Scores of new witnesses have emerged with more footage of the eerie'drone' UFO swarms buzzing key US military sites, including'a big fireball in a cube' over Area 51. The Las Vegas-area witness who reported this bizarre cube-shaped object claims to have observed similar strange aerial lights in the area'over 100 times' since June 2020, adding that these craft'always seem to head towards Nellis Air Force base.' Nevada's Nellis base and its sprawling complex about 40 miles northwest of Vegas -- including top secret Area 51, now legendary within UFO lore -- appear to have faced incursions by craft similar to those that plagued the Air Force in Virginia. For at least 17 nights last December, swarms of noisy small UFOs were seen'moving at rapid speeds' and displaying'flashing red, green, and white lights' within the highly restricted airspace over Virginia's Joint Base Langley–Eustis. Vegas natives have posted videos confirming they too have seen more than one red, green or white UFO that'wasn't flashing like a regular aircraft [or] like a satellite.' Another witness, who documented one September 4, 2024 case from their own 60-night experience with the odd lights, hoped coming forward might help get answers.

Russia and Ukraine have both launched record drone attacks on each other overnight, with Ukrainian attacks on Moscow temporarily shutting down three of the Russian capital's airports. Russia fired 145 drones at Ukraine overnight, Ukrainian President Volodymyr Zelenskyy said on Sunday – more than in any single nighttime attack so far during their two-and-a-half-year conflict. "Last night, Russia launched a record 145 Shaheds and other strike drones against Ukraine," Zelenskyy said on social media, urging Kyiv's Western allies to do more to help Ukraine's defence. Kyiv said its air defences downed 62 of the drones. Russia also said it had downed 34 Ukrainian attack drones targeting Moscow on Sunday, the largest attempted attack on the capital since the start of the offensive in 2022, with Moscow regional Governor Andrei Vorobyov calling the attack "massive".

Ukraine's attempted strike on Moscow was reportedly its largest attack on the capital since the war began, and was described as "massive" by the region's governor. One person was reported injured as drones were shot down near the Russian capital. Images on social media showed a residential building on fire. Most of the drones were downed in the Ramenskoye, Kolomna and Domodedovo districts, officials said. In September a woman was killed in a drone attack that hit Ramenskoye.

Intelligent aerial platforms such as Unmanned Aerial Vehicles (UAVs) are expected to revolutionize various fields, including transportation, traffic management, field monitoring, industrial production, and agricultural management. Among these, precise control is a critical task that determines the performance and capabilities of UAV systems. However, current research primarily focuses on trajectory tracking and minimizing flight errors, with limited attention to improving flight time. In this paper, we propose a Model Predictive Control (MPC) approach aimed at minimizing flight time while addressing the limitations of the commonly used classical MPC controllers. Furthermore, the MPC method and its application for UAV control are presented in detail. Finally, the results demonstrate that the proposed controller outperforms the standard MPC in terms of efficiency. Moreover, this approach shows potential to become a foundation for integrating intelligent algorithms into basic controllers.

North Korean troops are said to have clashed with Ukrainian forces in the Russian region of Kursk for the first time on Tuesday, the same day American voters re-elected Donald Trump for president, an isolationist who has argued against sending further military aid to Ukraine. "The first battles with North Korean soldiers open a new page of instability in the world," said Ukrainian President Volodymyr Zelenskyy in his evening address. "We must do everything to make this Russian step to expand the war – to really escalate it – to make this step a failure." Ukrainian Defence Minister Rustem Umerov said the clashes were "small scale" and that the North Korean troops were not fighting as separate formations but were embedded in Russian units disguised as Buryats from the Russian Federation. On Saturday, Ukraine's military intelligence (GUR) had said Russia transferred more than 7,000 North Korean military personnel "to areas near Ukraine" in the last week of October – a much higher figure than the 3,000 North Korean soldiers South Korean and United States intelligence had said were in Russia's Kursk region on October 30.

