Ukraine has said it used sea drones to attack and destroy a Russian warship in the Black Sea near the Russian-annexed Crimean peninsula. The military intelligence agency, known by its Ukrainian acronym GUR, published a video on Thursday that it said depicted a naval drone attack on the missile-armed corvette Ivanovets the night before. The grainy footage, running about 2 and a half minutes and accompanied by a dramatic soundtrack, showed a number of explosions, and the ship eventually listing to one side. It ended with the vessel sinking stern-first into the sea. "As a result of a number of direct hits to the hull, the Russian ship suffered damage incompatible with further movement," the intelligence agency said in a statement accompanying the video, apparently made up of live feeds from the drones.

Oleksandr Prokudin, the governor of the southern Ukrainian region of Kherson, said two French volunteer aid workers were killed after a Russian drone attack on the town of Beryslav. Four people were injured, three of them foreigners. One person was killed and two injured in Russian shelling and rocket attacks on villages in the eastern Donetsk region, the Ukrainian presidential office said. Ukraine said four people were injured in a Russian missile attack on a medical facility in the eastern Kharkiv region, near the front line town of Kupiansk. Ukraine's military intelligence agency GUR, said it attacked and sank the Russian corvette Ivanovets in the Black Sea using undersea drones.

Abstract-- Unmanned Aerial Vehicles (UAVs) are emerging as very important tools in search and rescue (SAR) missions at sea, enabling swift and efficient deployment for locating individuals or vessels in distress. The successful execution of these critical missions heavily relies on effective path planning algorithms that navigate UAVs through complex maritime environments while considering dynamic factors such as water currents and wind flow. Furthermore, they need to account for the uncertainty in search target locations. However, existing path planning methods often fail to address the inherent uncertainty associated with the precise location of search targets and the uncertainty of oceanic forces. In this paper, we develop a framework to develop and investigate trajectory planning algorithms for maritime SAR scenarios employing UAVs. We adopt it to compare multiple planning strategies, some of them used in practical applications by the United States Coast Guard. Furthermore, we propose a novel planner that aims at bridging the gap between computation heavy, precise algorithms and lightweight strategies applicable to real-world scenarios.

Multimodal UAVs (Unmanned Aerial Vehicles) are rarely capable of more than two modalities, i.e., flying and walking or flying and perching. However, being able to fly, perch, and walk could further improve their usefulness by expanding their operating envelope. For instance, an aerial robot could fly a long distance, perch in a high place to survey the surroundings, then walk to avoid obstacles that could potentially inhibit flight. Birds are capable of these three tasks, and so offer a practical example of how a robot might be developed to do the same. In this paper, we present a specialized avian-inspired claw design to enable UAVs to perch passively or walk. The key innovation is the combination of a Hoberman linkage leg with Fin Ray claw that uses the weight of the UAV to wrap the claw around a perch, or hyperextend it in the opposite direction to form a curved-up shape for stable terrestrial locomotion. Because the design uses the weight of the vehicle, the underactuated design is lightweight and low power. With the inclusion of talons, the 45g claws are capable of holding a 700g UAV to an almost 20-degree angle on a perch. In scenarios where cluttered environments impede flight and long mission times are required, such a combination of flying, perching, and walking is critical.

During a call with the parents of fallen service member Spc. Kennedy Ladon Sanders, Biden claimed he "lost" his son, Beau Biden, to the war in Iraq. President Biden repeated a dubious claim about the death of his son, Beau Biden, during a call with the parents of a U.S. service member who was recently killed in an attack on a base in Jordan near the border with Syria. While speaking on Tuesday to the parents of 24-year-old Specialist Kennedy Ladon Sanders, who lost her life in an Iran-backed drone strike this month in northeast Jordan that killed three service members total and injured 25 others, Biden said he lost his son to the war in Iraq. During the call, which was first shared by the Atlanta Journal Constitution, Biden told Shawn Sanders and Oneida Oliver-Sanders that their daughter was being posthumously promoted to sergeant.

