Kyiv, Ukraine – On Sunday, a top Russian security official in Moscow lauded dozens of servicemen who used an abandoned natural gas pipeline as a tunnel to infiltrate a Ukraine-occupied area in the western Russian region of Kursk. "The lid of a boiling cauldron is almost closed! Good job!" Dmitry Medvedev, who served as president and prime minister before becoming deputy head of Russia's Security Council, wrote on Telegram. But a Ukrainian serviceman deployed in Kursk offered a starkly different version of how the Russians barely got out of the pipeline on Saturday – only to be reportedly killed en masse. "Some suffocated right [in the pipeline], some turned back. About a hundred came out in our rear, split into two groups and were almost immediately ambushed by our special forces. And [also killed by] a massive squall of artillery," Evhen Sazonov wrote on Telegram.

Ukraine has targeted Moscow with a large overnight drone attack as Russia's Ministry of Defence says it has shot down 337 unmanned aircraft across the country. "The Defence Ministry's air defence continues to repel a massive attack by enemy drones on Moscow," Moscow Mayor Sergei Sobyanin said on Telegram early on Tuesday. Three people are reported to have been killed and three wounded in the southern suburbs of Moscow, according to Governor Andrei Vorobyov. He added that drone debris damaged at least seven units in a residential building in another suburb southeast of the city. The attack on the Russian capital, hundreds of kilometres from the Ukrainian border, comes before a meeting between United States and Ukrainian officials in Saudi Arabia.

Atlantic Council senior fellow Ariel Cohen and Heritage Foundation senior fellow Charles'Cully' Stimson discuss the state of the war amid White House tensions with President Zelenskyy. Ukraine launched its largest-ever drone attack on Moscow on Tuesday as a senior delegation met with Secretary of State Marco Rubio and National Security Advisor Mike Waltz in Saudi Arabia for talks about ending the war with Russia. A total of 337 drones were shot down Tuesday over Russia, including 91 in the Moscow area and 126 in the Kursk region bordering Ukraine, Reuters reported, citing Russia's defense ministry. Moscow-based meat producer Miratorg said two of its employees were killed by falling debris, while 18 other people – including three children – were injured after residential buildings were struck, officials told Reuters. Images taken in Russia showed damage to cars and apartment buildings in the wake of the attack, which temporarily shut down Moscow's four airports.

The use of drones by several African countries in their fight against armed groups is causing significant harm to civilians, according to a new report. More than 943 civilians have been killed in at least 50 incidents across six African countries from November 2021 to November 2024, according to the report by Drone Wars UK. The report, titled Death on Delivery, reveals that strikes regularly fail to distinguish between civilians and combatants in their operations. Experts told Al Jazeera that the death toll is likely only the tip of the iceberg because many countries run secretive drone campaigns. As drones rapidly become the weapon of choice for governments across the continent, what are the consequences for civilians in conflict zones?

One civilian was killed and three more were reportedly injured in one of the biggest Ukrainian drone attacks on Moscow in months. Moscow's Mayor Sergei Sobyanin said Russian air defence units destroyed at least 69 drones flying towards Moscow in a "massive" attack that later reports said involved more than 90 drones. Four airports in the Moscow region and the Domodedovo train network were forced to suspend services due to the attack. Several apartments were also damaged while Russia's TASS news agency reported a large fire in a car park near the Russian capital. Pro-Russian war bloggers said Kremlin forces have advanced further into the country's Kursk region as part of a major encirclement operation to push out thousands of Ukrainian soldiers holding territory inside Russia.

Ukraine has launched a "massive" early morning drone attack against the Russian capital that killed at least one person, injured several others and saw the shutdown of airports and damaged residential buildings, Moscow officials and aviation authorities said. The drone raid, the largest against Moscow in months, comes as Ukraine is poised to present the United States with a plan for a partial ceasefire with Russia during talks on Tuesday in Saudi Arabia. Andrei Vorobyov, governor of the Moscow region, said that one person was killed and three more wounded as a result of the raid, which began at 4am local time (01:00 GMT). The wave of attack drones damaged seven apartments in a residential building in the Moscow region's Ramenskoye district, Vorobyov said. Russia's Ministry of Defence said that air defences destroyed a total of 337 Ukrainian drones overnight, with 91 of them over the Moscow region.

