Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Bostonians who gathered at City Hall Plaza on Tuesday night to watch the Boston Celtics' opening night watch party and further celebrate the team's recent NBA Championship were greeted by a falling drone that injured some and sent at least one person to the hospital. Boston police say at least three people sustained non-life-threatening injuries from a falling drone outside the plaza around 7:30 p.m., just as the Celtics were tipping off against the New York Knicks. Yousef Kobeissi, who was hit by the drone, told Boston 25 that it sounded like "a banging sound" when the drone crashed into them.

Autonomous drone racing requires powerful perception, planning, and control and has become a benchmark and test field for autonomous, agile flight. Existing work usually assumes static race tracks with known maps, which enables offline planning of time-optimal trajectories, performing localization to the gates to reduce the drift in visual-inertial odometry (VIO) for state estimation or training learning-based methods for the particular race track and operating environment. In contrast, many real-world tasks like disaster response or delivery need to be performed in unknown and dynamic environments. To close this gap and make drone racing more robust against unseen environments and moving gates, we propose a control algorithm that does not require a race track map or VIO and uses only monocular measurements of the line of sight (LOS) to the gates. For this purpose, we adopt the law of proportional navigation (PN) to accurately fly through the gates despite gate motions or wind. We formulate the PN-informed vision-based control problem for drone racing as a constrained optimization problem and derive a closed-form optimal solution. We demonstrate through extensive simulations and real-world experiments that our method can navigate through moving gates at high speeds while being robust to different gate movements, model errors, wind, and delays.

The intersection of LLMs (Large Language Models) and UAV (Unoccupied Aerial Vehicles) technology represents a promising field of research with the potential to enhance UAV capabilities significantly. This study explores the application of LLMs in UAV control, focusing on the opportunities for integrating advanced natural language processing into autonomous aerial systems. By enabling UAVs to interpret and respond to natural language commands, LLMs simplify the UAV control and usage, making them accessible to a broader user base and facilitating more intuitive human-machine interactions. The paper discusses several key areas where LLMs can impact UAV technology, including autonomous decision-making, dynamic mission planning, enhanced situational awareness, and improved safety protocols. Through a comprehensive review of current developments and potential future directions, this study aims to highlight how LLMs can transform UAV operations, making them more adaptable, responsive, and efficient in complex environments. A template development framework for integrating LLMs in UAV control is also described. Proof of Concept results that integrate existing LLM models and popular robotic simulation platforms are demonstrated. The findings suggest that while there are substantial technical and ethical challenges to address, integrating LLMs into UAV control holds promising implications for advancing autonomous aerial systems.

Multirotor unmanned aerial vehicle is a prevailing type of aerial robots with wide real-world applications. The energy efficiency of the robot is a critical aspect of its performance, determining the range and duration of the missions that can be performed. This paper studies the energy-optimal planning of the multirotor, which aims at finding the optimal ordering of waypoints with the minimum energy consumption for missions in 3D space. The study is performed based on a previously developed model capturing first-principle energy dynamics of the multirotor. We found that in majority of the cases (up to 95%) the solutions of the energy-optimal planning are different from those of the traditional traveling salesman problem which minimizes the total distance. The difference can be as high as 14.9%, with the average at 1.6%-3.3% and 90th percentile at 3.7%-6.5% depending on the range and number of waypoints in the mission. We then identified and explained the key features of the minimum-energy order by correlating to the underlying flight energy dynamics. It is shown that instead of minimizing the distance, coordination of vertical and horizontal motion to promote aerodynamic efficiency is the key to optimizing energy consumption.

A bird's-eye view of campus featuring Maseeh Hall, captured by a DJI Mavic 3 drone in late August. Given airspace restrictions, the photographer, an FAA-certified drone pilot, had to get FAA clearance three days ahead of time--and hope the weather forecast would hold--to get this shot.

