Veteran and former intel officer Don Bramer joined Fox & Friends First to discuss his reaction to Trump tapping Keith Kellogg to be his Ukraine-Russia envoy and the Biden admin working with the Trump team on peace in the Middle East. Following an overnight missile and drone attack by Russia targeting Ukraine's key energy infrastructure, Russian President Vladimir Putin now says that government buildings in Kyiv could be targeted next using a new hypersonic missile that could also potentially reach the U.S. Russian attacks have not so far struck "decision-making centers" in the Ukrainian capital as Kyiv is heavily protected by air defenses. But Putin says Russia's Oreshnik hypersonic missile, which it fired for the first time at a Ukrainian city last week, is incapable of being intercepted. Russia fired the Oreshnik at the Ukrainian city of Dnipro on Nov. 21, striking a weapons production plant. This was in retaliation against Ukrainian strikes on a Russian military facility in Bryansk two days earlier with U.S. made long-range missiles called ATACMS, after President Biden had given Ukrainian President Volodymyr Zelenskyy permission to do so.

Fox News' Stephanie Bennett has updates on the war in Ukraine on'Fox News Live.' Russia launched another "massive" attack on Ukraine's energy infrastructure on Thursday, knocking out power for more than a million households, according to Ukranian officials. Thursday's attack, which involved more than 200 missiles and drones, marks the second on Ukraine's power grid in less than two weeks. Energy Minister Herman Halushchenko said on Facebook that "attacks on energy facilities are happening all over Ukraine." He added that emergency power outages have been implemented nationwide. Areas affected include the Lviv region in western Ukraine, the northwestern Rivne region, the bordering Volyn region and the western Ivano Frankivsk region, according to The Associated Press. A Su-34 bomber of the Russian air force drops bombs on Ukrainian positions at an undisclosed location.

This study presents acoustic-based methods for the control of multiple autonomous underwater vehicles (AUV). This study proposes two different models for implementing boundary and path control on low-cost AUVs using acoustic communication and a single central acoustic beacon. Two methods are presented: the Range Variation-Based (RVB) model completely relies on range data obtained by acoustic modems, whereas the Heading Estimation-Based (HEB) model uses ranges and range rates to estimate the position of the central boundary beacon and perform assigned behaviors. The models are tested on two boundary control behaviors: Fencing and Milling. Fencing behavior ensures AUVs return within predefined boundaries, while Milling enables the AUVs to move cyclically on a predefined path around the beacon. Models are validated by successfully performing the boundary control behaviors in simulations, pool tests, including artificial underwater currents, and field tests conducted in the ocean. All tests were performed with fully autonomous platforms, and no external input or sensor was provided to the AUVs during validation. Quantitative and qualitative analyses are presented in the study, focusing on the effect and application of a multi-robot system.

This paper presents a novel control method for a group of UAVs in obstacle-laden environments while preserving sensing network connectivity without data transmission between the UAVs. By leveraging constraints rooted in control barrier functions (CBFs), the proposed method aims to overcome the limitations, such as oscillatory behaviors and frequent constraint violations, of the existing method based on artificial potential fields (APFs). More specifically, the proposed method first determines desired control inputs by considering CBF-based constraints rather than repulsive APFs. The desired inputs are then minimally modified by solving a numerical optimization problem with soft constraints. In addition to the optimization-based method, we present an approximate method without numerical optimization. The effectiveness of the proposed methods is evaluated by extensive simulations to compare the performance of the CBF-based methods with an APF-based approach. Experimental results using real quadrotors are also presented.

Fox News chief national security correspondent Jennifer Griffin has the latest on efforts to find out about the unusual drone activity on'Special Report.' Unidentified drones have been spotted over joint U.S.-U.K. bases in the United Kingdom for nearly a week. Fox News' Jennifer Griffin reports that four U.S. military bases in the U.K. that house the American F-15 Strike Eagle and F-35 fighter jets have been targeted by "swarms of small drones" since Wednesday, Nov. 20. Military officials say they are "alarmed" at what appears to be a coordinated effort to test security at RAF Lakenheath, RAF Mildenhall and RAF Feltwell in eastern England, as well as RAF Fairford in southwestern England. The U.K. military has sent around 60 personnel to protect the bases being targeted by multiple drone incursions.

