Advanced Air Mobility (AAM) is a growing field that demands accurate modeling of legal concepts and restrictions in navigating intelligent vehicles. In addition, any implementation of AAM needs to face the challenges posed by inherently dynamic and uncertain human-inhabited spaces robustly. Nevertheless, the employment of Unmanned Aircraft Systems (UAS) beyond visual line of sight (BVLOS) is an endearing task that promises to enhance significantly today's logistics and emergency response capabilities. To tackle these challenges, we present a probabilistic and neuro-symbolic architecture to encode legal frameworks and expert knowledge over uncertain spatial relations and noisy perception in an interpretable and adaptable fashion. More specifically, we demonstrate Probabilistic Mission Design (ProMis), a system architecture that links geospatial and sensory data with declarative, Hybrid Probabilistic Logic Programs (HPLP) to reason over the agent's state space and its legality. As a result, ProMis generates Probabilistic Mission Landscapes (PML), which quantify the agent's belief that a set of mission conditions is satisfied across its navigation space. Extending prior work on ProMis' reasoning capabilities and computational characteristics, we show its integration with potent machine learning models such as Large Language Models (LLM) and Transformer-based vision models. Hence, our experiments underpin the application of ProMis with multi-modal input data and how our method applies to many important AAM scenarios.

At times, Israeli tanks have deployed along main roads where aid trucks travel. And Israeli ministers have said they debated authorizing private security contractors to protect international aid convoys inside Gaza. Until recently, Israeli forces largely did not target the looters unless they were affiliated with Hamas or other militant groups, according to U.N. officials. But that appears to have changed in recent weeks. In Israeli military drone footage viewed by The Times, looters can be seen confiscating white sacks of aid from cars in southern Gaza in November.

Fuzz testing effectively uncovers software vulnerabilities; however, it faces challenges with Autonomous Systems (AS) due to their vast search spaces and complex state spaces, which reflect the unpredictability and complexity of real-world environments. This paper presents a universal framework aimed at improving the efficiency of fuzz testing for AS. At its core is SaFliTe, a predictive component that evaluates whether a test case meets predefined safety criteria. By leveraging the large language model (LLM) with information about the test objective and the AS state, SaFliTe assesses the relevance of each test case. We evaluated SaFliTe by instantiating it with various LLMs, including GPT-3.5, Mistral-7B, and Llama2-7B, and integrating it into four fuzz testing tools: PGFuzz, DeepHyperion-UAV, CAMBA, and TUMB. These tools are designed specifically for testing autonomous drone control systems, such as ArduPilot, PX4, and PX4-Avoidance. The experimental results demonstrate that, compared to PGFuzz, SaFliTe increased the likelihood of selecting operations that triggered bug occurrences in each fuzzing iteration by an average of 93.1\%. Additionally, after integrating SaFliTe, the ability of DeepHyperion-UAV, CAMBA, and TUMB to generate test cases that caused system violations increased by 234.5\%, 33.3\%, and 17.8\%, respectively. The benchmark for this evaluation was sourced from a UAV Testing Competition.

Advanced Aerial Mobility encompasses many outstanding applications that promise to revolutionize modern logistics and pave the way for various public services and industry uses. However, throughout its history, the development of such systems has been impeded by the complexity of legal restrictions and physical constraints. While airspaces are often tightly shaped by various legal requirements, Unmanned Aerial Vehicles (UAV) must simultaneously consider, among others, energy demands, signal quality, and noise pollution. In this work, we address this challenge by presenting a novel architecture that integrates methods of Probabilistic Mission Design (ProMis) and Many-Objective Optimization for UAV routing. Hereby, our framework is able to comply with legal requirements under uncertainty while producing effective paths that minimize various physical costs a UAV needs to consider when traversing human-inhabited spaces. To this end, we combine hybrid probabilistic first-order logic for spatial reasoning with mixed deterministic-stochastic route optimization, incorporating physical objectives such as energy consumption and radio interference with a logical, probabilistic model of legal requirements. We demonstrate the versatility and advantages of our system in a large-scale empirical evaluation over real-world, crowd-sourced data from a map extract from the city of Paris, France, showing how a network of effective and compliant paths can be formed.

