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.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by Refinitiv Lipper .

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.

While most recent 3DGS-based SLAM works focus on small-scale indoor scenes, developing 3DGS-based SLAM methods for large-scale forest scenes holds great potential for many real-world applications, especially for wildfire emergency response and forest management. However, this line of research is impeded by the absence of a comprehensive and high-quality dataset, and collecting such a dataset over real-world scenes is costly and technically infeasible. To this end, we have built a large-scale, comprehensive, and high-quality synthetic dataset for SLAM in wildfire and forest environments. Leveraging the Unreal Engine 5 Electric Dreams Environment Sample Project, we developed a pipeline to easily collect aerial and ground views, including ground-truth camera poses and a range of additional data modalities from unmanned aerial vehicle. Our pipeline also provides flexible controls on environmental factors such as light, weather, and types and conditions of wildfire, supporting the need for various tasks covering forest mapping, wildfire emergency response, and beyond. The resulting pilot dataset, WildfireX-SLAM, contains 5.5k low-altitude RGB-D aerial images from a large-scale forest map with a total size of 16 km . On top of WildfireX-SLAM, a thorough benchmark is also conducted, which not only reveals the unique challenges of 3DGS-based SLAM in the forest but also highlights potential improvements for future works. The dataset and code will be publicly available.

Why did Israel launch air strikes on Gaza? What life is like in Gaza's crowded tents How is Israel using PR firms to frame its war? Will the US plan for Gaza fail? Hamas has denied accusations by the US Central Command (CENTCOM) that the Palestinian group looted aid trucks in the Gaza Strip. CENTCOM had published drone footage that allegedly showed an aid truck being looted in the enclave.

Trump-Xi meeting: Who has the upper hand? Could Trump go for a third term? Is the US eyeing its next Latin American target? Why is Trump tearing down parts of the White House? Russia's Ministry of Defence said its forces took control of the villages of Krasnohirske in Ukraine's Zaporizhia region and Sadove in the Kharkiv region, Russian state news agencies reported.