At least one person has been killed and three injured in a "massive" overnight drone attack on Moscow and the capital region, local officials say. Regional Governor Andrei Vorobyev says the casualties were in the towns of Vidnoye and Domodedovo, just outside the capital. Seven apartments in a residential building were damaged. Moscow Mayor Sergei Sobyanin says 73 drones heading towards the city were shot down. The roof of one building was damaged by drone wreckage.

Absolute Visual Localization (AVL) enables Unmanned Aerial Vehicle (UAV) to determine its position in GNSS-denied environments by establishing geometric relationships between UAV images and geo-tagged reference maps. While many previous works have achieved AVL with image retrieval and matching techniques, research in low-altitude multi-view scenarios still remains limited. Low-altitude Multi-view condition presents greater challenges due to extreme viewpoint changes. To explore the best UAV AVL approach in such condition, we proposed this benchmark. Firstly, a large-scale Low-altitude Multi-view dataset called AnyVisLoc was constructed. This dataset includes 18,000 images captured at multiple scenes and altitudes, along with 2.5D reference maps containing aerial photogrammetry maps and historical satellite maps. Secondly, a unified framework was proposed to integrate the state-of-the-art AVL approaches and comprehensively test their performance. The best combined method was chosen as the baseline and the key factors that influencing localization accuracy are thoroughly analyzed based on it. This baseline achieved a 74.1% localization accuracy within 5m under Low-altitude, Multi-view conditions. In addition, a novel retrieval metric called PDM@K was introduced to better align with the characteristics of the UAV AVL task. Overall, this benchmark revealed the challenges of Low-altitude, Multi-view UAV AVL and provided valuable guidance for future research. The dataset and codes are available at https://github.com/UAV-AVL/Benchmark

Neuromorphic vision, inspired by biological neural systems, has recently gained significant attention for its potential in enhancing robotic autonomy. This paper presents a systematic exploration of a proposed Neuromorphic Navigation framework that uses event-based neuromorphic vision to enable efficient, real-time navigation in robotic systems. We discuss the core concepts of neuromorphic vision and navigation, highlighting their impact on improving robotic perception and decision-making. The proposed reconfigurable Neuromorphic Navigation framework adapts to the specific needs of both ground robots (Turtlebot) and aerial robots (Bebop2 quadrotor), addressing the task-specific design requirements (algorithms) for optimal performance across the autonomous navigation stack -- Perception, Planning, and Control. We demonstrate the versatility and the effectiveness of the framework through two case studies: a Turtlebot performing local replanning for real-time navigation and a Bebop2 quadrotor navigating through moving gates. Our work provides a scalable approach to task-specific, real-time robot autonomy leveraging neuromorphic systems, paving the way for energy-efficient autonomous navigation.

Autonomous navigation in open-world outdoor environments faces challenges in integrating dynamic conditions, long-distance spatial reasoning, and semantic understanding. Traditional methods struggle to balance local planning, global planning, and semantic task execution, while existing large language models (LLMs) enhance semantic comprehension but lack spatial reasoning capabilities. Although diffusion models excel in local optimization, they fall short in large-scale long-distance navigation. To address these gaps, this paper proposes KiteRunner, a language-driven cooperative local-global navigation strategy that combines UAV orthophoto-based global planning with diffusion model-driven local path generation for long-distance navigation in open-world scenarios. Our method innovatively leverages real-time UAV orthophotography to construct a global probability map, providing traversability guidance for the local planner, while integrating large models like CLIP and GPT to interpret natural language instructions. Experiments demonstrate that KiteRunner achieves 5.6% and 12.8% improvements in path efficiency over state-of-the-art methods in structured and unstructured environments, respectively, with significant reductions in human interventions and execution time.