This paper presents the Visual Optical Recognition Telemetry EXtraction (VORTEX) system for extracting and analyzing drone telemetry data from First Person View (FPV) Uncrewed Aerial System (UAS) footage. VORTEX employs MMOCR, a PyTorch-based Optical Character Recognition (OCR) toolbox, to extract telemetry variables from drone Heads Up Display (HUD) recordings, utilizing advanced image preprocessing techniques, including CLAHE enhancement and adaptive thresholding. The study optimizes spatial accuracy and computational efficiency through systematic investigation of temporal sampling rates (1s, 5s, 10s, 15s, 20s) and coordinate processing methods. Results demonstrate that the 5-second sampling rate, utilizing 4.07% of available frames, provides the optimal balance with a point retention rate of 64% and mean speed accuracy within 4.2% of the 1-second baseline while reducing computational overhead by 80.5%. Comparative analysis of coordinate processing methods reveals that while UTM Zone 33N projection and Haversine calculations provide consistently similar results (within 0.1% difference), raw WGS84 coordinates underestimate distances by 15-30% and speeds by 20-35%. Altitude measurements showed unexpected resilience to sampling rate variations, with only 2.1% variation across all intervals. This research is the first of its kind, providing quantitative benchmarks for establishing a robust framework for drone telemetry extraction and analysis using open-source tools and spatial libraries.

Video shows the moment drones started falling from the sky during a drone show at Eola Lake in Orlando, Florida on Dec. 21, 2024. A 7-year-old Florida boy who was injured when drones collided and fell into a crowd at a holiday airshow over the weekend underwent open heart surgery, his parents said. Adriana Edgerton and Jessica Lumsden, parents of Alexander, said one of the red and green-lit drones struck him and knocked him out upon impact, causing a chest injury, Fox Orlando reported. Hundreds of drones being used as part of a Saturday night aerial light show in Lake Eola Park in downtown Orlando appeared to be flying into position before several started falling from the sky before slamming to the ground, according to videos posted online. Alexander, a 7-year-old boy, has undergone heart surgery after he was struck by a falling drone during a holiday airshow in Orlando, his parents said.

Video shows the moment drones started falling from the sky during a drone show at Eola Lake in Orlando, Florida on Dec. 21, 2024. A child was hospitalized on Saturday after being hit by a drone that was part of an Orlando, Florida holiday drone show. According to the Orlando Fire Department, a 7-year-old boy was transported to the hospital because of injuries sustained from the falling drones, FOX 35 in Orlando reported. In a video posted online by X user MosquitoCoFl, hundreds of drones being used as part of an aerial light show appeared to be flying into position before several started falling from the sky before slamming to the ground. A man could be heard saying to children nearby, "Oh no! I don't believe they're supposed to be falling."

This paper presents a novel approach to utilizing underactuated quadrotor Unmanned Aerial Vehicles (UAVs) as assistive devices in cooperative payload transportation task through human guidance and physical interaction. The proposed system consists of two underactuated UAVs rigidly connected to the transported payload. This task involves the collaboration between human and UAVs to transport and manipulate a payload. The goal is to reduce the workload of the human and enable seamless interaction between the human operator and the aerial vehicle. An Admittance-Nonsingular Fast Terminal Sliding Mode Control (NFTSMC) is employed to control and asymptotically stabilize the system while performing the task, where forces are applied to the payload by the human operator dictate the aerial vehicle's motion. The stability of the proposed controller is confirmed using Lyapunov analysis. Extensive simulation studies were conducted using MATLAB, Robot Operating System (ROS), and Gazebo to validate robustness and effectiveness of the proposed controller in assisting with payload transportation tasks. Results demonstrates feasibility and potential benefits utilizing quadrotor UAVs as assistive devices for payload transportation through intuitive human-guided control. Keywords Cooperative payload transportation, Admittance control, Sliding mode control, Quadrotor control

Russian President Vladimir Putin has pledged retaliation after Ukrainian drones struck residential buildings in the city of Kazan in Russia's Tatarstan region. Putin made the comments via videolink on Sunday while addressing the local leader of Tatarstan in a road-opening ceremony. "Whoever, and however much they try to destroy, they will face many times more destruction themselves and will regret what they are trying to do in our country," Putin said. On Saturday morning, six Ukrainian drones hit residential buildings in Kazan and a seventh struck an industrial facility. No injuries from the attack were officially reported, while media reports indicated that three people suffered cuts from shattered window glass.

As drone sightings over New Jersey continue to raise questions, a new tool could bring answers about the source of these flying vehicles -- if the government could get it off the ground. Earlier this year, the Federal Aviation Administration (FAA) began requiring all unmanned aircraft systems to be equipped with Remote ID technology, which makes every equipped drone uniquely identifiable to authorities, like a license plate on a car. The FAA announced that it would provide a database that could be accessed by local law enforcement, but nearly one year later, local authorities still can't get into it themselves. "The FAA is working on developing Remote ID data sharing capabilities for law enforcement so they can have access to FAA registration information," the agency said in a statement to Fox News Digital. A sign marks the entrance to the FAA headquarters in Washington, D.C., on Oct. 7. (J.