Fox News chief national security correspondent Jennifer Griffin has the latest on Iran's claims of developing an advanced hypersonic missile on'Special Report.' United States officials believe Iran is sending Russia materials to build a drone manufacturing plant east of Moscow to produce more Iranian drones to use in Ukraine. The intelligence was made public by the National Security Council's Coordinator for Strategic Communications John Kirby on Friday. "As of May, Russia received hundreds of one-way attack [unmanned aerial vehicles], as well as UAV production-related equipment, from Iran," Kirby said. Russian President Vladimir Putin takes part in the ceremony of signing an agreement on the construction of the Rasht-Astara railway via a video link together with Iranian President Ebrahim Raisi, at the Kremlin in Moscow.

The military partnership between Moscow and Tehran is deepening, White House officials said on Friday as they released newly declassified information about a drone factory that Iran is helping Russia build. Russia has repeatedly used Iranian-made drones to attack Ukraine in recent months, including strikes on civilian targets, buildings and electrical infrastructure as part of a push to break Ukrainian morale. And as Moscow's own weapons stocks have diminished, Iran has become a key supplier of military aid to Russia. The new factory, which is planned for a warehouse in the Yelabuga region several hundred miles east of Moscow, would allow Russia's military to have its own domestically produced source of attack drones. Iran is providing materials for the plant, said John Kirby, the National Security Council spokesman, who added that the facility could be operational next year.

Drone footage from Coconino County Emergency Management shows the aftermath of a train derailment in Williams, Arizona. A drone video captured the aftermath of a massive train derailment in Arizona involving a freight train that emergency officials say was "carrying a variety of new cars, vans and trucks." The train, operated by BNSF, derailed around midnight Wednesday in Williams, located outside of Flagstaff, according to Coconino County Emergency Management. "A total of 23 cars derailed and sustained heavy damage. The train cars involved were carrying a variety of new cars, vans and trucks," Coconino County officials said.

Three people were lightly wounded after a drone crashed into a residential building in Russia's Voronezh city, the regional governor said. The latest drone attack to target Russian cities in recent weeks comes as Ukraine has been intensifying its efforts to expel Russian forces from a vast swath of southern and eastern Ukraine that they invaded more than 15 months ago on orders from President Vladimir Putin. In a Telegram post, regional Governor Alexander Gusev said the three residents were hurt by shards of glass from broken windows, and received help on the spot. Russian state media published photos showing a high-rise apartment building with some windows blown out and damage to the facade. Such drone strikes – which have previously hit residential areas in southern Krasnodar and even one at the Kremlin – along with cross-border raids in southwestern Russia, have exposed glaring breaches in Russian air defences and porous border security.

Did you hear about the Air Force AI drone that went rogue and attacked its operators inside a simulation? The cautionary tale was told by Colonel Tucker Hamilton, chief of AI test and operations at the US Air Force, during a speech at an aerospace and defense event in London late last month. It apparently involved taking the kind of learning algorithm that has been used to train computers to play video games and board games like Chess and Go and using it to train a drone to hunt and destroy surface-to-air missiles. "At times, the human operator would tell it not to kill that threat, but it got its points by killing that threat," Hamilton was widely reported as telling the audience in London. It sounds like just the sort of thing AI experts have begun warning that increasingly clever and maverick algorithms might do.

Smart buildings are increasingly using Internet of Things (IoT)-based wireless sensing systems to reduce their energy consumption and environmental impact. As a result of their compact size and ability to sense, measure, and compute all electrical properties, Internet of Things devices have become increasingly important in our society. A major contribution of this study is the development of a comprehensive IoT-based framework for smart city energy management, incorporating multiple components of IoT architecture and framework. An IoT framework for intelligent energy management applications that employ intelligent analysis is an essential system component that collects and stores information. Additionally, it serves as a platform for the development of applications by other companies. Furthermore, we have studied intelligent energy management solutions based on intelligent mechanisms. The depletion of energy resources and the increase in energy demand have led to an increase in energy consumption and building maintenance. The data collected is used to monitor, control, and enhance the efficiency of the system.

