In upcoming 6G networks, unmanned aerial vehicles (UAVs) are expected to play a fundamental role by acting as mobile base stations, particularly for demanding vehicle-to-everything (V2X) applications. In this scenario, one of the most challenging problems is the design of trajectories for multiple UAVs, cooperatively serving the same area. Such joint trajectory design can be performed using multi-agent deep reinforcement learning (MADRL) algorithms, but ensuring collision-free paths among UAVs becomes a critical challenge. Traditional methods involve imposing high penalties during training to discourage unsafe conditions, but these can be proven to be ineffective, whereas binary masks can be used to restrict unsafe actions, but naively applying them to all agents can lead to suboptimal solutions and inefficiencies. To address these issues, we propose a rank-based binary masking approach. Higher-ranked UAVs move optimally, while lower-ranked UAVs use this information to define improved binary masks, reducing the number of unsafe actions. This approach allows to obtain a good trade-off between exploration and exploitation, resulting in enhanced training performance, while maintaining safety constraints.

Unmanned aerial vehicles (UAVs) offer a flexible and cost-effective solution for wildfire monitoring. However, their widespread deployment during wildfires has been hindered by a lack of operational guidelines and concerns about potential interference with aircraft systems. Consequently, the progress in developing deep-learning models for wildfire detection and characterization using aerial images is constrained by the limited availability, size, and quality of existing datasets. This paper introduces a solution aimed at enhancing the quality of current aerial wildfire datasets to align with advancements in camera technology. The proposed approach offers a solution to create a comprehensive, standardized large-scale image dataset. This paper presents a pipeline based on CycleGAN to enhance wildfire datasets and a novel fusion method that integrates paired RGB images as attribute conditioning in the generators of both directions, improving the accuracy of the generated images.

In this work, we propose a dynamic landing solution without the need for onboard exteroceptive sensors and an expensive computation unit, where all localization and control modules are carried out on the ground in a non-inertial frame. Our system starts with a relative state estimator of the aerial robot from the perspective of the landing platform, where the state tracking of the UAV is done through a set of onboard LED markers and an on-ground camera; the state is expressed geometrically on manifold, and is returned by Iterated Extended Kalman filter (IEKF) algorithm. Subsequently, a motion planning module is developed to guide the landing process, formulating it as a minimum jerk trajectory by applying the differential flatness property. Considering visibility and dynamic constraints, the problem is solved using quadratic programming, and the final motion primitive is expressed through piecewise polynomials. Through a series of experiments, the applicability of this approach is validated by successfully landing 18 cm x 18 cm quadrotor on a 43 cm x 43 cm platform, exhibiting performance comparable to conventional methods. Finally, we provide comprehensive hardware and software details to the research community for future reference.

Iran's Islamic Revolutionary Guard Corps (IRGC) has blamed Israel for a strike in Syria that killed four senior members of the group. "The Revolutionary Guards' Syria intel chief, his deputy and two other Guards members were martyred in the attack on Syria by Israel," Iran's Mehr news agency announced, citing an unnamed source. Nour News, another Iranian news agency that allegedly has close ties to the country's intelligence networks, identified Gen. Sadegh Omidzadeh, intelligence deputy of the IRGC's Quds Force in Syria, and his deputy among the dead. The Syrian Observatory for Human Rights said that another Iranian and a Syrian -- unidentified at this time -- also died in the strike. The strike destroyed a building in the western Damascus neighborhood of Mazzeh that the IRGC officials had allegedly used as a base of operations.

He isn't satisfied: This is the only work he's gotten in the past two weeks. Sharma is from Bihar, one of India's poorest states. But he's earned a decent living as a migrant agricultural laborer since moving to the northern state of Haryana 12 years ago. Haryana's agricultural sector relies on hundreds of thousands of Bihari laborers, and in the small village of Ghuskani, where Sharma lives, more than 87 migrant laborers work in fields, clean cattle sheds, and perform factory jobs. Sharma has sprayed insecticides on practically every farm in the village over the past 12 years.

Space-Air-Ground Integrated Networks (SAGINs), which incorporate space and aerial networks with terrestrial wireless systems, are vital enablers of the emerging sixth-generation (6G) wireless networks. Besides bringing significant benefits to various applications and services, SAGINs are envisioned to extend high-speed broadband coverage to remote areas, such as small towns or mining sites, or areas where terrestrial infrastructure cannot reach, such as airplanes or maritime use cases. However, due to the limited power and storage resources, as well as other constraints introduced by the design of terrestrial networks, SAGINs must be intelligently configured and controlled to satisfy the envisioned requirements. Meanwhile, Artificial Intelligence (AI) is another critical enabler of 6G. Due to massive amounts of available data, AI has been leveraged to address pressing challenges of current and future wireless networks. By adding AI and facilitating the decision-making and prediction procedures, SAGINs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics. In this work, we aim to investigate the interplay of AI and SAGINs by providing a holistic overview of state-of-the-art research in AI-enabled SAGINs. Specifically, we present a comprehensive overview of some potential applications of AI in SAGINs. We also cover open issues in employing AI and detail the contributions of SAGINs in the development of AI. Finally, we highlight some limitations of the existing research works and outline potential future research directions.

In the past decade, Unmanned Aerial Vehicles (UAVs) have grabbed the attention of researchers in academia and industry for their potential use in critical emergency applications, such as providing wireless services to ground users and collecting data from areas affected by disasters, due to their advantages in terms of maneuverability and movement flexibility. The UAVs' limited resources, energy budget, and strict mission completion time have posed challenges in adopting UAVs for these applications. Our system model considers a UAV swarm that navigates an area collecting data from ground IoT devices focusing on providing better service for strategic locations and allowing UAVs to join and leave the swarm (e.g., for recharging) in a dynamic way. In this work, we introduce an optimization model with the aim of minimizing the total energy consumption and provide the optimal path planning of UAVs under the constraints of minimum completion time and transmit power. The formulated optimization is NP-hard making it not applicable for real-time decision making. Therefore, we introduce a light-weight meta-reinforcement learning solution that can also cope with sudden changes in the environment through fast convergence. We conduct extensive simulations and compare our approach to three state-of-the-art learning models. Our simulation results prove that our introduced approach is better than the three state-of-the-art algorithms in providing coverage to strategic locations with fast convergence.

An oil depot in Russia was set on fire after the military downed a Ukrainian drone in the area. A Ukrainian military drone was flying over the town of Klintsy when Russian military forces forced it down, causing it to release its munitions into the oil field. "An aeroplane-style drone was brought down by the defense ministry using radio-electronic means. When the aerial target was destroyed, its munitions were dropped on the territory of the Klintsy oil depot," regional governor Alexander Bogomaz wrote on social media. Firefighters extinguish oil tanks at a storage facility that local authorities say caught fire after the military brought down a Ukrainian drone in the town of Klintsy in the Bryansk Region, Russia, in this still image taken from video.

Ukraine's president Volodymyr Zelensky has promised that in 2024 the country's military will have a million drones. His nation already deploys hundreds of thousands of small drones, but this is a step change – a transition to an armed forces with more drones than soldiers. What does that mean for the future of war? This technology has already transformed the conflict between Russia and Ukraine.

Nato commanders announced on Thursday that some 90,000 troops would take part in the alliance's biggest exercise since the Cold War. Steadfast Defender begins next week and will continue until May, involving all 31 member states and Sweden, which is set to join the alliance in the coming months.