Neurosymbolic AI combines neural network adaptability with symbolic reasoning, promising an approach to address the complex regulatory, operational, and safety challenges in Advanced Air Mobility (AAM). This survey reviews its applications across key AAM domains such as demand forecasting, aircraft design, and real-time air traffic management. Our analysis reveals a fragmented research landscape where methodologies, including Neurosymbolic Reinforcement Learning, have shown potential for dynamic optimization but still face hurdles in scalability, robustness, and compliance with aviation standards. We classify current advancements, present relevant case studies, and outline future research directions aimed at integrating these approaches into reliable, transparent AAM systems. By linking advanced AI techniques with AAM's operational demands, this work provides a concise roadmap for researchers and practitioners developing next-generation air mobility solutions.

Unmanned aerial vehicles (UAVs) have been recently utilized in multi-access edge computing (MEC) as edge servers. It is desirable to design UAVs' trajectories and user to UAV assignments to ensure satisfactory service to the users and energy efficient operation simultaneously. The posed optimization problem is challenging to solve because: (i) The formulated problem is non-convex, (ii) Due to the mobility of ground users, their future positions and channel gains are not known in advance, (iii) Local UAVs' observations should be communicated to a central entity that solves the optimization problem. The (semi-) centralized processing leads to communication overhead, communication/processing bottlenecks, lack of flexibility and scalability, and loss of robustness to system failures. To simultaneously address all these limitations, we advocate a fully decentralized setup with no centralized entity. Each UAV obtains its local observation and then communicates with its immediate neighbors only. After sharing information with neighbors, each UAV determines its next position via a locally run deep reinforcement learning (DRL) algorithm. None of the UAVs need to know the global communication graph. Two main components of our proposed solution are (i) Graph attention layers (GAT), and (ii) Experience and parameter sharing proximal policy optimization (EPS-PPO). Our proposed approach eliminates all the limitations of semi-centralized MADRL methods such as MAPPO and MA deep deterministic policy gradient (MADDPG), while guaranteeing a better performance than independent local DRLs such as in IPPO. Numerical results reveal notable performance gains in several different criteria compared to the existing MADDPG algorithm, demonstrating the potential for offering a better performance, while utilizing local communications only.

With the growing demand for Uncrewed Aerial Vehicle (UAV) networks in sensitive applications, such as urban monitoring, emergency response, and secure sensing, ensuring reliable connectivity and covert communication has become increasingly vital. However, dynamic mobility and exposure risks pose significant challenges. To tackle these challenges, this paper proposes a self-organizing UAV network framework combining Graph Diffusion-based Policy Optimization (GDPO) with a Stackelberg Game (SG)-based incentive mechanism. The GDPO method uses generative AI to dynamically generate sparse but well-connected topologies, enabling flexible adaptation to changing node distributions and Ground User (GU) demands. Meanwhile, the Stackelberg Game (SG)-based incentive mechanism guides self-interested UAVs to choose relay behaviors and neighbor links that support cooperation and enhance covert communication. Extensive experiments are conducted to validate the effectiveness of the proposed framework in terms of model convergence, topology generation quality, and enhancement of covert communication performance.

Kyiv, Ukraine – Taras, a seasoned Ukrainian serviceman recovering from a contusion, expects "no miracles" from United States President Donald Trump's August 15 summit with his Russian counterpart, Vladimir Putin. "There's going to be no miracles, no peace deal in a week, and Putin will try to make Trump believe that it is Ukraine that doesn't want peace," the fair-haired 32-year-old with a deep brown tan acquired in the trenches of eastern Ukraine, told Al Jazeera. Taras, who spent more than three years on the front line and said he had recently shot down an explosives-laden Russian drone barging at him in a field covered with explosion craters, withheld his last name in accordance with the wartime protocol. Putin wants to dupe Trump by pandering to the US president's self-image as a peacemaker to avoid further economic sanctions, while the Russian leader seeks a major military breakthrough in eastern Ukraine, Taras said. "Putin really believes that until this winter, he will seize something sizeable, or that [his troops] will break through the front line and will dictate terms to Ukraine," Taras said.

