ITH increasing deployment in a vast range of applications, including inspection, delivery, and search-and-rescue, aerial robots have gained immense popularity. Multi-rotor systems of varying scales have taken diverse roles and forms ranging from large vehicles with significant payload-carrying capacity to racing micro drones and reconfigurable robots capable of changing their shape actively or passively for traversal [1]-[4] or manipulation [5], [6]. Critically, each unique robot configuration requires addressing embodiment-and task-specific challenges in terms of control, sensing capabilities, perception, and planning. With changes in the number of propellers, structural materials, overall platform size, payloads, the onboard sensor suite, as well as the environment within which a system is expected to operate, autonomy design and optimization need to exploit high-end simulation toward a safer and faster path to resilient deployment.

CognitiveDrone: A VLA Model and Evaluation Benchmark for Real-Time Cognitive T ask Solving and Reasoning in UA Vs Artem Lykov, V alerii Serpiva, Muhammad Haris Khan, Oleg Sautenkov, Artyom Myshlyaev, Grik Tadevosyan, Y asheerah Y aqoot, and Dzmitry Tsetserukou Abstract -- This paper introduces CognitiveDrone, a novel Vision-Language-Action (VLA) model tailored for complex Unmanned Aerial V ehicles (UA Vs) tasks that demand advanced cognitive abilities. Trained on a dataset comprising over 8,000 simulated flight trajectories across three key categories--Human Recognition, Symbol Understanding, and Reasoning--the model generates real-time 4D action commands based on first-person visual inputs and textual instructions. T o further enhance performance in intricate scenarios, we propose CognitiveDrone-R1, which integrates an additional Vision-Language Model (VLM) reasoning module to simplify task directives prior to high-frequency control. Experimental evaluations using our open-source benchmark, CognitiveDroneBench, reveal that while a racing-oriented model (RaceVLA) achieves an overall success rate of 31.3%, the base CognitiveDrone model reaches 59.6%, and CognitiveDrone-R1 attains a success rate of 77.2%. These results demonstrate improvements of up to 30% in critical cognitive tasks, underscoring the effectiveness of incorporating advanced reasoning capabilities into UA V control systems. Our contributions include the development of a state-of-the-art VLA model for UA V control and the introduction of the first dedicated benchmark for assessing cognitive tasks in drone operations.

How to endow aerial robots with the ability to operate in close proximity remains an open problem. The core challenges lie in the propulsion system's dual-task requirement: generating manipulation forces while simultaneously counteracting gravity. These competing demands create dynamic coupling effects during physical interactions. Furthermore, rotor-induced airflow disturbances critically undermine operational reliability. Although fully-actuated unmanned aerial vehicles (UAVs) alleviate dynamic coupling effects via six-degree-of-freedom (6-DoF) force-torque decoupling, existing implementations fail to address the aerodynamic interference between drones and environments. They also suffer from oversized designs, which compromise maneuverability and limit their applications in various operational scenarios. To address these limitations, we present FLOAT Drone (FuLly-actuated cOaxial Aerial roboT), a novel fully-actuated UAV featuring two key structural innovations. By integrating control surfaces into fully-actuated systems for the first time, we significantly suppress lateral airflow disturbances during operations. Furthermore, a coaxial dual-rotor configuration enables a compact size while maintaining high hovering efficiency. Through dynamic modeling, we have developed hierarchical position and attitude controllers that support both fully-actuated and underactuated modes. Experimental validation through comprehensive real-world experiments confirms the system's functional capabilities in close-proximity operations.

Designing ground-aerial robots is challenging due to the increased actuation requirements which can lead to added weight and reduced locomotion efficiency. Morphobots mitigate this by combining actuators into multi-functional groups and leveraging ground transformation to achieve different locomotion modes. However, transforming on the ground requires dealing with the complexity of ground-vehicle interactions during morphing, limiting applicability on rough terrain. Mid-air transformation offers a solution to this issue but demands operating near or beyond actuator limits while managing complex aerodynamic forces. We address this problem by introducing the Aerially Transforming Morphobot (ATMO), a robot which transforms near the ground achieving smooth transition between aerial and ground modes. To achieve this, we leverage the near ground aerodynamics, uncovered by experimental load cell testing, and stabilize the system using a model-predictive controller that adapts to ground proximity and body shape. The system is validated through numerous experimental demonstrations. We find that ATMO can land smoothly at body postures past its actuator saturation limits by virtue of the uncovered ground-effect.