'Special Report' all-star panelists discuss the Biden admin's foreign policy and U.S. preparations for a response to the deadly Jordan drone attack. A United Kingdom citizen was sentenced to 18 months in prison after pleading guilty to conspiring to procure a high-powered microwave system and counter-drone system from the United States to Iran, the U.S. Attorney's Office announced Thursday. U.S. Attorney Matthew Graves said Saber Fakih, 48, conspired with Bader Fakih, 43, of Canada, Altaf Faquih, 72, of the United Arab Emirates, and Alireza Taghavi, 48, of Iran, to export and attempt to export an industrial microwave system (IMS) and counter-drone system to Iran. "The potential military uses of the IMS could include high-power microwave-based directed-energy weapon systems. The counter-drone system, which has both commercial and military uses, can be used to stop, identify, redirect, land or take total control of a target unmanned aerial vehicle," the attorney's office said.

The United States military has carried out new strikes against 10 drones belonging to the Iran-aligned Houthi rebels in Yemen as well as a ground control centre. On Thursday, US forces targeted a "Houthi UAV ground control station and 10 Houthi one-way UAVs" that "presented an imminent threat to merchant vessels and the US Navy ships in the region", the US military's Central Command (CENTCOM) said in a statement referring to unmanned aerial vehicles. "This action will protect freedom of navigation and make international waters safer and more secure for US Navy vessels and merchant vessels," it added. The group said on Wednesday that all US and British warships participating in "aggression" against Yemen are targets, heightening concerns over the escalating tensions in the region as well as the increased disruption to world trade. CENTCOM said earlier that the USS Carney had shot down an antiship ballistic missile fired by the Houthis and downed three Iranian drones less than an hour later.

Recent advancements in deep reinforcement learning (DRL) techniques have sparked its multifaceted applications in the automation sector. Managing complex decision-making problems with DRL encourages its use in the nuclear industry for tasks such as optimizing radiation exposure to the personnel during normal operating conditions and potential accidental scenarios. However, the lack of efficient reward function and effective exploration strategy thwarted its implementation in the development of radiation-aware autonomous unmanned aerial vehicle (UAV) for achieving maximum radiation protection. Here, in this article, we address these intriguing issues and introduce a deep Q-learning based architecture (RadDQN) that operates on a radiation-aware reward function to provide time-efficient minimum radiation-exposure pathway in a radiation zone. We propose a set of unique exploration strategies that fine-tune the extent of exploration and exploitation based on the state-wise variation in radiation exposure during training. Further, we benchmark the predicted path with grid-based deterministic method. We demonstrate that the formulated reward function in conjugation with adequate exploration strategy is effective in handling several scenarios with drastically different radiation field distributions. When compared to vanilla DQN, our model achieves a superior convergence rate and higher training stability.

Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, weeding, cropping, and disease control. This work will introduce a machine learning framework for automatized large-scale plant-specific trait annotation for the use case disease severity scoring for Cercospora Leaf Spot (CLS) in sugar beet. With concepts of Deep Label Distribution Learning (DLDL), special loss functions, and a tailored model architecture, we develop an efficient Vision Transformer based model for disease severity scoring called SugarViT. One novelty in this work is the combination of remote sensing data with environmental parameters of the experimental sites for disease severity prediction. Although the model is evaluated on this special use case, it is held as generic as possible to also be applicable to various image-based classification and regression tasks. With our framework, it is even possible to learn models on multi-objective problems as we show by a pretraining on environmental metadata.

White House national security spokesman John Kirby reiterated Wednesday that the U.S. will respond after three American soldiers were killed in a drone attack by an Iran-backed proxy group. President Biden on Tuesday blamed Iran for providing weapons to the militant groups that perpetuated the attack and said he had decided how to respond but did not offer further details. But with no public action in the days since the attack, a reporter asked Kirby whether the White House had missed an opportunity to signal resolve. "I think we signal resolve pretty well. And as I said the other day, we'll respond on our own time, on our own schedule, and we'll do that," Kirby said at the daily White House press briefing.