Path planning is essential for unmanned aerial vehicles (UAVs) as it determines the path that the UAV needs to follow to complete a task. This work addresses this problem by introducing a new algorithm called navigation variable-based multi-objective particle swarm optimization (NMOPSO). It first models path planning as an optimization problem via the definition of a set of objective functions that include optimality and safety requirements for UAV operation. The NMOPSO is then used to minimize those functions through Pareto optimal solutions. The algorithm features a new path representation based on navigation variables to include kinematic constraints and exploit the maneuverable characteristics of the UAV. It also includes an adaptive mutation mechanism to enhance the diversity of the swarm for better solutions. Comparisons with various algorithms have been carried out to benchmark the proposed approach. The results indicate that the NMOPSO performs better than not only other particle swarm optimization variants but also other state-of-the-art multi-objective and metaheuristic optimization algorithms. Experiments have also been conducted with real UAVs to confirm the validity of the approach for practical flights. The source code of the algorithm is available at https://github.com/ngandng/NMOPSO.

In its notice, the Commerce Department said that drones could be used to damage physical infrastructure in a collision, deliver an explosive payload or gather information about critical infrastructure, including building layouts. In addition, with critical infrastructure in the United States increasingly reliant on drones, any efforts to remotely incapacitate them would create a risk to national security. The department added that in the past, drone companies based in China had pushed updates to their devices to create no-fly restrictions that disabled them in conflict zones defined by the companies. The notice said that the Commerce Department was also considering whether any measures could mitigate the risks and allow the sale of Chinese drones to continue, such as certain design requirements or cybersecurity software. The proposed rule is part of a broader effort by the Biden administration to examine and eliminate vulnerabilities in high-tech products and communications infrastructure that collect huge amounts of data about Americans.

The banks of a canal in Cheshire collapsed on New Year's Day after parts of the UK were lashed by heavy rain. Drone footage showed how fields near the village of Little Bollington, which is close to Altrincham, were inundated with water pouring from the Bridgewater Canal. Cheshire Police said nearby properties were evacuated and a section of the M56 had to be closed. It came on the same day that Greater Manchester police declared a major incident due to floodwater trapping people in their homes across the north west of England.

Aviva Siegel, who was held hostage by Hamas for 51 days, tells'Fox & Friends' about her husband, who is still being held in captivity in Gaza, and recounts the harrowing experiences she witnessed during the terror attacks of Oct. 7, 2023. A top Hamas commander responsible for the heinous Oct. 7 attack on Kibbutz Nir Oz has been killed by a targeted drone strike, the Israel Defense Force (IDF) announced. Abd al-Hadi Sabah, who led the infiltration into Kibbutz Nir Oz, which ravaged the community near the Gaza border on Oct. 7, was killed on Tuesday local time in the Western Khan Yunis Battalion. The IDF said in a release on social media Tuesday that they conducted the intelligence-based strike alongside the Israeli Security Agency (ISA). The agencies said that Sabah was hiding in a shelter in the designated humanitarian area in Khan Yunis, in southern Gaza.

Compact UAV systems, while advancing delivery and surveillance, pose significant security challenges due to their small size, which hinders detection by traditional methods. This paper presents a cost-effective, unsupervised UAV detection method using spatial-temporal sequence processing to fuse multiple LiDAR scans for accurate UAV tracking in real-world scenarios. Our approach segments point clouds into foreground and background, analyzes spatial-temporal data, and employs a scoring mechanism to enhance detection accuracy. Tested on a public dataset, our solution placed 4th in the CVPR 2024 UG2+ Challenge, demonstrating its practical effectiveness. We plan to open-source all designs, code, and sample data for the research community github.com/lianghanfang/UnLiDAR-UAV-Est.

