Fox News Greg Palkot reports from Kyiv on another deadly Russian missile strike and Moscows efforts to block Ukraine food exports. Russia continued to target Ukrainian grain infrastructure in attacks overnight Wednesday, a sign the country could be struggling to achieve a victory in its full-scale invasion of Ukraine. "By targeting Ukraine's grain depots, Putin seeks to starve Ukrainians and create a food crisis, in order to compel [Ukrainian President Volodymyr] Zelenskyy to capitulate and Western nations to withdraw support from Ukraine," Rebekah Koffler, a strategic military intelligence analyst, former senior official at the Defense Intelligence Agency, and author of "Putin's Playbook," told Fox News Digital. "Putin's goal at this stage is to turn Ukraine into a dysfunctional state, that is unable to govern itself and feed its people, thus raising the cost of rebuilding it for the U.S. and European countries." Koffler's comments come after another round of attacks against Ukraine's southern Odesa region, where overnight Russian drones hit storage facilities and ports that Ukraine has been using for grain transport, according to a report from The Associated Press.

Ukraine's air force said a wave of Russian military drones had entered the mouth of the Danube River and were headed towards the country's Izmail river port near the border with Romania. Social media groups monitoring the war reported hearing air defence systems firing in the area near Ukraine's Danube ports of Izmail and Reni early on Wednesday morning. The governor of southern Odesa region, Oleh Kiper, asked residents of Izmail district to take shelter at around 1:30 a.m. Ukraine's Danube River ports accounted for around a quarter of all grain exports from Ukraine before Russia recently pulled out of a deal allowing safe passage for the export of Ukrainian grain via the country's Black Sea ports. Danube River ports have now become the main export route, with grain shipments sent on barges from Ukraine across the Danube to Romania and its Black Sea port of Constanta for onward shipment.

Robots have been successfully used to perform tasks with high precision. In real-world environments with sparse rewards and multiple goals, learning is still a major challenge and Reinforcement Learning (RL) algorithms fail to learn good policies. Training in simulation environments and then fine-tuning in the real world is a common approach. However, adapting to the real-world setting is a challenge. In this paper, we present a method named Ready for Production Hierarchical RL (ReProHRL) that divides tasks with hierarchical multi-goal navigation guided by reinforcement learning. We also use object detectors as a pre-processing step to learn multi-goal navigation and transfer it to the real world. Empirical results show that the proposed ReProHRL method outperforms the state-of-the-art baseline in simulation and real-world environments in terms of both training time and performance. Although both methods achieve a 100% success rate in a simple environment for single goal-based navigation, in a more complex environment and multi-goal setting, the proposed method outperforms the baseline by 18% and 5%, respectively. For the real-world implementation and proof of concept demonstration, we deploy the proposed method on a nano-drone named Crazyflie with a front camera to perform multi-goal navigation experiments.

Navigation in Global Positioning Systems (GPS)-denied environments requires robust estimators reliant on fusion of inertial sensors able to estimate rigid-body's orientation, position, and linear velocity. Ultra-wideband (UWB) and Inertial Measurement Unit (IMU) represent low-cost measurement technology that can be utilized for successful Inertial Navigation. This paper presents a nonlinear deterministic navigation observer in a continuous form that directly employs UWB and IMU measurements. The estimator is developed on the extended Special Euclidean Group $\mathbb{SE}_{2}\left(3\right)$ and ensures exponential convergence of the closed loop error signals starting from almost any initial condition. The discrete version of the proposed observer is tested using a publicly available real-world dataset of a drone flight. Keywords: Ultra-wideband, Inertial measurement unit, Sensor Fusion, Positioning system, GPS-denied navigation.

In the UAM space, strategic deconfliction provides an all-essential layer to airspace automation by providing safe, pre-emptive deconfliction or assignment of airspace resources to airspace users pre-flight. Strategic deconfliction approaches provide an elegant solution to pre-flight deconfliction operations. This overall creates safer and more efficient airspace and reduces the workload on controllers. In this research, we propose a method that constructs routes between start and end nodes in airspace, assigns a contract of operational volumes (OVs) and ensures that these OVs are sufficiently deconflicted against static no-fly zones and OVs of other airspace users. Our approach uses the A* optimal cost path algorithm to generate the shortest routes between the origin and destination. We present a method for generating OVs based on the distribution of aircraft positions from simulated flights; volumes are constructed such that this distribution is conservatively described.

