Russia has launched another large drone attack on Ukraine, striking Kyiv and damaging a maternity ward in the southern port of Odesa, regional officials said early on Tuesday. The overnight attacks follow Russia's biggest drone strike on Ukraine on Monday -- part of intensified operations that Moscow said were retaliatory measures for Kyiv's recent brazen attacks inside Russia. Medics were called to four districts of Kyiv a couple hours after midnight on Tuesday, including the historic Podil neighborhood, Mayor Vitali Klitschko said on the Telegram messaging app. The military said the strikes were still ongoing and urged people to seek bomb shelters. The full scale of the attack was not immediately clear.

Grounding language to a navigating agent's observations can leverage pretrained multimodal foundation models to match perceptions to object or event descriptions. However, previous approaches remain disconnected from environment mapping, lack the spatial precision of geometric maps, or neglect additional modality information beyond vision. To address this, we propose multimodal spatial language maps as a spatial map representation that fuses pretrained multimodal features with a 3D reconstruction of the environment. We build these maps autonomously using standard exploration. We present two instances of our maps, which are visual-language maps (VLMaps) and their extension to audio-visual-language maps (AVLMaps) obtained by adding audio information. When combined with large language models (LLMs), VLMaps can (i) translate natural language commands into open-vocabulary spatial goals (e.g., "in between the sofa and TV") directly localized in the map, and (ii) be shared across different robot embodiments to generate tailored obstacle maps on demand. Building upon the capabilities above, AVLMaps extend VLMaps by introducing a unified 3D spatial representation integrating audio, visual, and language cues through the fusion of features from pretrained multimodal foundation models. This enables robots to ground multimodal goal queries (e.g., text, images, or audio snippets) to spatial locations for navigation. Additionally, the incorporation of diverse sensory inputs significantly enhances goal disambiguation in ambiguous environments. Experiments in simulation and real-world settings demonstrate that our multimodal spatial language maps enable zero-shot spatial and multimodal goal navigation and improve recall by 50% in ambiguous scenarios. These capabilities extend to mobile robots and tabletop manipulators, supporting navigation and interaction guided by visual, audio, and spatial cues.

--Navigation of UA Vs in unknown environments with obstacles is essential for applications in disaster response and infrastructure monitoring. However, existing obstacle avoidance algorithms such as Artificial Potential Field (APF) are unable to generalize across environments with different obstacle configurations. Furthermore, the precise location of the final target may not be available in applications such search and rescue, in which case approaches such as RF source seeking can be used to align towards the target location. This paper proposes a real-time trajectory planning method, which involves real time adaptation of APF through a sampling-based approach. The proposed approach utilizes only the bearing angle of the target without its precise location, and adjusts the potential field parameters according to the environment with new obstacle configurations in real time. The main contributions of the article are i) RF source seeking algorithm to provide a bearing angle estimate using RF signal calculations based on antenna placement, and ii) modified APF for adaptable collision avoidance in changing environments, which are evaluated separately in the simulation software Gazebo, using ROS2 for communication. Simulation results show that the RF source-seeking algorithm achieves high accuracy, with an average angular error of just 1.48 degrees, and with this estimate, the proposed navigation algorithm improves the success rate of reaching the target by 46% and reduces the trajectory length by 1.2% compared to standard potential fields. The increasing use of drones in various applications has been facilitated by advancements in sensor technology, enabling better localization and obstacle detection methods. These technologies allow drones to effectively navigate through complex environments, avoiding obstacles in real time. The demand for autonomous drone navigation is growing in sectors like search and rescue [1], inspection of unknown areas [2], and other critical applications requiring drones to operate in unfamiliar and potentially hazardous environments. In these scenarios, drones must autonomously identify and locate targets, update environmental maps in real time, detect obstacles, and plan safe trajectories. The variability of these environments, such as changes in obstacle sizes, distances, and spatial constraints, poses a significant challenge to creating a unified navigation system that can adapt to such differing conditions.

