A Russian drone strike has killed five people in the northern town of Pryluky in Chernihiv region, including three members of one family, Ukrainian authorities said. Interior Minister Ihor Klymenko said on Thursday morning that a local first responder's wife, daughter and one-year-old grandson were killed in the attack. Regional Governor Viacheslav Chaus said the family was among five people killed when Russia launched six drones to attack the town overnight. Six others were admitted to hospital, he said. Ukrainian President Volodymyr Zelenskyy slammed the attacks and accused Moscow of "constantly trying to buy time for itself to continue killing. "When it does not feel strong enough condemnation and pressure from the world – it kills again," he wrote on X. Zelenskyy said Russia launched 103 drones and one ballistic missile overnight targeting the Donetsk, Kharkiv, Odesa, Sumy, Chernihiv, Dnipro and Kherson regions. "This is yet another reason to impose maximum sanctions and put pressure together.

Russian President Vladimir Putin told U.S. President Donald Trump on Wednesday that he would have to respond to Ukrainian drone attacks on Russia's nuclear-capable bomber fleet, while also describing peace talks with Ukraine as "useful." The war in Ukraine is intensifying after nearly four months of cajoling and threats to both Moscow and Kyiv from Trump, who says he wants peace after more than three years of the deadliest conflict in Europe since World War II. After Ukraine bombed bridges and attacked Russia's fleet of bombers deep in Siberia and Russia's far north, Putin on Wednesday said he did not think Ukraine's leaders wanted peace.

With the widespread application of Unmanned Aerial Vehicles (UAVs) in domains like military reconnaissance, emergency rescue, and logistics delivery, efficiently planning the shortest flight path has become a critical challenge. Traditional heuristic-based methods often suffer from the inability to escape from local optima, which limits their effectiveness in finding the shortest path. To address these issues, a novel Improved Grey Wolf Optimizer (IGWO) is presented in this study. The proposed IGWO incorporates an Advanced Cooperative Predation (ACP) and a Lens Opposition-based Learning Strategy (LOBL) in order to improve the optimization capability of the method. Simulation results show that IGWO ranks first in optimization performance on benchmark functions F1-F5, F7, and F9-F12, outperforming all other compared algorithms. Subsequently, IGWO is applied to UAV shortest path planning in various obstacle-laden environments. Simulation results show that the paths planned by IGWO are, on average, shorter than those planned by GWO, PSO, and WOA by 1.70m, 1.68m, and 2.00m, respectively, across four different maps.

New UAV technologies and the NewSpace era are transforming Earth Observation missions and data acquisition. Numerous small platforms generate large data volume, straining bandwidth and requiring onboard decision-making to transmit high-quality information in time. While Machine Learning allows real-time autonomous processing, FPGAs balance performance with adaptability to mission-specific requirements, enabling onboard deployment. This review systematically analyzes 66 experiments deploying ML models on FPGAs for Remote Sensing applications. We introduce two distinct taxonomies to capture both efficient model architectures and FPGA implementation strategies. For transparency and reproducibility, we follow PRISMA 2020 guidelines and share all data and code at https://github.com/CedricLeon/Survey_RS-ML-FPGA.

As the number of Unmanned Aerial Vehicles (UAVs) operating in low-altitude airspace continues to increase, non-cooperative targets pose growing challenges to low-altitude operations. To address this issue, this paper proposes a multi-UAV-tethered netted system as a non-lethal solution for capturing non-cooperative targets. To validate the proposed system, we develop mySim, a multibody dynamics-based UAV simulation environment that integrates high-precision physics modeling, vision-based motion tracking, and reinforcement learning-driven control strategies. In mySim, the spring-damper model is employed to simulate the dynamic behavior of the tethered net, while the dynamics of the entire system is modeled using multibody dynamics (MBD) to achieve accurate representations of system interactions. The motion of the UAVs and the target are estimated using VINS-MONO and DETR, and the system autonomously executes the capture strategy through MAPPO. Simulation results demonstrate that mySim accurately simulates dynamics and control of the system, successfully enabling the multi-UAV-tethered netted system to capture both non-propelled and maneuvering non-cooperative targets. By providing a high-precision simulation platform that integrates dynamics modeling with perception and learning-based control, mySim enables efficient testing and optimization of UAV-based control policies before real-world deployment. This approach offers significant advantages for simulating complex UAVs coordination tasks and has the potential to be applied to the design of other UAV-based systems.

