Fox News anchor Bret Baier takes a look at key shipping routes in the Middle East and breaks down how Iran's actions in the Red Sea could impact global trade on'Special Report.' Oil prices are on the rise amid attacks on ships in the Red Sea. Prices are at around $80 a barrel, which is far lower than prices were when Russia invaded Ukraine. Oil futures had then jumped to more than $100 a barrel. The prices have remained low since June 2022.

For intelligent quadcopter UAVs, a robust and reliable autonomous planning system is crucial. Most current trajectory planning methods for UAVs are suitable for static environments but struggle to handle dynamic obstacles, which can pose challenges and even dangers to flight. To address this issue, this paper proposes a vision-based planning system that combines tracking and trajectory prediction of dynamic obstacles to achieve efficient and reliable autonomous flight. We use a lightweight object detection algorithm to identify dynamic obstacles and then use Kalman Filtering to track and estimate their motion states. During the planning phase, we not only consider static obstacles but also account for the potential movements of dynamic obstacles. For trajectory generation, we use a B-spline-based trajectory search algorithm, which is further optimized with various constraints to enhance safety and alignment with the UAV's motion characteristics. We conduct experiments in both simulation and real-world environments, and the results indicate that our approach can successfully detect and avoid obstacles in dynamic environments in real-time, offering greater reliability compared to existing approaches. Furthermore, with the advancements in Natural Language Processing (NLP) technology demonstrating exceptional zero-shot generalization capabilities, more user-friendly human-machine interactions have become feasible, and this study also explores the integration of autonomous planning systems with Large Language Models (LLMs).

Russia has warned that it will react robustly to Western moves to seize its assets or deploy missiles. Moscow could sever diplomatic relations with the United States should it confiscate Russian assets frozen under sanctions, Deputy Foreign Minister Sergey Ryabkov said on Friday. Officials also said the Kremlin would respond to the deployment of missiles in Europe or Asia, even as Ukraine reported that Russia had unleashed another barrage of attack drones overnight. Ryabkov threatened that Moscow could cut diplomatic ties with Washington should it hand frozen Russian assets to Kyiv, which is desperate for funds, according to the Russian state news agency Interfax. Western countries are discussing the confiscation of more than $1bn in Russian assets frozen due to sanctions over the war in Ukraine.

It is challenging to model and control a tail-sitter unmanned aerial vehicle (UAV) because its blended wing body generates complicated nonlinear aerodynamic effects, such as wing lift, fuselage drag, and propeller-wing interactions. We therefore devised a hybrid aerodynamic modeling method and model predictive control (MPC) design for a quadrotor tail-sitter UAV. The hybrid model consists of the Newton-Euler equation, which describes quadrotor dynamics, and a feedforward neural network, which learns residual aerodynamic effects. This hybrid model exhibits high predictive accuracy at a low computational cost and was used to implement hybrid MPC, which optimizes the throttle, pitch angle, and roll angle for position tracking. The controller performance was validated in real-world experiments, which obtained a 57% tracking error reduction compared with conventional nonlinear MPC. External wind disturbance was also introduced and the experimental results confirmed the robustness of the controller to these conditions.

Unmanned Aerial Systems (UAS) have gained significant traction for their application in infrastructure inspections. However, considering the enormous scale and complex nature of infrastructure, automation is essential for improving the efficiency and quality of inspection operations. One of the core problems in this regard is electing an optimal automated flight path that can achieve the mission objectives while minimizing flight time. This paper presents an effective formulation for the path planning problem in the context of structural inspections. Coverage is guaranteed as a constraint to ensure damage detectability and path length is minimized as an objective, thus maximizing efficiency while ensuring inspection quality. A two-stage algorithm is then devised to solve the path planning problem, composed of a genetic algorithm for determining the positions of viewpoints and a greedy algorithm for calculating the poses. A comprehensive sensitivity analysis is conducted to demonstrate the proposed algorithm's effectiveness and range of applicability. Applied examples of the algorithm, including partial space inspection with no-fly zones and focused inspection, are also presented, demonstrating the flexibility of the proposed method to meet real-world structural inspection requirements. In conclusion, the results of this study highlight the feasibility of the proposed approach and establish the groundwork for incorporating automation into UAS-based structural inspection mission planning.