Unmanned aerial vehicles (UAVs) have been widely adopted in various real-world applications. However, the control and optimization of multi-UAV systems remain a significant challenge, particularly in dynamic and constrained environments. This work explores the joint motion and communication control of multiple UAVs operating within integrated terrestrial and non-terrestrial networks that include high-altitude platform stations (HAPS). Specifically, we consider an aerial highway scenario in which UAVs must accelerate, decelerate, and change lanes to avoid collisions and maintain overall traffic flow. Different from existing studies, we propose a novel hierarchical and collaborative method based on large language models (LLMs). In our approach, an LLM deployed on the HAPS performs UAV access control, while another LLM onboard each UAV handles motion planning and control. This LLM-based framework leverages the rich knowledge embedded in pre-trained models to enable both high-level strategic planning and low-level tactical decisions. This knowledge-driven paradigm holds great potential for the development of next-generation 3D aerial highway systems. Experimental results demonstrate that our proposed collaborative LLM-based method achieves higher system rewards, lower operational costs, and significantly reduced UAV collision rates compared to baseline approaches.

Drone shows are becoming ever more popular. Once rarities, they are now appearing at occasions ranging from birthday parties and weddings, to major sporting events. Some theme parks even have resident drone shows that take place multiple nights in a row. Glastonbury music festival had its first drone show in 2024. And record-breaking displays are pushing the technology to its limits – the biggest drone show in history took place in China last October.

Ukraine and Russia completed the first in a series of planned prisoner swaps that could result in at least 1,200 POWs being freed by each side.

Russia has launched 479 drones against Ukraine in the biggest overnight drone bombardment of the three-year war, according to the Ukrainian air force. The air force said early on Monday that it had downed 460 drones as well as 19 missiles launched overnight. Russia's continued to step up its drone and missile attacks on Ukraine, despite declaring, under pressure from United States President Donald Trump, that it is interested in pursuing peace talks. The record launch came just ahead of the start of a prisoner swap agreed at recent talks between the pair. Of the hundreds of projectiles fired at numerous targets, only 10 reached their destination, Kyiv officials said.

Fox News senior national correspondent Rich Edson has the latest on a Chinese pair charged with smuggling a'devastating' pathogen to the U.S. on'The Story.' By all appearances, the world is edging perilously close to the brink of a catastrophic global conflict. In just the past few days, five deeply troubling developments have emerged -- each significant on its own -- but taken together, they form a pattern too urgent to dismiss. Viewed in context, these events expose a rapidly deteriorating international order, where diplomacy is failing, deterrence is weakening, and the risk of multi-theater war is rising sharply. First, Ukraine's audacious drone strike deep inside Russian territory -- reportedly destroying or damaging a significant share of Russia's strategic bomber fleet -- bears the hallmarks of Western involvement.

Unmanned Aerial Vehicles (UAVs) are one of the most revolutionary inventions of 21st century. At the core of a UAV lies the central processing system that uses wireless signals to control their movement. The most popular UAVs are quadcopters that use a set of four motors, arranged as two on either side with opposite spin. An autonomous UAV is called a drone. Drones have been in service in the US army since the 90's for covert missions critical to national security. It would not be wrong to claim that drones make up an integral part of the national security and provide the most valuable service during surveillance operations. While UAVs are controlled using wireless signals, there reside some challenges that disrupt the operation of such vehicles such as signal quality and range, real time processing, human expertise, robust hardware and data security. These challenges can be solved by programming UAVs to be autonomous, using object detection and tracking, through Computer Vision algorithms. Computer Vision is an interdisciplinary field that seeks the use of deep learning to gain a high-level understanding of digital images and videos for the purpose of automating the task of human visual system. Using computer vision, algorithms for detecting and tracking various objects can be developed suitable to the hardware so as to allow real time processing for immediate judgement. This paper attempts to review the various approaches several authors have proposed for the purpose of autonomous navigation of UAVs by through various algorithms of object detection and tracking in real time, for the purpose of applications in various fields such as disaster management, dense area exploration, traffic vehicle surveillance etc.

Russian forces have shot down 10 Ukrainian drones heading towards Moscow, according to the city's mayor, Sergei Sobyanin, as Ukraine reported at least one person killed in Russian attacks on the war-torn country. There were no reports of any damage in Moscow on Sunday, but a Ukrainian drone attack led to a short-lived fire at the Azot chemical plant in the neighbouring Tula region, injuring two people, and seven drones were destroyed above the Kaluga region, regional governors said. Rosaviatsia, Russia's civil aviation authority, said on Telegram that, to ensure air safety, it halted flights at Moscow's Vnukovo and Domodedovo, as well as nearby Kaluga (Grabtsevo) airports. The drone attack comes as Kyiv launched an unprecedented drone operation last weekend deep inside Russia, targeting nuclear-capable military aircraft at Russian airbases. Moscow promised to retaliate, unleashing a barrage of attacks in recent days.