Autonomous aerial monitoring is an important task aimed at gathering information from areas that may not be easily accessible by humans. At the same time, this task often requires recognizing anomalies from a significant distance and/or not previously encountered in the past. In this paper, we propose a novel framework that leverages the advanced capabilities provided by Large Language Models (LLMs) to actively collect information and perform anomaly detection in novel scenes. To this end, we propose an LLM-based model dialogue approach, in which two deep learning models engage in a dialogue to actively control a drone to increase perception and anomaly detection accuracy. We conduct our experiments in a high fidelity simulation environment where an LLM is provided with a predetermined set of natural language movement commands mapped into executable code functions. Additionally, we deploy a multimodal Visual Question Answering (VQA) model charged with the task of visual question answering and captioning. By engaging the two models in conversation, the LLM asks exploratory questions while simultaneously flying a drone into different parts of the scene, providing a novel way to implement active perception. By leveraging LLM's reasoning ability, we output an improved detailed description of the scene going beyond existing static perception approaches. In addition to information gathering, our approach is utilized for anomaly detection and our results demonstrate the proposed method's effectiveness in informing and alerting about potential hazards.

An influx of unexplained drone sightings in parts of the United States began to make headlines in November, and although authorities have said there is no evidence that the drones are a threat to national security or public safety, experts explain the mystery behind the uptick in sightings. Fox News' Peter Doocy questioned President Donald Trump about the drones on Monday, asking, "Anything with these drones -- is it anything to be worried about?" "I would like to find out what it is and tell the people. In fact, I'd like to do that," Trump responded. He then redirected to White House Chief of Staff Susie Wiles. "Could we find out what that was, Susie? Why don't we find out immediately?" "I can't imagine it's an enemy, or there would have been, you know, people would have gotten blown up, all of them. Maybe they were testing things. They wouldn't have said what it was," Trump said.

Inertial odometry (IO) using only Inertial Measurement Units (IMUs) offers a lightweight and cost-effective solution for Unmanned Aerial Vehicle (UAV) applications, yet existing learning-based IO models often fail to generalize to UAVs due to the highly dynamic and non-linear-flight patterns that differ from pedestrian motion. In this work, we identify that the conventional practice of transforming raw IMU data to global coordinates undermines the observability of critical kinematic information in UAVs. By preserving the body-frame representation, our method achieves substantial performance improvements, with a 66.7% average increase in accuracy across three datasets. Furthermore, explicitly encoding attitude information into the motion network results in an additional 23.8% improvement over prior results. Combined with a data-driven IMU correction model (AirIMU) and an uncertainty-aware Extended Kalman Filter (EKF), our approach ensures robust state estimation under aggressive UAV maneuvers without relying on external sensors or control inputs. Notably, our method also demonstrates strong generalizability to unseen data not included in the training set, underscoring its potential for real-world UAV applications.

The control of quadrotor Unmanned Aerial Vehicles (UAVs) presents unique challenges due to their nonlinear dynamics, underactuation, and the need for precise trajectory tracking in dynamic environments. Traditional control techniques often struggle to handle these challenges efficiently while maintaining computational tractability for real-time applications. To address these issues, this work outlines an error-state Linear Quadratic Regulator (LQR) approach, leveraging the compact and singularity-free representation of orientation errors using exponential coordinates. Exponential coordinates provide a robust way to represent orientation errors without the singularities inherent in other parameterizations such as Euler angles. By formulating the controller in terms of error-state dynamics, this approach avoids the complexity of directly controlling the nonlinear dynamics, focusing instead on minimizing deviations from a nominal trajectory. This is achieved by driving the error-state--which includes position, velocity, and orientation errors--toward zero. The proposed controller uses an LQR formulation, a well-established concept in classical control theory for linear systems, to minimize a quadratic cost function balancing state deviations and control effort. Although the quadrotor dynamics are nonlinear, the error-state dynamics can be re-linearized about the current tracking error at a sufficiently high frequency, allowing the LQR controller to operate effectively in real time. This iterative re-linearization ensures that the controller remains responsive to changes in the tracking error while maintaining computational efficiency.