Recent advances in reinforcement learning (RL) and Human-in-the-Loop (HitL) learning have made human-AI collaboration easier for humans to team with AI agents. Leveraging human expertise and experience with AI in intelligent systems can be efficient and beneficial. Still, it is unclear to what extent human-AI collaboration will be successful, and how such teaming performs compared to humans or AI agents only. In this work, we show that learning from humans is effective and that human-AI collaboration outperforms human-controlled and fully autonomous AI agents in a complex simulation environment. In addition, we have developed a new simulator for critical infrastructure protection, focusing on a scenario where AI-powered drones and human teams collaborate to defend an airport against enemy drone attacks. We develop a user interface to allow humans to assist AI agents effectively. We demonstrated that agents learn faster while learning from policy correction compared to learning from humans or agents. Furthermore, human-AI collaboration requires lower mental and temporal demands, reduces human effort, and yields higher performance than if humans directly controlled all agents. In conclusion, we show that humans can provide helpful advice to the RL agents, allowing them to improve learning in a multi-agent setting.

The advent of fifth generation (5G) networks has opened new avenues for enhancing connectivity, particularly in challenging environments like remote areas or disaster-struck regions. Unmanned aerial vehicles (UAVs) have been identified as a versatile tool in this context, particularly for improving network performance through the Integrated access and backhaul (IAB) feature of 5G. However, existing approaches to UAV-assisted network enhancement face limitations in dynamically adapting to varying user locations and network demands. This paper introduces a novel approach leveraging deep reinforcement learning (DRL) to optimize UAV placement in real-time, dynamically adjusting to changing network conditions and user requirements. Our method focuses on the intricate balance between fronthaul and backhaul links, a critical aspect often overlooked in current solutions. The unique contribution of this work lies in its ability to autonomously position UAVs in a way that not only ensures robust connectivity to ground users but also maintains seamless integration with central network infrastructure. Through various simulated scenarios, we demonstrate how our approach effectively addresses these challenges, enhancing coverage and network performance in critical areas. This research fills a significant gap in UAV-assisted 5G networks, providing a scalable and adaptive solution for future mobile networks.

The United States Senate will not vote on an aid package for Ukraine this year, chamber leaders have said. Democratic and Republican negotiators continue to seek a compromise on the delayed funding deal to support Kyiv as it defends against Russia's invasion, a joint statement released on Tuesday said. Ukraine has become increasingly desperate to secure funding from the West in recent weeks, with political delays to both US and European Union aid bolstering Russian confidence amid the bogged-down conflict. Having already handed Ukraine about $100bn in aid since the February 2022 invasion, President Joe Biden has asked the lower house Congress to approve another $60bn. However, Republicans have blocked the move, using the issue to demand new immigration legislation.

The United States has imposed sanctions on a network accused of evading trade restrictions to supply Iran with components to build drones. The US Treasury Department on Tuesday announced the measures have been implemented against a web of foreign front companies that have been sending Tehran sensitive equipment. Iranian-built unmanned aerial vehicles (UAVs) are being used in the wars in the Middle East and Ukraine. The sanctions target 10 entities and four individuals. The firms affected include "intermediary companies, front companies, and logistics businesses" based in Iran, Malaysia, Hong Kong and Indonesia.

Abstract--In recent years, formation control of unmanned vehicles has received considerable interest, driven by the progress in autonomous systems and the imperative for multiple vehicles to carry out diverse missions. In this paper, we address the problem of behavior-based formation control of mobile robots, where we use safe multi-agent reinforcement learning (MARL) to ensure the safety of the robots by eliminating all collisions during training and execution. To ensure safety, we implemented distributed model predictive control safety filters to override unsafe actions. We focus on achieving behavior-based formation without having individual reference targets for the robots, and instead use targets for the centroid of the formation. This formulation facilitates the deployment of formation control on real robots and improves the scalability of our approach to Figure 1: Real-world example for behavior-based formation control of more robots. The task cannot be addressed through optimizationbased mobile robots based on centroid reference targets. The formation is controllers without specific individual reference targets for defined by the distances between the three robots. The robots start the robots and information about the relative locations of each from random locations and then navigate cooperatively to move the robot to the others. That is why, for our formulation we use target for the centroid of the formation while aiming to maintain the MARL to train the robots. Moreover, in order to account for the predefined distances with respect to each other. The centroid of the interactions between the agents, we use attention-based critics to formation reaches the first goal and is then moved to the second goal improve the training process.