Overnight Russian drone attacks on east, south and central Ukraine damaged civilian infrastructure and businesses in the Poltava region and injured civilians in the Odesa region, Ukrainian officials said early on Wednesday. Odesa came under a "massive attack" by Russian drones overnight on Tuesday, wounding at least three people, the head of the regional administration, Oleh Kiper, wrote on his Telegram page. A residential building in a densely populated urban area of Odesa, civilian infrastructure and an educational facility were hit, he said. Air defence units repelled Russian air attacks on the Kyiv region and Ukraine's second largest city of Kharkiv, regional governors said in posts on Telegram channels. Russian forces said they have retaken St Nicholas Belogorsky monastery in the village of Gornal in Russia's Kursk region, where Ukrainian troops had been based, Russia's TASS news agency quoted a security source as saying.

With the exponential growth of Internet of Things (IoT) devices, edge computing (EC) is gradually playing an important role in providing cost-effective services. However, existing approaches struggle to perform well in graph-structured scenarios where user data is correlated, such as traffic flow prediction and social relationship recommender systems. In particular, graph neural network (GNN)-based approaches lead to expensive server communication cost. To address this problem, we propose GraphEdge, an efficient GNN-based EC architecture. It considers the EC system of GNN tasks, where there are associations between users and it needs to take into account the task data of its neighbors when processing the tasks of a user. Specifically, the architecture first perceives the user topology and represents their data associations as a graph layout at each time step. Then the graph layout is optimized by calling our proposed hierarchical traversal graph cut algorithm (HiCut), which cuts the graph layout into multiple weakly associated subgraphs based on the aggregation characteristics of GNN, and the communication cost between different subgraphs during GNN inference is minimized. Finally, based on the optimized graph layout, our proposed deep reinforcement learning (DRL) based graph offloading algorithm (DRLGO) is executed to obtain the optimal offloading strategy for the tasks of users, the offloading strategy is subgraph-based, it tries to offload user tasks in a subgraph to the same edge server as possible while minimizing the task processing time and energy consumption of the EC system. Experimental results show the good effectiveness and dynamic adaptation of our proposed architecture and it also performs well even in dynamic scenarios.

Aerial object detection using unmanned aerial vehicles (UAVs) faces critical challenges including sub-10px targets, dense occlusions, and stringent computational constraints. Existing detectors struggle to balance accuracy and efficiency due to rigid receptive fields and redundant architectures. To address these limitations, we propose Variable Receptive Field DETR (VRF-DETR), a transformer-based detector incorporating three key components: 1) Multi-Scale Context Fusion (MSCF) module that dynamically recalibrates features through adaptive spatial attention and gated multi-scale fusion, 2) Gated Convolution (GConv) layer enabling parameter-efficient local-context modeling via depthwise separable operations and dynamic gating, and 3) Gated Multi-scale Fusion (GMCF) Bottleneck that hierarchically disentangles occluded objects through cascaded global-local interactions. Experiments on VisDrone2019 demonstrate VRF-DETR achieves 51.4\% mAP\textsubscript{50} and 31.8\% mAP\textsubscript{50:95} with only 13.5M parameters. This work establishes a new efficiency-accuracy Pareto frontier for UAV-based detection tasks.

Performing striking aerobatic flight in complex environments demands manual designs of key maneuvers in advance, which is intricate and time-consuming as the horizon of the trajectory performed becomes long. This paper presents a novel framework that leverages diffusion models to automate and scale up aerobatic trajectory generation. Our key innovation is the decomposition of complex maneuvers into aerobatic primitives, which are short frame sequences that act as building blocks, featuring critical aerobatic behaviors for tractable trajectory synthesis. The model learns aerobatic primitives using historical trajectory observations as dynamic priors to ensure motion continuity, with additional conditional inputs (target waypoints and optional action constraints) integrated to enable user-editable trajectory generation. During model inference, classifier guidance is incorporated with batch sampling to achieve obstacle avoidance. Additionally, the generated outcomes are refined through post-processing with spatial-temporal trajectory optimization to ensure dynamical feasibility. Extensive simulations and real-world experiments have validated the key component designs of our method, demonstrating its feasibility for deploying on real drones to achieve long-horizon aerobatic flight.

