The region's governor, Veniamin Kondratyev, said it was caused by two Ukrainian drones: "There is some damage to buildings, but critical infrastructure was not damaged. And most importantly, there were no casualties."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Cyprus authorities say they're taking extra efforts to ensure flight safety isn't compromised from Turkish warplanes and military drones flying inside Cypriot-monitored airspace without filing either flight plans or communicating with air traffic control. The issue over unregulated Turkish military flights again came to the fore earlier this month when Cypriot authorities said a Turkish warplane "illegally" flew low over a United Nations-controlled buffer zone that cuts across the ethnically-divided island nation on what was believed to be a surveillance mission. "Despite these illegal acts by Turkey, and the illegal operation of the self-styled air traffic control by the secessionist entity, the Department of Civil Aviation of Cyprus is doing its utmost to ensure the safe provision of air traffic services within the Nicosia FIR in its entirety," the Cyprus government told The Associated Press late Wednesday.

The conflict in Ukraine is about to enter a new high-intensity phase as Kyiv's troops gear up for an anticipated counteroffensive. Ukraine's persistent lobbying of allies has yielded significant results as NATO members have gradually relented to supplying high-tech weapons. The fighting in the coming weeks is likely to be bloody, as Ukraine aims to take back territory Russia took from it in the opening weeks of the invasion in 2022. What are these weapons, and why are they needed? More than 230 Western main battle tanks have been transferred to Ukraine, including United States-made Abrams M1s and British Challenger 2s.

This paper presents a technique to cope with the gap between high-level planning, e.g., reference trajectory tracking, and low-level controlling using a learning-based method in the plan-based control paradigm. The technique improves the smoothness of maneuvering through cluttered environments, especially targeting low-speed velocity profiles. In such a profile, external aerodynamic effects that are applied on the quadrotor can be neglected. Hence, we used a simplified motion model to represent the motion of the quadrotor when formulating the Nonlinear Model Predictive Control (NMPC)-based local planner. However, the simplified motion model causes residual dynamics between the high-level planner and the low-level controller. The Sparse Gaussian Process Regression-based technique is proposed to reduce these residual dynamics. The proposed technique is compared with Data-Driven MPC. The comparison results yield that an augmented residual dynamics model-based planner helps to reduce the nominal model error by a factor of 2 on average. Further, we compared the proposed complete framework with four other approaches. The proposed approach outperformed the others in terms of tracking the reference trajectory without colliding with obstacles with less flight time without losing computational efficiency.

This paper introduces a wall construction planner for Unmanned Aerial Vehicles (UAVs), which uses a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic to generate near-time-optimal building plans for even large walls within seconds. This approach addresses one of the most time-consuming and labor-intensive tasks, while also minimizing workers' safety risks. To achieve this, the wall-building problem is modeled as a variant of the Team Orienteering Problem and is formulated as Mixed-Integer Linear Programming (MILP), with added precedence and concurrence constraints that ensure bricks are built in the correct order and without collision between cooperating agents. The GRASP planner is validated in a realistic simulation and demonstrated to find solutions with similar quality as the optimal MILP, but much faster. Moreover, it outperforms all other state-of-the-art planning approaches in the majority of test cases. This paper presents a significant advancement in the field of automated wall construction, demonstrating the potential of UAVs and optimization algorithms in improving the efficiency and safety of construction projects.

Recently, Unmanned Aerial Vehicles (UAVs) have shown impressive performance in many critical applications, such as surveillance, search and rescue operations, environmental monitoring, etc. In many of these applications, the UAVs capture images as well as other sensory data and then send the data processing requests to remote servers. Nevertheless, this approach is not always practical in real-time-based applications due to unstable connections, limited bandwidth, limited energy, and strict end-to-end latency. One promising solution is to divide the inference requests into subtasks that can be distributed among UAVs in a swarm based on the available resources. Moreover, these tasks create intermediate results that need to be transmitted reliably as the swarm moves to cover the area. Our system model deals with real-time requests, aiming to find the optimal transmission power that guarantees higher reliability and low latency. We formulate the Low Latency and High-Reliability (LLHR) distributed inference as an optimization problem, and due to the complexity of the problem, we divide it into three subproblems. In the first subproblem, we find the optimal transmit power of the connected UAVs with guaranteed transmission reliability. The second subproblem aims to find the optimal positions of the UAVs in the grid, while the last subproblem finds the optimal placement of the CNN layers in the available UAVs. We conduct extensive simulations and compare our work to two baseline models demonstrating that our model outperforms the competing models.

