In this study, a novel technique for the autonomous visual inspection of rotating wind turbine rotor blades utilizing an unmanned aerial vehicle (UAV) was developed. This approach addresses the challenges presented by the dynamic environment at hand and the requirement of maintaining a safe distance from the moving rotor blades. The application of UAV-based inspection techniques mitigates these dangers and the expenses associated with traditional wind turbine inspection methods which involve halting normal wind farm operations. Our proposed system incorporates algorithms and sensor technologies. It relies on a light detection and ranging (LiDAR) sensor system, an inertial measurement unit, and a GPS to accurately identify the relative position of the rotating wind turbine with respect to the UAV's own position. Once this position is determined, a non-destructive visual analysis of the rotating rotor blades is performed by generating a suitable trajectory and triggering a camera fitted on a gimbal system as the blades approach. This new technique, built upon the existing research on UAV inspection of rotating wind turbines, has been empirically validated using data collected from real-world wind farm applications. This article contributes to the ongoing trend of enhancing the safety and efficiency of infrastructure inspection. It also presents a good base for future research, with potential applications for other types of infrastructure, such as bridges or power lines.

In recent years, there is a noteworthy advancement in autonomous drone racing. However, the primary focus is on attaining execution times, while scant attention is given to the challenges of dynamic environments. The high-speed nature of racing scenarios, coupled with the potential for unforeseeable environmental alterations, present stringent requirements for online replanning and its timeliness. For racing in dynamic environments, we propose an online replanning framework with an efficient polynomial trajectory representation. We trade off between aggressive speed and flexible obstacle avoidance based on an optimization approach. Additionally, to ensure safety and precision when crossing intermediate racing waypoints, we formulate the demand as hard constraints during planning. For dynamic obstacles, parallel multi-topology trajectory planning is designed based on engineering considerations to prevent racing time loss due to local optimums. The framework is integrated into a quadrotor system and successfully demonstrated at the DJI Robomaster Intelligent UAV Championship, where it successfully complete the racing track and placed first, finishing in less than half the time of the second-place.

The Roller-Quadrotor is a novel quadrotor that combines the maneuverability of aerial drones with the endurance of ground vehicles. This work focuses on the design, modeling, and experimental validation of the Roller-Quadrotor. Flight capabilities are achieved through a quadrotor configuration, with four thrust-providing actuators. Additionally, rolling motion is facilitated by a unicycle-driven and rotor-assisted turning structure. By utilizing terrestrial locomotion, the vehicle can overcome rolling and turning resistance, thereby conserving energy compared to its flight mode. This innovative approach not only tackles the inherent challenges of traditional rotorcraft but also enables the vehicle to navigate through narrow gaps and overcome obstacles by taking advantage of its aerial mobility. We develop comprehensive models and controllers for the Roller-Quadrotor and validate their performance through experiments. The results demonstrate its seamless transition between aerial and terrestrial locomotion, as well as its ability to safely navigate through gaps half the size of its diameter. Moreover, the terrestrial range of the vehicle is approximately 2.8 times greater, while the operating time is about 41.2 times longer compared to its aerial capabilities. These findings underscore the feasibility and effectiveness of the proposed structure and control mechanisms for efficient navigation through challenging terrains while conserving energy.

Catching high-speed targets in the flight is a complex and typical highly dynamic task. In this paper, we propose Catch Planner, a planning-with-decision scheme for catching. For sequential decision making, we propose a policy search method based on deep reinforcement learning. In order to make catching adaptive and flexible, we propose a trajectory optimization method to jointly optimize the highly coupled catching time and terminal state while considering the dynamic feasibility and safety. We also propose a flexible constraint transcription method to catch targets at any reasonable attitude and terminal position bias. The proposed Catch Planner provides a new paradigm for the combination of learning and planning and is integrated on the quadrotor designed by ourselves, which runs at 100hz on the onboard computer. Extensive experiments are carried out in real and simulated scenes to verify the robustness of the proposed method and its expansibility when facing a variety of high-speed flying targets.

The wars of the tomorrow will not be fought with mushroom clouds but with devastating cyber attacks unleashed by'quantum computers,' experts have predicted. Former soldiers and intelligence agents revealed this ominous future to DailyMail.com, These range from quantum computers millions of times more powerful than the machines used today to robotic exoskeletons, which will give soldiers superhuman strength, and AI-controlled drones that will kill without human input. Augmented reality will offer soldiers and pilots'superhuman' senses Virtual reality and augmented reality will allow soldiers to'see through' drones or pilot robotic vehicles, with augmented reality'heads-up displays' (HUDs) overlaying the view of pilots and soldiers. Adam Seamons, Information Security Manager, GRC International Group, said, Technologies like VR have already been used for many years to train both military and commercial pilots, along with other expensive hardware such as armor vehicles and tanks.

