Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Europe lacks coordination as Russia'prepares for war with NATO': Experts Europe is unprepared to counteract a new chapter of Russian military and intelligence activities in the Baltic and North Seas, experts have told Al Jazeera. At the same time, they said, a growing rift between European and United States intelligence services is leaving the continent unsupported.

Traditional fixed architectures struggle to cope with dynamic and unpredictable environments, leading to inefficiencies in energy consumption and connectivity. This paper addresses this gap by proposing an adaptive architecture for drone swarms, leveraging a Large Language Model (LLM) to dynamically select the optimal architecture--centralized, hierarchical, or holonic--based on real-time mission parameters such as task complexity, swarm size, and communication stability. Our system addresses the challenges of scalability, adaptability, and robustness, ensuring efficient energy consumption and maintaining connectivity under varying conditions. Extensive simulations demonstrate that our adaptive architecture outperforms traditional static models in terms of scalability, energy efficiency, and connectivity. These results highlight the potential of our approach to provide a scalable, adaptable, and resilient solution for real-world disaster response scenarios.

Swarm robotics plays a crucial role in enabling autonomous operations in dynamic and unpredictable environments. However, a major challenge remains ensuring safe and efficient navigation in environments filled with both dynamic alive (e.g., humans) and dynamic inanimate (e.g., non-living objects) obstacles. In this paper, we propose ImpedanceGPT, a novel system that combines a Vision-Language Model (VLM) with retrieval-augmented generation (RAG) to enable real-time reasoning for adaptive navigation of mini-drone swarms in complex environments. The key innovation of ImpedanceGPT lies in the integration of VLM and RAG, which provides the drones with enhanced semantic understanding of their surroundings. This enables the system to dynamically adjust impedance control parameters in response to obstacle types and environmental conditions. Our approach not only ensures safe and precise navigation but also improves coordination between drones in the swarm. Experimental evaluations demonstrate the effectiveness of the system. The VLM-RAG framework achieved an obstacle detection and retrieval accuracy of 80 % under optimal lighting. In static environments, drones navigated dynamic inanimate obstacles at 1.4 m/s but slowed to 0.7 m/s with increased separation around humans. In dynamic environments, speed adjusted to 1.0 m/s near hard obstacles, while reducing to 0.6 m/s with higher deflection to safely avoid moving humans.

Catalyzed by advancements in hardware and software, drone performances are increasingly making their mark in the entertainment industry. However, designing smooth and safe choreographies for drone swarms is complex and often requires expert domain knowledge. In this work, we introduce SwarmGPT-Primitive, a language-based choreographer that integrates the reasoning capabilities of large language models (LLMs) with safe motion planning to facilitate deployable drone swarm choreographies. The LLM composes choreographies for a given piece of music by utilizing a library of motion primitives; the language-based choreographer is augmented with an optimization-based safety filter, which certifies the choreography for real-world deployment by making minimal adjustments when feasibility and safety constraints are violated. The overall SwarmGPT-Primitive framework decouples choreographic design from safe motion planning, which allows non-expert users to re-prompt and refine compositions without concerns about compliance with constraints such as avoiding collisions or downwash effects or satisfying actuation limits. We demonstrate our approach through simulations and experiments with swarms of up to 20 drones performing choreographies designed based on various songs, highlighting the system's ability to generate effective and synchronized drone choreographies for real-world deployment.

When a sophisticated Chinese spy balloon floated over America in early 2023, lawmakers and the public were outraged at the Biden-Harris administration's passivity and initial inclination to keep it quiet – only acknowledging the balloon after two civilian photographers forced their hand. Now, the Wall Street Journal has broken news on an even more stupendous U.S. national security breach, reporting that drones flew over a sensitive nuclear weapons testing facility for three days last October and then, two months later, flew over Langley Air Force Base in Virginia for 17 straight nights while the Biden White House, and the military officers it promoted, dawdled and argued over what to do about it. The swarms started on Dec. 7, 2023. Drones, some as large as 20 feet long, flew at night over the Air Combat Command headquarters with its squadrons of advanced F-22 Raptor fighters. The blame-casting and responsibility-shirking reveal a dangerous pattern of hesitation and risk-averse decision-making.

A retired, senior Pentagon official has confirmed that UFO'mother ships' were spotted'releasing swarms of smaller craft' -- adding further mystery to the still-unexplained intrusions over multiple US military bases. His statements come amid the release of 50 pages of Air Force records related to provocative'drone' incursions, that one general calls'Close Encounters at Langley.' For at least 17 nights last December, swarms of noisy, small UFOs were seen at dusk'moving at rapid speeds' and displaying'flashing red, green, and white lights' penetrating the highly restricted airspace above Langley Air Force Base in Virginia. Senior ex-Pentagon security official Chris Mellon told DailyMail.com'Two of the notable aspects,' he said, 'are the fact our drone signal-jamming devices have proven ineffective and these craft are making no effort to remain concealed.'

This study proposes a novel artificial intelligence (AI) driven flight computer, integrating an online free-retraining-prediction model, a swarm control, and an obstacle avoidance strategy, to track dynamic targets using a distributed drone swarm for military applications. To enable dynamic target tracking the swarm requires a trajectory prediction capability to achieve intercept allowing for the tracking of rapid maneuvers and movements while maintaining efficient path planning. Traditional predicative methods such as curve fitting or Long ShortTerm Memory (LSTM) have low robustness and struggle with dynamic target tracking in the short term due to slow convergence of single agent-based trajectory prediction and often require extensive offline training or tuning to be effective. Consequently, this paper introduces a novel robust adaptive bidirectional fuzzy brain emotional learning prediction (BFBEL-P) methodology to address these challenges. The controller integrates a fuzzy interface, a neural network enabling rapid adaption, predictive capability and multi-agent solving enabling multiple solutions to be aggregated to achieve rapid convergence times and high accuracy in both the short and long term. This was verified through the use of numerical simulations seeing complex trajectory being predicted and tracked by a swarm of drones. These simulations show improved adaptability and accuracy to state of the art methods in the short term and strong results over long time domains, enabling accurate swarm target tracking and predictive capability.

Drones could soon be working together in swarms to put out flames before they become wildfires, UK researchers hope. A team of firefighters, scientists and engineers are working on a project they say will allow swarms of up to 30 autonomous planes to spot and extinguish fires by working collectively using artificial intelligence. Drones piloted by people are already used in firefighting, for example to detect hidden blazes and assess safety risks. The research is still in the test phase and has not been used on a wildfire, but the team claims it is the first to combine unpiloted drone technology with swarm engineering in the field of firefighting.

Abstract-- Achieving safe and precise landings for a swarm of drones poses a significant challenge, primarily attributed to conventional control and planning methods. This paper presents the implementation of multi-agent deep reinforcement learning (MADRL) techniques for the precise landing of a drone swarm at relocated target locations. The system is trained in a realistic simulated environment with a maximum velocity of 3 m/s in training spaces of 4 x 4 x 4 m and deployed utilizing Crazyflie drones with a Vicon indoor localization system. This research highlights drone landing technologies that eliminate the need for analytical centralized systems, potentially offering scalability and revolutionizing applications in logistics, safety, and rescue missions. Swarm drones, characterized by their collaborative behavior, are driving research due to their disruptive potential across industries like agriculture, construction, entertainment, and logistics [1], [2].

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