Drones
Russian strikes on Kyiv kill three as Ukraine envoys travel to US for talks
What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? Russian drone and missile strikes in and around Ukraine's capital, Kyiv, have killed at least three people and wounded dozens of others, officials said, as Ukrainian representatives travelled to the United States for talks on a renewed push to end the war. "Russia shot dozens of cruise and ballistic missiles and over 500 drones at ordinary homes, the energy grid, and critical infrastructure," Foreign Minister Andrii Sybiha wrote on X on Saturday.
Israeli attack kills two children in southern Gaza despite ceasefire
Can Pakistan join the Gaza stabilisation force? Two Palestinian children have been killed in an Israeli drone attack on the town of Bani Suheila, east of Khan Younis in the southern Gaza Strip, according to medics. Witnesses told Al Jazeera that drones dropped a bomb on a group of civilians near al-Farabi School on Saturday morning, killing two brothers, Juma and Fadi Tamer Abu Assi. The area targeted by the attack lies beyond the so-called Yellow Line marking the Israeli forces' redeployment boundary agreed under the ceasefire agreement, which took effect on October 10, sources said. Earlier on Saturday, the Israeli military launched ground, naval, and air attacks on several parts of Gaza.
Russian drone and missile strikes hit residential buildings in several Kyiv districts
A Russian drone and missile attack on the Ukrainian capital Kyiv has killed at least one person and injured seven others, city officials say. Early on Saturday morning residential buildings in several districts were hit and loud explosions could be heard across the city. Kyiv's mayor Vitaly Klitschko said a 13-year-old child was among the injured and four people had been taken to hospital. Earlier this week a similar attack on Kyiv killed seven people, Ukrainian officials said. The latest bombardment came as Ukrainian negotiators were preparing for talks with US officials this weekend on an amended US peace plan.
Russia-Ukraine war: List of key events, day 1,374
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' Here's where things stand on Saturday, November 29. Russian drones struck six locations in Kyiv's city centre and eastern suburbs early on Saturday, injuring four people, as apartment buildings and other dwellings were hit, said the head of Kyiv's military administration, Tymur Tkachenko.
Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness
Gong, Yanwei, Fan, Junchao, Zhang, Ruichen, Niyato, Dusit, Yao, Yingying, Chang, Xiaolin
The rapid growth of the low-altitude economy has driven the widespread adoption of unmanned aerial vehicles (UAVs). This growing deployment presents new challenges for UAV trajectory planning in complex urban environments. However, existing studies often overlook key factors, such as urban airspace constraints and economic efficiency, which are essential in low-altitude economy contexts. Deep reinforcement learning (DRL) is regarded as a promising solution to these issues, while its practical adoption remains limited by low learning efficiency. To overcome this limitation, we propose a novel UAV trajectory planning framework that combines DRL with large language model (LLM) reasoning to enable safe, compliant, and economically viable path planning. Experimental results demonstrate that our method significantly outperforms existing baselines across multiple metrics, including data collection rate, collision avoidance, successful landing, regulatory compliance, and energy efficiency. These results validate the effectiveness of our approach in addressing UAV trajectory planning key challenges under constraints of the low-altitude economy networking.
Russia says talks to end Ukraine war 'serious' but rules out concessions
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' Russia says talks to end Ukraine war'serious' but rules out concessions Russia says the United States-brokered talks to end the war with Ukraine are "serious", but its officials caution that an agreement is a long way off and Moscow would offer no major concessions to Kyiv. Kremlin spokesman Dmitry Peskov said in televised comments on Wednesday that the negotiations were ongoing and "the process is serious."
LIVE: Ukraine and Russia launch drone strikes during US-led peace talks
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' At least 18 Ukrainians have been wounded in Russian drone attacks on the Zaporizhzhia area as tens of thousands of invading troops continue their advance on the southeastern region. Ukraine says it supports the "essence" of a United States plan to end its war with Russia, as US President Donald Trump says "progress" is being made and dispatches special envoy Steve Witkoff to Russia for talks with President Vladimir Putin.
Maritime Small Object Detection from UAVs using Deep Learning with Altitude-Aware Dynamic Tiling
Unmanned Aerial Vehicles (UAVs) are crucial in Search and Rescue (SAR) missions due to their ability to monitor vast maritime areas. However, small objects often remain difficult to detect from high altitudes due to low object-to-background pixel ratios. We propose an altitude-aware dynamic tiling method that scales and adaptively subdivides the image into tiles for enhanced small object detection. By integrating altitude-dependent scaling with an adaptive tiling factor, we reduce unnecessary computation while maintaining detection performance. Tested on the SeaDronesSee dataset [1] with YOLOv5 [2] and Slicing Aided Hyper Inference (SAHI) framework [3], our approach improves Mean Average Precision (mAP) for small objects by 38% compared to a baseline and achieves more than double the inference speed compared to static tiling. This approach enables more efficient and accurate UAV-based SAR operations under diverse conditions.
Flow-Based Path Planning for Multiple Homogenous UAVs for Outdoor Formation-Flying
Ibrahim, Mahmud Suhaimi, Rahman, Shantanu, Hasan, Muhammad Samin, Ahmad, Minhaj Uddin, Abrar, Abdullah
Collision-free path planning is the most crucial component in multi-UAV formation-flying (MFF). We use unlabeled homogenous quadcopters (UAVs) to demonstrate the use of a flow network to create complete (inter-UAV) collision-free paths. This procedure has three main parts: 1) Creating a flow network graph from physical GPS coordinates, 2) Finding a path of minimum cost (least distance) using any graph-based path-finding algorithm, and 3) Implementing the Ford-Fulkerson Method to find the paths with the maximum flow (no collision). Simulations of up to 64 UAVs were conducted for various formations, followed by a practical experiment with 3 quadcopters for testing physical plausibility and feasibility. The results of these tests show the efficacy of this method's ability to produce safe, collision-free paths.
Count Every Rotation and Every Rotation Counts: Exploring Drone Dynamics via Propeller Sensing
Chen, Xuecheng, Xu, Jingao, Ding, Wenhua, Wang, Haoyang, Luo, Xinyu, Duan, Ruiyang, Chen, Jialong, Wang, Xueqian, Liu, Yunhao, Chen, Xinlei
As drone-based applications proliferate, paramount contactless sensing of airborne drones from the ground becomes indispensable. This work demonstrates concentrating on propeller rotational speed will substantially improve drone sensing performance and proposes an event-camera-based solution, \sysname. \sysname features two components: \textit{Count Every Rotation} achieves accurate, real-time propeller speed estimation by mitigating ultra-high sensitivity of event cameras to environmental noise. \textit{Every Rotation Counts} leverages these speeds to infer both internal and external drone dynamics. Extensive evaluations in real-world drone delivery scenarios show that \sysname achieves a sensing latency of 3$ms$ and a rotational speed estimation error of merely 0.23\%. Additionally, \sysname infers drone flight commands with 96.5\% precision and improves drone tracking accuracy by over 22\% when combined with other sensing modalities. \textit{ Demo: {\color{blue}https://eventpro25.github.io/EventPro/.} }