If you've ever wanted to get into drone piloting but you felt it was too expensive to justify the hobby, then you've come to the right place. This long-range, high-speed 4K camera drone is designed with a modular large-capacity body battery that's easy to install and supports a significantly longer flight time than other drones on the market. It's also stabler than other drones, with a smart hover function to stay solid during windy conditions and a 360º stunt roll to amaze and delight onlookers down below. Of course, the 4K camera can capture high-quality images and videos as you fly with ease thanks to headless mode and one-key automatic return. With the companion app, you can take photos with the point of a finger and the included remote control allows you to go beyond the one-button beginner flying mode to navigate from greater distances.

A joint statement from the U.S. Representative to the United Nations on behalf of a coalition of European countries has urged the U.N. to investigate Russia's use of Iranian drones in Ukraine. "Earlier this month, the United States released further information documenting how Iran has provided Russia with hundreds of one-way attack UAVs (unmanned aerial vehicles), as well as UAV production-related equipment. Ukraine and the U.K. also submitted evidence to the U.N. of Iranian UAVs recovered by the Ukrainian armed forces," Linda Thomas-Greenfield, United States Ambassador to the United Nations, told reporters. "Russia has not only procured hundreds of Mohajer and Shahed series UAVs from Iran in clear violation of Resolution 2231, but it is also now working with Iran to produce these weapons inside Russia," she continued, reading a statement on behalf of the U.S., the U.K., France, Ukraine and Albania. "Russia has been using these UAVs in recent weeks to strike Kyiv, destroy Ukrainian infrastructure, and kill and terrorize Ukrainian civilians. Media reports indicate just this week Russia targeted Kyiv and other Ukrainian cities with dozens of Iranian-made drones," she said, adding, "The United Nations must respond to growing calls from the international community to investigate these violations."

As the two Koreas near the anniversary of the start of their conflict in 1950, both sides are pouring money into drone programs to bolster their militaries along a border dubbed "the Cold War's last frontier." South Korea's cabinet this week approved plans for a new drone command to be set up by the military around September to provide what the government called an "overwhelming response" to any provocations by North Korea's unmanned aerial vehicles, or UAVs. North Korea appears to have started testing a new large drone at its Panghyon airbase, NK News reported last week based on satellite images. The aircraft was the largest it has seen to date, with an estimated wingspan of about 115 feet (35 meters), bigger than the 65-foot drone spotted at the airbase earlier this month, it said. This could be due to a conflict with your ad-blocking or security software.

In this paper, we address the problem of autonomous search and vessel detection in an unknown GNSS-denied maritime environment with fixed-wing UAVs. The main challenge in such environments with limited localization, communication range, and the total number of UAVs and sensors is to implement an appropriate search strategy so that a target vessel can be detected as soon as possible. Thus we present informed and non-informed methods used to search the environment. The informed method relies on an obtained probabilistic map, while the non-informed method navigates the UAVs along predefined paths computed with respect to the environment. The vessel detection method is trained on synthetic data collected in the simulator with data annotation tools. Comparative experiments in simulation have shown that our combination of sensors, search methods and a vessel detection algorithm leads to a successful search for the target vessel in such challenging environments.

Developing optimal controllers for aggressive high-speed quadcopter flight poses significant challenges in robotics. Recent trends in the field involve utilizing neural network controllers trained through supervised or reinforcement learning. However, the sim-to-real transfer introduces a reality gap, requiring the use of robust inner loop controllers during real flights, which limits the network's control authority and flight performance. In this paper, we investigate for the first time, an end-to-end neural network controller, addressing the reality gap issue without being restricted by an inner-loop controller. The networks, referred to as G\&CNets, are trained to learn an energy-optimal policy mapping the quadcopter's state to rpm commands using an optimal trajectory dataset. In hover-to-hover flights, we identified the unmodeled moments as a significant contributor to the reality gap. To mitigate this, we propose an adaptive control strategy that works by learning from optimal trajectories of a system affected by constant external pitch, roll and yaw moments. In real test flights, this model mismatch is estimated onboard and fed to the network to obtain the optimal rpm command. We demonstrate the effectiveness of our method by performing energy-optimal hover-to-hover flights with and without moment feedback. Finally, we compare the adaptive controller to a state-of-the-art differential-flatness-based controller in a consecutive waypoint flight and demonstrate the advantages of our method in terms of energy optimality and robustness.