Cooperative control of multi-UAV systems has attracted substantial research attention due to its significance in various application sectors such as emergency response, search and rescue missions, and critical infrastructure inspection. This paper proposes a distributed control algorithm to generate collision-free trajectories that drive the multi-UAV system to completely inspect a set of 3D points on the surface of an object of interest. The objective of the UAVs is to cooperatively inspect the object of interest in the minimum amount of time. Extensive numerical simulations for a team of quadrotor UAVs inspecting a real 3D structure illustrate the validity and effectiveness of the proposed approach.

Witnesses described unthinkable scenes of heavy flooding in northern Italy as Storm Boris continued its journey across Europe on Thursday. People were seen climbing on roofs to escape the water as buildings collapsed in Traversara di Bagnacavallo. The Italian emergency services carried out helicopter rescues after what one eyewitness said was 36 hours of rain. Storm Boris had earlier swept across Poland, the Czech Republic, Romania and Austria, killing at least 23 people. One of Kyiv's main government buildings was hit in overnight missile and drone strikes by Russia.

This paper proposes a novel framework for large-scale scene reconstruction based on 3D Gaussian splatting (3DGS) and aims to address the scalability and accuracy challenges faced by existing methods. For tackling the scalability issue, we split the large scene into multiple cells, and the candidate point-cloud and camera views of each cell are correlated through a visibility-based camera selection and a progressive point-cloud extension. To reinforce the rendering quality, three highlighted improvements are made in comparison with vanilla 3DGS, which are a strategy of the ray-Gaussian intersection and the novel Gaussians density control for learning efficiency, an appearance decoupling module based on ConvKAN network to solve uneven lighting conditions in large-scale scenes, and a refined final loss with the color loss, the depth distortion loss, and the normal consistency loss. Finally, the seamless stitching procedure is executed to merge the individual Gaussian radiance field for novel view synthesis across different cells. Evaluation of Mill19, Urban3D, and MatrixCity datasets shows that our method consistently generates more high-fidelity rendering results than state-of-the-art methods of large-scale scene reconstruction. We further validate the generalizability of the proposed approach by rendering on self-collected video clips recorded by a commercial drone.

This paper introduces a learning-based low-level controller for quadcopters, which adaptively controls quadcopters with significant variations in mass, size, and actuator capabilities. Our approach leverages a combination of imitation learning and reinforcement learning, creating a fast-adapting and general control framework for quadcopters that eliminates the need for precise model estimation or manual tuning. The controller estimates a latent representation of the vehicle's system parameters from sensor-action history, enabling it to adapt swiftly to diverse dynamics. Extensive evaluations in simulation demonstrate the controller's ability to generalize to unseen quadcopter parameters, with an adaptation range up to 16 times broader than the training set. In real-world tests, the controller is successfully deployed on quadcopters with mass differences of 3.7 times and propeller constants varying by more than 100 times, while also showing rapid adaptation to disturbances such as off-center payloads and motor failures. These results highlight the potential of our controller in extreme adaptation to simplify the design process and enhance the reliability of autonomous drone operations in unpredictable environments. The video and code are at: https://github.com/muellerlab/xadapt_ctrl

A Ukrainian drone strike launched at an arms depot in Russia's Tver region caused a massive explosion picked up by NASA space satellites. Ukrainian drones allegedly attacked a massive arms depot some 240 miles west of Moscow on Wednesday, causing an earthquake-sized blast and forcing the evacuation of thousands from the area. Ukrainian officials have not yet claimed responsibility for the attack, though sources in the Security Service of Ukraine (SBU) reportedly confirmed the attack to the Kyiv Independent and other reports pointed to military bloggers and local officials who said Ukrainian drones had been shot down over the Tver region in Russia, where a known military arsenal was located. Video footage obtained by Fox News Digital depicted a huge blast erupting during the early morning hours on Wednesday, though the cause of the explosions or the targets could not be independently verified. A cloud rises after an explosion in Toropets, Tver region, Russia, in this screen grab obtained from a social media video released on Sept. 18, 2024.

The China fight is on. Last week, the House of Representatives stepped up to the Herculean task of passing 25 bills targeting Chinese intrusions into America's economy and technology. This first-ever "China Week" took aim at drones, bad Chinese network routers, batteries and federal biotech contracts with Chinese firms. "House indulges in Mad Hatter's Tea Party," screamed state-run China Daily on Thursday, lamenting "40 years of mutually beneficial relationships. President, Xi Jinping waves as he leaves after speaking at a press event on Oct. 23, 2022, in Beijing, China. Rep. John Moolenaar, R-Mich., chairman of the House Select Committee on China, put it clearly. "This week, we will draw a line in the sand.

A Ukrainian drone attack targeting an armoury has caused a giant fireball, leading to a partial evacuation in western Russia. The attack, reported early on Wednesday, targeted a large arsenal close to the town of Toropets, some 400km (250 miles) northwest of Moscow in the Tver region. It illustrates Ukraine's continued effort to show it can strike at targets deep inside Russia. The drone attack caused an "extremely powerful detonation" and destroyed a large warehouse of the Main Missile and Artillery Directorate of the Russian Ministry of Defence and sparked a fire 6km (3.7 miles) wide, an unnamed source from the Ukrainian security services said. "The warehouse contained missiles intended for Iskander tactical missile systems, Tochka-U tactical missile systems, guided aerial bombs and artillery ammunition," the source told news wires.

This study uses machine vision and drone technologies to propose a unique method for the diagnosis of cucumber disease in agriculture. The backbone of this research is a painstakingly curated dataset of hyperspectral photographs acquired under genuine field conditions. Unlike earlier datasets, this study included a wide variety of illness types, allowing for precise early-stage detection. The model achieves an excellent 87.5\% accuracy in distinguishing eight unique cucumber illnesses after considerable data augmentation. The incorporation of drone technology for high-resolution images improves disease evaluation. This development has enormous potential for improving crop management, lowering labor costs, and increasing agricultural productivity. This research, which automates disease detection, represents a significant step toward a more efficient and sustainable agricultural future.

Handling orientations of robots and objects is a crucial aspect of many applications. Yet, ever so often, there is a lack of mathematical correctness when dealing with orientations, especially in learning pipelines involving, for example, artificial neural networks. In this paper, we investigate reinforcement learning with orientations and propose a simple modification of the network's input and output that adheres to the Lie group structure of orientations. As a result, we obtain an easy and efficient implementation that is directly usable with existing learning libraries and achieves significantly better performance than other common orientation representations. We briefly introduce Lie theory specifically for orientations in robotics to motivate and outline our approach. Subsequently, a thorough empirical evaluation of different combinations of orientation representations for states and actions demonstrates the superior performance of our proposed approach in different scenarios, including: direct orientation control, end effector orientation control, and pick-and-place tasks.

The proposed solution enables safe and feasible trajectories while accommodating heterogeneous time-bound constraints and vehicle physical limits. An optimization problem is formulated to meet mission objectives and temporal requirements encoded as Signal Temporal Logic (STL) specifications. Additionally, an event-triggered replanner is introduced to address unforeseen events and compensate for lost time. Furthermore, a generalized robustness scoring method is employed to reflect user preferences and mitigate task conflicts. The effectiveness of the proposed approach is demonstrated through MATLAB and Gazebo simulations, as well as field multi-robot experiments in a mock-up scenario.