Communications system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel. However, in the era of cyber-physical systems, the effectiveness of communications is not dictated simply by the bit rate, but most importantly by the efficient completion of the task in hand, e.g., controlling remotely a robot, automating a production line or collaboratively sensing through a drone swarm. In parallel, it is projected that by 2023, half of the worldwide network connections will be among machines rather than humans. In this context, it is crucial to establish a new paradigm for designing communications strategies for multi-agent cyber-physical systems. This is a daunting task, since it requires a combination of principles from information, communication, control theories and computer science in order to formalize a general framework for task-oriented communication design. In this direction, this paper reviews and structures the relevant theoretical work across a wide range of scientific communities. Subsequently, it proposes a general conceptual framework for task-oriented communication design, along with its specializations according to the targeted use case. Furthermore, it provides a survey of relevant contributions in dominant applications, such as industrial internet of things, multi-UAV systems, tactile internet, autonomous vehicles, distributed learning systems, smart manufacturing plants and 5G and beyond self-organizing networks. Finally, it highlights the most important open research topics from both the theoretical framework and application points of view.

Failure detection and fault tolerant control are fundamental safety features of any aerial vehicle. With the emergence of complex, multi-body flying systems such as jet-powered humanoid robots, it becomes of crucial importance to design fault detection and control strategies for these systems, too. In this paper we propose a fault detection and control framework for the flying humanoid robot iRonCub in case of loss of one turbine. The framework is composed of a failure detector based on turbines rotational speed, a momentum-based flight control for fault response, and an offline reference generator that produces far-from-singularities configurations and accounts for self and jet exhausts collision avoidance. Simulation results with Gazebo and MATLAB prove the effectiveness of the proposed control strategy.

One major barrier to advancing aerial autonomy has been collecting large-scale aerial datasets for training machine learning models. Due to costly and time-consuming real-world data collection through deploying drones, there has been an increasing shift towards using synthetic data for training models in drone applications. However, to increase widespread generalization and transferring models to real-world, increasing the diversity of simulation environments to train a model over all the varieties and augmenting the training data, has been proved to be essential. Current synthetic aerial data generation tools either lack data augmentation or rely heavily on manual workload or real samples for configuring and generating diverse realistic simulation scenes for data collection. These dependencies limit scalability of the data generation workflow. Accordingly, there is a major challenge in balancing generalizability and scalability in synthetic data generation. To address these gaps, we introduce a scalable Aerial Synthetic Data Augmentation (ASDA) framework tailored to aerial autonomy applications. ASDA extends a central data collection engine with two scriptable pipelines that automatically perform scene and data augmentations to generate diverse aerial datasets for different training tasks. ASDA improves data generation workflow efficiency by providing a unified prompt-based interface over integrated pipelines for flexible control. The procedural generative approach of our data augmentation is performant and adaptable to different simulation environments, training tasks and data collection needs. We demonstrate the effectiveness of our method in automatically generating diverse datasets and show its potential for downstream performance optimization.

Fox News contributor Dan Hoffman joined'America's Newsroom' to discuss the alleged attack and how Ukraine has responded in wake of the strike. A drone attack on the Kremlin earlier this month was most likely orchestrated by Ukraine, which has conducted a series of attacks on Russian targets, U.S. officials said. Russia has claimed Ukrainian forces attempted to kill President Vladimir Putin in the failed attack on May 3. Two drones were used in the "assassination attempt" at the president's residence within the Kremlin compound, but were disabled by Russian defense systems, Russia said. Russian President Vladimir Putin is seen on May 3, 2023. A drone was purportedly shot down over the Kremlin.