This paper introduces MILUV, a Multi-UAV Indoor Localization dataset with UWB and Vision measurements. This dataset comprises 217 minutes of flight time over 36 experiments using three quadcopters, collecting ultra-wideband (UWB) ranging data such as the raw timestamps and channel-impulse response data, vision data from a stereo camera and a bottom-facing monocular camera, inertial measurement unit data, height measurements from a laser rangefinder, magnetometer data, and ground-truth poses from a motion-capture system. The UWB data is collected from up to 12 transceivers affixed to mobile robots and static tripods in both line-of-sight and non-line-of-sight conditions. The UAVs fly at a maximum speed of 4.418 m/s in an indoor environment with visual fiducial markers as features. MILUV is versatile and can be used for a wide range of applications beyond localization, but the primary purpose of MILUV is for testing and validating multi-robot UWB- and vision-based localization algorithms. The dataset can be downloaded at https://doi.org/10.25452/figshare.plus.28386041.v1. A development kit is presented alongside the MILUV dataset, which includes benchmarking algorithms such as visual-inertial odometry, UWB-based localization using an extended Kalman filter, and classification of CIR data using machine learning approaches. The development kit can be found at https://github.com/decargroup/miluv, and is supplemented with a website available at https://decargroup.github.io/miluv/.

Integrated Navigation (IN) techniques have emerged as a promising solution by combining multiple sensor measurements, such as those obtained from Inertial Measurement Units (IMU), Global Navigation Satellite Systems (GNSS), and vision-based sensors. IN approaches offer significant advantages, including robustness, improved accuracy, and the ability to overcome the limitations of individual sensors. Among the various mathematical tools employed in IN, quaternions have garnered considerable attention for estimating a vehicle's attitude (orientation). Quaternions provide an elegant and compact representation of orientation, avoiding the limitations of traditional Euler angles, such as singularities and ambiguity.

A day before Easter, Russian President Vladimir Putin announced a temporary ceasefire for the Christian holiday. Like other Russian promises, this one was broken too. Ukrainian media reported Russian drone attacks, shelling and firefights across the front lines. Ukrainian civilians were also targeted. This ceasefire that wasn't came on the tail of another one: a 30-day ceasefire that was supposed to cover energy infrastructure.

Drones could be used for NHS-related missions in remote areas, inspecting offshore wind turbines and supplying oil rigs by 2026 as part of a new regulatory regime in the UK. David Willetts, the head of a new government unit helping to deploy new technologies in Britain, said there were obvious situations where drones could be used if the changes go ahead next year. Ministers announced plans this month to allow drones to fly long distances without their operators seeing them. Drones cannot be flown "beyond visual line of sight" under current regulations, making their use for lengthy journeys impossible. In an interview with the Guardian, Lord Willetts, chair of the Regulatory Innovation Office (RIO), said the changes could come as soon as 2026, but that they would apply in "atypical" aviation environments at first, which would mean remote areas and over open water. Referring to the NHS, Willetts said there was potentially a huge market for drone operators.

A new bill moving through the Florida Senate would give homeowners the right to use "reasonable force" to take down drones infringing on their right to privacy, directly conflicting with federal airspace regulations while raising new legal questions regarding how far a person can go to defend their home from surveillance. The bill primarily focuses on further regulating the use of unmanned aircraft systems (UAS) while broadening the scope of locations that are protected from drone flights within the state, such as airports and correctional facilities. Notably, the bill would permit homeowners to use "reasonable force" to stop a drone from infringing on their expectation of privacy. A bill proposed in the Florida Senate would allow homeowners to use "reasonable force" to take down drones infringing on their right to privacy. "No one wants to have a drone sitting over their property, filming what they do for any number of reasons," Florida-based attorney Raul Gastesi told Fox News Digital.

Russia unleashed a barrage of missile and drone strikes on Ukraine as a short-lived Easter ceasefire expired. Russian forces launched 96 drones and three missiles on eastern and southern Ukraine overnight, Ukraine's Air Force reported on Monday. The swift return to major hostilities following a pause declared by Russian President Vladimir Putin comes as the United States struggles to persuade Moscow to agree on a longer-term ceasefire. The overnight assault targeted Ukraine's Kharkiv, Dnipropetrovsk and Cherkasy regions, the Air Force wrote on Telegram. Air defence units intercepted 42 drones and redirected another 47.