Russian forces attacked Ukraine's capital, Kyiv, early on Monday, wounding five people and damaging a residential building, according to the head of the city's military administration, Tymur Tkachenko. A Russian drone hit a Ukrainian bus carrying 39 evacuees in the eastern Sumy region, near Ukraine's border with Russia, on Sunday, killing three people and wounding 19 others, according to the regional governor. Two others were killed in a landmine explosion in Sumy's Esman community on Saturday, while two more were killed in Russian attacks on the front-line Donetsk region, according to officials, taking the death toll from attacks across Ukraine on that day to at least six. Ukraine's forces also launched drone attacks at Russia on Sunday, with the governor of the Leningrad region reporting that at least 10 Ukrainian unmanned aircraft were downed over the areas surrounding the city of St Petersburg. Falling debris injured a woman, Governor Alexander Drozdenko said.

Prior work has demonstrated that incorporating well-known quantum tunnelling (QT) probability into neural network models effectively captures important nuances of human perception, particularly in the recognition of ambiguous objects and sentiment analysis. In this paper, we employ novel QT-based neural networks and assess their effectiveness in distinguishing customised CIFAR-format images of military and civilian vehicles, as well as sentiment, using a proprietary military-specific vocabulary. We suggest that QT-based models can enhance multimodal AI applications in battlefield scenarios, particularly within human-operated drone warfare contexts, imbuing AI with certain traits of human reasoning.

The Nuclear Regulation Authority (NRA) on Sunday corrected its announcement that three drones were found flying over Kyushu Electric Power's Genkai nuclear plant in Saga Prefecture around 9 p.m. on Saturday. The corrected announcement stated that three lights that appeared to be drones were spotted at the time. It cannot be said with certainty that they were drones, NRA officials said. According to the NRA, no drones or flying objects have been found on or around the plant's grounds following the incident. There have also been no abnormalities at the plant, such as a leak of nuclear materials.

Falling debris from destroyed Ukrainian drones disrupted railway power supply and train operations in part of the Volgograd region, the administration of the region in Russia's south said on Sunday. There were no injuries as a result of the attacks, the administration said on Telegram, quoting Governor Andrei Bocharov. Russia downed 99 drones overnight over 12 Russian regions, the Crimean Peninsula and the Black Sea, the Russian Ministry of Defence said. Meanwhile, Russia launched a barrage of drones and missiles in an overnight attack that killed three people in Ukraine's Dnipro and the nearby region on Saturday, Ukrainian officials said. Ukraine's air force said it intercepted 183 drones and 17 missiles, but hits from 10 missiles and 25 drones were recorded in nine locations.

Zelensky said Russian strikes also targeted Kharkiv and Sumy, and added on X that the strikes "cannot be left without response, and Ukrainian long-range drones ensure one". He added that Russian military sites and airports "must see that Russia's own war is now hitting them back with real consequences" and said Ukraine's drone attacks were "some of the arguments that will surely bring peace closer". Six people were injured in the strikes on Dnipropetrovsk, the region's head said. A residential block and industrial areas were hit in the city of Dnipro while a shopping centre was hit in the wider district. Also on Saturday, Russia's ministry of defence claimed its army had captured two villages, Zelenyi Hai in the Donetsk region and Maliivka in the Dnipropetrovsk region.

A solar-powered surveillance drone with a wingspan larger than a Boeing 747 jumbo jet could fly for weeks or months at a time, according to its operator, while watching for drug-smuggling vessels, pirates or naval warships. It has been performing test flights off the US Gulf Coast this month. The Skydweller drone, operated by US-Spanish firm Skydweller Aero, has a wingspan of 72 metres – exceeding the width of most commercial passenger jets. But it weighs only about 2500 kilograms – as much as a Ford F-150 truck. It is based on the Solar Impulse 2 aircraft, which performed the first solar-powered flight around the world in 2016. Skydweller Aero purchased and converted the pioneering aircraft with the goal of building a fleet of similar solar-powered, carbon-fibre drones capable of "perpetual flight" at altitudes exceeding 13 kilometres in daytime hours.

