Rapid response to natural disasters such as earthquakes is a crucial element in ensuring the safety of civil infrastructures and minimizing casualties. Traditional manual inspection is labour-intensive, time-consuming, and can be dangerous for inspectors and rescue workers. This paper proposed an autonomous inspection approach for structural damage inspection and survivor detection in the post-disaster building indoor scenario, which incorporates an autonomous navigation method, deep learning-based damage and survivor detection method, and a customized low-cost micro aerial vehicle (MAV) with onboard sensors. Experimental studies in a pseudo-post-disaster office building have shown the proposed methodology can achieve high accuracy in structural damage inspection and survivor detection. Overall, the proposed inspection approach shows great potential to improve the efficiency of existing manual post-disaster building inspection.

Video Individual Counting (VIC) has received increasing attentions recently due to its importance in intelligent video surveillance. Existing works are limited in two aspects, i.e., dataset and method. Previous crowd counting datasets are captured with fixed or rarely moving cameras with relatively sparse individuals, restricting evaluation for a highly varying view and time in crowded scenes. While VIC methods have been proposed based on localization-then-association or localization-then-classification, they may not perform well due to difficulty in accurate localization of crowded and small targets under challenging scenarios. To address these issues, we collect a MovingDroneCrowd Dataset and propose a density map based VIC method. Different from existing datasets, our dataset consists of videos captured by fast-moving drones in crowded scenes under diverse illuminations, shooting heights and angles. Other than localizing individuals, we propose a Depth-wise Cross-Frame Attention (DCFA) module, which directly estimate inflow and outflow density maps through learning shared density maps between consecutive frames. The inflow density maps across frames are summed up to obtain the number of unique pedestrians in a video. Experiments on our datasets and publicly available ones show the superiority of our method over the state of the arts for VIC in highly dynamic and complex crowded scenes. Our dataset and codes will be released publicly.

We consider a probabilistic model for large-scale task allocation problems for multi-agent systems, aiming to determine an optimal deployment strategy that minimizes the overall transport cost. Specifically, we assign transportation agents to delivery tasks with given pick-up and drop-off locations, pairing the spatial distribution of transport resources with the joint distribution of task origins and destinations. This aligns with the optimal mass transport framework where the problem and is in the unequal-dimensional setting. The task allocation problem can be thus seen as a linear programming problem that minimizes a quadratic transport cost functional, optimizing the energy of all transport units. The problem is motivated by time-sensitive medical deliveries using drones, such as emergency equipment and blood transport. In this paper, we establish the existence, uniqueness, and smoothness of the optimal solution, and illustrate its properties through numerical simulations.

Turbojet-powered VTOL UAVs have garnered increased attention in heavy-load transport and emergency services, due to their superior power density and thrust-to-weight ratio compared to existing electronic propulsion systems. The main challenge with jet-powered UAVs lies in the complexity of thrust vectoring mechanical systems, which aim to mitigate the slow dynamics of the turbojet. In this letter, we introduce a novel turbojet-powered UAV platform named Hex-Jet. Our concept integrates thrust vectoring and differential thrust for comprehensive attitude control. This approach notably simplifies the thrust vectoring mechanism. We utilize a predictor-based time delay control method based on the frequency domain model in our Hex-Jet controller design to mitigate the delay in roll attitude control caused by turbojet dynamics. Our comparative studies provide valuable insights for the UAV community, and flight tests on the scaled prototype demonstrate the successful implementation and verification of the proposed predictor-based time delay control technique.