Russian forces have captured the village of Kopanky in Ukraine's northeastern Kharkiv region, Russia's Ministry of Defence has said. Two civilians have died in Russian shelling in the city of Sumy in northeastern Ukraine, President Volodymyr Zelenskyy said, adding that a rescue operation is under way and more people could be under debris. Ukraine has hit Russia with Army Tactical Missile Systems (ATACMS), produced by the United States, twice over the last three days and Russia is preparing retaliatory measures, according to the Defence Ministry in Moscow. It said both strikes targeted Russian forces in the Kursk region, without reporting any casualties. Russia conducted its largest ever drone attack on Ukraine overnight, damaging the grid in Ternopil and cutting power to about 70 percent of the region, as well as damaging residential buildings in the Kyiv region, Ukrainian officials said.

Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI), enabling agents to learn optimal behaviors through interactions with their environments. Drawing from the foundations of trial and error, RL equips agents to make informed decisions through feedback in the form of rewards or penalties. This paper presents a comprehensive survey of RL, meticulously analyzing a wide range of algorithms, from foundational tabular methods to advanced Deep Reinforcement Learning (DRL) techniques. We categorize and evaluate these algorithms based on key criteria such as scalability, sample efficiency, and suitability. We compare the methods in the form of their strengths and weaknesses in diverse settings. Additionally, we offer practical insights into the selection and implementation of RL algorithms, addressing common challenges like convergence, stability, and the exploration-exploitation dilemma. This paper serves as a comprehensive reference for researchers and practitioners aiming to harness the full potential of RL in solving complex, real-world problems.

The common theory is that human pilot's performance degrades when responsible for an increased number of uncrewed aircraft systems (UAS). This theory was developed in the early 2010's for ground robots and not highly autonomous UAS. It has been shown that increasing autonomy can mitigate some performance impacts associated with increasing the number of UAS. Overall, the Oregon State University-Wing collaboration seeks to understand what factors negatively impact a pilot's ability to maintain responsibility and control over an assigned set of active UAS. The Phase I evaluation establishes baseline data focused on the number of UAS and the number of nests increase. This evaluation focuses on nominal operations as well as crewed aircraft encounters and adverse weather changes. The results demonstrate that the pilots were actively engaged and had very good situation awareness. Manipulation of the conditions did not result in any significant differences in overall workload. The overall results debunk the theory that increasing the number of UAS is detrimental to pilot's performance.

Interesting and efficient collective behavior observed in multi-robot or swarm systems emerges from the individual behavior of the robots. The functional space of individual robot behaviors is in turn shaped or constrained by the robot's morphology or physical design. Thus the full potential of multi-robot systems can be realized by concurrently optimizing the morphology and behavior of individual robots, informed by the environment's feedback about their collective performance, as opposed to treating morphology and behavior choices disparately or in sequence (the classical approach). This paper presents an efficient concurrent design or co-design method to explore this potential and understand how morphology choices impact collective behavior, particularly in an MRTA problem focused on a flood response scenario, where the individual behavior is designed via graph reinforcement learning. Computational efficiency in this case is attributed to a new way of near exact decomposition of the co-design problem into a series of simpler optimization and learning problems. This is achieved through i) the identification and use of the Pareto front of Talent metrics that represent morphology-dependent robot capabilities, and ii) learning the selection of Talent best trade-offs and individual robot policy that jointly maximizes the MRTA performance. Applied to a multi-unmanned aerial vehicle flood response use case, the co-design outcomes are shown to readily outperform sequential design baselines. Significant differences in morphology and learned behavior are also observed when comparing co-designed single robot vs. co-designed multi-robot systems for similar operations.

This letter presents a distributed trajectory planning method for multi-agent aerial tracking. The proposed method uses a Dynamic Buffered Voronoi Cell (DBVC) and a Dynamic Inter-Visibility Cell (DIVC) to formulate the distributed trajectory generation. Specifically, the DBVC and the DIVC are time-variant spaces that prevent mutual collisions and occlusions among agents, while enabling them to maintain suitable distances from the moving target. We combine the DBVC and the DIVC with an efficient Bernstein polynomial motion primitive-based tracking generation method, which has been refined into a less conservative approach than in our previous work. The proposed algorithm can compute each agent's trajectory within several milliseconds on an Intel i7 desktop. We validate the tracking performance in challenging scenarios, including environments with dozens of obstacles.