Ukrainian President Volodymyr Zelensky says he has visited troops near the town of Pokrovsk, where the fiercest front line battle between Russia and Ukraine is currently taking place. Zelensky posted photos showing him meeting personnel at a command post in the Dobropillya sector, some 20km (12 miles) north of Pokrovsk in the Donetsk region. Kyiv's top military commander, Oleksandr Syrskiy, said on Monday that Ukraine was increasing pressure on the Dobropillya front to force the enemy to disperse its forces and make it impossible to concentrate their main efforts in the Pokrovsk area. Russia has been trying to seize Pokrovsk - a strategic frontline town and logistic hub - for over a year. Although it has taken them months to approach the town's borders, Russian soldiers have now infiltrated it and on Friday, Zelensky said Russia had amassed 170,000 troops on its outskirts.

Unmanned Aerial Vehicles (UAVs), particularly multi-rotor platforms, have rapidly advanced in research and applications due to their unique capabilities, including vertical takeoff and landing (VTOL), hovering, and high maneuverability. These features make them ideal for complex environments and have driven their adoption in fields such as environmental monitoring, precision agriculture, infrastructure inspection, and emergency response, among others. A key area of recent interest is the control and coordination of multiple UAVs in formation. Formation control enables groups of UAVs to maintain specific geometric arrangements while performing tasks, offering advantages such as enhanced coverage, efficiency, and redundancy [24]. These benefits are critical for applications ranging from search and rescue to cooperative tasks like cargo transport and aerial cinematography.

Drone fleets equipped with onboard cameras, computer vision, and Deep Neural Network (DNN) models present a powerful paradigm for real-time spatio-temporal decision-making. In wildfire response, such drones play a pivotal role in monitoring fire dynamics, supporting firefighter coordination, and facilitating safe evacuation. In this paper, we introduce AeroResQ, an edge-accelerated UAV framework designed for scalable, resilient, and collaborative escape route planning during wildfire scenarios. AeroResQ adopts a multi-layer orchestration architecture comprising service drones (SDs) and coordinator drones (CDs), each performing specialized roles. SDs survey fire-affected areas, detect stranded individuals using onboard edge accelerators running fire detection and human pose identification DNN models, and issue requests for assistance. CDs, equipped with lightweight data stores such as Apache IoTDB, dynamically generate optimal ground escape routes and monitor firefighter movements along these routes. The framework proposes a collaborative path-planning approach based on a weighted A* search algorithm, where CDs compute context-aware escape paths. AeroResQ further incorporates intelligent load-balancing and resilience mechanisms: CD failures trigger automated data redistribution across IoTDB replicas, while SD failures initiate geo-fenced re-partitioning and reassignment of spatial workloads to operational SDs. We evaluate AeroResQ using realistic wildfire emulated setup modeled on recent Southern California wildfires. Experimental results demonstrate that AeroResQ achieves a nominal end-to-end latency of <=500ms, much below the 2s request interval, while maintaining over 98% successful task reassignment and completion, underscoring its feasibility for real-time, on-field deployment in emergency response and firefighter safety operations.

Rivers are critical corridors for environmental monitoring and disaster response, where Unmanned Aerial Vehicles (UAVs) guided by vision-driven policies can provide fast, low-cost coverage. However, deployment exposes simulation-trained policies with distribution shift and safety risks and requires efficient adaptation from limited human interventions. We study human-in-the-loop (HITL) learning with a conservative overseer who vetoes unsafe or inefficient actions and provides statewise preferences by comparing the agent's proposal with a corrective override. We introduce Statewise Hybrid Preference Alignment for Robotics (SPAR-H), which fuses direct preference optimization on policy logits with a reward-based pathway that trains an immediate-reward estimator from the same preferences and updates the policy using a trust-region surrogate. With five HITL rollouts collected from a fixed novice policy, SPAR-H achieves the highest final episodic reward and the lowest variance across initial conditions among tested methods. The learned reward model aligns with human-preferred actions and elevates nearby non-intervened choices, supporting stable propagation of improvements. We benchmark SPAR-H against imitation learning (IL), direct preference variants, and evaluative reinforcement learning (RL) in the HITL setting, and demonstrate real-world feasibility of continual preference alignment for UAV river following. Overall, dual statewise preferences empirically provide a practical route to data-efficient online adaptation in riverine navigation.

