Abstract--With the rapid advancement of Large Language Models (LLMs), their capabilities in various automation domains, particularly Unmanned Aerial V ehicle (UA V) operations, have garnered increasing attention. Current research remains predominantly constrained to small-scale UA V applications, with most studies focusing on isolated components such as path planning for toy drones, while lacking comprehensive investigation of medium-and long-range UA V systems in real-world operational contexts. Larger UA V platforms introduce distinct challenges, including stringent requirements for airport-based take-off and landing procedures, adherence to complex regulatory frameworks, and specialized operational capabilities with elevated mission expectations. LV system processes natural language instructions to orchestrate short-, medium-, and long-range UA V missions through five key technical components: (i) LLM-as-Parser for instruction interpretation, (ii) Route Planner for Points of Interest (POI) determination, (iii) Path Planner for waypoint generation, (iv) Control Platform for executable trajectory implementation, and (v) UA V monitoring. We demonstrate the system's feasibility through three representative use cases spanning different operational scales: multi-UA V patrol, multi-POI delivery, and multi-hop relocation. Beyond the current implementation, we establish a five-level automation taxonomy that charts the evolution from current LLM-as-Parser capabilities (Level 1) to fully autonomous LLMas-Autopilot systems (Level 5), identifying technical prerequisites and research challenges at each stage. The rise of Large Language Models (LLMs) has transformed numerous domains, such as mobile services, vehicles, and robotics [1]-[3]. These fields have become increasingly intelligent and user-friendly through LLM integration, enabling command and control through natural language. Equal contribution L. Y uan and C. G. Brinton are with the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA. C. Deng and I. Hwang are with the School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907, USA. Han is with the Department of Computer Science and Engineering, Y onsei University, Seoul, South Korea. E-mail: djh@yonsei.ac.kr S. Brunswicker is with the Polytechnic Institute, Purdue University, West Lafayette, IN 47907, USA. LLMs fulfill diverse roles within these systems. LLM-as-Router can orchestrate task allocation and model selection for human pilots, LLM-as-Agent can execute actions on behalf of humans, and LLM-as-Judge can conduct evaluations in place of human judgment.

Can Israel annex the West Bank if the US says no? Will the US plan for Gaza fail? 'We survived the war, we may not survive the ceasefire' Who are the 95 healthcare workers held by Israel? Hamas has handed over the remains of another dead captive to Israel, hours after an Israeli drone attack in southern Gaza killed two Palestinians amid a fragile ceasefire. The Israeli military said on Monday that the Red Cross had taken custody of the coffin and was in the process of transporting it to the army's troops in Gaza. The remains of 16 had been handed over as of Monday.

Can Israel annex the West Bank if the US says no? Will the US plan for Gaza fail? 'We survived the war, we may not survive the ceasefire' Who are the 95 healthcare workers held by Israel? The United Nations and France have condemned an Israeli attack that hit UN peacekeeping troops in southern Lebanon. UN spokesperson Stephane Dujarric said on Monday that the previous day's attack on UNIFIL troops, which he said involved an Israeli drone dropping a grenade in the vicinity of a patrol, as well as a tank opening fire on peacekeepers near the border town of Kfar Kila, was "very, very dangerous". Israel has violated the truce on a near-daily basis.

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.

In Russia's'blitz' of Ukraine, the question of appeasement is back Following another week of intensive and lethal Russian bombardment of Ukraine's cities, a composite image has been doing the rounds on Ukrainian social media. Underneath an old, black-and-white photo of Londoners queuing at a fruit and vegetable stall surrounded by the bombed-out rubble of the Blitz, a second image - this time in colour - creates a striking juxtaposition. Taken on Saturday, it shows shoppers thronging to similar stalls in a northern suburb of the Ukrainian capital, Kyiv, while a column of black smoke rises ominously in the background. Bombs can't stop markets, reads the caption linking the two images. The night before, as the city's sleep was interrupted once again by the now all-too-familiar booms of missile and drone strikes, two people were killed and nine others injured.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Russian drone attacks on the Ukrainian capital, Kyiv, early on Sunday killed at least three people and wounded 29 others, according to Ukrainian Minister of Internal Affairs Ihor Klymenko. The wounded included seven children, Klymenko said.

