Ukraine has launched a barrage of drone and missile strikes against Russia, igniting two oil refineries. The overnight attacks, some of which reached deep into Russian territory, hit refineries in towns hundreds of miles apart in the Nizhny Novgorod and Oryol regions. No casualties were reported, according to regional officials. In Kstovo, a town located 828km (514 miles) from the Ukraine border in Nizhny Novgorod, a fuel and energy complex, reportedly owned by Lukoil – Russia's largest privately-owned company – was attacked by drones, according to regional Governor Gleb Nikitin. "The special services are working on the spot, using all the necessary force and means to localise the fire at one of the oil refining installations," he said on the Telegram messaging app.

It is, however, naval drones that have made Russia's Black Sea fleet particularly vulnerable. Under relentless attacks, Moscow was forced to withdraw the core of its fleet from Crimea and move them further east, to Novorossiysk. And even there, Russian ships remain within reach of Ukrainian drones.

Mastering autonomous drone landing on dynamic platforms presents formidable challenges due to unpredictable velocities and external disturbances caused by the wind, ground effect, turbines or propellers of the docking platform. This study introduces an advanced Deep Reinforcement Learning (DRL) agent, Lander:AI, designed to navigate and land on platforms in the presence of windy conditions, thereby enhancing drone autonomy and safety. Lander:AI is rigorously trained within the gym-pybullet-drone simulation, an environment that mirrors real-world complexities, including wind turbulence, to ensure the agent's robustness and adaptability. The agent's capabilities were empirically validated with Crazyflie 2.1 drones across various test scenarios, encompassing both simulated environments and real-world conditions. The experimental results showcased Lander:AI's high-precision landing and its ability to adapt to moving platforms, even under wind-induced disturbances. Furthermore, the system performance was benchmarked against a baseline PID controller augmented with an Extended Kalman Filter, illustrating significant improvements in landing precision and error recovery. Lander:AI leverages bio-inspired learning to adapt to external forces like birds, enhancing drone adaptability without knowing force magnitudes.This research not only advances drone landing technologies, essential for inspection and emergency applications, but also highlights the potential of DRL in addressing intricate aerodynamic challenges.

Drones are also known as UAVs are originally designed for military purposes. With the technological advances, they can be seen in most of the aspects of life from filming to logistics. The increased use of drones made it sometimes essential to form a collaboration between them to perform the task efficiently in a defined process. This paper investigates the use of a combined centralised and decentralised architecture for the collaborative operation of drones in a parts delivery scenario to enable and expedite the operation of the factories of the future. The centralised and decentralised approaches were extensively researched, with experimentation being undertaken to determine the appropriateness of each approach for this use-case. Decentralised control was utilised to remove the need for excessive communication during the operation of the drones, resulting in smoother operations. Initial results suggested that the decentralised approach is more appropriate for this use-case. The individual functionalities necessary for the implementation of a decentralised architecture were proven and assessed, determining that a combination of multiple individual functionalities, namely VSLAM, dynamic collision avoidance and object tracking, would give an appropriate solution for use in an industrial setting. A final architecture for the parts delivery system was proposed for future work, using a combined centralised and decentralised approach to combat the limitations inherent in each architecture.

The Yemen-based branch of Al Qaeda said on Sunday that its leader, Khaled Batarfi, had died. Al Qaeda in the Arabian Peninsula, known as A.Q.A.P., released a video announcing Mr. Batarfi's death, showing images of him wrapped in a white funeral shroud overlaid with a black Al Qaeda flag. It did not explain how he had died. The United States government once considered Al Qaeda in the Arabian Peninsula to be one of the world's most dangerous terrorist organizations. The group tried and failed at least three times to blow up American airliners, and has been targeted by American drone strikes for two decades.

