NATO member Turkey on Thursday carried out a second day of aerial attacks on what it said are Kurdish militant positions in Iraq and Syria, following a terrorist attack on a state-run defense agency this week in which five people were killed. Turkey's National Intelligence Organization reportedly targeted numerous "strategic locations" allegedly used by the Kurdistan Workers' Party (PKK) – which was deemed a terrorist organization in the U.S. in 1997 – as well as targets used by Syrian Kurdish militia affiliated with the militant group. Armed drones were used to hit military, intelligence, energy and infrastructure facilities and ammunition depots, The Associated Press reported. Smoke rises as emergency rescue teams and police officers attend outside Turkish Aerospace Industries Inc. on the outskirts of Ankara, Turkey, on Wednesday, Oct. 23, 2024. However, according to General Commander Mazloum Abdi of the Syrian Democratic Forces (SDF), who is Kurdish, the Turkish attacks have been "indiscriminate" and have targeted civilian areas and health centers.

United States defence secretary Lloyd Austin has waded in on reports that North Korea was preparing to enter the Ukraine war with troops. "If they are co-belligerents, if their intention is to participate in this war on Russia's behalf, that is a very, very serious issue," Austin said. Austin was returning from his fourth visit to Kyiv, where he announced a 400m package of US weapons for Ukraine. John Kirby, White House national security spokesman, said Washington believes that at least 3,000 North Korean soldiers arrived this month by sea to Vladivostok, Russia's largest Pacific port. "These soldiers then travelled onward to multiple Russian military training sites in eastern Russia, where they are currently undergoing training," Kirby said on Wednesday.

Electroaerodynamic propulsion, where force is produced via the momentum-transferring collisions between accelerated ions and neutral air molecules, is a promising alternative mechanism for flight at the micro air vehicle scale due to its silent and solid-state nature. Its relatively low efficiency, however, has thus far precluded its use in a power-autonomous vehicle; leveraging the efficiency benefits of operation close to a fixed surface is a potential solution. While proximity effects like the ground and ceiling effects have been well-investigated for rotorcraft and flapping wing micro air vehicles, they have not been for electroaerodynamically-propelled fliers. In this work, we investigate the change in performance when centimeter-scale thrusters are operated close to a "ceiling" plane about the inlet. We show a surprising and, until now, unreported effect; a major electrostatic attractive component, analogous to electroadhesive pressure but instead mediated by a stable atmospheric plasma. The isolated electrostatic and fluid dynamic components of the ceiling effect are shown for different distances from the plane and for different materials. We further show that a flange attached to the inlet can vastly increase both components of force. A peak efficiency improvement of 600% is shown close to the ceiling. This work points the way towards effective use of the ceiling effect for power autonomous vehicles, extending flight duration, or as a perching mechanism.

In our recent work [5], we proposed a novel Differential The increasing use of fixed-wing Unmanned Aerial Vehicles Evolution-based path planner that handles kinematicconstrained (UAVs) is driven by several factors, such as longrange, UAVs. In this approach, we also show that high speeds, and superior payload capacity compared using the Non-Uniform Rational B-spline (NURBS) curve as to quadrotors. Combined with motion planning strategies, the path representation can provide a more flexible planner these advantages enable fixed-wing UAVs also to navigate than using the B-spline representation.

Formation control of multiple Unmanned Aerial Vehicles (UAVs) is vital for practical applications. This paper tackles the task of behavior-based UAV formation while avoiding static and dynamic obstacles during directed flight. We present a two-stage reinforcement learning (RL) training pipeline to tackle the challenge of multi-objective optimization, large exploration spaces, and the sim-to-real gap. The first stage searches in a simplified scenario for a linear utility function that balances all task objectives simultaneously, whereas the second stage applies the utility function in complex scenarios, utilizing curriculum learning to navigate large exploration spaces. Additionally, we apply an attention-based observation encoder to enhance formation maintenance and manage varying obstacle quantity. Experiments in simulation and real world demonstrate that our method outperforms planning-based and RL-based baselines regarding collision-free rate and formation maintenance in scenarios with static, dynamic, and mixed obstacles.

