Garrett Butts details military drone innovation effort aimed at speeding deployment and reducing cost in an exclusive interview with Fox News Digital. As the war between Israel and Iran intensifies, one Army drone expert is warning that the U.S. must stay ready, and fast. Garrett Butts is helping lead the charge by building smarter, cheaper unmanned aircraft systems (UAS) in-house for the battlefield. In an exclusive interview with Fox News Digital on Tuesday, Butts described how his team is creating drone technology from scratch, often using parts it took nearly a year to legally obtain. "We're a transformation and contact unit," said Butts, who serves with the 1st Cavalry Division.

Russia's increased sense of vulnerability may be the most important result of a recent large-scale Ukrainian drone attack named Operation Spiderweb, experts tell Al Jazeera. The operation destroyed as much as a third of Russia's strategic bomber fleet on the tarmac of four airfields deep inside Russia on June 1. Days later, Russia started to build shelters for its bombers and relocate them. An open source intelligence (OSINT) researcher nicknamed Def Mon posted time-lapse satellite photographs on social media showing major excavations at the Kirovskoe airfield in annexed Crimea as well as in Sevastopol, Gvardiyskoye and Saki, where Russia was constructing shelters for military aircraft. They reported similar work at several airbases in Russia, including the Engels base, which was targeted in Ukraine's attacks on June 1.

Autonomous drones capable of interpreting and executing high-level language instructions in unstructured environments remain a long-standing goal. Yet existing approaches are constrained by their dependence on hand-crafted skills, extensive parameter tuning, or computationally intensive models unsuitable for onboard use. We introduce GRaD-Nav++, a lightweight Vision-Language-Action (VLA) framework that runs fully onboard and follows natural-language commands in real time. Our policy is trained in a photorealistic 3D Gaussian Splatting (3DGS) simulator via Differentiable Reinforcement Learning (DiffRL), enabling efficient learning of low-level control from visual and linguistic inputs. At its core is a Mixture-of-Experts (MoE) action head, which adaptively routes computation to improve generalization while mitigating forgetting. In multi-task generalization experiments, GRaD-Nav++ achieves a success rate of 83% on trained tasks and 75% on unseen tasks in simulation. When deployed on real hardware, it attains 67% success on trained tasks and 50% on unseen ones. In multi-environment adaptation experiments, GRaD-Nav++ achieves an average success rate of 81% across diverse simulated environments and 67% across varied real-world settings. These results establish a new benchmark for fully onboard Vision-Language-Action (VLA) flight and demonstrate that compact, efficient models can enable reliable, language-guided navigation without relying on external infrastructure.

A U.S. citizen died during a Russian missile attack on the Ukrainian capital of Kyiv, the State Department confirmed Tuesday afternoon. An American citizen was among the 15 killed in Russian drone and missile strikes on the Ukrainian capital city, Kyiv, on Tuesday, State Department spokesperson Tammy Bruce confirmed in a press conference Wednesday. In response to a reporter's question on U.S. diplomats in Kyiv having to spend the night in a bunker, Bruce said "we can confirm the death of a U.S. citizen in Ukraine." "We are aware of last night's attack on Kyiv that resulted in numerous casualties, including the tragic death of a U.S. citizen," she said, noting, "We condemn those strikes and extend our deepest condolences to the victims and to the families of all those affected." Bruce did not offer any more details on the identity of the citizen killed by the Russian strikes, citing "respect to the family during this obviously horrible time."

Russia has launched a large-scale drone and missile attack on Ukraine's capital Kyiv, killing several people and wounding more than a hundred others according to local officials. Al Jazeera's Assed Baig has been to one of the attack sites there.

Waves of Russian missile and drone strikes have killed at least 15 people and injured 116 others, with most of the casualties in Kyiv, Ukrainian officials have reported. The massive aerial assault overnight into Tuesday struck 27 locations in the Ukrainian capital, damaging residential buildings and critical infrastructure, according to Interior Minister Ihor Klymenko. Ukrainian officials were quick to call for international attention on the attacks as Kyiv pushes diplomatic efforts to raise pressure on Moscow to agree a ceasefire. "Today, the enemy spared neither drones nor missiles," Klymenko said, describing the attack as one of the largest against Kyiv since Russia launched its full-scale invasion of the country in February 2022. Thirty apartments were destroyed in a single residential block, and emergency services were searching through the rubble for possible survivors, Klymenko added.