In May 2022, just months after Russia's full-scale invasion of Ukraine, a disparate group of people from across the globe decided that they wanted to fight back. This turned into the North Atlantic Fella Organization (NAFO), a decentralized online activist network designed to combat pro-Kremlin propaganda, primarily focusing on the platform then known as Twitter. The members, identified by cartoon Shiba Inu avatars, mocked Russian government accounts and used meme warfare to disrupt Moscow's propaganda over the invasion. But in November 2022, Elon Musk took control of the platform, changed its name to X, and effectively allowed Russia's propagandists free rein to do whatever they wanted. While NAFO members continued to push back, posting memes in response to any posts from official Russian accounts, they knew they had to do something else to alter the course of the war.

The Lebanese armed group Hezbollah has claimed responsibility for a drone attack last week on Israeli Prime Minister Benjamin Netanyahu's holiday residence in Caesarea in northern Israel. "The Islamic Resistance claims responsibility for the Caesarea operation and targeting Netanyahu's home," the head of Hezbollah's media office, Mohammad Afif, said at a news conference on Tuesday. One of three drones launched from Lebanon hit Netanyahu's holiday residence on Saturday. His spokesperson said the prime minister was not in the vicinity at the time of the attack and there were no casualties. Afif said that if in the previous attack Netanyahu was not hurt, "the coming days and nights and the [battle]fields are between us."

The swift advancement of unmanned aerial vehicle (UAV) technologies necessitates new standards for developing human-drone interaction (HDI) interfaces. Most interfaces for HDI, especially first-person view (FPV) goggles, limit the operator's ability to obtain information from the environment. This paper presents a novel interface, FlightAR, that integrates augmented reality (AR) overlays of UAV first-person view (FPV) and bottom camera feeds with head-mounted display (HMD) to enhance the pilot's situational awareness. Using FlightAR, the system provides pilots not only with a video stream from several UAV cameras simultaneously, but also the ability to observe their surroundings in real time. User evaluation with NASA-TLX and UEQ surveys showed low physical demand ($\mu=1.8$, $SD = 0.8$) and good performance ($\mu=3.4$, $SD = 0.8$), proving better user assessments in comparison with baseline FPV goggles. Participants also rated the system highly for stimulation ($\mu=2.35$, $SD = 0.9$), novelty ($\mu=2.1$, $SD = 0.9$) and attractiveness ($\mu=1.97$, $SD = 1$), indicating positive user experiences. These results demonstrate the potential of the system to improve UAV piloting experience through enhanced situational awareness and intuitive control. The code is available here: https://github.com/Sautenich/FlightAR

New reporting about over a dozen unidentified drones that were allowed to fly over Langley Air Force Base has prompted fresh calls for change to a threat that experts say will only become more prevalent. For more than two weeks in December 2023, the mystery drones traipsed into restricted airspace over the installation, home to key national security facilities and the F-22 Raptor stealth fighters. Experts say the incident is likely one of many that U.S. authorities are underprepared to tackle in an evolving threat environment. Lack of a standard protocol for such incursions left Langley officials unsure of what to do – other than allow the 20-foot-long drones to hover near their classified facilities. The Pentagon has said little about the incidents other than to confirm they occurred after a Wall Street Journal report this month.

Aerial robotics for transporting suspended payloads as the form of freely-floating manipulator are growing great interest in recent years. However, the prior information of the payload, such as the mass, is always hard to obtain accurately in practice. The force/torque caused by payload and residual dynamics will introduce unmodeled perturbations to the system, which negatively affects the closed-loop performance. Different from estimation-like methods, this paper proposes Neural Predictor, a learning-based approach to model force/torque caused by payload and residual dynamics as a dynamical system. It results a hybrid model including both the first-principles dynamics and the learned dynamics. This hybrid model is then integrated into a MPC framework to improve closed-loop performance. Effectiveness of proposed framework is verified extensively in both numerical simulations and real-world flight experiments. The results indicate that our approach can capture force/torque caused by payload and residual dynamics accurately, respond quickly to the changes of them and improve the closed-loop performance significantly. In particular, Neural Predictor outperforms a state-of-the-art learning-based estimator and has reduced the force and torque estimation errors by up to 66.15% and 33.33% while using less samples.