In the last twenty years, unmanned aerial vehicles (UAVs) have garnered growing interest due to their expanding applications in both military and civilian domains. Detecting non-cooperative aerial vehicles with efficiency and estimating collisions accurately are pivotal for achieving fully autonomous aircraft and facilitating Advanced Air Mobility (AAM). This paper presents a deep-learning framework that utilizes optical sensors for the detection, tracking, and distance estimation of non-cooperative aerial vehicles. In implementing this comprehensive sensing framework, the availability of depth information is essential for enabling autonomous aerial vehicles to perceive and navigate around obstacles. In this work, we propose a method for estimating the distance information of a detected aerial object in real time using only the input of a monocular camera. In order to train our deep learning components for the object detection, tracking and depth estimation tasks we utilize the Amazon Airborne Object Tracking (AOT) Dataset. In contrast to previous approaches that integrate the depth estimation module into the object detector, our method formulates the problem as image-to-image translation. We employ a separate lightweight encoder-decoder network for efficient and robust depth estimation. In a nutshell, the object detection module identifies and localizes obstacles, conveying this information to both the tracking module for monitoring obstacle movement and the depth estimation module for calculating distances. Our approach is evaluated on the Airborne Object Tracking (AOT) dataset which is the largest (to the best of our knowledge) air-to-air airborne object dataset.

Small-scale autonomous airborne vehicles, such as micro-drones, are expected to be a central component of a broad spectrum of applications ranging from exploration to surveillance and delivery. This class of vehicles is characterized by severe constraints in computing power and energy reservoir, which impairs their ability to support the complex state-of-the-art neural models needed for autonomous operations. The main contribution of this paper is a new class of neural navigation models -- NaviSlim -- capable of adapting the amount of resources spent on computing and sensing in response to the current context (i.e., difficulty of the environment, current trajectory, and navigation goals). Specifically, NaviSlim is designed as a gated slimmable neural network architecture that, different from existing slimmable networks, can dynamically select a slimming factor to autonomously scale model complexity, which consequently optimizes execution time and energy consumption. Moreover, different from existing sensor fusion approaches, NaviSlim can dynamically select power levels of onboard sensors to autonomously reduce power and time spent during sensor acquisition, without the need to switch between different neural networks. By means of extensive training and testing on the robust simulation environment Microsoft AirSim, we evaluate our NaviSlim models on scenarios with varying difficulty and a test set that showed a dynamic reduced model complexity on average between 57-92%, and between 61-80% sensor utilization, as compared to static neural networks designed to match computing and sensing of that required by the most difficult scenario.

In this paper, we present an innovative technique for the path planning of flying robots in a 3D environment in Rough Mereology terms. The main goal was to construct the algorithm that would generate the mereological potential fields in 3-dimensional space. To avoid falling into the local minimum, we assist with a weighted Euclidean distance. Moreover, a searching path from the start point to the target, with respect to avoiding the obstacles was applied. The environment was created by connecting two cameras working in real-time. To determine the gate and elements of the world inside the map was responsible the Python Library OpenCV [1] which recognized shapes and colors. The main purpose of this paper is to apply the given results to drones.

With the rapid development of drone technology, the application of multi-drones is becoming increasingly widespread in various fields. However, the task planning technology for multi-drones still faces challenges such as the complexity of remote operation and the convenience of human-machine interaction. To address these issues, this paper proposes a prompt-driven task planning method for multi-drones based on large language models. By introducing the Prompt technique, appropriate prompt information is provided for the multi-drone system.

Object detection techniques for Unmanned Aerial Vehicles (UAVs) rely on Deep Neural Networks (DNNs), which are vulnerable to adversarial attacks. Nonetheless, adversarial patches generated by existing algorithms in the UAV domain pay very little attention to the naturalness of adversarial patches. Moreover, imposing constraints directly on adversarial patches makes it difficult to generate patches that appear natural to the human eye while ensuring a high attack success rate. We notice that patches are natural looking when their overall color is consistent with the environment. Therefore, we propose a new method named Environmental Matching Attack(EMA) to address the issue of optimizing the adversarial patch under the constraints of color. To the best of our knowledge, this paper is the first to consider natural patches in the domain of UAVs. The EMA method exploits strong prior knowledge of a pretrained stable diffusion to guide the optimization direction of the adversarial patch, where the text guidance can restrict the color of the patch. To better match the environment, the contrast and brightness of the patch are appropriately adjusted. Instead of optimizing the adversarial patch itself, we optimize an adversarial perturbation patch which initializes to zero so that the model can better trade off attacking performance and naturalness. Experiments conducted on the DroneVehicle and Carpk datasets have shown that our work can reach nearly the same attack performance in the digital attack(no greater than 2 in mAP$\%$), surpass the baseline method in the physical specific scenarios, and exhibit a significant advantage in terms of naturalness in visualization and color difference with the environment.

