The increasing demand for data usage in wireless communications requires using wider bands in the spectrum, especially for backhaul links. Yet, allocations in the spectrum for non-communication systems inhibit merging bands to achieve wider bandwidth. To overcome this issue, spectrum-sharing or opportunistic spectrum utilization by secondary users stands out as a promising solution. However, both approaches must minimize interference to primary users. Therefore, spectrum sensing becomes vital for such opportunistic usage, ensuring the proper operation of the primary users. Although this problem has been investigated for 2D networks, unmanned aerial vehicle (UAV) networks need different points of view concerning 3D space, its challenges, and opportunities. For this purpose, we propose a federated learning (FL)-based method for spectrum sensing in UAV networks to account for their distributed nature and limited computational capacity. FL enables local training without sharing raw data while guaranteeing the privacy of local users,lowering communication overhead, and increasing data diversity. Furthermore, we develop a federated aggregation method, namely FedSNR, that considers the signal-to-noise ratio observed by UAVs to acquire a global model. The numerical results show that the proposed architecture and the aggregation method outperform traditional methods.

The first time that Terry Vandenbos watched a bear run from a drone, on a spring day two years ago, he was chasing the animal himself. After he saw the grizzly cross a road near his property, the Montana rancher hopped on his all-terrain vehicle, planning to scare it away from his cattle if needed. But the bear began sprinting away when he was still far from it, looking over its shoulder as it ran, and Vandenbos looked up too; overhead, a small drone was following the bear, its four propellers emitting a high-pitched whine as it sent the animal towards a nearby lake. "I don't think I need to be here," Vandenbos remembers thinking. The bear never touched his cows.

In perhaps the most adorable hacker story of the year, a trio of technologists in India found an innovative way to circumvent Apple's location restrictions on AirPod Pro 2s so they could enable the earbuds' hearing aid feature for their grandmas. The hack involved a homemade Faraday cage, a microwave, and a lot of trial and error. On the other end of the tech-advancements spectrum, the US military is currently testing an AI-enabled machine gun that is capable of auto-targeting swarms of drones. The Bullfrog, built by Allen Control Systems, is one of several advanced weapons technologies in the works to combat the growing threat of cheap, small drones on the battlefield. The US Department of Justice announced this week that an 18-year-old from California has admitted to making or orchestrating more than 375 swatting attacks across the United States.

A laser beam downs drones by heating up and "frying" their electronics – each invisible, soundless "shot" is more precise and less costly than an air defence missile. Hanwha Aerospace, a South Korean defence company, has fire-tested and is about to start mass-producing the world's first-ever optical fibre laser weapon. And Hanwha is ready to supply it to Ukraine if Seoul lifts a ban on the export of lethal weapons to Kyiv "in light of North Korean military activities", South Korean President Yoon Suk Yeol said in late October. His statement followed North Korea's deployment of about 10,000 soldiers to western Russia, becoming the first foreign power to step into the Russia-Ukrainian war. However, the US president-elect may stand in the way.

North Korean leader Kim Jong Un has called for accelerating the mass production of attack drones, according to state media, as international concerns mount over the country's deepening military cooperation with Russia. The official Korean Central News Agency (KCNA) reported on Friday that Kim supervised the latest tests of "various types of suicide attack drones" produced by Pyongyang's Unmanned Aerial Technology Complex. The unmanned drones can hit land and sea targets, effectively acting as guided missiles. Kim "underscored the need to build a serial production system as early as possible and go into full-scale mass production", noting how drones are becoming crucial in modern warfare as he oversaw the tests on Thursday, KCNA said. North Korea first unveiled its suicide drones in August and military experts said the capability could be attributed to the country's growing alliance with Russia, with both sides signing a mutual defence pact.

Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.

Uncrewed aerial systems have tightly coupled energy and motion dynamics which must be accounted for by onboard planning algorithms. This work proposes a strategy for coupled motion and energy planning using model predictive control (MPC). A reduced-order linear time-invariant model of coupled energy and motion dynamics is presented. Constrained zonotopes are used to represent state and input constraints, and hybrid zonotopes are used to represent non-convex constraints tied to a map of the environment. The structures of these constraint representations are exploited within a mixed-integer quadratic program solver tailored to MPC motion planning problems. Results apply the proposed methodology to coupled motion and energy utilization planning problems for 1) a hybrid-electric vehicle that must restrict engine usage when flying over regions with noise restrictions, and 2) an electric package delivery drone that must track waysets with both position and battery state of charge requirements. By leveraging the structure-exploiting solver, the proposed mixed-integer MPC formulations can be implemented in real time.

The application of Multiple Unmanned Aerial Vehicles (Multi-UAV) in Wilderness Search and Rescue (WiSAR) significantly enhances mission success due to their rapid coverage of search areas from high altitudes and their adaptability to complex terrains. This capability is particularly crucial because time is a critical factor in searching for a lost person in the wilderness; as time passes, survival rates decrease and the search area expands. The probability of success in such searches can be further improved if UAVs leverage terrain features to predict the lost person's position. In this paper, we aim to enhance search missions by proposing a smart agent-based probability model that combines Monte Carlo simulations with an agent strategy list, mimicking the behavior of a lost person in the wildness areas. Furthermore, we develop a distributed Multi-UAV receding horizon search strategy with dynamic partitioning, utilizing the generated probability density model as prior information to prioritize locations where the lost person is most likely to be found. Simulated search experiments across different terrains have been conducted to validate the search efficiency of the proposed methods compared to other benchmark methods.

Autonomous multi-agent systems such as hospital robots and package delivery drones often operate in highly uncertain environments and are expected to achieve complex temporal task objectives while ensuring safety. While learning-based methods such as reinforcement learning are popular methods to train single and multi-agent autonomous systems under user-specified and state-based reward functions, applying these methods to satisfy trajectory-level task objectives is a challenging problem. Our first contribution is the use of weighted automata to specify trajectory-level objectives, such that, maximal paths induced in the weighted automaton correspond to desired trajectory-level behaviors. We show how weighted automata-based specifications go beyond timeliness properties focused on deadlines to performance properties such as expeditiousness. Our second contribution is the use of evolutionary game theory (EGT) principles to train homogeneous multi-agent teams targeting homogeneous task objectives. We show how shared experiences of agents and EGT-based policy updates allow us to outperform state-of-the-art reinforcement learning (RL) methods in minimizing path length by nearly 30\% in large spaces. We also show that our algorithm is computationally faster than deep RL methods by at least an order of magnitude. Additionally our results indicate that it scales better with an increase in the number of agents as compared to other methods.

The New York police department (NYPD) announced it will begin using drones to respond to reports of robberies and alerts from a city-wide gunshot detection system. The drones will fly to the scene, piloted by an NYPD officer, and record video and audio that will be sent to police officers' smartphones in real time, according to a press release. The integration of these two surveillance technologies is part of a broader "Drone as First Responder" program that has existed since 2018. The New York city mayor, Eric Adams, and the city's interim police commissioner, Tom Donlan, announced the expansion on Wednesday afternoon. It will be initially rolled out to five precincts in Brooklyn, the Bronx and Manhattan.