Autonomous unmanned aerial vehicle (UAV) navigation in orchards presents significant challenges due to obstacles and GPS-deprived environments. In this work, we introduce a learning-based approach to achieve vision-based navigation of UAVs within orchard rows. Our method employs a variational autoencoder (VAE)-based controller, trained with an intervention-based learning framework that allows the UAV to learn a visuomotor policy from human experience. Field experiments demonstrate that after only a few iterations of training, the proposed VAE-based controller can autonomously navigate the UAV based on a front-mounted camera stream. The controller exhibits strong obstacle avoidance performance, achieves longer flying distances with less human assistance, and outperforms existing algorithms. Furthermore, we show that the policy generalizes effectively to novel environments and maintains competitive performance across varying conditions and speeds. This research not only advances UAV autonomy but also holds significant potential for precision agriculture, improving efficiency in orchard monitoring and management. Introduction Unmanned aerial vehicle (UAV) technology has made significant progress in recent years, particularly for applications in agriculture. The ability to navigate within orchard rows allows UAVs to perform tasks such as crop inspection and yield estimation (Zhang et al., 2021). This capability provides a valuable tool for remote sensing and precision agriculture (Chen et al., 2022), leading to more efficient and improved orchard management. However, most existing UAVs still depend on GPS for navigation in agricultural settings. This reliance limits their ability to operate in confined orchard rows, where dense tree canopies can block GPS signals. Additionally, in environments with unknown obstacles, such as tree branches in orchard rows, human pilots are frequently queried to provide avoidance maneuvers, which significantly increases their workload. The ability to navigate autonomously and safely in orchard scenes with weak GPS signals and obstacles presents several challenges and largely hinders the deployment of UAVs in orchard operations. Corresponding author Email address: zdkong@ucdavis.edu The view of the onboard camera is provided. When the GPS signal is attenuated, the UAV may rely on exteroceptive sensors to sense the environment and navigate. Advanced techniques to enable UAV autonomous operations without GPS include: 1) lidar-based, and 2) camera-based approaches.

Ukraine's military has said it struck an oil refinery in Russia's Saratov region in an overnight drone attack, causing explosions and destruction, according to an army statement, as daily aerial exchanges intensify with diplomatic momentum to end the war in play. Saratov's governor said on Sunday that one person was killed and several residential apartments and an industrial facility were damaged, but did not mention the oil refinery being struck. "[Ukrainian] drones are targeting … deeper into Russian territory [than] in the past, where previous attacks have been focused on the line of contact in the south and the western parts of Russia," said Al Jazeera's Osama Bin Javaid, reporting from Moscow. It is still unclear whether Ukraine's claims that it hit a refinery are true, he added. Ukraine's military also said on Sunday that it had taken back a village in the Sumy region from the Russian army, which has made significant recent gains there.

One person was killed and several apartments and an industrial facility were damaged in a Ukrainian drone attack on the south Russian region of Saratov, the governor said on Sunday. Roman Busargin posted on the Telegram messaging app that residents were evacuated after debris from a destroyed drone damaged three apartments in the overnight attack. "Several residents required medical assistance," Busargin said. "Aid was provided onsite, and one person has been hospitalized. Unfortunately, one person has died."

Russian forces launched a drone attack on a bus in Ukraine's Kherson region, killing at least two people and wounding 16 others, according to Ukrainian officials. Another drone hit the bus as the police were responding to the attack, injuring three officers, the police added. Russian forces also launched 36 other attacks on settlements across the Kherson region through Friday and Saturday, killing at least one more person and injuring three, according to Governor Oleksandr Prokudin. Russian attacks on Ukraine's Zaporizhia region killed two people travelling in a car in the Bilenkivska community on Saturday morning, and a 61-year-old woman who was in her home in the Vasylivka district, a local official reported. In Ukraine's Dnipropetrovsk region, a Russian attack killed a 56-year-old woman and wounded a 62-year-old man in the city of Nikopol, while in the Donetsk region, other Russian attacks killed four people and wounded nine, according to officials.

Campus Guardian Angel founder and CEO Justin Marston tells'Fox & Friends First' about his Texas-based company's drones designed to make schools safer by confronting an active shooter within seconds. A drone system designed to confront a school shooter within seconds could soon become a frontline defense in classrooms across America. Texas-based Campus Guardian Angel has developed the technology which stations drones inside schools, ready to deploy the moment an emergency alert is triggered. The drones, all controlled remotely at a central operation center in Austin, Texas, are stored in charging boxes inside schools. Once activated, they are designed to fire powder pellets to incapacitate a shooter within 60 seconds and buy time for local law enforcement to arrive at the scene.

The Ukrainian military said its drone units hit the Afipsky oil refinery in Russia's Krasnodar region. It was not immediately clear what the extent of the damage was at the refinery, which, together with the Krasnodar refinery, processed 7.2 million metric tonnes of crude oil in 2024. Local Russian emergency services said they had extinguished a fire at the Afipsky refinery, saying it was caused by fallen drone debris. The Russian Ministry of Defence said air defence systems had shot down nine Ukrainian drones in the region overnight. Russia's Defence Ministry said air defence systems also shot down eight British-made Storm Shadow missiles launched by the Ukrainian army over the past 24 hours.