Footage of a Japanese naval destroyer that circulated on Chinese social media is likely genuine, Tokyo's Defense Ministry said Thursday, after initial speculation the video may have been generated by artificial intelligence. No obvious military activity can be seen in the clip, which appears to show the docked Izumo helicopter carrier. According to officials, footage "purportedly shot by a drone" was first uploaded to Chinese video-sharing platform Bilibili on March 26.

This article presents the world's first rapid drone flocking control using natural language through generative AI. The described approach enables the intuitive orchestration of a flock of any size to achieve the desired geometry. The key feature of the method is the development of a new interface based on Large Language Models to communicate with the user and to generate the target geometry descriptions. Users can interactively modify or provide comments during the construction of the flock geometry model. By combining flocking technology and defining the target surface using a signed distance function, smooth and adaptive movement of the drone swarm between target states is achieved. Our user study on FlockGPT confirmed a high level of intuitive control over drone flocking by users. Subjects who had never previously controlled a swarm of drones were able to construct complex figures in just a few iterations and were able to accurately distinguish the formed swarm drone figures. The results revealed a high recognition rate for six different geometric patterns generated through the LLM-based interface and performed by a simulated drone flock (mean of 80% with a maximum of 93\% for cube and tetrahedron patterns). Users commented on low temporal demand (19.2 score in NASA-TLX), high performance (26 score in NASA-TLX), attractiveness (1.94 UEQ score), and hedonic quality (1.81 UEQ score) of the developed system. The FlockGPT demo code repository can be found at: coming soon

Consumer-grade drones have become effective multimedia collection tools, spring-boarded by rapid development in embedded CPUs, GPUs, and cameras. They are best known for their ability to cheaply collect high-quality aerial video, 3D terrain scans, infrared imagery, etc., with respect to manned aircraft. However, users can also create and attach custom sensors, actuators, or computers, so the drone can collect different data, generate composite data, or interact intelligently with its environment, e.g., autonomously changing behavior to land in a safe way, or choosing further data collection sites. Unfortunately, developing custom payloads is prohibitively difficult for many researchers outside of engineering. We provide guidelines for how to create a sophisticated computational payload that integrates a Raspberry Pi 5 into a DJI Matrice 350. The payload fits into the Matrice's case like a typical DJI payload (but is much cheaper), is easy to build and expand (3D-printed), uses the drone's power and telemetry, can control the drone and its other payloads, can access the drone's sensors and camera feeds, and can process video and stream it to the operator via the controller in real time. We describe the difficulties and proprietary quirks we encountered, how we worked through them, and provide setup scripts and a known-working configuration for others to use.

Invasive plant species are detrimental to the ecology of both agricultural and wildland areas. Euphorbia esula, or leafy spurge, is one such plant that has spread through much of North America from Eastern Europe. When paired with contemporary computer vision systems, unmanned aerial vehicles, or drones, offer the means to track expansion of problem plants, such as leafy spurge, and improve chances of controlling these weeds. We gathered a dataset of leafy spurge presence and absence in grasslands of western Montana, USA, then surveyed these areas with a commercial drone. We trained image classifiers on these data, and our best performing model, a pre-trained DINOv2 vision transformer, identified leafy spurge with 0.84 accuracy (test set). This result indicates that classification of leafy spurge is tractable, but not solved. We release this unique dataset of labelled and unlabelled, aerial drone imagery for the machine learning community to explore. Improving classification performance of leafy spurge would benefit the fields of ecology, conservation, and remote sensing alike. Code and data are available at our website: leafy-spurge-dataset.github.io.

Polish Foreign Minister Radosław Sikorski provides his analysis of the Russia-Ukraine war as it marks its third Easter, the passing of the foreign aid package and his expectations for the upcoming NATO summit. Rumbling out of its forest hideout, the hulking German-supplied howitzer has only a few minutes to fire before slipping back under cover to evade Russian surveillance in the skies above. Across the hills and valleys of the east, Ukrainian artillery units play a cat-and-mouse game with Russian drones hunting high-value artillery weapons such as this self-propelled Panzerhaubitze 2000. Moscow's troops have stepped up ground attacks along the 621-mile front in the south and east of Ukraine, threatening some of the industrialized Donetsk region's last big cities held by Kyiv more than two years after Russia's full-scale invasion. Counterbattery efforts are crucial to suppressing enemy fire that rains on Ukrainian lines and artillery units, and paves the way for Russian advances.