Commercial UAVs are an emerging security threat as they are capable of carrying hazardous payloads or disrupting air traffic. To counter UAVs, we introduce an autonomous 3D target encirclement and interception strategy. Unlike traditional ground-guided systems, this strategy employs autonomous drones to track and engage non-cooperative hostile UAVs, which is effective in non-line-of-sight conditions, GPS denial, and radar jamming, where conventional detection and neutralization from ground guidance fail. Using two noisy real-time distances measured by drones, guardian drones estimate the relative position from their own to the target using observation and velocity compensation methods, based on anti-synchronization (AS) and an X$-$Y circular motion combined with vertical jitter. An encirclement control mechanism is proposed to enable UAVs to adaptively transition from encircling and protecting a target to encircling and monitoring a hostile target. Upon breaching a warning threshold, the UAVs may even employ a suicide attack to neutralize the hostile target. We validate this strategy through real-world UAV experiments and simulated analysis in MATLAB, demonstrating its effectiveness in detecting, encircling, and intercepting hostile drones. More details: https://youtu.be/5eHW56lPVto.

Recent breakthroughs in multimodal large language models (MLLMs) have endowed AI systems with unified perception, reasoning and natural-language interaction across text, image and video streams. Meanwhile, Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in dynamic, safety-critical missions that demand rapid situational understanding and autonomous adaptation. This paper explores potential solutions for integrating MLLMs with UAV swarms to enhance the intelligence and adaptability across diverse tasks. Specifically, we first outline the fundamental architectures and functions of UAVs and MLLMs. Then, we analyze how MLLMs can enhance the UAV system performance in terms of target detection, autonomous navigation, and multi-agent coordination, while exploring solutions for integrating MLLMs into UAV systems. Next, we propose a practical case study focused on the forest fire fighting. To fully reveal the capabilities of the proposed framework, human-machine interaction, swarm task planning, fire assessment, and task execution are investigated. Finally, we discuss the challenges and future research directions for the MLLMs-enabled UAV swarm. An experiment illustration video could be found online at https://youtu.be/zwnB9ZSa5A4.

Quantum communication is poised to become a foundational element of next-generation networking, offering transformative capabilities in security, entanglement-based connectivity, and computational offloading. However, the classical OSI model-designed for deterministic and error-tolerant systems-cannot support quantum-specific phenomena such as coherence fragility, probabilistic entanglement, and the no-cloning theorem. This paper provides a comprehensive survey and proposes an architectural redesign of the OSI model for quantum networks in the context of 7G. We introduce a Quantum-Converged OSI stack by extending the classical model with Layer 0 (Quantum Substrate) and Layer 8 (Cognitive Intent), supporting entanglement, teleportation, and semantic orchestration via LLMs and QML. Each layer is redefined to incorporate quantum mechanisms such as enhanced MAC protocols, fidelity-aware routing, and twin-based applications. This survey consolidates over 150 research works from IEEE, ACM, MDPI, arXiv, and Web of Science (2018-2025), classifying them by OSI layer, enabling technologies such as QKD, QEC, PQC, and RIS, and use cases such as satellite QKD, UAV swarms, and quantum IoT. A taxonomy of cross-layer enablers-such as hybrid quantum-classical control, metadata-driven orchestration, and blockchain-integrated quantum trust-is provided, along with simulation tools including NetSquid, QuNetSim, and QuISP. We present several domain-specific applications, including quantum healthcare telemetry, entangled vehicular networks, and satellite mesh overlays. An evaluation framework is proposed based on entropy throughput, coherence latency, and entanglement fidelity. Key future directions include programmable quantum stacks, digital twins, and AI-defined QNet agents, laying the groundwork for a scalable, intelligent, and quantum-compliant OSI framework for 7G and beyond.

--In this paper, we propose an equilibrium-driven controller that enables a marsupial carrier-passenger robotic system to achieve smooth carrier-passenger separation and then to navigate the passenger robot toward a predetermined target point. This introduces multiple equilibrium points corresponding to the zero state of the error dynamic system during carrier-passenger separation. The change of equilibrium points is associated with the change in their attraction regions, enabling smooth carrier-passenger separation and afterwards seamless navigation toward the target. Finally, simulations demonstrate the effectiveness and adaptability of the proposed controller in environments containing obstacles. Over the years, the coordination of multi-robot systems (MRSs) has played an important role in a wide range of applications, including urban search and rescue, environmental monitoring, delivery, and collaborative convoying [1]-[3].