The research presents an automated method for determining the trajectory of an unmanned aerial vehicle (UAV) for wind turbine inspection. The proposed method enables efficient data collection from multiple wind installations using UAV optical sensors, considering the spatial positioning of blades and other components of the wind energy installation. It includes component segmentation of the wind energy unit (WEU), determination of the blade pitch angle, and generation of optimal flight trajectories, considering safe distances and optimal viewing angles. The results of computational experiments have demonstrated the advantage of the proposed method in monitoring WEU, achieving a 78% reduction in inspection time, a 17% decrease in total trajectory length, and a 6% increase in average blade surface coverage compared to traditional methods. Furthermore, the process minimizes the average deviation from the optimal trajectory by 68%, indicating its high accuracy and ability to compensate for external influences.

This paper presents a wireless power transfer (WPT) for a mid-sized inspection mobile robot. The objective is to transmit 100 W of power over 1 meter of distance, achieved through lightweight Litz wire coils weighing 320 g held together with a coil structure of 3.54 kg. The Wireless Power Transfer System (WPTS) is mounted onto an unmanned ground vehicle (UGV). The study addresses an investigation of coil design, accounting for misalignment and tolerance issues in resonance-coupled coils. In experimental validation, the system effectively transmits 109.7 W of power over a 1-meter distance, with obstacles present. This achievement yields a system efficiency of 47.14%, a value that is remarkably close to the maximum power transfer point (50%) when the WPTS utilises the full voltage allowance of the capacitor. The paper shows the WPTS charging speed of 5 minutes for 12 V, 0.8 Ah lead acid batteries.

Russia's air defence systems destroyed seven Ukrainian missiles overnight over the Kursk region, Kursk regional governor Alexei Smirnov wrote on his Telegram channel. Falling debris from destroyed Ukrainian drones sparked a fire at an industrial facility in Russia's Kaluga, according to regional governor Vladislav Shapsha. He said there were no injuries and that three drones were destroyed. Russian forces captured a British mercenary, who identified himself as James Scott Rhys, who is fighting with the Ukrainian army in Russia's Kursk region, a security source told Russia's RIA Novosti state news agency. Air defences were in operation in Kyiv in response to a new Russian drone attack, Mayor Vitali Klitschko said on Telegram.

Huge flames can be seen engulfing a closely-built shanty community in the port area of Manila, in drone footage released by the city's disaster management office. Hundreds of residents have been left without homes, after around 1,000 houses were destroyed in the blaze on Sunday, Manila Fire District said. Dozens of fire engines and fire boats were deployed to tackle the blaze, and the Philippine Air Force sent two helicopters which scooped up up water from Manila Bay to help extinguish the fire. Emergency services have reported no casualties so far, and are yet to identify the cause. The incident comes months after 11 people died in residential fire in the Chinatown district of the Philippine capital.

Trajectory generation for fully autonomous flights of tail-sitter unmanned aerial vehicles (UAVs) presents substantial challenges due to their highly nonlinear aerodynamics. In this paper, we introduce, to the best of our knowledge, the world's first fully autonomous tail-sitter UAV capable of high-speed navigation in unknown, cluttered environments. The UAV autonomy is enabled by cutting-edge technologies including LiDAR-based sensing, differential-flatness-based trajectory planning and control with purely onboard computation. In particular, we propose an optimization-based tail-sitter trajectory planning framework that generates high-speed, collision-free, and dynamically-feasible trajectories. To efficiently and reliably solve this nonlinear, constrained \textcolor{black}{problem}, we develop an efficient feasibility-assured solver, EFOPT, tailored for the online planning of tail-sitter UAVs. We conduct extensive simulation studies to benchmark EFOPT's superiority in planning tasks against conventional NLP solvers. We also demonstrate exhaustive experiments of aggressive autonomous flights with speeds up to 15m/s in various real-world environments, including indoor laboratories, underground parking lots, and outdoor parks. A video demonstration is available at https://youtu.be/OvqhlB2h3k8, and the EFOPT solver is open-sourced at https://github.com/hku-mars/EFOPT.

