Video captures the moment and aftermath of what appears to be a drone, allegedly of Ukrainian origin, striking Russian drone production facility. Russian officials claimed that only a worker's dormitory was hit. A massive missile and drone attack destroyed one of Ukraine's largest power plants and damaged others, officials said Thursday, part of a renewed Russian campaign targeting energy infrastructure. The Trypilska plant, which was the biggest energy supplier for the Kyiv, Cherkasy and Zhytomyr regions, was struck numerous times, destroying the transformer, turbines and generators and leaving the plant ablaze. As the first drone approached, workers hid in a shelter, saving their lives, said Andrii Gota, chairman of the supervisory board of the state company that runs the plant, Centrenergo.

South Africa's Aerobotics is utilizing artificial intelligence (AI) in helping fruit and nut farmers in over 18 countries. JOHANNESBURG - South Africa's Aerobotics is utilizing artificial intelligence (AI) in helping fruit and nut farmers improve crop yields. Although the Cape Town-based company only started nine years ago, it is already operating in 18 countries, with the U.S. being their largest market, followed by South Africa, Australia, Spain and Portugal. Its customers produce tens of millions of tons of fresh produce every year. California is now ground zero for Aerobotics – where the company has the biggest concentration of customers.

In recent years, reports of illegal drones threatening public safety have increased. For the invasion of fully autonomous drones, traditional methods such as radio frequency interference and GPS shielding may fail. This paper proposes a scheme that uses an autonomous multicopter with a strapdown camera to intercept a maneuvering intruder UAV. The interceptor multicopter can autonomously detect and intercept intruders moving at high speed in the air. The strapdown camera avoids the complex mechanical structure of the electro-optical pod, making the interceptor multicopter compact. However, the coupling of the camera and multicopter motion makes interception tasks difficult. To solve this problem, an Image-Based Visual Servoing (IBVS) controller is proposed to make the interception fast and accurate. Then, in response to the time delay of sensor imaging and image processing relative to attitude changes in high-speed scenarios, a Delayed Kalman Filter (DKF) observer is generalized to predict the current image position and increase the update frequency. Finally, Hardware-in-the-Loop (HITL) simulations and outdoor flight experiments verify that this method has a high interception accuracy and success rate. In the flight experiments, a high-speed interception is achieved with a terminal speed of 20 m/s.

Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present FusionPortableV2, a multi-sensor SLAM dataset featuring notable sensor diversity, varied motion patterns, and a wide range of environmental scenarios. Our dataset comprises $27$ sequences, spanning over $2.5$ hours and collected from four distinct platforms: a handheld suite, wheeled and legged robots, and vehicles. These sequences cover diverse settings, including buildings, campuses, and urban areas, with a total length of $38.7km$. Additionally, the dataset includes ground-truth (GT) trajectories and RGB point cloud maps covering approximately $0.3km^2$. To validate the utility of our dataset in advancing SLAM research, we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we demonstrate the dataset's broad applicability beyond traditional SLAM tasks by investigating its potential for monocular depth estimation. The complete dataset, including sensor data, GT, and calibration details, is accessible at https://fusionportable.github.io/dataset/fusionportable_v2.

Video captures the moment and aftermath of what appears to be a drone, allegedly of Ukrainian origin, striking Russian drone production facility. Russian officials claimed that only a worker's dormitory was hit. Russia has started to make steady progress against Ukraine as Kyiv's forces face dwindling ammunition supplies, much to Ukrainian President Volodymyr Zelenskyy's frustration. "There can be no question, Ukraine could be quickly overwhelmed by both men and arms by odds as great as 10 to 1 within weeks without additional U.S. assistance," Kenneth Braithwaite, a former ambassador and former Navy secretary during the Trump administration, told Fox News Digital. "This is a critical juncture in the war and time is of the essence for Congress to act on a comprehensive package," Braithwaite said.

Drone video shows rows of houses and streets underwater in Russia and Kazakhstan, where thousands of people have had to evacuate their homes due to flooding caused by unusually severe melting snow and ice.

Ukraine needs military aid and air defence systems in the face of Russia's intensifying attacks, President Volodymyr Zelenskyy said, as he criticised his country's allies for engaging in "lengthy discussions" and "turning a blind eye". The Russian military launched attacks on five regions across Ukraine, killing at least seven people and damaging infrastructure including substations and power generation facilities, Ukrainian officials said on Thursday. Zelenskyy said Russia fired more than 40 missiles and about 40 attack drones overnight, many targeting energy infrastructure. The attacks show how "critical" air defence has become for Ukraine, he posted on X, adding that the Russian missiles and Iranian-designed one-way drones must not be allowed to hit Ukraine. In southern Odesa, Governor Oleh Kiper said on his Telegram channel on Wednesday night that Russian missile strikes killed four people, including a 10-year-old girl, and left several others in critical condition.

A year into Sudan's civil war, Iranian-made armed drones have helped the army turn the tide of the conflict, halting the progress of the rival paramilitary Rapid Support Force and regaining territory around the capital, a senior army source has said. Six Iranian sources, regional officials and diplomats -- who, like the army source, asked not to be identified because of the sensitivity of the information -- also said the military had acquired Iranian-made unmanned aerial vehicles (UAVs) over the past few months. The Sudanese Armed Forces (SAF) used some older UAVs in the first months of the war alongside artillery batteries and fighter jets, but had little success in rooting out RSF fighters embedded in heavily populated neighborhoods in Khartoum and other cities, more than a dozen Khartoum residents said.

In the era of cutting-edge autonomous systems, Unmanned Aerial Vehicles (UAVs) are becoming an essential part of the solutions for numerous complex challenges. This paper evaluates UAV peer-to-peer telemetry communication, highlighting its security vulnerabilities and explores a transition to a het-erogeneous multi-hop mesh all-to-all communication architecture to increase inter-swarm connectivity and reliability. Additionally, we suggest a symmetric key agreement and data encryption mechanism implementation for inter - swarm communication, to ensure data integrity and confidentiality without compromising performance.

Executing safe and precise flight maneuvers in dynamic high-speed winds is important for the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the relationship between various wind conditions and its effect on aircraft maneuverability is not well understood, it is challenging to design effective robot controllers using traditional control design methods. We present Neural-Fly, a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly builds on two key observations that aerodynamics in different wind conditions share a common representation and that the wind-specific part lies in a low-dimensional space. To that end, Neural-Fly uses a proposed learning algorithm, domain adversarially invariant meta-learning (DAIML), to learn the shared representation, only using 12 minutes of flight data. With the learned representation as a basis, Neural-Fly then uses a composite adaptation law to update a set of linear coefficients for mixing the basis elements. When evaluated under challenging wind conditions generated with the Caltech Real Weather Wind Tunnel, with wind speeds up to 43.6 kilometers/hour (12.1 meters/second), Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers. In addition to strong empirical performance, the exponential stability of Neural-Fly results in robustness guarantees. Last, our control design extrapolates to unseen wind conditions, is shown to be effective for outdoor flights with only onboard sensors, and can transfer across drones with minimal performance degradation.