The United States has accused Russian fighter jets of flying dangerously close to several of its drones over Syria, setting off flares and forcing the MQ-9 Reapers to take evasive action. US Air Forces Central released a video of Wednesday's encounter, showing a Russian SU-35 fighter closing in on the drone. Footage showed the Russian pilot positioning his aircraft in front of the Reaper and turning on the afterburner, dramatically increasing speed and air pressure and making it harder to operate the drone, the air force said in comments accompanying the video. So-called parachute flares were also released. "The Russian SU-35 fighter aircraft employed parachute flares in the flight path of US MQ-9 aircraft," the air force said.

Nowadays, there are few unmanned aerial vehicles (UAVs) capable of flying, walking and grasping. A drone with all these functionalities can significantly improve its performance in complex tasks such as monitoring and exploring different types of terrain, and rescue operations. This paper presents MorphoArms, a novel system that consists of a morphogenetic chassis and a hand gesture recognition teleoperation system. The mechanics, electronics, control architecture, and walking behavior of the morphogenetic chassis are described. This robot is capable of walking and grasping objects using four robotic limbs. Robotic limbs with four degrees-of-freedom are used as pedipulators when walking and as manipulators when performing actions in the environment. The robot control system is implemented using teleoperation, where commands are given by hand gestures. A motion capture system is used to track the user's hands and to recognize their gestures. The method of controlling the robot was experimentally tested in a study involving 10 users. The evaluation included three questionnaires (NASA TLX, SUS, and UEQ). The results showed that the proposed system was more user-friendly than 56% of the systems, and it was rated above average in terms of attractiveness, stimulation, and novelty.

Moscow – Moscow has said that Ukrainian drone attacks on Russian territory would "not be possible" without U.S. and NATO help, escalating its rhetoric after reporting it had downed five drones near the capital on Tuesday. Ukraine meanwhile accused Russia of planning "dangerous provocations" at the Moscow-controlled Zaporizhzhia nuclear power plant, while Russia in turn claimed Kyiv was planning to attack the facility -- Europe's largest. Moscow said the West had enabled Ukraine to carry out the drone attacks, after earlier condemning what it called a "terrorist act." This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

This paper presents a novel approach for optical flow control of Micro Air Vehicles (MAVs). The task is challenging due to the nonlinearity of optical flow observables. Our proposed Incremental Nonlinear Dynamic Inversion (INDI) control scheme incorporates an efficient data-driven method to address the nonlinearity. It directly estimates the inverse of the time-varying control effectiveness in real-time, eliminating the need for the constant assumption and avoiding high computation in traditional INDI. This approach effectively handles fast-changing system dynamics commonly encountered in optical flow control, particularly height-dependent changes. We demonstrate the robustness and efficiency of the proposed control scheme in numerical simulations and also real-world flight tests: multiple landings of an MAV on a static and flat surface with various tracking setpoints, hovering and landings on moving and undulating surfaces. Despite being challenged with the presence of noisy optical flow estimates and the lateral and vertical movement of the landing surfaces, the MAV is able to successfully track or land on the surface with an exponential decay of both height and vertical velocity at almost the same time, as desired.

The U.S. and other NATO countries made possible an alleged Ukrainian drone attack on Moscow, Russian officials claimed Tuesday. Russia says the U.S provided intelligence.

Drones being evaluated by the US military could soon be equipped with an artificial intelligence that is claimed to be better than humans at identifying targets, although the classified nature of the work makes it difficult to verify this claim. Stephen Bornstein of Athena AI, the Australian company behind the system, says the AI will assist human drone operators, who can lose concentration after hours spent looking at streaming video. "The AI will do a lot of the heavy lifting for them," says …

Senior foreign affairs correspondent Greg Palkot reports 80% of all Ukrainians have a relative or friend killed as the result of the war on'America's Newsroom.' The Russian military claimed Tuesday that it shot down several Ukrainian drones that were headed for the capital city of Moscow, an attack that also prompted officials to briefly close one of the city's airports. The Russian Defense Ministry said air defenses on the outskirts of Moscow shot down four of five drones heading toward the capital, adding that the fifth was jammed by electronic means. Ukrainian authorities, who usually do not comment on attacks inside Russia's proper territory, have not claimed responsibility. Moscow Mayor Sergei Sobyanin said no one was harmed in the attack and no buildings were damaged.

"An attempt by the Kyiv regime to attack a zone where civil infrastructure is located, including an airport that receives international flights, is a new terrorist act," foreign ministry spokeswoman Maria Zakharova said on Telegram.

Nowadays, there is a wide availability of datasets that enable the training of common object detectors or human detectors. These come in the form of labelled real-world images and require either a significant amount of human effort, with a high probability of errors such as missing labels, or very constrained scenarios, e.g. VICON systems. On the other hand, uncommon scenarios, like aerial views, animals, like wild zebras, or difficult-to-obtain information, such as human shapes, are hardly available. To overcome this, synthetic data generation with realistic rendering technologies has recently gained traction and advanced research areas such as target tracking and human pose estimation. However, subjects such as wild animals are still usually not well represented in such datasets. In this work, we first show that a pre-trained YOLO detector can not identify zebras in real images recorded from aerial viewpoints. To solve this, we present an approach for training an animal detector using only synthetic data. We start by generating a novel synthetic zebra dataset using GRADE, a state-of-the-art framework for data generation. The dataset includes RGB, depth, skeletal joint locations, pose, shape and instance segmentations for each subject. We use this to train a YOLO detector from scratch. Through extensive evaluations of our model with real-world data from i) limited datasets available on the internet and ii) a new one collected and manually labelled by us, we show that we can detect zebras by using only synthetic data during training. The code, results, trained models, and both the generated and training data are provided as open-source at https://eliabntt.github.io/grade-rr.

The Ghana Cashew Disease Identification with Artificial Intelligence (CADI AI) project demonstrates the importance of sound data work as a precondition for the delivery of useful, localized datacentric solutions for public good tasks such as agricultural productivity and food security. Dronecollected data and machine learning are utilized to determine crop stressors. Data, model and the final app are developed jointly and made available to local farmers via a desktop application. Cashew is a significant cash crop in Ghana (Rabany et al., 2015), with small and medium farmers relying on it for income. Cashew cultivation is concentrated in specific regions of Ghana. However, farmers face challenges including insect, plant disease and abiotic stress factors that reduce their Figure 1: A visual summary of the application lifecycle: yields (ICAR; Jayaprakash et al., 2023; Mensah et al., 2023; data work (data collection with farmers, data annotation Timothy et al., 2021). To address these issues, the Cashew and labelling), model work (model training and fine-tuning), Disease Identification With Artificial Intelligence (CADI and UI application (software deployment and release to AI) project was launched to provide a data-centric solution.