Dozens of patients have been killed in a drone attack on one of the last functioning hospitals in el-Fasher in Sudan's Darfur region. While it was not immediately clear who targeted the Saudi Hospital on Friday, medical sources quoted by AFP news agency said the same building was hit by a Rapid Support Forces (RSF) drone "a few weeks ago". Friday's attack killed at least 30 patients in the emergency department, the report added. Regional governor Mini Minawi posted graphic images of bloodied bodies on his X account on Saturday, saying that the attack "exterminated" more than 70 patients, including women and children. The Sudanese army has been at war with the paramilitary RSF, who have seized nearly the entire vast western region of Darfur, since April 2023.

Edge sensing and computing is rapidly becoming part of intelligent infrastructure architecture leading to operational reliance on such systems in disaster or emergency situations. In such scenarios there is a high chance of power supply failure due to power grid issues, and communication system issues due to base stations losing power or being damaged by the elements, e.g., flooding, wildfires etc. Mobile edge computing in the form of unmanned aerial vehicles (UAVs) has been proposed to provide computation offloading from these devices to conserve their battery, while the use of UAVs as relay network nodes has also been investigated previously. This paper considers the use of UAVs with further constraints on power and connectivity to prolong the life of the network while also ensuring that the data is received from the edge nodes in a timely manner. Reinforcement learning is used to investigate numerous scenarios of various levels of power and communication failure. This approach is able to identify the device most likely to fail in a given scenario, thus providing priority guidance for maintenance personnel. The evacuations of a rural town and urban downtown area are also simulated to demonstrate the effectiveness of the approach at extending the life of the most critical edge devices.

Ensuring a stable power supply in rural areas relies heavily on effective inspection of power equipment, particularly transmission lines (TLs). However, detecting TLs from aerial imagery can be challenging when dealing with misalignments between visible light (RGB) and infrared (IR) images, as well as mismatched high- and low-level features in convolutional networks. To address these limitations, we propose a novel Hierarchical Multi-Modal Enhancement Network (HMMEN) that integrates RGB and IR data for robust and accurate TL detection. Our method introduces two key components: (1) a Mutual Multi-Modal Enhanced Block (MMEB), which fuses and enhances hierarchical RGB and IR feature maps in a coarse-to-fine manner, and (2) a Feature Alignment Block (FAB) that corrects misalignments between decoder outputs and IR feature maps by leveraging deformable convolutions. We employ MobileNet-based encoders for both RGB and IR inputs to accommodate edge-computing constraints and reduce computational overhead. Experimental results on diverse weather and lighting conditionsfog, night, snow, and daytimedemonstrate the superiority and robustness of our approach compared to state-of-the-art methods, resulting in fewer false positives, enhanced boundary delineation, and better overall detection performance. This framework thus shows promise for practical large-scale power line inspections with unmanned aerial vehicles.

An Israeli drone attack on a vehicle near the occupied West Bank town of Qabatiya has killed two people, the Palestinian Ministry of Health says on the fourth day of a large-scale Israeli operation in and around the nearby city of Jenin. The Israeli military said the air attack on Friday in the Jenin governorate hit a vehicle with what it said was a "terrorist cell" inside, but it gave no further details. The official Palestinian news agency Wafa reported that it was a drone attack that happened just before Israeli forces stormed Qabatiya and began "sweeping operations". The air attack coincided with the ongoing military operation against Palestinian fighters in Jenin and its adjacent refugee camp, which has already resulted in the deaths of 14 Palestinians and injured about 50 others, according to the Palestinian Health Ministry in Ramallah. The Israeli military also announced the arrests of 20 people it considers "wanted suspects" and said it had seized weapons.

A New Jersey Mayor has shared new footage of'glowing orbs transforming into drones' over Long Island, adding more intrigue to this ongoing mystery. Michael Melham, the Mayor of Belleville, has been outspoken about the unexplained phenomena plaguing his state and the greater tri-state area since mid-November when the drones first appeared. He shared the bizarre footage on X, saying the clips'appears to show glowing orbs turning into drones. Verified not to be planes via flight tracker. In a recent interview with NewsNation, Melham said he is still getting reports of drone sightings'all over New Jersey, and even Long Island.' 'Here in New Jersey, we are about 500 mayors strong, we are still waiting for answers because our residents are still gravely concerned over what's flying just over our homes,' he said.

People were filmed fleeing a fire at a Russian oil refinery in Ryazan, about 200 kilometres southeast of Moscow, after a Ukrainian drone attack. The facility accounted for almost 5% of the country's oil refining output last year.