What is in the 28-point US plan for Ukraine? Why is Europe opposing Trump's peace plan? Is the fall of Pokrovsk inevitable? 'A corruption scandal may well end the Ukraine war' Russian drones attacked targets in Ukraine hours after the US and Kyiv announced revisions to a controversial peace plan proposed by Donald Trump. Speaking after talks in Geneva, US and Ukrainian officials agreed any deal should "fully uphold" Ukraine's sovereignty.

Is the fall of Pokrovsk inevitable? Is Trump losing patience with Putin? Here's where things stand on Monday, November 24. United States Secretary of State Marco Rubio told reporters in Geneva that "a tremendous amount of progress" was made during talks in the Swiss city on Sunday and that he was "very optimistic" that an agreement could be reached in "a very reasonable period of time, very soon". Rubio also said that specific areas still being worked on from a 28-point peace plan for Ukraine, championed by US President Donald Trump, included the role of NATO and security guarantees for Ukraine.

In multi UAV scenarios,the traditional Artificial Potential Field (APF) method often leads to redundant flight paths and frequent abrupt heading changes due to unreasonable obstacle avoidance path planning,and is highly prone to inter UAV collisions during the obstacle avoidance process.To address these issues,this study proposes a novel hybrid algorithm that combines the improved Multi-Robot Formation Obstacle Avoidance (MRF IAPF) algorithm with an enhanced APF optimized for single UAV path planning.Its core ideas are as follows:first,integrating three types of interaction forces from MRF IAPF obstacle repulsion force,inter UAV interaction force,and target attraction force;second,incorporating a refined single UAV path optimization mechanism,including collision risk assessment and an auxiliary sub goal strategy.When a UAV faces a high collision threat,temporary waypoints are generated to guide obstacle avoidance,ensuring eventual precise arrival at the actual target.Simulation results demonstrate that compared with traditional APF based formation algorithms,the proposed algorithm achieves significant improvements in path length optimization and heading stability,can effectively avoid obstacles and quickly restore the formation configuration,thus verifying its applicability and effectiveness in static environments with unknown obstacles.

In Ukraine's'kill-zone', robots are a lifeline to troops trapped on perilous eastern front The toy is delivered, a Ukrainian soldier whispers into the radio. In the dead of night, he and his partner move quickly to roll out their cargo from a van. Speed is crucial as they are within the range of deadly Russian drones. The fifth brigade's new toy is an unmanned ground vehicle (UGV), a robot that provides a lifeline for Ukrainian troops at the front in Pokrovsk and Myrnograd, a strategic hub in eastern Ukraine. Russian forces are relentlessly trying to cut off Ukraine's supply routes in the area.

Police logs contradict official reports about a November 22, 2024 incident where a UK police helicopter took emergency evasive action after unidentified objects pursued it.

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This paper introduces SPIRAL (Self-Play Incremental Racing Algorithm for Learning), a novel approach for training autonomous drones in multi-agent racing competitions. SPIRAL distinctively employs a self-play mechanism to incrementally cultivate complex racing behaviors within a challenging, dynamic environment. Through this self-play core, drones continuously compete against increasingly proficient versions of themselves, naturally escalating the difficulty of competitive interactions. This progressive learning journey guides agents from mastering fundamental flight control to executing sophisticated cooperative multi-drone racing strategies. Our method is designed for versatility, allowing integration with any state-of-the-art Deep Reinforcement Learning (DRL) algorithms within its self-play framework. Simulations demonstrate the significant advantages of SPIRAL and benchmark the performance of various DRL algorithms operating within it. Consequently, we contribute a versatile, scalable, and self-improving learning framework to the field of autonomous drone racing. SPIRAL's capacity to autonomously generate appropriate and escalating challenges through its self-play dynamic offers a promising direction for developing robust and adaptive racing strategies in multi-agent environments. This research opens new avenues for enhancing the performance and reliability of autonomous racing drones in increasingly complex and competitive scenarios.