UAV swarms are widely used in emergency communications, area monitoring, and disaster relief. Coordinated by control centers, they are ideal for federated learning (FL) frameworks. However, current UAV-assisted FL methods primarily focus on single tasks, overlooking the need for multi-task training. In disaster relief scenarios, UAVs perform tasks such as crowd detection, road feasibility analysis, and disaster assessment, which exhibit time-varying demands and potential correlations. In order to meet the time-varying requirements of tasks and complete multiple tasks efficiently under resource constraints, in this paper, we propose a UAV swarm based multi-task FL framework, where ground emergency vehicles (EVs) collaborate with UAVs to accomplish multiple tasks efficiently under constrained energy and bandwidth resources. Through theoretical analysis, we identify key factors affecting task performance and introduce a task attention mechanism to dynamically evaluate task importance, thereby achieving efficient resource allocation. Additionally, we propose a task affinity (TA) metric to capture the dynamic correlation among tasks, thereby promoting task knowledge sharing to accelerate training and improve the generalization ability of the model in different scenarios. To optimize resource allocation, we formulate a two-layer optimization problem to jointly optimize UAV transmission power, computation frequency, bandwidth allocation, and UAV-EV associations. For the inner problem, we derive closed-form solutions for transmission power, computation frequency, and bandwidth allocation and apply a block coordinate descent method for optimization. For the outer problem, a two-stage algorithm is designed to determine optimal UAV-EV associations. Furthermore, theoretical analysis reveals a trade-off between UAV energy consumption and multi-task performance.

The Ukrainian military launched its largest drone barrage to date against Russia as talks got underway with US officials in Saudi Arabia. Moscow said Kyiv's strikes on civilian targets undermine the effort to settle the war, despite its own history of hitting civilian infrastructure.

Kyiv, Ukraine – On Sunday, a top Russian security official in Moscow lauded dozens of servicemen who used an abandoned natural gas pipeline as a tunnel to infiltrate a Ukraine-occupied area in the western Russian region of Kursk. "The lid of a boiling cauldron is almost closed! Good job!" Dmitry Medvedev, who served as president and prime minister before becoming deputy head of Russia's Security Council, wrote on Telegram. But a Ukrainian serviceman deployed in Kursk offered a starkly different version of how the Russians barely got out of the pipeline on Saturday – only to be reportedly killed en masse. "Some suffocated right [in the pipeline], some turned back. About a hundred came out in our rear, split into two groups and were almost immediately ambushed by our special forces. And [also killed by] a massive squall of artillery," Evhen Sazonov wrote on Telegram.

Ukraine has targeted Moscow with a large overnight drone attack as Russia's Ministry of Defence says it has shot down 337 unmanned aircraft across the country. "The Defence Ministry's air defence continues to repel a massive attack by enemy drones on Moscow," Moscow Mayor Sergei Sobyanin said on Telegram early on Tuesday. Three people are reported to have been killed and three wounded in the southern suburbs of Moscow, according to Governor Andrei Vorobyov. He added that drone debris damaged at least seven units in a residential building in another suburb southeast of the city. The attack on the Russian capital, hundreds of kilometres from the Ukrainian border, comes before a meeting between United States and Ukrainian officials in Saudi Arabia.

Atlantic Council senior fellow Ariel Cohen and Heritage Foundation senior fellow Charles'Cully' Stimson discuss the state of the war amid White House tensions with President Zelenskyy. Ukraine launched its largest-ever drone attack on Moscow on Tuesday as a senior delegation met with Secretary of State Marco Rubio and National Security Advisor Mike Waltz in Saudi Arabia for talks about ending the war with Russia. A total of 337 drones were shot down Tuesday over Russia, including 91 in the Moscow area and 126 in the Kursk region bordering Ukraine, Reuters reported, citing Russia's defense ministry. Moscow-based meat producer Miratorg said two of its employees were killed by falling debris, while 18 other people – including three children – were injured after residential buildings were struck, officials told Reuters. Images taken in Russia showed damage to cars and apartment buildings in the wake of the attack, which temporarily shut down Moscow's four airports.

The use of drones by several African countries in their fight against armed groups is causing significant harm to civilians, according to a new report. More than 943 civilians have been killed in at least 50 incidents across six African countries from November 2021 to November 2024, according to the report by Drone Wars UK. The report, titled Death on Delivery, reveals that strikes regularly fail to distinguish between civilians and combatants in their operations. Experts told Al Jazeera that the death toll is likely only the tip of the iceberg because many countries run secretive drone campaigns. As drones rapidly become the weapon of choice for governments across the continent, what are the consequences for civilians in conflict zones?