This paper introduces a high-performance artificial intelligence operating system tailored for low-altitude aviation, designed to address key challenges such as real-time task execution, computational efficiency, and seamless modular collaboration. Built on a powerful hardware platform and leveraging the UNIX architecture, the system implements a distributed data processing strategy that ensures rapid and efficient synchronization across critical modules, including vision, navigation, and perception. By adopting dynamic resource management, it optimally allocates computational resources, such as CPU and GPU, based on task priority and workload, ensuring high performance for demanding tasks like real-time video processing and AI model inference. Furthermore, the system features an advanced interrupt handling mechanism that allows for quick responses to sudden environmental changes, such as obstacle detection, by prioritizing critical tasks, thus improving safety and mission success rates. Robust security measures, including data encryption, access control, and fault tolerance, ensure the system's resilience against external threats and its ability to recover from potential hardware or software failures. Complementing these core features are modular components for image analysis, multi-sensor fusion, dynamic path planning, multi-drone coordination, and ground station monitoring. Additionally, a low-code development platform simplifies user customization, making the system adaptable to various mission-specific needs. This comprehensive approach ensures the system meets the evolving demands of intelligent aviation, providing a stable, efficient, and secure environment for complex drone operations.

The Federal Aviation Administration issued temporary flight restrictions prohibiting drone flights over parts of New Jersey following an influx of sightings in recent weeks. The notice, which expires Jan. 17, 2025, said drone operations in support of national defense, homeland security, law enforcement, firefighting, search and rescue or disaster response missions are not included in the restrictions. Commercial drone operations are allowed with a valid statement of work, but there must be an approved special governmental interest airspace waiver and all applicable FAA regulations must be followed. House Speaker Mike Johnson, R-La., said the White House, and more broadly the U.S. government, does not seem concerned about the increased sightings in New Jersey and other northeastern states. "Look, I'm the speaker of the House. I have the exact same frustrations that you do and all of us do. We don't have the answers. The administration is not providing them," Johnson said in a Fox News appearance.

We propose LLM-DaaS, a novel Drone-as-a-Service (DaaS) framework that leverages Large Language Models (LLMs) to transform free-text user requests into structured, actionable DaaS operation tasks. Our approach addresses the key challenge of interpreting and structuring natural language input to automate drone service operations under uncertain conditions. The system is composed of three main components: free-text request processing, structured request generation, and dynamic DaaS selection and composition. First, we fine-tune different LLM models such as Phi-3.5, LLaMA-3.2 7b and Gemma 2b on a dataset of text user requests mapped to structured DaaS requests. Users interact with our model in a free conversational style, discussing package delivery requests, while the fine-tuned LLM extracts DaaS metadata such as delivery time, source and destination locations, and package weight. The DaaS service selection model is designed to select the best available drone capable of delivering the requested package from the delivery point to the nearest optimal destination. Additionally, the DaaS composition model composes a service from a set of the best available drones to deliver the package from the source to the final destination. Second, the system integrates real-time weather data to optimize drone route planning and scheduling, ensuring safe and efficient operations. Simulations demonstrate the system's ability to significantly improve task accuracy, operational efficiency, and establish LLM-DaaS as a robust solution for DaaS operations in uncertain environments.

Large Language Models (LLMs) are transforming the robotics domain by enabling robots to comprehend and execute natural language instructions. The cornerstone benefits of LLM include processing textual data from technical manuals, instructions, academic papers, and user queries based on the knowledge provided. However, deploying LLM-generated code in robotic systems without safety verification poses significant risks. This paper outlines a safety layer that verifies the code generated by ChatGPT before executing it to control a drone in a simulated environment. The safety layer consists of a fine-tuned GPT-4o model using Few-Shot learning, supported by knowledge graph prompting (KGP). Our approach improves the safety and compliance of robotic actions, ensuring that they adhere to the regulations of drone operations.

With the rapid development of drone technology, the application of multi-drones is becoming increasingly widespread in various fields. However, the task planning technology for multi-drones still faces challenges such as the complexity of remote operation and the convenience of human-machine interaction. To address these issues, this paper proposes a prompt-driven task planning method for multi-drones based on large language models. By introducing the Prompt technique, appropriate prompt information is provided for the multi-drone system.