Safe Periodic Trochoidal Paths for Fixed-Wing UA Vs in Confined Windy Environments Jaeyoung Lim 1, David Rohr 1, Thomas Stastny 1, Roland Siegwart 1 Abstract -- Due to their energy-efficient flight characteristics, fixed-wing type uncrewed aerial vehicles (UA Vs) are useful robotic tools for long-range and duration flight applications in large-scale environments. However, flying fixed-wing UA V in confined environments, such as mountainous regions, can be challenging due to their limited maneuverability and sensitivity to uncertain wind conditions. In this work, we first analyze periodic trochoidal paths that can be used to define wind-aware terminal loitering states. We then propose a wind-invariant safe set of trochoidal paths along with a switching strategy for selecting the corresponding minimum-extent periodic path type. Finally, we show that planning with this minimum-extent set allows us to safely reach up to 10 times more locations in mountainous terrain compared to planning with a single, conservative loitering maneuver . I. INTRODUCTION Uncrewed aerial vehicles (UA Vs) have become crucial tools for information-gathering applications, such as surveying and inspection [1], search and rescue [2], and environment monitoring [3], [4]. For large-scale coverage or long-range applications, fixed-wing type UA Vs are preferred over rotary-wing type systems due to their high endurance and speed. While the wing-borne aerodynamic lift enables energy-efficient flight, it also poses challenges for operating safely.

A long-cherished vision of drones is to autonomously traverse through clutter to reach every corner of the world using onboard sensing and computation. In this paper, we combine onboard 3D lidar sensing and sim-to-real reinforcement learning (RL) to enable autonomous flight in cluttered environments. Compared to vision sensors, lidars appear to be more straightforward and accurate for geometric modeling of surroundings, which is one of the most important cues for successful obstacle avoidance. On the other hand, sim-to-real RL approach facilitates the realization of low-latency control, without the hierarchy of trajectory generation and tracking. We demonstrate that, with design choices of practical significance, we can effectively combine the advantages of 3D lidar sensing and RL to control a quadrotor through a low-level control interface at 50Hz. The key to successfully learn the policy in a lightweight way lies in a specialized surrogate of the lidar's raw point clouds, which simplifies learning while retaining a fine-grained perception to detect narrow free space and thin obstacles. Simulation statistics demonstrate the advantages of the proposed system over alternatives, such as performing easier maneuvers and higher success rates at different speed constraints. With lightweight simulation techniques, the policy trained in the simulator can control a physical quadrotor, where the system can dodge thin obstacles and safely traverse randomly distributed obstacles.

One morning last month, while I was waiting at a bus stop on the western edge of the western Ukrainian city of Lviv, I struck up a conversation with a man in his early forties named Mykola Hryhoryan. Across from the bus stop was a bombed-out museum. I asked if he knew what had happened to it. "It was hit by a Russian drone," he said. Mykola was wearing jeans and a black parka with the hood pulled over his head. He told me that he was a soldier.

Technologies such as robotics, Artificial Intelligence (AI), and Computer Vision (CV) can be applied to crime scene analysis (CSA) to help protect lives, facilitate justice, and deter crime, but an overview of the tasks that can be automated has been lacking. Here we follow a speculate prototyping approach: First, the STAIR tool is used to rapidly review the literature and identify tasks that seem to have not received much attention, like accessing crime sites through a window, mapping/gathering evidence, and analyzing blood smears. Secondly, we present a prototype of a small drone that implements these three tasks with 75%, 85%, and 80% performance, to perform a minimal analysis of an indoor crime scene. Lessons learned are reported, toward guiding next work in the area.

A security camera in eastern Lebanon captured the moments a car was hit in a suspected Israeli drone attack. Lebanon's health ministry said one person was killed and another injured.

JTB and two other Japanese companies said Wednesday that they will launch services employing drones to shoot videos from the sky of customers at tourist spots. The travel agency, drone business operator Fly and Japan Airlines expect their services to help spread the attractions of tourist spots. Drones will start video-recording after customers scan quick response codes prepared at tourist spots and complete payments via a website. Customers will receive one or two minutes of video footage edited automatically by artificial intelligence about 30 minutes after recording. The services will cost 2,000 per use.