Famously, the Battle of the Bulge in the winter of 1944/45 was one of the largest and bloodiest armed conflict of the Second World War. Taking place in densely forested Ardennes region between Belgium and Luxembourg, it was the last major German offensive campaign on the Western Front during World War II. Researchers have used drone-mounted LiDAR – which emits pulses of light to create 3D models and maps – to'see through' the thick forest canopy. They found nearly 1,000 features within the landscape, including dugouts, bomb craters and even artillery emplacements where troops positioned their guns. Pictured are LiDAR images from the study.

Unmanned aerial vehicle (UAV)-enabled edge federated learning (FL) has sparked a rise in research interest as a result of the massive and heterogeneous data collected by UAVs, as well as the privacy concerns related to UAV data transmissions to edge servers. However, due to the redundancy of UAV collected data, e.g., imaging data, and non-rigorous FL participant selection, the convergence time of the FL learning process and bias of the FL model may increase. Consequently, we investigate in this paper the problem of selecting UAV participants for edge FL, aiming to improve the FL model's accuracy, under UAV constraints of energy consumption, communication quality, and local datasets' heterogeneity. We propose a novel UAV participant selection scheme, called data-efficient energy-aware participant selection strategy (DEEPS), which consists of selecting the best FL participant in each sub-region based on the structural similarity index measure (SSIM) average score of its local dataset and its power consumption profile. Through experiments, we demonstrate that the proposed selection scheme is superior to the benchmark random selection method, in terms of model accuracy, training time, and UAV energy consumption.

LiDAR has become one of the primary sensors in robotics and autonomous system for high-accuracy situational awareness. In recent years, multi-modal LiDAR systems emerged, and among them, LiDAR-as-a-camera sensors provide not only 3D point clouds but also fixed-resolution 360{\deg}panoramic images by encoding either depth, reflectivity, or near-infrared light in the image pixels. This potentially brings computer vision capabilities on top of the potential of LiDAR itself. In this paper, we are specifically interested in utilizing LiDARs and LiDAR-generated images for tracking Unmanned Aerial Vehicles (UAVs) in real-time which can benefit applications including docking, remote identification, or counter-UAV systems, among others. This is, to the best of our knowledge, the first work that explores the possibility of fusing the images and point cloud generated by a single LiDAR sensor to track a UAV without a priori known initialized position. We trained a custom YOLOv5 model for detecting UAVs based on the panoramic images collected in an indoor experiment arena with a MOCAP system. By integrating with the point cloud, we are able to continuously provide the position of the UAV. Our experiment demonstrated the effectiveness of the proposed UAV tracking approach compared with methods based only on point clouds or images. Additionally, we evaluated the real-time performance of our approach on the Nvidia Jetson Nano, a popular mobile computing platform.

Russian shelling has killed seven people, including a 23-day-old infant, and wounded 20 others in Ukraine's southern region of Kherson, prompting local officials to declare a day of mourning. Kyiv reclaimed part of Kherson from Russian occupation last November, but Kremlin troops have continued shelling the regional capital and areas around it from across the Dnipro River. A couple, their 23-day-old child and another man were killed in the village of Shyroka Balka, Interior Minister Ihor Klymenko said on Sunday. The couple's 12-year-old son was critically wounded and died in hospital. "The terrorists will never willingly stop killing civilians," Klymenko wrote in a Telegram post.

Recently emerged Vision-and-Language Navigation (VLN) tasks have drawn significant attention in both computer vision and natural language processing communities. Existing VLN tasks are built for agents that navigate on the ground, either indoors or outdoors. However, many tasks require intelligent agents to carry out in the sky, such as UAV-based goods delivery, traffic/security patrol, and scenery tour, to name a few. Navigating in the sky is more complicated than on the ground because agents need to consider the flying height and more complex spatial relationship reasoning. To fill this gap and facilitate research in this field, we propose a new task named AerialVLN, which is UAV-based and towards outdoor environments. We develop a 3D simulator rendered by near-realistic pictures of 25 city-level scenarios. Our simulator supports continuous navigation, environment extension and configuration. We also proposed an extended baseline model based on the widely-used cross-modal-alignment (CMA) navigation methods. We find that there is still a significant gap between the baseline model and human performance, which suggests AerialVLN is a new challenging task. Dataset and code is available at https://github.com/AirVLN/AirVLN.