Developing robust drone detection systems is often constrained by the limited availability of large-scale annotated training data and the high costs associated with real-world data collection. However, leveraging synthetic data generated via game engine-based simulations provides a promising and cost-effective solution to overcome this issue. Therefore, we present SynDroneVision, a synthetic dataset specifically designed for RGB-based drone detection in surveillance applications. Featuring diverse backgrounds, lighting conditions, and drone models, SynDroneVision offers a comprehensive training foundation for deep learning algorithms. To evaluate the dataset's effectiveness, we perform a comparative analysis across a selection of recent YOLO detection models. Our findings demonstrate that SynDroneVision is a valuable resource for real-world data enrichment, achieving notable enhancements in model performance and robustness, while significantly reducing the time and costs of real-world data acquisition. SynDroneVision will be publicly released upon paper acceptance.

Deep reinforcement learning (DRL) is currently the most popular AI-based approach to autonomous vehicle control. An agent, trained for this purpose in simulation, can interact with the real environment with a human-level performance. Despite very good results in terms of selected metrics, this approach has some significant drawbacks: high computational requirements and low explainability. Because of that, a DRL-based agent cannot be used in some control tasks, especially when safety is the key issue. Therefore we propose to use Tangled Program Graphs (TPGs) as an alternative for deep reinforcement learning in control-related tasks. In this approach, input signals are processed by simple programs that are combined in a graph structure. As a result, TPGs are less computationally demanding and their actions can be explained based on the graph structure. In this paper, we present our studies on the use of TPGs as an alternative for DRL in control-related tasks. In particular, we consider the problem of navigating an unmanned aerial vehicle (UAV) through the unknown environment based solely on the on-board LiDAR sensor. The results of our work show promising prospects for the use of TPGs in control related-tasks.

Lebanese armed group Hezbollah has said that it targeted an Israeli military base south of Tel Aviv with a swarm of drones "for the first time", as Israel launched renewed air strikes on the southern suburbs of Beirut city. Hezbollah fighters launched a "squadron of attack drones at the Bilu base south of Tel Aviv, for the first time", late on Wednesday, the group said in a statement. There were no immediate reports of casualties or damage to infrastructure from Israeli authorities. Earlier, Hezbollah also claimed a slew of attacks, including two that targeted naval bases near the Israeli port city of Haifa, and another base near Israel's main international airport close to Tel Aviv. The Israel Airports Authority said operations at the airport were not affected by the attack.

Path planning in narrow passages is a challenging problem in various applications. Traditional planning algorithms often face challenges in complex environments like mazes and traps, where narrow entrances require special orientation control for successful navigation. In this work, we present a novel approach that combines superquadrics (SQ) representation and Voronoi diagrams to solve the narrow passage problem in both 2D and 3D environment. Our method utilizes the SQ formulation to expand obstacles, eliminating impassable passages, while Voronoi hyperplane ensures maximum clearance path. Additionally, the hyperplane provides a natural reference for robot orientation, aligning its long axis with the passage direction. We validate our framework through a 2D object retrieval task and 3D drone simulation, demonstrating that our approach outperforms classical planners and a cutting-edge drone planner by ensuring passable trajectories with maximum clearance.

Deep reinforcement learning (DRL) has been extensively applied to Multi-Unmanned Aerial Vehicle (UAV) network (MUN) to effectively enable real-time adaptation to complex, time-varying environments. Nevertheless, most of the existing works assume a stationary user distribution (UD) or a dynamic one with predicted patterns. Such considerations may make the UD-specific strategies insufficient when a MUN is deployed in unknown environments. To this end, this paper investigates distributed user connectivity maximization problem in a MUN with generalization to arbitrary UDs. Specifically, the problem is first formulated into a time-coupled combinatorial nonlinear non-convex optimization with arbitrary underlying UDs. To make the optimization tractable, a multi-agent CNN-enhanced deep Q learning (MA-CDQL) algorithm is proposed. The algorithm integrates a ResNet-based CNN to the policy network to analyze the input UD in real time and obtain optimal decisions based on the extracted high-level UD features. To improve the learning efficiency and avoid local optimums, a heatmap algorithm is developed to transform the raw UD to a continuous density map. The map will be part of the true input to the policy network. Simulations are conducted to demonstrate the efficacy of UD heatmaps and the proposed algorithm in maximizing user connectivity as compared to K-means methods.