In this paper, we propose a novel swashplateless-elevon actuation (SEA) for dual-rotor tail-sitter vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs). In contrast to the conventional elevon actuation (CEA) which controls both pitch and yaw using elevons, the SEA adopts swashplateless mechanisms to generate an extra moment through motor speed modulation to control pitch and uses elevons solely for controlling yaw, without requiring additional actuators. This decoupled control strategy mitigates the saturation of elevons' deflection needed for large pitch and yaw control actions, thus improving the UAV's control performance on trajectory tracking and disturbance rejection performance in the presence of large external disturbances. Furthermore, the SEA overcomes the actuation degradation issues experienced by the CEA when the UAV is in close proximity to the ground, leading to a smoother and more stable take-off process. We validate and compare the performances of the SEA and the CEA in various real-world flight conditions, including take-off, trajectory tracking, and hover flight and position steps under external disturbance. Experimental results demonstrate that the SEA has better performances than the CEA. Moreover, we verify the SEA's feasibility in the attitude transition process and fixed-wing-mode flight of the VTOL UAV. The results indicate that the SEA can accurately control pitch in the presence of high-speed incoming airflow and maintain a stable attitude during fixed-wing mode flight. Video of all experiments can be found in youtube.com/watch?v=Sx9Rk4Zf7sQ

In this work, we introduce OmniDrones, an efficient and flexible platform tailored for reinforcement learning in drone control, built on Nvidia's Omniverse Isaac Sim. It employs a bottom-up design approach that allows users to easily design and experiment with various application scenarios on top of GPU-parallelized simulations. It also offers a range of benchmark tasks, presenting challenges ranging from single-drone hovering to over-actuated system tracking. In summary, we propose an open-sourced drone simulation platform, equipped with an extensive suite of tools for drone learning. It includes 4 drone models, 5 sensor modalities, 4 control modes, over 10 benchmark tasks, and a selection of widely used RL baselines. To showcase the capabilities of OmniDrones and to support future research, we also provide preliminary results on these benchmark tasks. We hope this platform will encourage further studies on applying RL to practical drone systems.

Russian aerial defence systems destroyed a wave of 19 Ukrainian drones that were launched overnight in attacks against targets in the Russia-annexed Crimean peninsula, the surrounding Black Sea and other regions of Russia. The Russian defence ministry said early on Thursday that it had "thwarted" the attacks by Ukraine's aircraft-type unmanned aerial vehicles (UAVs). "In the night from 20th to 21st September, an attempt by the Kyiv regime to commit a terrorist attack with lethal drones on sites in the Russian Federation was intercepted," the defence ministry said on the Telegram messaging app. "Air defence systems destroyed 19 Ukrainian UAVs over the Black Sea and the territory of the Republic of Crimea, and one each over the territories of Kursk, Belgorod and Oryol regions," the ministry said. The Belgorod and Kursk regions of Russia border eastern Ukraine, while Oryol is closer to the capital, Moscow.

Benchmarking, standards and certification are closely related processes. Standards can provide normative requirements that robotics and AI systems may or may not conform to. Certification generally relies upon conformance with one or more standards as the key determinant of granting a certificate to operate. And benchmarks are sets of standardised tests against which robots and AI systems can be measured. Benchmarks therefore can be thought of as informal standards. In this paper we will develop these themes with examples from benchmarking, standards and certification, and argue that these three linked processes are not only useful but vital to the broader practice of Responsible Innovation.

Perceiving and mapping the surroundings are essential for enabling autonomous navigation in any robotic platform. The algorithm class that enables accurate mapping while correcting the odometry errors present in most robotics systems is Simultaneous Localization and Mapping (SLAM). Today, fully onboard mapping is only achievable on robotic platforms that can host high-wattage processors, mainly due to the significant computational load and memory demands required for executing SLAM algorithms. For this reason, pocket-size hardware-constrained robots offload the execution of SLAM to external infrastructures. To address the challenge of enabling SLAM algorithms on resource-constrained processors, this paper proposes NanoSLAM, a lightweight and optimized end-to-end SLAM approach specifically designed to operate on centimeter-size robots at a power budget of only 87.9 mW. We demonstrate the mapping capabilities in real-world scenarios and deploy NanoSLAM on a nano-drone weighing 44 g and equipped with a novel commercial RISC-V low-power parallel processor called GAP9. The algorithm is designed to leverage the parallel capabilities of the RISC-V processing cores and enables mapping of a general environment with an accuracy of 4.5 cm and an end-to-end execution time of less than 250 ms.

Monitoring propeller failures is vital to maintain the safe and reliable operation of quadrotor UAVs. The simulation-to-reality UAV fault diagnosis technique offer a secure and economical approach to identify faults in propellers. However, classifiers trained with simulated data perform poorly in real flights due to the wind disturbance in outdoor scenarios. In this work, we propose an uncertainty-based fault classifier (UFC) to address the challenge of sim-to-real UAV fault diagnosis in windy scenarios. It uses the ensemble of difference-based deep convolutional neural networks (EDDCNN) to reduce model variance and bias. Moreover, it employs an uncertainty-based decision framework to filter out uncertain predictions. Experimental results demonstrate that the UFC can achieve 100% fault-diagnosis accuracy with a data usage rate of 33.6% in the windy outdoor scenario.

Tehran, Iran – Russian Defence Minister Sergey Shoigu, has met senior military and security officials in Iran and toured an exhibition of Iranian missiles and drones. Shoigu arrived in Tehran on Tuesday and was officially received by Mohammad Bagheri, chief of staff of the Iranian armed forces. He has met Iranian Defence Minister Mohammad Reza Ashtiani, Islamic Revolutionary Guard Corps (IRGC) aerospace chief Amir Ali Hajizadeh and security chief Ali Akbar Ahmadian. Bagheri told Shoigu that military cooperation is at the vanguard of expanding relations between Tehran and Moscow, who have been working on a new long-term cooperation plan for months. "This document has serious military and defence dimensions, and can act as suitable grounds to expand long-term cooperation between the two countries," he was quoted as saying by Iranian state media.

Ukraine and Russia launched waves of drone attacks overnight with reports of a fire at an oil refinery in Ukraine's Poltava region and four Ukrainian unmanned aerial vehicles (UAVs) being shot down over two regions in Russia's west, officials say. A Russian drone hit the Kremenchuk oil refinery in the central Poltava region of Ukraine, causing a fire, the regional governor, Dmytro Lunin, said on Wednesday. "Last night, Russians repeatedly attacked Poltava region. Our air defence system did a good job against enemy UAVs," he said on the Telegram messaging app. The General Staff of Ukraine's Armed Forces said air defence systems shot down 17 of 24 drones that Russia launched against targets in Ukraine.

Researchers at the University of Washington developed small robotic devices that can change how they move through the air by "snapping" into a folded position during their descent. Shown here is a timelapse photo of the "microflier" falling in its unfolded state, which makes it tumble chaotically and spread outward in the wind. On a cool afternoon at the heart of the University of Washington's campus, autumn, for a few fleeting moments, appears to have arrived early. Tiny golden squares resembling leaves flutter then fall, switching from a frenzied tumble to a graceful descent with a snap. Aptly named "microfliers" and inspired by Miura-fold origami, these small robotic devices can fold closed during their descent after being dropped from a drone.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Russia launched a massive drone attack on the western city of Lviv early Tuesday, burning down a warehouse said to house humanitarian supplies and killing one man, Ukrainian authorities said. It was one of at least three deadly attacks in different cities. Ukraine intercepted most of the 30 Shahed drones overnight, the country's air force said.