The next time you order a takeaway, it might be flown directly to your door. Today, Deliveroo has launched its first drone delivery service for customers in Ireland. Drones travelling at speeds of up to 50 miles per hour (80 kph) will carry food from restaurants to customers in as little as three minutes. Upon arrival, the drone will hover above the customer's home and gently lower the food to the ground on a tether before returning to the delivery hub. Launching in Blanchardstown, on the outskirts of Dublin, the trial will cover a 1.8-mile (3km) radius, reaching up to 150,000 people.

New satellite images and drone footage show serious damage inflicted on aircraft at several Russian airbases during Ukraine's surprise drone strike on Sunday. The images of two Russian airbases in north-western and central Russia, taken on Wednesday morning, show 12 aircraft damaged or destroyed. Meanwhile, drone footage, released by the Security Service of Ukraine (SBU) on Wednesday, showed attacks on these two bases as well as two more targeted elsewhere. Ukraine claims that it targeted 41 strategic bombers in the operation, adding that "at least" 13 were destroyed. Security officials say the shock incursion took 18 months to plan and saw many drones smuggled into Russia.

Russian President Vladimir Putin has told Donald Trump in a telephone conversation that Moscow would have to respond to the recent Ukrainian drone attacks, the US president said. Trump said on Wednesday that the two men "discussed the attack on Russia's docked airplanes, by Ukraine, and also various other attacks that have been taking place by both sides." Putin "did say, and very strongly, that he will have to respond to the recent attack on the airfields", Trump said in a social media post. Al Jazeera's Kimberly Halkett said that Trump described his 85-minute phone call with Putin as "a good conversation but not one that would lead to immediate peace". "You have to remember that Donald Trump, when he came into office, was very confident that he could end this war on day one, but here we are now in June and the fact is … this is far from resolved," she said from the White House.

As UAV popularity soars, so does the mission planning associated with it. The classical approaches suffer from the triple problems of decoupled of task assignment and path planning, poor real-time performance and limited adaptability. Aiming at these challenges, this paper proposes a dynamic real-time multi-UAV collaborative mission planning algorithm based on Dubins paths under a distributed formation structure. Dubins path with multiple advantages bridges the gap between task assignment and path planning, leading to a coupled solution for mission planning. Then, a series of acceleration techniques, task clustering preprocessing, highly efficient distance cost functions, low-complexity and less iterative task allocation strategies, are employed to guarantee the real-time performance of the algorithms. To cope with different emergencies and their simultaneous extremes, real-time planning of emerging tasks and mission replanning due to the reduction of available UAVs are appropriately handled. Finally, the developed algorithm is comprehensively exemplified and studied through simulations, highlighting that the proposed method only sacrifices 9.57% of the path length, while achieving a speed improvement of 4-5 orders of magnitude over the simulated annealing method, with a single mission planning of about 0.0003s.

A public safety Unmanned Aerial Vehicle (UAV) enhances situational awareness in emergency response. Its agility and ability to optimize mobility and establish Line-of-Sight (LoS) communication make it increasingly vital for managing emergencies such as disaster response, search and rescue, and wildfire monitoring. While Deep Reinforcement Learning (DRL) has been applied to optimize UAV navigation and control, its high training complexity, low sample efficiency, and simulation-to-reality gap limit its practicality in public safety. Recent advances in Large Language Models (LLMs) offer a compelling alternative. With strong reasoning and generalization capabilities, LLMs can adapt to new tasks through In-Context Learning (ICL), which enables task adaptation via natural language prompts and example-based guidance, without retraining. Deploying LLMs at the network edge, rather than in the cloud, further reduces latency and preserves data privacy, thereby making them suitable for real-time, mission-critical public safety UAVs. This paper proposes the integration of LLM-enabled ICL with public safety UAV to address the key functions, such as path planning and velocity control, in the context of emergency response. We present a case study on data collection scheduling where the LLM-enabled ICL framework can significantly reduce packet loss compared to conventional approaches, while also mitigating potential jailbreaking vulnerabilities. Finally, we discuss LLM optimizers and specify future research directions. The ICL framework enables adaptive, context-aware decision-making for public safety UAV, thus offering a lightweight and efficient solution for enhancing UAV autonomy and responsiveness in emergencies.