Abstract--Driven by the unceasing development of maritime services, tasks of unmanned aerial vehicle (UA V)-assisted maritime data collection (MDC) are becoming increasingly diverse, complex and personalized. As a result, effective task allocation for MDC is becoming increasingly critical. In this work, integrating the concept of spatial crowdsourcing (SC), we develop an SCbased MDC network model and investigate the task allocation problem for UA V-assisted MDC. In variable maritime service scenarios, tasks are allocated to UA Vs based on the spatial and temporal requirements of the tasks, as well as the mobility of the UA Vs. T o address this problem, we design an SCbased task allocation algorithm for the MDC (SC-MDC-T A). The quality estimation is utilized to assess and regulate task execution quality by evaluating signal to interference plus noise ratio and the UA V energy consumption. The reverse auction is employed to potentially reduce the task waiting time as much as possible while ensuring timely completion. Additionally, we establish typical task allocation scenarios based on maritime service requirements indicated by electronic navigational charts. Simulation results demonstrate that the proposed SC-MDC-T A algorithm effectively allocates tasks for various MDC scenarios. Furthermore, compared to the benchmark, the SC-MDC-T A algorithm can also reduce the task completion time and lower the UA V energy consumption. RIVEN by the continuous development of maritime services such as marine resources exploration, reconnaissance and surveillance, anti-submarine, marine tourism, marine transportation and emergency collection, tasks of unmanned aerial vehicle (UA V)-assisted maritime data collection (MDC) are becoming increasingly diverse, complex and personalized [1]-[3]. Personal use of this material is permitted.

China's president Xi caught knifing Trump in brutal attack just hours after historic summit World's'most trusted' broadcaster the BBC doctored Trump speech a week before the election, whistleblower reveals I won't ever forget what I saw at Andy Cohen's party. He may admit he's hooking up with guys on every dating app but this is the truth about men like him: KENNEDY'Venomous' Republican split over Israel hits new low as fiery feud reaches White House America's most dangerous cities revealed: Crime, natural disaster risks and financial safety top the list of growing concerns Drivers mock new design for world's best-selling car: 'Did it already get into a wreck?' I learned the horrifying risks of'miracle' ADHD drugs and stopped taking them... but it was too late Roller coaster camera caught utter terror on people's faces after seat belt failed on 208ft ride that travels at 75mph The leafy suburb under an hour from Manhattan where wealthy New Yorkers are fleeing to escape'woke' Mamdani's socialist dystopia The five cities with America's most pleasant climate revealed - and they're all in the same state A girl, 15, bludgeoned to death in a gated enclave, a Kennedy cousin released and the brother who'knows the truth' about the death that haunts Camelot Sex aids and poppers... the sordid discoveries made by royal aides after party Andrew threw for Epstein and Ghislaine Maxwell - and the truth about those massages: ROBERT JOBSON READ MORE: New Jersey UFO mystery solved! Mysterious drones were spotted near Belgium's Kleine Brogel air base, where US nuclear weapons are stored, prompting fears of a potential espionage operation. Belgium's Defense Minister Theo Francken confirmed that drones entered the base's airspace in two waves on Saturday and Sunday night.

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Drones have been seen flying over a Belgian military base near the Dutch border for a third night in a row, according to the country's defence minister said. A helicopter was deployed to the Kleine-Brogel base after drones were spotted - they then flew off towards the Netherlands, national broadcaster VRT reported. Defence Minister Theo Francken said an investigation was under way, calling it a clear mission targeting Kleine Broge. Francken told Belgian radio on Monday that it looked like an espionage operation, but said he would not speculate on who could be behind it. I have some ideas, but I'm going to be cautious, he added.

Drone teams competing for points under the'Army of Drones Bonus System' killed or wounded 18,000 Russian soldiers in September. Drone teams competing for points under the'Army of Drones Bonus System' killed or wounded 18,000 Russian soldiers in September. Ukrainian computer game-style drone attack system goes'viral' A computer game-style drone attack system has gone "viral" among Ukrainian military units and is being extended to reconnaissance, artillery and logistics operations, the nation's first deputy prime minister, Mykhailo Fedorov, has told the Guardian. Drone teams competing for points under the "Army of Drones Bonus System" killed or wounded 18,000 Russian soldiers in September, with 400 drone units now taking part in the competition, up from 95 in August, Ukrainian officials said. The system, which launched more than a year ago, rewards soldiers who achieve strikes with points that can be exchanged to buy more weapons in an "Amazon-for-war" online store called Brave1 filled with more than 100 different drones, autonomous vehicles and other drone war material.

In this paper a modular and scalable architecture for heterogeneous swarm-based Counter Unmanned Aerial Systems (C-UASs) built on PX4-Autopilot and Robot Operating System 2 (ROS 2) framework is presented. The proposed architecture emphasizes seamless integration of hardware components by introducing independent ROS 2 nodes for each component of a Unmanned Aerial Vehicle (UAV). Communication between swarm participants is abstracted in software, allowing the use of various technologies without architectural changes. Key functionalities are supported, e.g. leader following and formation flight to maneuver the swarm. The system also allows computer vision algorithms to be integrated for the detection and tracking of UAVs. Additionally, a ground station control is integrated for the coordination of swarm operations. Swarm-based Unmanned Aerial System (UAS) architecture is verified within a Gazebo simulation environment but also in real-world demonstrations.