Excerpts from Walter Isaacson's Elon Musk biography are coming to light ahead of its release next week, revealing some new details about the billionaire's decision to provide Ukraine with Starlink access amid the country's war with Russia. According to an excerpt CNN reported on, Musk allegedly told SpaceX workers to shut down Starlink access close to the Crimea coast to prevent a Ukrainian drone attack on Russia's naval fleet. Musk, who has reportedly been in contact with Russian officials including President Vladimir Putin, is said to have been worried that the attack would lead to Russia retaliating with nuclear weapons. Ukrainian leaders seemingly begged Musk to reactivate Starlink access but drones that were approaching Russian warships "lost connectivity and washed ashore harmlessly," CNN cites Isaacson as stating. Musk's alleged actions have had significant consequences for Ukraine, according to Mykhailo Podolyak, an advisor to President Volodymyr Zelensky.

High-tech giants and start-ups are investing in drone technologies to provide urban air delivery service, which is expected to solve the last-mile problem and mitigate road traffic congestion. However, air delivery service will not scale up without proper traffic management for drones in dense urban environment. Currently, a range of Concepts of Operations (ConOps) for unmanned aircraft system traffic management (UTM) are being proposed and evaluated by researchers, operators, and regulators. Among these, the tube-based (or corridor-based) ConOps has emerged in operations in some regions of the world for drone deliveries and is expected to continue serving certain scenarios that with dense and complex airspace and requires centralized control in the future. Towards the tube-based ConOps, we develop a route network planning method to design routes (tubes) in a complex urban environment in this paper. In this method, we propose a priority structure to decouple the network planning problem, which is NP-hard, into single-path planning problems. We also introduce a novel space cost function to enable the design of dense and aligned routes in a network. The proposed method is tested on various scenarios and compared with other state-of-the-art methods. Results show that our method can generate near-optimal route networks with significant computational time-savings.

A new report from Drone Industry Insights says the commercial industry can expect progress globally. DRONEII Editor Ed Alvarado writes that around the world, drone regulations – and the regulatory framework – are evolving rapidly. "This is a very welcome development given that the drone industry sees this as the most important driving factor. The movement on drone regulation in 2022 is global. In Korea, significant movement towards urban air mobility is underway: continuing the progress made this year with trial flights and the government committment to an early implementation of passenger VTOL aircraft.

Artificial Intelligence (AI) has gained increasing popularity across industries. Worldwide revenues for the AI market, including software, hardware, and services, are forecast to grow 40.2% annually, topping $997.77 billion by the end of 2028, according to a latest report by Grand View Research, an international consulting firm that helps Fortune 500 companies understand the global and regional business environment. Earlier, Connecticut-based Aquiline Drones Corporation (AD) announced the acquisition of ElluminAI Labs, LLC to support further development of its AI framework called Spartacus. This is AD's second strategic acquisition in the company's pre-IPO plan. It was just last month that AD completed the purchase of 50% of Netherlands-based AerialTronics, a renowned drone manufacturer, for $9.0M USD from Paris-based Drone Volt (ALDRV).

DEME Offshore and Sabca have carried out a series of tests at the Rentel offshore wind farm with an aim to automate critical and ad hoc operations in the near future by using autonomous aerial vehicles (AAVs) and artificial intelligence (AI). The companies, which teamed up two years ago, have performed the first commercial, cross-border, "beyond visual line of sight" (BVLOS) drone operations at the wind farm 35 kilometres off the Belgian coast, where tests in Search & Rescue operations, environmental surveys, turbine and substation inspections, as well as parcel deliveries took place. During the tests, both a multicopter drone and a fixed-wing surveillance drone with a wing span of more than 3 metres were deployed in parallel. The long endurance surveillance drone took off from the Belgian coast and flew to the Rentel offshore wind farm. Meanwhile, an automated resident drone performed inspections and cargo flights from the substation and vessels.