To meet the demands for ubiquitous communication and temporary edge computing in 6G networks, aerial mobile edge computing (MEC) networks have been envisioned as a new paradigm. However, dynamic user requests pose challenges for task assignment strategies. Most of the existing research assumes that the strategy is deployed on ground-based stations or UAVs, which will be ineffective in an environment lacking infrastructure and continuous energy supply. Moreover, the resource mutual exclusion problem of dynamic task assignment has not been effectively solved. Toward this end, we introduce the digital twin (DT) into the aerial MEC network to study the resource coalition cooperation approach with the generative model (GM), which provides a preliminary coalition structure for the coalition game. Specifically, we propose a novel network framework that is composed of an application plane, a physical plane, and a virtual plane. After that, the task assignment problem is simplified to convex optimization programming with linear constraints. And then, we also propose a resource coalition cooperation approach that is based on a transferable utility (TU) coalition game to obtain an approximate optimal solution. Numerical results confirm the effectiveness of our proposed approach in terms of energy consumption and utilization of resources.

Fox News State Department and foreign policy correspondent Gillian Turner has the latest on the Israel-Hamas war on'Special Report.' Two Nigerian military personnel will face a court martial over the killing of 85 villagers in a military drone attack in December in the West African nation's conflict-battered north, authorities said, prompting calls from a rights group Friday for more transparency and justice for victims. The two personnel will be subjected to military justice proceedings "for acts of omission or commission" after investigations found that the civilians killed by the strike "were mistaken for terrorists," Nigeria's Defense Headquarters spokesperson Maj. Gen. Edward Buba said in a statement Thursday without providing further details. Nigeria's military often conducts air raids as it fights the extremist violence and rebel attacks that have destabilized Nigeria's northern region for more than a decade, often leaving civilian casualties in its wake.

Monitoring large scale environments is a crucial task for managing remote alpine environments, especially for hazardous events such as avalanches. One key information for avalanche risk forecast is imagery of released avalanches. As these happen in remote and potentially dangerous locations this data is difficult to obtain. Fixed-wing vehicles, due to their long range and travel speeds are a promising platform to gather aerial imagery to map avalanche activities. However, operating such vehicles in mountainous terrain remains a challenge due to the complex topography, regulations, and uncertain environment. In this work, we present a system that is capable of safely navigating and mapping an avalanche using a fixed-wing aerial system and discuss the challenges arising when executing such a mission. We show in our field experiments that we can effectively navigate in steep terrain environments while maximizing the map quality. We expect our work to enable more autonomous operations of fixed-wing vehicles in alpine environments to maximize the quality of the data gathered.

The United States Department of Defense has acknowledged that a drone strike in Syria, initially said to have successfully targeted an al-Qaeda leader, actually killed a farmer. The Pentagon stated on Thursday that the drone strike on May 3, 2023, killed a 56-year-old shepherd named Lutfi Hasan Masto, whom they initially misidentified as a senior member of al-Qaeda. US Central Command, which oversees military activities in the Middle East, wrote that it "acknowledges and regrets the civilian harm that resulted from the airstrike". A year ago a US strike in Syria killed a'senior Al-Qaeda leader' The US military has now officially accepted it was a mistake, blaming'confirmation bias'https://t.co/WG9Mf0Rdq2 The killing of Masto is the latest incident to raise questions about the impact of US drone warfare on civilians, who often pay the price for botched strikes.

On the top floor of a building somewhere in Ukraine is a drone workshop. Inside is a chaotic workbench covered in logic boards, antennas, batteries, augmented reality headsets, and rotor blades. On one end of the room is a makeshift photo studio--a jet-black quadcopter drone sits on a long white sheet, waiting for its close-up. He grins as he shows off his creations, flittering around with a lit cigarette in his mouth, dangling ash, grabbing different models. Yvan holds up a mid-size drone: This model successfully hit a target from 11 kilometers away, he says, but it should be capable of traveling at least 20. He's trying different batteries and controllers to try to extend the range.