Abstract-- The inspection of confined critical infrastructure such as attics or crawlspaces is challenging for human operators due to insufficient task space, limited visibility, and the presence of hazardous materials. This paper introduces a prototype of PARIS (Precision Application Robot for Inaccessible Spaces): a use-inspired teleoperated mobile robot manipulator system that was conceived, developed, and tested for--and selected as a Phase I winner of--the U.S. Department of Energy's E-ROBOT Prize. To improve the thermal efficiency of buildings, the PARIS platform supports: 1) teleoperated mapping and navigation, enabling the human operator to explore compact spaces; 2) inspection and sensing, facilitating the identification and localization of under-insulated areas; and 3) air-sealing targeted gaps and cracks through which thermal energy is lost. The resulting versatile platform can also be tailored for targeted application of treatments and remediation in constrained spaces. Approximately 75% of the world's greenhouse gas (GHG) emissions result from the cumulative energy sector [1].

Drone swarms coupled with data intelligence can be the future of wildfire fighting. However, drone swarm firefighting faces enormous challenges, such as the highly complex environmental conditions in wildfire scenes, the highly dynamic nature of wildfire spread, and the significant computational complexity of drone swarm operations. We develop a predict-then-optimize approach to address these challenges to enable effective drone swarm firefighting. First, we construct wildfire spread prediction convex neural network (Convex-NN) models based on real wildfire data. Then, we propose a mixed-integer programming (MIP) model coupled with dynamic programming (DP) to enable efficient drone swarm task planning. We further use chance-constrained robust optimization (CCRO) to ensure robust firefighting performances under varying situations. The formulated model is solved efficiently using Benders Decomposition and Branch-and-Cut algorithms. After 75 simulated wildfire environments training, the MIP+CCRO approach shows the best performance among several testing sets, reducing movements by 37.3\% compared to the plain MIP. It also significantly outperformed the GA baseline, which often failed to fully extinguish the fire. Eventually, we will conduct real-world fire spread and quenching experiments in the next stage for further validation.

With the rapid increase in wildfires in the past decade, it has become necessary to detect and predict these disasters to mitigate losses to ecosystems and human lives. In this paper, we present a novel solution -- Hyper-Drive3D -- consisting of snapshot hyperspectral imaging and LiDAR, mounted on an Unmanned Ground Vehicle (UGV) that identifies areas inside forests at risk of becoming fuel for a forest fire. This system enables more accurate classification by analyzing the spectral signatures of forest vegetation. We conducted field trials in a controlled environment simulating forest conditions, yielding valuable insights into the system's effectiveness. Extensive data collection was also performed in a dense forest across varying environmental conditions and topographies to enhance the system's predictive capabilities for fire hazards and support a risk-informed, proactive forest management strategy. Additionally, we propose a framework for extracting moisture data from hyperspectral imagery and projecting it into 3D space.

Researchers from the Israel Antiquities Authority and Tel Aviv University have uncovered fortifications that help reassess the limits of the ancient city of Jerusalem. Archaeologists, with the help of drones and Google Earth imagery, have discovered 4,000-year-old canals in Belize that were once used by the predecessors of the ancient Mayans to catch freshwater fish. "The aerial imagery was crucial to identify this really distinctive pattern of zigzag linear canals" study co-author Eleanor Harrison-Buck of the University of New Hampshire said of the pre-Christopher Columbus discovery. The fish-trapping canals, built around 2000 BCE, continued to be used by their Mayan descendants until around 200 CE. Altar Q that depicts 16 kings in the dynastic succession of the city is seen inside the archeological site of Copan, in Copan Ruinas, Honduras.

This paper presents a comprehensive overview of exploration strategies utilized in both 2D and 3D environments, focusing on autonomous multi-robot systems designed for building exploration and fire detection. We explore the limitations of traditional algorithms that rely on prior knowledge and predefined maps, emphasizing the challenges faced when environments undergo changes that invalidate these maps. Our modular approach integrates localization, mapping, and trajectory planning to facilitate effective exploration using an OctoMap framework generated from point cloud data. The exploration strategy incorporates obstacle avoidance through potential fields, ensuring safe navigation in dynamic settings. Additionally, I propose future research directions, including decentralized map creation, coordinated exploration among unmanned aerial vehicles (UAVs), and adaptations to time-varying environments. This work serves as a foundation for advancing coordinated multi-robot exploration algorithms, enhancing their applicability in real-world scenarios.