This paper presents a multi-agent reinforcement learning (MARL) framework for cooperative collision avoidance of UA V swarms leveraging domain knowledge-driven reward. The reward is derived from knowledge in the domain of image processing, approximating contours on a two-dimensional field. By modeling obstacles as maxima on the field, collisions are inherently avoided as contours never go through peaks or intersect. Additionally, counters are smooth and energy-efficient. Our framework enables training with large swarm sizes as the agent interaction is minimized and the need for complex credit assignment schemes or observation sharing mechanisms in state-of-the-art MARL approaches are eliminated. Moreover, UA Vs obtain the ability to adapt to complex environments where contours may be nonviable or non-existent through intensive training. Extensive experiments are conducted to evaluate the performances of our framework against state-of-the-art MARL algorithms.

The rapid development of urban low-altitude unmanned aerial vehicle (UAV) economy poses new challenges for dynamic site selection of UAV landing points and supply stations. Traditional deep reinforcement learning methods face computational complexity bottlenecks, particularly with standard attention mechanisms, when handling large-scale urban-level location problems. This paper proposes GeoHopNet, a Hopfield-augmented sparse spatial attention network specifically designed for dynamic UAV site location problems. Our approach introduces four core innovations: (1) distance-biased multi-head attention mechanism that explicitly encodes spatial geometric information; (2) K-nearest neighbor sparse attention that reduces computational complexity from $O(N^2)$ to $O(NK)$; (3) a modern Hopfield external memory module; and (4) a memory regularization strategy. Experimental results demonstrate that GeoHopNet extends the boundary of solvable problem sizes. For large-scale instances with 1,000 nodes, where standard attention models become prohibitively slow (over 3 seconds per instance) and traditional solvers fail, GeoHopNet finds high-quality solutions (0.22\% optimality gap) in under 0.1 seconds. Compared to the state-of-the-art ADNet baseline on 100-node instances, our method improves solution quality by 22.2\% and is 1.8$\times$ faster.

Arizona Attorney General Kris Mayes explains how drones are frequently used at the southern border to transport drugs, raising concerns from both sides of the aisle. As reported crossings have dropped dramatically at the border, there is still work to be done on matters of stopping drugs from making their way into the United States, especially in the border state of Arizona, a top state official says. One of the ways that cartels transport drugs is by using drones, a tactic that gained attention after bipartisan legislation signed in the Grand Canyon State gave law enforcement the power to shoot down the small aircraft. "I think what has changed is that we have gotten more control over people crossing over the border, but unfortunately what has not changed is we still have a huge amount of fentanyl that is coming across our border here in Arizona, and that is being flown over the by the Mexican drug cartels with drones," Democratic Arizona Attorney General Kris Mayes said. Fentanyl is being delivered across the border by cartels on drones.

Breakthroughs, discoveries, and DIY tips sent every weekday. Researchers from the University of Florida have uncovered the largest known nesting site for the threatened giant South American river turtle (Podocnemis expansa). How did they find over 41,000 nesting reptiles? The turtles were found gathered along the Amazon's Guaporé River between Brazil and Bolivia. This innovative use of drones opens up new avenues for conservationists, as detailed in a study recently published in the Journal of Applied Ecology.

Kyiv, Ukraine – Heavy thuds that resemble fast hip-hop beats fill the night air when MIM-104 Patriots, air defence systems made in the United States, get to work. Each Patriot surface-to-air launcher can shoot up to 32 missiles within seconds – and hit Russian ballistic missiles closing in on their targets. The missiles fly at supersonic speeds, and the collision triggers a bright, split-second blast followed by a thunderous shock-wave. "That's the kind of explosion that makes me feel safe," Ihor Lysenko, a 17-year-old in the capital Kyiv told Al Jazeera. He believes that the "technology is pretty reliable".

Russian forces launched drone attacks on Ukraine's eastern regions of Kharkiv and Sumy, killing at least one person and wounding 21 others, the Kyiv Independent reported, citing local authorities. The Ukrainian Red Cross said the attacks also damaged buildings in Sumy, including an educational and medical facility. The death toll from Russian attacks on Ukraine on Sunday has risen to six, including three people in Sumy, two others in Donetsk and one more in Kherson, the Kyiv Independent reported, citing local officials. Russia's Ministry of Defence claimed control of two more villages in eastern Ukraine: Malynivka in the Zaporizhia region and Mayak in the Donetsk region. Ukrainian drone attacks wounded two people in Russia's Kursk region, and another person in the city of Kamianka-Dniprovska in Ukraine's Zaporizhia region, which Moscow partially occupies, according to the Russian state TASS news agency.

