by pitch-roll coupling. These improvements enhance energy efficiency and flight stability in constrained soaring environments. The flight endurance of micro air vehicles (MA Vs) significantly constrains operational capabilities, limiting the scope of missions they can perform [1], [2]. This limitation is not solely due to inherently short flight durations, but also because take-off and landing procedures typically demand substantial time, energy, effort, and space. One potential solution to this problem lies in the advancement of battery technology, which could lead to improved efficiency. However, progress in this area has been relatively slow [3], [4]. Consequently, researchers have been exploring alternative solutions, such as using energy sources with higher energy densities or enabling mid-air refueling or recharging [5], [6]. Nevertheless, these approaches require considerable investment in hardware and system infrastructure, and often necessitate larger, heavier platforms--undermining the fundamental advantage of MA Vs being small. An alternative approach is to exploit soaring, a flight technique widely employed by birds [7]-[9] and human-piloted glider aircraft [10], [11]. Soaring takes advantage of wind energy, specifically upward vertical winds, to gain altitude or remain airborne with minimal energy expenditure. A key strength of soaring is its compatibility with existing systems: it can be integrated into any fixed-wing aircraft without requiring hardware modifications, making it a valuable complement to other endurance-enhancing strategies. V arious types of soaring techniques exist [12].

We may earn revenue from the products available on this page and participate in affiliate programs. At, we've published our prestigious Best of What's New list since 1988. For 153 years, we've celebrated the science and technology that shapes our everyday lives and launches humanity forward. Innovation doesn't follow a straight path, and the detours, stumbles, and dead ends force great minds to pioneer change. Looking back at the early days of our Best of What's New lists, we see technologies that now seem quaint or have been completely forgotten, but we also see the roots of future greatness. Our list this year is the culmination of countless hours of debate, hands-on testing, and expert conversations. This is the Best of What's New 2025. From the most detailed movie of the night sky ever made to the first commercial soft landing on the moon, this year has been an inflection point for exploring and understanding the vast expanse above our heads. We also saw breakthroughs in small changes to commercial airliners that improve efficiency, as well as a new type of rocket engine that might be the future of extremely high speed air travel, plus the closest view of Mercury we've ever seen! Vera C. Rubin Observatory by U.S. National Science Foundation & Department of Energy: World's largest digital camera to conduct 10-year survey of the night sky Prepare to see space like never before. The Vera C. Rubin Observatory is a groundbreaking US-funded project that will capture the most detailed, dynamic map of the night sky ever made. Using the world's largest digital camera, it will capture a time-lapse of the entire sky every few nights to reveal billions of objects and catch fast-changing events like supernovae and near-Earth asteroids. Its massive dataset will help scientists better understand dark matter, dark energy, and the structure of the universe while also improving planetary defense. The 3,200-megapixel Legacy Survey of Space and Time (LSST) camera is the size of a small car and twice as heavy, tipping the scales at 6,000 pounds. The sensor's huge number of megapixels is equivalent to 260 modern cell phone sensors. The camera is so powerful, it could snap a clear image of a golf ball from 15 miles away. By making its data widely available, the observatory will also open new doors for discovery for researchers, students, and citizen scientists around the world. Deployed on Boeing 787-9 aircraft starting in January, the coating uses tiny, sharkskin-like grooves called riblets to guide airflow smoothly along the aircraft's surface.

A drone flies over a railroad track during a trial in Minamisoma, Fukushima Prefecture, on Tuesday. It was the first drone flight above a railroad track out of the sight of an operator in an inhabited area in the country. The test was conducted jointly with Deloitte Tohmatsu. Sagawa Express and East Japan Railway cooperated with the test. The drone flew in an area of 3.5 kilometers from east to west by 2.4 kilometers from north to south.

A member of the Ukrainian State Emergency Service attends a transfer ceremony of special vehicles from Japan to Ukraine in Kyiv in November 2023. With Russia making incremental territorial gains in Ukraine, Kyiv is urging Tokyo to boost its support in areas such as cybersecurity, demining and counterdrone systems, transforming what started as ad hoc, nonlethal assistance into a long-term security partnership. "Ukraine has identified several priority areas," Ukrainian Deputy Defense Minister Serhiy Boev told The Japan Times in an exclusive interview. These include counterdrone systems that can be integrated with the country's air-defense network, logistics and maintenance support for nonlethal equipment, as well as surveillance and reconnaissance capabilities to enhance maritime domain awareness, particularly in the Black Sea. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Abstract-- This report presents a heterogeneous robotic system designed for remote primary triage in mass-casualty incidents (MCIs). The system employs a coordinated air-ground team of unmanned aerial vehicles (UA Vs) and unmanned ground vehicles (UGVs) to locate victims, assess their injuries, and prioritize medical assistance without risking the lives of first responders. The UA V identify and provide overhead views of casualties, while UGVs equipped with specialized sensors measure vital signs and detect and localize physical injuries. Unlike previous work that focused on exploration or limited medical evaluation, this system addresses the complete triage process: victim localization, vital sign measurement, injury severity classification, mental status assessment, and data consolidation for first responders. Developed as part of the DARPA Triage Challenge, this approach demonstrates how multi-robot systems can augment human capabilities in disaster response scenarios to maximize lives saved. I. INTRODUCTION Robotics has long sought to augment human capabilities in hazardous scenarios. Mass-casualty incidents (MCIs), such as those resulting from natural disasters, bombings, plane crashes, or industrial chemical spills, present an opportunity for robotic systems to assist first responders. The critical first step of providing medical assistance during MCIs is primary triage: the initial process of locating victims at the site of the MCI and assessing the severity of their injuries to prioritize treatment, which is essential to optimizing survival outcomes. Traditionally, primary triage relies on human responders who may face significant risk and information overload [1], underscoring the potential for automated systems to mitigate these challenges. While prior efforts have explored the use of air-ground robotic teams for search and exploration in disaster zones [2]-[5], few systems have focused specifically on rapid triage. Existing approaches typically solve parts of the problem in isolation without integrating comprehensive triage functions. For example, air-ground teams have also been developed to find and localize objects of interest [3], [6] Authors are with the GRASP Lab, School of Engineering and Applied Sciences, University of Pennsylvania. Authors are with the Perelman School of Medicine, University of Pennsylvania. This work was supported by the DARP A Triage Challenge under grant #HR001123S0011.