Abstract--The proliferation of unmanned aerial vehicles (UA Vs) has enabled a wide range of mission-critical applications and is becoming a cornerstone of low-altitude networks, supporting smart cities, emergency response, and more. However, the open wireless environment, dynamic topology, and resource constraints of UA Vs expose low-altitude networks to severe Denial-of-Service (DoS) threats, undermining their reliability and security. Traditional defense approaches, which rely on fixed configurations or centralized decision-making, cannot effectively respond to the rapidly changing conditions in UA V swarm environments. T o address these challenges, we propose a novel federated multi-agent deep reinforcement learning (FMADRL)- driven moving target defense (MTD) framework for proactive DoS mitigation in low-altitude networks. Specifically, we design lightweight and coordinated MTD mechanisms, including leader switching, route mutation, and frequency hopping, to disrupt attacker efforts and enhance network resilience. The defense problem is formulated as a multi-agent partially observable Markov decision process (POMDP), capturing the uncertain nature of UA V swarms under attack. Each UA V is equipped with a policy agent that autonomously selects MTD actions based on partial observations and local experiences. By employing a policy gradient-based FMADRL algorithm, UA Vs collaboratively optimize their policies via reward-weighted aggregation, enabling distributed learning without sharing raw data and thus reducing communication overhead. Extensive simulations demonstrate that our approach significantly outperforms state-of-the-art baselines, achieving up to a 34.6% improvement in attack mitigation rate, a reduction in average recovery time of up to 94.6%, and decreases in energy consumption and defense cost by as much as 29.3% and 98.3%, respectively, under various DoS attack strategies. These results highlight the potential of intelligent, distributed defense mechanisms to protect low-altitude networks, paving the way for reliable and scalable low-altitude economy. HE rapid development of unmanned aerial vehicle (UA V) technology [1] has enabled a wide range of applications, including environmental monitoring, disaster response, precision agriculture, logistics, aerial photography, and intelligent surveillance [2]. Y uyang Zhou, Guang Cheng, Kang Du, Zihan Chen, Tian Qin, and Y uyu Zhao are with the School of Cyber Science and Engineering, Southeast University, Purple Mountain Laboratories, and Jiangsu Province Engineering Research Center of Security for Ubiquitous Network, Nanjing 211189, China. Guang Cheng is the corresponding author. It is expected to play an increasingly important role in smart cities, emergency management, and next-generation communication infrastructures, forming the backbone of low-altitude networks. Nevertheless, the widespread adoption of UA V swarms also brings new security challenges [7], [8] to low-altitude networks.

A drone explodes during a Russian drone strike in Kyiv on Nov. 14. LONDON - Explosive weapons killed or injured children at record levels last year, as wars increasingly move into urban areas, Save the Children said in a report published Thursday. Nearly 12,000 children were killed or injured in conflict last year worldwide, said the U.K.-based charity, citing U.N. figures. This is the highest number since records began in 2006, and is 42% higher than the 2020 total. Previously, children in war zones were more likely to die from malnutrition, disease or failing health systems. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Hu et al. [1] suggested using uncrewed vehicles in civil infrastructure asset management. Similarly, Bechtsis et al. [2] propose using uncrewed ground vehicles (UGVs) in precision farming. One of the emerging areas where such vehicles can prove helpful is assisting in postdisaster evacuation. Natural disasters, including earthquakes, tsunamis, hurricanes, and volcanic eruptions, can severely damage the urban infrastructure, leading to considerable losses. Following such events, providing timely relief and disseminating crucial information, such as safe evacuation routes, becomes essential for affected individuals' safe and organized movement. Recently, among the advanced technologies integrated into disaster response missions include uncrewed aerial vehicles (UAVs) that have been crucial in assessing the state of critical infrastructure essential services, including telecommunications, transportation, and buildings, to facilitate efficient disaster response and evacuation [3]. UAV systems have proven to be increasingly valuable in disaster relief and emergency response (DRER) efforts by enhancing the capabilities of the first responders, offering advanced predictive insights, and enabling early warning systems [4]. UAVs have assisted in diverse tasks, including remote sensing, search and rescue, forest fire detection, survey and surveillance [5].