Collaborative perception enhances environmental awareness through inter-agent communication and is regarded as a promising solution to intelligent transportation systems. However, existing collaborative methods for Unmanned Aerial Vehicles (UAVs) overlook the unique characteristics of the UAV perspective, resulting in substantial communication overhead. To address this issue, we propose a novel communication-efficient collaborative perception framework based on late-intermediate fusion, dubbed LIF. The core concept is to exchange informative and compact detection results and shift the fusion stage to the feature representation level. In particular, we leverage vision-guided positional embedding (VPE) and box-based virtual augmented feature (BoBEV) to effectively integrate complementary information from various agents. Additionally, we innovatively introduce an uncertainty-driven communication mechanism that uses uncertainty evaluation to select high-quality and reliable shared areas. Experimental results demonstrate that our LIF achieves superior performance with minimal communication bandwidth, proving its effectiveness and practicality. Code and models are available at https://github.com/uestchjw/LIF.

This paper introduces a cooperative and decentralized collision avoidance algorithm (CoDe) for small-scale UAV swarms consisting of up to three UAVs. CoDe improves energy efficiency of UAVs by achieving effective cooperation among UAVs. Moreover, CoDe is specifically tailored for UAV's operations by addressing the challenges faced by existing schemes, such as ineffectiveness in selecting actions from continuous action spaces and high computational complexity. CoDe is based on Multi-Agent Reinforcement Learning (MARL), and finds cooperative policies by incorporating a novel credit assignment scheme. The novel credit assignment scheme estimates the contribution of an individual by subtracting a baseline from the joint action value for the swarm. The credit assignment scheme in CoDe outperforms other benchmarks as the baseline takes into account not only the importance of a UAV's action but also the interrelation between UAVs. Furthermore, extensive experiments are conducted against existing MARL-based and conventional heuristic-based algorithms to demonstrate the advantages of the proposed algorithm.

This paper aims to propose a novel machine learning (ML) approach incorporating Homomorphic Encryption (HE) to address privacy limitations in Unmanned Aerial Vehicles (UAV)-based face detection. Due to challenges related to distance, altitude, and face orientation, high-resolution imagery and sophisticated neural networks enable accurate face recognition in dynamic environments. However, privacy concerns arise from the extensive surveillance capabilities of UAVs. To resolve this issue, we propose a novel framework that integrates HE with advanced neural networks to secure facial data throughout the inference phase. This method ensures that facial data remains secure with minimal impact on detection accuracy. Specifically, the proposed system leverages the Cheon-Kim-Kim-Song (CKKS) scheme to perform computations directly on encrypted data, optimizing computational efficiency and security. Furthermore, we develop an effective data encoding method specifically designed to preprocess the raw facial data into CKKS form in a Single-Instruction-Multiple-Data (SIMD) manner. Building on this, we design a secure inference algorithm to compute on ciphertext without needing decryption. This approach not only protects data privacy during the processing of facial data but also enhances the efficiency of UAV-based face detection systems. Experimental results demonstrate that our method effectively balances privacy protection and detection performance, making it a viable solution for UAV-based secure face detection. Significantly, our approach (while maintaining data confidentially with HE encryption) can still achieve an accuracy of less than 1% compared to the benchmark without using encryption.

-- Unmanned Aerial Vehicle s (UAV) have emerged as versatile platforms, driving the demand for accurate modeling to support developmental testing. This paper proposes data - driven modeling software for UAV. Emphasizes the utilization of cost - effective sensors to obtain orientation and location data subsequently processed through the application of data filtering algorithms and sensor fusion techniques to improve the data quality to make a precise model visualization on the software. UAV's orientation is obtained using processed Inertial Measurement Unit (IMU) data and represented using Quaternion Representation to avoid the gimbal lock problem. The UAV's location is determined by combining data from the Global Positioning System (GPS), which provides stable geographic coordinates but slower data update frequency, and the accelerometer, which has higher data update frequency but integrating it to get position data is unstable due to its accumulative error. By combining data from these two sensors, the software is able to calculate and continuously update the UAV's real - time position during its flight operations. The result shows that the software effectively renders UAV orientation and position with high degree of accuracy and fluidity. Unmanned Aerial Vehicle s (UAV) have rapidly evolved as a versatile platform for various applications [ 1 ] . The increasing demand for UAV development to solve complex environment s necessitates raising the need to develop accurate and reliable simulation models that faithfully represent the dynamic behavior of the UAV. An accurate simulation model of UAV that has been tested allows developers to perform cost - effective analysis and evaluation while also validating the performance of UAV under real - world scenarios.