Battery endurance represents a key challenge for long-term autonomy and long-range operations, especially in the case of aerial robots. In this paper, we propose AutoCharge, an autonomous charging solution for quadrotors that combines a portable ground station with a flexible, lightweight charging tether and is capable of universal, highly efficient, and robust charging. We design and manufacture a pair of circular magnetic connectors to ensure a precise orientation-agnostic electrical connection between the ground station and the charging tether. Moreover, we supply the ground station with an electromagnet that largely increases the tolerance to localization and control errors during the docking maneuver, while still guaranteeing smooth un-docking once the charging process is completed. We demonstrate AutoCharge on a perpetual 10 hours quadrotor flight experiment and show that the docking and un-docking performance is solidly repeatable, enabling perpetual quadrotor flight missions.

Autonomous vehicles are suited for continuous area patrolling problems. However, finding an optimal patrolling strategy can be challenging for many reasons. Firstly, patrolling environments are often complex and can include unknown environmental factors, such as wind or landscape. Secondly, autonomous vehicles can have failures or hardware constraints, such as limited battery life. Importantly, patrolling large areas often requires multiple agents that need to collectively coordinate their actions. In this work, we consider these limitations and propose an approach based on model-free, deep multi-agent reinforcement learning. In this approach, the agents are trained to patrol an environment with various unknown dynamics and factors. They can automatically recharge themselves to support continuous collective patrolling. A distributed homogeneous multi-agent architecture is proposed, where all patrolling agents execute identical policies locally based on their local observations and shared location information. This architecture provides a patrolling system that can tolerate agent failures and allow supplementary agents to be added to replace failed agents or to increase the overall patrol performance. The solution is validated through simulation experiments from multiple perspectives, including the overall patrol performance, the efficiency of battery recharging strategies, the overall fault tolerance, and the ability to cooperate with supplementary agents.

The development of urban-air-mobility (UAM) is rapidly progressing with spurs, and the demand for efficient transportation management systems is a rising need due to the multifaceted environmental uncertainties. Thus, this paper proposes a novel air transportation service management algorithm based on multi-agent deep reinforcement learning (MADRL) to address the challenges of multi-UAM cooperation. Specifically, the proposed algorithm in this paper is based on communication network (CommNet) method utilizing centralized training and distributed execution (CTDE) in multiple UAMs for providing efficient air transportation services to passengers collaboratively. Furthermore, this paper adopts actual vertiport maps and UAM specifications for constructing realistic air transportation networks. By evaluating the performance of the proposed algorithm in data-intensive simulations, the results show that the proposed algorithm outperforms existing approaches in terms of air transportation service quality. Furthermore, there are no inferior UAMs by utilizing parameter sharing in CommNet and a centralized critic network in CTDE. Therefore, it can be confirmed that the research results in this paper can provide a promising solution for autonomous air transportation management systems in city-wide urban areas.

This paper proposes a novel algorithm, named quantum multi-agent actor-critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmanned aerial vehicles (UAVs). In the context of facilitating collaboration among multiple unmanned aerial vehicles (UAVs), the application of multi-agent reinforcement learning (MARL) techniques is regarded as a promising approach. These methods enable UAVs to learn collectively, optimizing their actions within a shared environment, ultimately leading to more efficient cooperative behavior. Furthermore, the principles of a quantum computing (QC) are employed in our study to enhance the training process and inference capabilities of the UAVs involved. By leveraging the unique computational advantages of quantum computing, our approach aims to boost the overall effectiveness of the UAV system. However, employing a QC introduces scalability challenges due to the near intermediate-scale quantum (NISQ) limitation associated with qubit usage. The proposed algorithm addresses this issue by implementing a quantum centralized critic, effectively mitigating the constraints imposed by NISQ limitations. Additionally, the advantages of the QMACN with performance improvements in terms of training speed and wireless service quality are verified via various data-intensive evaluations. Furthermore, this paper validates that a noise injection scheme can be used for handling environmental uncertainties in order to realize robust mobile access.