Unmanned aerial vehicles (UAVs) visual localization in planetary aims to estimate the absolute pose of the UAV in the world coordinate system through satellite maps and images captured by on-board cameras. However, since planetary scenes often lack significant landmarks and there are modal differences between satellite maps and UAV images, the accuracy and real-time performance of UAV positioning will be reduced. In order to accurately determine the position of the UAV in a planetary scene in the absence of the global navigation satellite system (GNSS), this paper proposes JointLoc, which estimates the real-time UAV position in the world coordinate system by adaptively fusing the absolute 2-degree-of-freedom (2-DoF) pose and the relative 6-degree-of-freedom (6-DoF) pose. Extensive comparative experiments were conducted on a proposed planetary UAV image cross-modal localization dataset, which contains three types of typical Martian topography generated via a simulation engine as well as real Martian UAV images from the Ingenuity helicopter. JointLoc achieved a root-mean-square error of 0.237m in the trajectories of up to 1,000m, compared to 0.594m and 0.557m for ORB-SLAM2 and ORB-SLAM3 respectively. The source code will be available at https://github.com/LuoXubo/JointLoc.

One common and desirable application of robots is exploring potentially hazardous and unstructured environments. Air-ground collaboration offers a synergistic approach to addressing such exploration challenges. In this paper, we demonstrate a system for large-scale exploration using a team of aerial and ground robots. Our system uses semantics as lingua franca, and relies on fully opportunistic communications. We highlight the unique challenges from this approach, explain our system architecture and showcase lessons learned during our experiments. All our code is open-source, encouraging researchers to use it and build upon.

IDF Special Operations veteran Aaron Cohen and executive director of The Lawfare Project Brooke Goldstein react to Israel's'limited' retaliatory strike on Iran on'Hannity.' The People's Mojahedin Organization of Iran (MEK), an exiled Iranian resistance group, provided a report to Fox News Digital presenting evidence of a top-secret unmanned aerial vehicle (UAV) site in the Islamic Republic of Iran, north of Qom City in the Ganjine region. According to the report, members of the Islamic Revolutionary Guard Corps (IRGC) are trained to use "all kinds of drones" at the base, including the Mohajer series, manufactured by Qods Aviation Industry. Employees of Qods Aviation Industry also reportedly use the site to train small groups of Iranian proxy operatives of Hezbollah, as well as members of Iranian proxy groups from Syria, Yemen and Iraq, to use the Mohajer-4 drone platform. The National Council of Resistance of Iran (NCRI), based on information from the MEK, told Fox News Digital that the site is a proving ground for Mohajer-4, Mohajer-6, and Mohajer-10 drones.

Fox News White House correspondent Jacqui Heinrich has the latest on the countries' alliance amid Chinese tensions on'Special Report.' Japan's defense chief Friday called for the bolstering of its anti-drone capability after a drone footage posted on Chinese social media showed a Japanese aircraft carrier docked at a restricted navy port west of Tokyo. Defense Minister Minoru Kihara called it a serious security threat. Kihara's acknowledgement of the vulnerability comes more than a month after a video filmed by a drone showed JS Izumo, one of two Japanese helicopter carriers, being retrofitted to carry stealth fighters to strengthen Japan's counter-strike capability in the face of China's assertive military actions in the Indo-Pacific. The footage, also showing plants, buildings and other facility at the Japan Maritime Self-Defense Force's Yokosuka naval base was posted on a Chinese social media site in March, prompting investigation by ministry officials.

Footage of a Japanese naval destroyer that circulated on Chinese social media is likely genuine, Tokyo's Defense Ministry said Thursday, after initial speculation the video may have been generated by artificial intelligence. No obvious military activity can be seen in the clip, which appears to show the docked Izumo helicopter carrier. According to officials, footage "purportedly shot by a drone" was first uploaded to Chinese video-sharing platform Bilibili on March 26.