Achieving both agile maneuverability and high energy efficiency in aerial robots, particularly in dynamic wind environments, remains challenging. Conventional thruster-powered systems offer agility but suffer from high energy consumption, while fixed-wing designs are efficient but lack hovering and maneuvering capabilities. We present Floaty, a shape-changing robot that overcomes these limitations by passively soaring, harnessing wind energy through intelligent morphological control inspired by birds. Floaty's design is optimized for passive stability, and its control policy is derived from an experimentally learned aerodynamic model, enabling precise attitude and position control without active propulsion. Wind tunnel experiments demonstrate Floaty's ability to hover, maneuver, and reject disturbances in vertical airflows up to 10 m/s. Crucially, Floaty achieves this with a specific power consumption of 10 W/kg, an order of magnitude lower than thruster-powered systems. This introduces a paradigm for energy-efficient aerial robotics, leveraging morphological intelligence and control to operate sustainably in challenging wind conditions.

Breakthroughs, discoveries, and DIY tips sent every weekday. For some adventurers, scaling Mount Everest represents the ultimate test of grit and determination: a visual signifier of humanity's epic struggle to overcome the elements. For others, the peak can seem more like a really tall trash can. Every year, around 600 climbers make the trek from the mountain's base camp to the summit. During their time on Everest, each person produces an estimated 18 pounds of waste, most of which is left behind.

An invasion of small metallic orbs has been spotted hovering over the US in recent years, leaving the Pentagon scrambling to identify these mysterious UFOs. A new report from the crowdsourced platform Enigma, which allows people to report sightings of unidentified flying objects (UFOs), reveals more than 8,000 sightings across the US between December 2022 and June 2025. Among these, 422 reports specifically describe metallic orbs, with the majority observed between 1am and 4am near military installations in New York, California, and Arizona. Eyewitnesses, including civilians, pilots, and military personnel, reported seeing the spheres hover silently before moving at extreme speeds, leaving no trace of their departure. Some of the sightings have been captured on video or radar, though many remain unexplained.

In his post on Wednesday, Zelensky said Russia had carried out almost 100 drone attacks overnight. Energy facilities were the main targets, but a school in the Kharkiv region and a high-rise building in Kherson were also hit, he said. "New steps are needed to put pressure on Russia to stop the strikes and truly guarantee security. We are working with partners for such pressure," Zelensky added. Three and a half years after Russia's full-scale invasion of Ukraine, fighting on the ground shows no sign of abating.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A missing hiker's dead body was finally found in July in Italy's rugged Piedmont region after 10 months. The recovery team credited the breakthrough to an AI-powered drone that spotted a critical clue within hours. The same process would have taken weeks or even months if done by the human eye.

Efficient and safe navigation of Unmanned Aerial Vehicles (UAVs) is critical for various applications, including combat support, package delivery and Search and Rescue Operations. This paper introduces the Tangent Intersection Guidance (TIG) algorithm, an advanced approach for UAV path planning in both static and dynamic environments. The algorithm uses the elliptic tangent intersection method to generate feasible paths. It generates two sub-paths for each threat, selects the optimal route based on a heuristic rule, and iteratively refines the path until the target is reached. Considering the UAV kinematic and dynamic constraints, a modified smoothing technique based on quadratic Bézier curves is adopted to generate a smooth and efficient route. Experimental results show that the TIG algorithm can generate the shortest path in less time, starting from 0.01 seconds, with fewer turning angles compared to A*, PRM, RRT*, Tangent Graph, and Static APPATT algorithms in static environments. Furthermore, in completely unknown and partially known environments, TIG demonstrates efficient real-time path planning capabilities for collision avoidance, outperforming APF and Dynamic APPATT algorithms.

Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making it well-suited for tasks where objectives are difficult to formalize or inherently subjective. Acrobatic flight poses a particularly challenging problem due to its complex dynamics, rapid movements, and the importance of precise execution. In this work, we explore the use of PbRL for agile drone control, focusing on the execution of dynamic maneuvers such as powerloops. Building on Preference-based Proximal Policy Optimization (Preference PPO), we propose Reward Ensemble under Confidence (REC), an extension to the reward learning objective that improves preference modeling and learning stability. Our method achieves 88.4% of the shaped reward performance, compared to 55.2% with standard Preference PPO. We train policies in simulation and successfully transfer them to real-world drones, demonstrating multiple acrobatic maneuvers where human preferences emphasize stylistic qualities of motion. Furthermore, we demonstrate the applicability of our probabilistic reward model in a representative MuJoCo environment for continuous control. Finally, we highlight the limitations of manually designed rewards, observing only 60.7% agreement with human preferences. These results underscore the effectiveness of PbRL in capturing complex, human-centered objectives across both physical and simulated domains.

Insects control unsteady aerodynamic forces on flapping wings to navigate complex environments. While understanding these forces is vital for biology, physics, and engineering, existing evaluation methods face trade-offs: high-fidelity simulations are computationally or experimentally expensive and lack explanatory power, whereas theoretical models based on quasi-steady assumptions offer insights but exhibit low accuracy. To overcome these limitations and thus enhance the accuracy of quasi-steady aerodynamic models, we applied a data-driven approach involving discovery and formulation of previously overlooked critical mechanisms. Through selection from 5,000 candidate kinematic functions, we identified mathematical expressions for three key additional mechanisms -- the effect of advance ratio, effect of spanwise kinematic velocity, and rotational Wagner effect -- which had been qualitatively recognized but were not formulated. Incorporating these mechanisms considerably reduced the prediction errors of the quasi-steady model using the computational fluid dynamics results as the ground truth, both in hawkmoth forward flight (at high Reynolds numbers) and fruit fly maneuvers (at low Reynolds numbers). The data-driven quasi-steady model enables rapid aerodynamic analysis, serving as a practical tool for understanding evolutionary adaptations in insect flight and developing bio-inspired flying robots.

This paper addresses the problem of trajectory optimization for unmanned aerial vehicles (UAVs) performing time-sensitive medical deliveries in urban environments. Specifically, we consider a single UAV with 3 degree-of-freedom dynamics tasked with delivering blood packages to multiple hospitals, each with a predefined time window and priority. Mission objectives are encoded using Signal Temporal Logic (STL), enabling the formal specification of spatial-temporal constraints. To ensure safety, city buildings are modeled as 3D convex obstacles, and obstacle avoidance is handled through a Convex Feasible Set (CFS) method. The entire planning problem-combining UAV dynamics, STL satisfaction, and collision avoidance-is formulated as a convex optimization problem that ensures tractability and can be solved efficiently using standard convex programming techniques. Simulation results demonstrate that the proposed method generates dynamically feasible, collision-free trajectories that satisfy temporal mission goals, providing a scalable and reliable approach for autonomous UAV-based medical logistics.

As drone use has become more widespread, there is a critical need to ensure safety and security. A key element of this is robust and accurate drone detection and localization. While cameras and other optical sensors like LiDAR are commonly used for object detection, their performance degrades under adverse lighting and environmental conditions. Therefore, this has generated interest in finding more reliable alternatives, such as millimeter-wave (mmWave) radar. Recent research on mmWave radar object detection has predominantly focused on 2D detection of road users. Although these systems demonstrate excellent performance for 2D problems, they lack the sensing capability to measure elevation, which is essential for 3D drone detection. To address this gap, we propose CubeDN, a single-stage end-to-end radar object detection network specifically designed for flying drones. CubeDN overcomes challenges such as poor elevation resolution by utilizing a dual radar configuration and a novel deep learning pipeline. It simultaneously detects, localizes, and classifies drones of two sizes, achieving decimeter-level tracking accuracy at closer ranges with overall $95\%$ average precision (AP) and $85\%$ average recall (AR). Furthermore, CubeDN completes data processing and inference at 10Hz, making it highly suitable for practical applications.