The integration of human-intuitive interactions into autonomous systems has been limited. Traditional Natural Language Processing (NLP) systems struggle with context and intent understanding, severely restricting human-robot interaction. Recent advancements in Large Language Models (LLMs) have transformed this dynamic, allowing for intuitive and high-level communication through speech and text, and bridging the gap between human commands and robotic actions. Additionally, autonomous navigation has emerged as a central focus in robotics research, with artificial intelligence (AI) increasingly being leveraged to enhance these systems. However, existing AI-based navigation algorithms face significant challenges in latency-critical tasks where rapid decision-making is critical. Traditional frame-based vision systems, while effective for high-level decision-making, suffer from high energy consumption and latency, limiting their applicability in real-time scenarios. Neuromorphic vision systems, combining event-based cameras and spiking neural networks (SNNs), offer a promising alternative by enabling energy-efficient, low-latency navigation. Despite their potential, real-world implementations of these systems, particularly on physical platforms such as drones, remain scarce. In this work, we present Neuro-LIFT, a real-time neuromorphic navigation framework implemented on a Parrot Bebop2 quadrotor. Leveraging an LLM for natural language processing, Neuro-LIFT translates human speech into high-level planning commands which are then autonomously executed using event-based neuromorphic vision and physics-driven planning. Our framework demonstrates its capabilities in navigating in a dynamic environment, avoiding obstacles, and adapting to human instructions in real-time.

This paper introduces a secure communication architecture for Unmanned Aerial Vehicles (UAVs) and ground stations in 5G networks, addressing critical challenges in network security. The proposed solution integrates the Advanced Encryption Standard (AES) with Elliptic Curve Cryptography (ECC) and CRYSTALS-Kyber for key encapsulation, offering a hybrid cryptographic approach. By incorporating CRYSTALS-Kyber, the framework mitigates vulnerabilities in ECC against quantum attacks, positioning it as a quantum-resistant alternative. The architecture is based on a server-client model, with UAVs functioning as clients and the ground station acting as the server. The system was rigorously evaluated in both VPN and 5G environments. Experimental results confirm that CRYSTALS-Kyber delivers strong protection against quantum threats with minimal performance overhead, making it highly suitable for UAVs with resource constraints. Moreover, the proposed architecture integrates an Artificial Intelligence (AI)-based Intrusion Detection System (IDS) to further enhance security. In performance evaluations, the IDS demonstrated strong results across multiple models with XGBoost, particularly in more demanding scenarios, outperforming other models with an accuracy of 97.33% and an AUC of 0.94. These findings underscore the potential of combining quantum-resistant encryption mechanisms with AI-driven IDS to create a robust, scalable, and secure communication framework for UAV networks, particularly within the high-performance requirements of 5G environments.

President Donald Trump has revealed the mysterious drones over New Jersey were'not the enemy' and had been authorized to conduct'research'. In the first press briefing of Trump's second administration, White House Press Secretary Karoline Leavitt said the Federal Aviation Administration (FAA) had been authorized to fly the drones for'research and various other reasons'. Leavitt said many of the drones were also'hobbyists, recreational and private individuals that enjoy flying drones' and claims that'in time, it got worse due to curiosity.' She added information had come'directly from the president of the United States that was just shared with me in the Oval Office'. But the White House's vague explanation has raised even more questions, especially after the FAA - which investigated the sightings after receiving reports from'concerned citizens' - failed to previously mention the alleged research.

White House press secretary Karoline Leavitt shared a message from President Donald Trump on the source of drone sightings reported over New Jersey. The White House on Tuesday revealed that some of the drones seen flying over New Jersey and other parts of the country in November were authorized to be flown by the Federal Aviation Administration. Press secretary Karoline Leavitt shared an update "directly" from President Donald Trump that clarified the origin of the drones, which caused a national stir and captured headlines for weeks late last year. "After research and study, the drones that were flying over New Jersey in large numbers were authorized by the FAA for research and various other reasons," Leavitt said. White House Press Secretary Karoline Leavitt holds her first news conference in the Brady Press Briefing Room at the White House on Jan. 28, 2025, in Washington, D.C.

We consider the trajectory replanning problem for a large-scale swarm in a cluttered environment. Our path planner replans for robots by utilizing a hierarchical approach, dividing the workspace, and computing collision-free paths for robots within each cell in parallel. Distributed trajectory optimization generates a deadlock-free trajectory for efficient execution and maintains the control feasibility even when the optimization fails. Our hierarchical approach combines the benefits of both centralized and decentralized methods, achieving a high task success rate while providing real-time replanning capability. Compared to decentralized approaches, our approach effectively avoids deadlocks and collisions, significantly increasing the task success rate. We demonstrate the real-time performance of our algorithm with up to 142 robots in simulation, and a representative 24 physical Crazyflie nano-quadrotor experiment.

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.