A YouTube based in Florida's iconic Haulover Inlet, set between Bal Harbour and Sunny Isles in Miami-Dade County, posted this video during a boozing weekend. The family of one of two teen boys facing felonies for dumping drums of trash into the Atlantic Ocean at Florida's annual Boca Bash issued an apology after their son turned himself in to the Florida Fish and Wildlife Conservation Commission (FWC). Now-viral drone footage shows the teens hefting two trash bins filled with bottles and other plastics over the railing of their fishing vessel as they speed away from the boozy water gathering on April 28. As the boat of partiers zoomed away into the choppy waters of the Boca Raton inlet, the video pans out to the spread of debris left floating in their wake. Footage from the front of the boat shows the teens waving and laughing.

Amazon plans to start flying delivery drones in Arizona this year--but don't count on them to bring you a refreshing drink on a hot day. The hexacopter can't operate when temperatures top 104 degrees Fahrenheit, or 40 degrees Celsius, the company says, and average daily highs exceed that for three months of the year in Tolleson, the city outside Phoenix where Amazon is preparing to offer aerial deliveries from inside a 7.5-mile radius. The drones can't help with midnight snacks either, because they'll be grounded after sunset. Potentially being inoperable for a quarter of the year might make launching drone deliveries in Tolleson and neighboring desert communities seem like an odd choice. It's far from the first challenge faced by Amazon's much-delayed drone project.

Routing protocols help in transmitting the sensed data from UAVs monitoring the targets (called target UAVs) to the BS. However, the highly dynamic nature of an autonomous, decentralized UAV network leads to frequent route breaks or traffic disruptions. Traditional routing schemes cannot quickly adapt to dynamic UAV networks and/or incur large control overhead and delays. To establish stable, high-quality routes from target UAVs to the BS, we design a hybrid reactive routing scheme called pipe routing that is mobility, congestion, and energy-aware. The pipe routing scheme discovers routes on-demand and proactively switches to alternate high-quality routes within a limited region around the active routes (called the pipe) when needed, reducing the number of route breaks and increasing data throughput. We then design a novel topology control-based pipe routing scheme to maintain robust connectivity in the pipe region around the active routes, leading to improved route stability and increased throughput with minimal impact on the coverage performance of the UAV network.

NORAD confirmed it detected four Russian military aircraft in the Alaska Air Defense Identification Zone on Thursday, but the aircraft didn't enter U.S. airspace. Russia said Monday it plans to hold drills simulating the use of battlefield nuclear weapons amid rising tensions following comments by senior Western officials about the possibility of deeper involvement in the war in Ukraine. The announcement came on the eve of Russian President Vladimir Putin's inauguration to a fifth term in office and in a week when Moscow on Thursday will celebrate Victory Day, its most important secular holiday, marking its defeat of Nazi Germany in World War II. The drills are a response to "provocative statements and threats of certain Western officials regarding the Russian Federation," the Defense Ministry said in a statement. PUTIN WARNS THE WEST THAT RUSSIA IS'READY' FOR NUCLEAR WAR: 'WEAPONS EXIST IN ORDER TO USE THEM' It was the first time Russia has publicly announced drills involving tactical nuclear weapons, although its strategic nuclear forces regularly hold exercises.

Here is the situation on Monday, May 6, 2024. At least one person was killed and 24 injured in Russian drone and bomb attacks on Ukraine's northeastern city of Kharkiv and the surrounding region. Power cuts were also reported. Ukraine's Air Force said that Russia had launched 24 Shahed attack drones and 23 were shot down. Russia's Ministry of Defence said its forces had captured the village of Ocheretyne, in Ukraine's eastern Donetsk region.