Three people were killed in Russian shelling and drone attacks on towns in Ukraine's eastern Donetsk region, while at least a dozen people were injured in a Russian missile attack in the early hours of Sunday morning on the town of Myrnohrad, about 40km (25 miles) from the front line in Donetsk. Kyiv said Russia launched 39 Iranian-made Shahed attack drones across central and southern regions, including the Kyiv region. The Air Force said 35 were shot down over 10 regions. It did not say whether there was any damage. St Petersburg's Pulkovo Airport was closed briefly after a Ukrainian drone was detected in the neighbouring Leningrad region.

This work presents a unified approach for collision avoidance using Collision-Cone Control Barrier Functions (CBFs) in both ground (UGV) and aerial (UAV) unmanned vehicles. We propose a novel CBF formulation inspired by collision cones, to ensure safety by constraining the relative velocity between the vehicle and the obstacle to always point away from each other. The efficacy of this approach is demonstrated through simulations and hardware implementations on the TurtleBot, Stoch-Jeep, and Crazyflie 2.1 quadrotor robot, showcasing its effectiveness in avoiding collisions with dynamic obstacles in both ground and aerial settings. The real-time controller is developed using CBF Quadratic Programs (CBF-QPs). Comparative analysis with the state-of-the-art CBFs highlights the less conservative nature of the proposed approach. Overall, this research contributes to a novel control formation that can give a guarantee for collision avoidance in unmanned vehicles by modifying the control inputs from existing path-planning controllers.

Abstract--Humans use UAVs to monitor changes in forest environments since they are lightweight and provide a large variety of surveillance data. However, their information does not present enough details for understanding the scene which is needed to assess the degree of deforestation. Deep learning algorithms must be trained on large amounts of data to output accurate interpretations, but ground truth recordings of annotated forest imagery are not available. To solve this problem, we introduce a new large aerial dataset for forest inspection which contains both real-world and virtual recordings of natural environments, with densely annotated semantic segmentation labels and depth maps, taken in different illumination conditions, at various altitudes and recording angles. We test the performance of two multiscale neural networks for solving the semantic segmentation task (HRNet and PointFlow network), studying the impact of the various acquisition conditions and the capabilities of transfer learning from virtual to real data. Our results showcase that the best results are obtained when the training is done on a dataset containing a large variety of scenarios, rather than separating the data into specific categories. However, it comes at the cost of large amounts of training data that need to contain I. Since Worldwide efforts are being made to protect forests, slow manual annotation is time-consuming, researchers have turned the rate of deforestation, and reduce the negative impacts toward video game engines that are able to emulate real world of environmental degradation. Researchers have successfully scenarios, from urban cities to natural habitats.

We present a fully autonomous self-recharging drone system capable of long-duration sustained operations near powerlines. The drone is equipped with a robust onboard perception and navigation system that enables it to locate powerlines and approach them for landing. A passively actuated gripping mechanism grasps the powerline cable during landing after which a control circuit regulates the magnetic field inside a split-core current transformer to provide sufficient holding force as well as battery recharging. The system is evaluated in an active outdoor three-phase powerline environment. We demonstrate multiple contiguous hours of fully autonomous uninterrupted drone operations composed of several cycles of flying, landing, recharging, and takeoff, validating the capability of extended, essentially unlimited, operational endurance.

The Pentagon will look to develop new artificial intelligence-guided planes, offering two contracts that several private companies have been competing to obtain. The Collaborative Combat Aircraft (CCA) project is part of a 6 billion program that will add at least 1,000 new drones to the U.S. Air Force. These drones would deploy alongside human-piloted jets and provide cover for them, acting as escorts with full weapons capabilities that could also act as scouts or communications hubs, The Wall Street Journal reported. Boeing, Lockheed Martin, Northrop Grumman, General Atomics and Anduril Industries have all taken up the challenge. General Atomics supplied the Reaper and Predator drones the U.S. has deployed in numerous campaigns in the Middle East, and Anduril is a newcomer to the field, founded in 2017 by inventor Palmer Luckey, an entrepreneur who founded Oculus VR.