This paper tackles the challenge of real-time 3D trajectory prediction for UAVs, which is critical for applications such as aerial surveillance and defense. Existing prediction models that rely primarily on position data struggle with accuracy, especially when UAV movements fall outside the position domain used in training. Our research identifies a gap in utilizing velocity estimates, first-order dynamics, to better capture the dynamics and enhance prediction accuracy and generalizability in any position domain. To bridge this gap, we propose a new trajectory prediction method using Gated Recurrent Units (GRUs) within sequence-based neural networks. Unlike traditional methods that rely on RNNs or transformers, this approach forecasts future velocities and positions based on historical velocity data instead of positions. This is designed to enhance prediction accuracy and scalability, overcoming challenges faced by conventional models in handling complex UAV dynamics. The methodology employs both synthetic and real-world 3D UAV trajectory data, capturing a wide range of flight patterns, speeds, and agility. Synthetic data is generated using the Gazebo simulator and PX4 Autopilot, while real-world data comes from the UZH-FPV and Mid-Air drone racing datasets. The GRU-based models significantly outperform state-of-the-art RNN approaches, with a mean square error (MSE) as low as 2 x 10^-8. Overall, our findings confirm the effectiveness of incorporating velocity data in improving the accuracy of UAV trajectory predictions across both synthetic and real-world scenarios, in and out of position data distributions. Finally, we open-source our 5000 trajectories dataset and a ROS 2 package to facilitate the integration with existing ROS-based UAV systems.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Bostonians who gathered at City Hall Plaza on Tuesday night to watch the Boston Celtics' opening night watch party and further celebrate the team's recent NBA Championship were greeted by a falling drone that injured some and sent at least one person to the hospital. Boston police say at least three people sustained non-life-threatening injuries from a falling drone outside the plaza around 7:30 p.m., just as the Celtics were tipping off against the New York Knicks. Yousef Kobeissi, who was hit by the drone, told Boston 25 that it sounded like "a banging sound" when the drone crashed into them.

Autonomous drone racing requires powerful perception, planning, and control and has become a benchmark and test field for autonomous, agile flight. Existing work usually assumes static race tracks with known maps, which enables offline planning of time-optimal trajectories, performing localization to the gates to reduce the drift in visual-inertial odometry (VIO) for state estimation or training learning-based methods for the particular race track and operating environment. In contrast, many real-world tasks like disaster response or delivery need to be performed in unknown and dynamic environments. To close this gap and make drone racing more robust against unseen environments and moving gates, we propose a control algorithm that does not require a race track map or VIO and uses only monocular measurements of the line of sight (LOS) to the gates. For this purpose, we adopt the law of proportional navigation (PN) to accurately fly through the gates despite gate motions or wind. We formulate the PN-informed vision-based control problem for drone racing as a constrained optimization problem and derive a closed-form optimal solution. We demonstrate through extensive simulations and real-world experiments that our method can navigate through moving gates at high speeds while being robust to different gate movements, model errors, wind, and delays.

The intersection of LLMs (Large Language Models) and UAV (Unoccupied Aerial Vehicles) technology represents a promising field of research with the potential to enhance UAV capabilities significantly. This study explores the application of LLMs in UAV control, focusing on the opportunities for integrating advanced natural language processing into autonomous aerial systems. By enabling UAVs to interpret and respond to natural language commands, LLMs simplify the UAV control and usage, making them accessible to a broader user base and facilitating more intuitive human-machine interactions. The paper discusses several key areas where LLMs can impact UAV technology, including autonomous decision-making, dynamic mission planning, enhanced situational awareness, and improved safety protocols. Through a comprehensive review of current developments and potential future directions, this study aims to highlight how LLMs can transform UAV operations, making them more adaptable, responsive, and efficient in complex environments. A template development framework for integrating LLMs in UAV control is also described. Proof of Concept results that integrate existing LLM models and popular robotic simulation platforms are demonstrated. The findings suggest that while there are substantial technical and ethical challenges to address, integrating LLMs into UAV control holds promising implications for advancing autonomous aerial systems.

Multirotor unmanned aerial vehicle is a prevailing type of aerial robots with wide real-world applications. The energy efficiency of the robot is a critical aspect of its performance, determining the range and duration of the missions that can be performed. This paper studies the energy-optimal planning of the multirotor, which aims at finding the optimal ordering of waypoints with the minimum energy consumption for missions in 3D space. The study is performed based on a previously developed model capturing first-principle energy dynamics of the multirotor. We found that in majority of the cases (up to 95%) the solutions of the energy-optimal planning are different from those of the traditional traveling salesman problem which minimizes the total distance. The difference can be as high as 14.9%, with the average at 1.6%-3.3% and 90th percentile at 3.7%-6.5% depending on the range and number of waypoints in the mission. We then identified and explained the key features of the minimum-energy order by correlating to the underlying flight energy dynamics. It is shown that instead of minimizing the distance, coordination of vertical and horizontal motion to promote aerodynamic efficiency is the key to optimizing energy consumption.