In this paper, we present a subsystem, using Unmanned Aerial Vehicles (UAV), for search and rescue missions, focusing on people detection, face recognition and tracking of identified individuals. The proposed solution integrates a UAV with ROS2 framework, that utilizes multiple convolutional neural networks (CNN) for search missions. System identification and PD controller deployment are performed for autonomous UAV navigation. The ROS2 environment utilizes the YOLOv11 and YOLOv11-pose CNNs for tracking purposes, and the dlib library CNN for face recognition. The system detects a specific individual, performs face recognition and starts tracking. If the individual is not yet known, the UAV operator can manually locate the person, save their facial image and immediately initiate the tracking process. The tracking process relies on specific keypoints identified on the human body using the YOLOv11-pose CNN model. These keypoints are used to track a specific individual and maintain a safe distance. To enhance accurate tracking, system identification is performed, based on measurement data from the UAVs IMU. The identified system parameters are used to design PD controllers that utilize YOLOv11-pose to estimate the distance between the UAVs camera and the identified individual. The initial experiments, conducted on 14 known individuals, demonstrated that the proposed subsystem can be successfully used in real time. The next step involves implementing the system on a large experimental UAV for field use and integrating autonomous navigation with GPS-guided control for rescue operations planning.

-- Multi-UA V Coverage Path Planning (mCPP) algorithms in popular commercial software typically treat a Region of Interest (RoI) only as a 2D plane, ignoring important 3D structure characteristics. This leads to incomplete 3D reconstructions, especially around occluded or vertical surfaces. In this paper, we propose a modular algorithm that can extend commercial two-dimensional path planners to facilitate terrain-aware planning by adjusting altitude and camera orientations. T o demonstrate it, we extend the well-known DARP (Divide Areas for Optimal Multi-Robot Coverage Path Planning) algorithm and produce DARP-3D. Compared to baseline, our approach consistently captures improved 3D reconstructions, particularly in areas with significant vertical features. An open-source implementation of the algorithm is available here: https://github.com/konskara/T

Path smoothness is often overlooked in path imitation learning from expert demonstrations. In this paper, we introduce a novel learning method, termed deep angular A* (DAA*), by incorporating the proposed path angular freedom (PAF) into A* to improve path similarity through adaptive path smoothness. The PAF aims to explore the effect of move angles on path node expansion by finding the trade-off between their minimum and maximum values, allowing for high adaptiveness for imitation learning. DAA* improves path optimality by closely aligning with the reference path through joint optimization of path shortening and smoothing, which correspond to heuristic distance and PAF, respectively. Throughout comprehensive evaluations on 7 datasets, including 4 maze datasets, 2 video-game datasets, and a real-world drone-view dataset containing 2 scenarios, we demonstrate remarkable improvements of our DAA* over neural A* in path similarity between the predicted and reference paths with a shorter path length when the shortest path is plausible, improving by 9.0% SPR, 6.9% ASIM, and 3.9% PSIM. Furthermore, when jointly learning pathfinding with both path loss and path probability map loss, DAA* significantly outperforms the state-of-the-art TransPath by 6.3% SPR, 6.0% PSIM, and 3.7% ASIM. We also discuss the minor trade-off between path optimality and search efficiency where applicable. Our code and model weights are available at https://github.com/zwxu064/DAAStar.git.

Retired Air Force Gen. Charles Wald joins'Fox News Live' to weigh in on Russia's increased attacks on Ukraine despite President Donald Trump's ultimatum to Vladimir Putin. Russia and Ukraine took aim at corresponding Black Sea resort cities early Thursday morning, just hours after ceasefire talks in Turkey once again failed to deliver results. The major Russian resort city of Sochi was rocked by a Ukrainian drone strike that began around 1 a.m. and lasted until 3 a.m., where one person was reportedly killed and another injured, according to Ukrainian media outlet the Kyiv Independent, though the Ukrainian military has not commented on the incident. An oil depot in the Krasnodar Krai region where Sochi is located was also struck, though the extent of the damage remains unclear. Russia's President Vladimir Putin chairs a meeting via a video conference at the Kremlin in Moscow on July 23, 2025.