New drone footage obtained by The Associated Press news agency showed how months of relentless Russian artillery pounding has devastated Chasiv Yar. The town was once home to 12,000 people, but the footage reveals it is now almost deserted and barely a building remains intact. The United States accused Russia of breaking the international ban on chemical weapons by using the choking agent chloropicrin against Ukrainian troops. Chloropicrin is listed as a banned agent by the Hague-based Organisation for the Prohibition of Chemical Weapons (OPCW). The US said Moscow was also deploying riot control agents "as a method of warfare" in Ukraine.

In autonomous and mobile robotics, a principal challenge is resilient real-time environmental perception, particularly in situations characterized by unknown and dynamic elements, as exemplified in the context of autonomous drone racing. This study introduces a perception technique for detecting drone racing gates under illumination variations, which is common during high-speed drone flights. The proposed technique relies upon a lightweight neural network backbone augmented with capabilities for continual learning. The envisaged approach amalgamates predictions of the gates' positional coordinates, distance, and orientation, encapsulating them into a cohesive pose tuple. A comprehensive number of tests serve to underscore the efficacy of this approach in confronting diverse and challenging scenarios, specifically those involving variable lighting conditions. The proposed methodology exhibits notable robustness in the face of illumination variations, thereby substantiating its effectiveness.

5G New Radio Time of Arrival (ToA) data has the potential to revolutionize indoor localization for micro aerial vehicles (MAVs). However, its performance under varying network setups, especially when combined with IMU data for real-time localization, has not been fully explored so far. In this study, we develop an error state Kalman filter (ESKF) and a pose graph optimization (PGO) approach to address this gap. We systematically evaluate the performance of the derived approaches for real-time MAV localization in realistic scenarios with 5G base stations in Line-Of-Sight (LOS), demonstrating the potential of 5G technologies in this domain. In order to experimentally test and compare our localization approaches, we augment the EuRoC MAV benchmark dataset for visual-inertial odometry with simulated yet highly realistic 5G ToA measurements. Our experimental results comprehensively assess the impact of varying network setups, including varying base station numbers and network configurations, on ToA-based MAV localization performance. The findings show promising results for seamless and robust localization using 5G ToA measurements, achieving an accuracy of 15 cm throughout the entire trajectory within a graph-based framework with five 5G base stations, and an accuracy of up to 34 cm in the case of ESKF-based localization. Additionally, we measure the run time of both algorithms and show that they are both fast enough for real-time implementation.

We address a joint trajectory planning, user association, resource allocation, and power control problem to maximize proportional fairness in the aerial IoT network, considering practical end-to-end quality-of-service (QoS) and communication schedules. Though the problem is rather ancient, apart from the fact that the previous approaches have never considered user- and time-specific QoS, we point out a prevalent mistake in coordinate optimization approaches adopted by the majority of the literature. Coordinate optimization approaches, which repetitively optimize radio resources for a fixed trajectory and vice versa, generally converge to local optima when all variables are differentiable. However, these methods often stagnate at a non-stationary point, significantly degrading the network utility in mixed-integer problems such as joint trajectory and radio resource optimization. We detour this problem by converting the formulated problem into the Markov decision process (MDP). Exploiting the beneficial characteristics of the MDP, we design a non-iterative framework that cooperatively optimizes trajectory and radio resources without initial trajectory choice. The proposed framework can incorporate various trajectory planning algorithms such as the genetic algorithm, tree search, and reinforcement learning. Extensive comparisons with diverse baselines verify that the proposed framework significantly outperforms the state-of-the-art method, nearly achieving the global optimum. Our implementation code is available at https://github.com/hslyu/dbspf.

This article presents a deep reinforcement learning-based approach to tackle a persistent surveillance mission requiring a single unmanned aerial vehicle initially stationed at a depot with fuel or time-of-flight constraints to repeatedly visit a set of targets with equal priority. Owing to the vehicle's fuel or time-of-flight constraints, the vehicle must be regularly refueled, or its battery must be recharged at the depot. The objective of the problem is to determine an optimal sequence of visits to the targets that minimizes the maximum time elapsed between successive visits to any target while ensuring that the vehicle never runs out of fuel or charge. We present a deep reinforcement learning algorithm to solve this problem and present the results of numerical experiments that corroborate the effectiveness of this approach in comparison with common-sense greedy heuristics.