As small unmanned aerial vehicles (UAVs) become increasingly prevalent, there is growing concern regarding their impact on public safety and privacy, highlighting the need for advanced tracking and trajectory estimation solutions. In response, this paper introduces a novel framework that utilizes audio array for 3D UAV trajectory estimation. Our approach incorporates a self-supervised learning model, starting with the conversion of audio data into mel-spectrograms, which are analyzed through an encoder to extract crucial temporal and spectral information. Simultaneously, UAV trajectories are estimated using LiDAR point clouds via unsupervised methods. These LiDAR-based estimations act as pseudo labels, enabling the training of an Audio Perception Network without requiring labeled data. In this architecture, the LiDAR-based system operates as the Teacher Network, guiding the Audio Perception Network, which serves as the Student Network. Once trained, the model can independently predict 3D trajectories using only audio signals, with no need for LiDAR data or external ground truth during deployment. To further enhance precision, we apply Gaussian Process modeling for improved spatiotemporal tracking. Our method delivers top-tier performance on the MMAUD dataset, establishing a new benchmark in trajectory estimation using self-supervised learning techniques without reliance on ground truth annotations.

This paper studies an integrated sensing and communications (ISAC) system for low-altitude economy (LAE), where a ground base station (GBS) provides communication and navigation services for authorized unmanned aerial vehicles (UAVs), while sensing the low-altitude airspace to monitor the unauthorized mobile target. The expected communication sum-rate over a given flight period is maximized by jointly optimizing the beamforming at the GBS and UAVs' trajectories, subject to the constraints on the average signal-to-noise ratio requirement for sensing, the flight mission and collision avoidance of UAVs, as well as the maximum transmit power at the GBS. Typically, this is a sequential decision-making problem with the given flight mission. Thus, we transform it to a specific Markov decision process (MDP) model called episode task. Based on this modeling, we propose a novel LAE-oriented ISAC scheme, referred to as Deep LAE-ISAC (DeepLSC), by leveraging the deep reinforcement learning (DRL) technique. In DeepLSC, a reward function and a new action selection policy termed constrained noise-exploration policy are judiciously designed to fulfill various constraints. To enable efficient learning in episode tasks, we develop a hierarchical experience replay mechanism, where the gist is to employ all experiences generated within each episode to jointly train the neural network. Besides, to enhance the convergence speed of DeepLSC, a symmetric experience augmentation mechanism, which simultaneously permutes the indexes of all variables to enrich available experience sets, is proposed. Simulation results demonstrate that compared with benchmarks, DeepLSC yields a higher sum-rate while meeting the preset constraints, achieves faster convergence, and is more robust against different settings.

This paper presents an advanced mapping system that combines drone imagery with machine learning and computer vision to overcome challenges in speed, accuracy, and adaptability across diverse terrains. By automating processes like feature detection, image matching, and stitching, the system produces seamless, high-resolution maps with minimal latency, offering strategic advantages in defense operations. Developed in Python, the system utilizes OpenCV for image processing, NumPy for efficient computations, and Concurrent[dot]futures for parallel execution. ORB (Oriented FAST and Rotated BRIEF) is employed for feature detection, while FLANN (Fast Library for Approximate Nearest Neighbors) ensures accurate keypoint matching. Homography transformations align overlapping images, resulting in distortion-free maps in real time. This automation eliminates manual intervention, enabling live updates essential in rapidly changing environments. Designed for versatility, the system performs reliably under various lighting conditions and rugged terrains, making it highly suitable for aerospace and defense applications. Testing has shown notable improvements in processing speed and accuracy compared to conventional methods, enhancing situational awareness and informed decision-making. This scalable solution leverages cutting-edge technologies to provide actionable, reliable data for mission-critical operations.

Exclusive drone footage reveals the extensive destruction in Zabadani, a city near Damascus, which endured heavy shelling from Bashar al-Assad's forces during Syria's war.

President Vladimir Putin on Saturday apologized to Azerbaijan's leader for what the Kremlin called a "tragic incident" over Russia in which an Azerbaijan Airlines plane crashed after Russian air defences were fired against Ukrainian drones. The extremely rare publicized apology from Putin was the closest Moscow had come to accepting some blame for Wednesday's disaster, although the Kremlin statement did not say Russia had shot down the plane, only noting that a criminal case had been opened. Flight J2-8243, en route from Baku to the Chechen capital Grozny, crash-landed on Wednesday near Aktau in Kazakhstan after diverting from southern Russia, where Ukrainian drones were reported to be attacking several cities. At least 38 people were killed.