Space-air-ground integrated multi-access edge computing (SAGIN-MEC) provides a promising solution for the rapidly developing low-altitude economy (LAE) to deliver flexible and wide-area computing services. However, fully realizing the potential of SAGIN-MEC in the LAE presents significant challenges, including coordinating decisions across heterogeneous nodes with different roles, modeling complex factors such as mobility and network variability, and handling real-time decision-making under partially observable environment with hybrid variables. To address these challenges, we first present a hierarchical SAGIN-MEC architecture that enables the coordination between user devices (UDs), uncrewed aerial vehicles (UAVs), and satellites. Then, we formulate a UD cost minimization optimization problem (UCMOP) to minimize the UD cost by jointly optimizing the task offloading ratio, UAV trajectory planning, computing resource allocation, and UD association. We show that the UCMOP is an NP-hard problem. To overcome this challenge, we propose a multi-agent deep deterministic policy gradient (MADDPG)-convex optimization and coalitional game (MADDPG-COCG) algorithm. Specifically, we employ the MADDPG algorithm to optimize the continuous temporal decisions for heterogeneous nodes in the partially observable SAGIN-MEC system. Moreover, we propose a convex optimization and coalitional game (COCG) method to enhance the conventional MADDPG by deterministically handling the hybrid and varying-dimensional decisions. Simulation results demonstrate that the proposed MADDPG-COCG algorithm significantly enhances the user-centric performances in terms of the aggregated UD cost, task completion delay, and UD energy consumption, with a slight increase in UAV energy consumption, compared to the benchmark algorithms. Moreover, the MADDPG-COCG algorithm shows superior convergence stability and scalability.

Micro aerial vehicles are becoming increasingly important in search and rescue operations due to their agility, speed, and ability to access confined spaces o r hazardous areas. However, designing lightweight aerial systems presents significant structural, aerodynamic, and computational challenges. This work addresses two key limitations in many low - cost aerial systems under two kilograms: their lack of structural durability during flight through rough terrains and inability to replan paths dynamically when new victims or obstacles are detected. We present a fully customised drone built from scratch using only commonly available components and materials, emphasising modularity, low cost, and ease of assembly. The structural frame is reinforced with lightweight yet durable materials to withstand impact, while the onboard control system is powered entirely by free, open - source software solutions. The proposed system demonstrates real - time perception and adaptive navigation capabilities without relying on expensive hardware accelerators by offering an affordable and practical solution for real - world search and rescue missions.

Nighttime UAV tracking faces significant challenges in real-world robotics operations. Low-light conditions not only limit visual perception capabilities, but cluttered backgrounds and frequent viewpoint changes also cause existing trackers to drift or fail during deployment. To address these difficulties, researchers have proposed solutions based on low-light enhancement and domain adaptation. However, these methods still have notable shortcomings in actual UAV systems: low-light enhancement often introduces visual artifacts, domain adaptation methods are computationally expensive and existing lightweight designs struggle to fully leverage dynamic object information. Based on an in-depth analysis of these key issues, we propose MATrack-a multiscale adaptive system designed specifically for nighttime UAV tracking. MATrack tackles the main technical challenges of nighttime tracking through the collaborative work of three core modules: Multiscale Hierarchy Blende (MHB) enhances feature consistency between static and dynamic templates. Adaptive Key Token Gate accurately identifies object information within complex backgrounds. Nighttime Template Calibrator (NTC) ensures stable tracking performance over long sequences. Extensive experiments show that MATrack achieves a significant performance improvement. On the UAVDark135 benchmark, its precision, normalized precision and AUC surpass state-of-the-art (SOTA) methods by 5.9%, 5.4% and 4.2% respectively, while maintaining a real-time processing speed of 81 FPS. Further tests on a real-world UAV platform validate the system's reliability, demonstrating that MATrack can provide stable and effective nighttime UAV tracking support for critical robotics applications such as nighttime search and rescue and border patrol.

In recent years, consumer-grade UAVs have been widely adopted by first responders. In general, they are operated manually, which requires trained pilots, especially in unknown GNSS-denied environments and in the vicinity of structures. Autonomous flight can facilitate the application of UAVs and reduce operator strain. However, autonomous systems usually require special programming interfaces, custom sensor setups, and strong onboard computers, which limits a broader deployment. We present a system for autonomous flight using lightweight consumer-grade DJI drones. They are controlled by an Android app for state estimation and obstacle avoidance directly running on the UAV's remote control. Our ground control station enables a single operator to configure and supervise multiple heterogeneous UAVs at once. Furthermore, it combines the observations of all UAVs into a joint 3D environment model for improved situational awareness.