There was no confirmation from Russia's navy that the Caesar Kunikov had been sunk in the Black Sea, merely that six Ukrainian drones had been destroyed. Video appearing to show the aftermath of the Ukrainian attack was uploaded only recently, BBC Verify confirmed.

A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAVs. We find an opportunity to accomplish the task by transforming AMAVs into mobile localization infrastructures for BMAVs. However, turning this insight into a practical system is non-trivial due to challenges in location estimation with BMAVs' unknown and diverse localization errors and resource allocation of AMAVs given coupled influential factors. This study proposes TransformLoc, a new framework that transforms AMAVs into mobile localization infrastructures, specifically designed for low-cost and resource-constrained BMAVs. We first design an error-aware joint location estimation model to perform intermittent joint location estimation for BMAVs and then design a proximity-driven adaptive grouping-scheduling strategy to allocate resources of AMAVs dynamically. TransformLoc achieves a collaborative, adaptive, and cost-effective localization system suitable for large-scale heterogeneous MAV swarms. We implement TransformLoc on industrial drones and validate its performance. Results show that TransformLoc outperforms baselines including SOTA up to 68\% in localization performance, motivating up to 60\% navigation success rate improvement.

Seven people, including three children, were killed in a Russian drone attack on a gas station in the Ukrainian city of Kharkiv on Saturday. Russia has lost more than 3,000 tanks in Ukraine - the equivalent of its entire pre-war active inventory - but has enough lower-quality armored vehicles in storage for years of replacements, a leading research center said on Tuesday. Ukraine has also suffered heavy losses since Russia invaded in February 2022, but Western military replenishments have allowed it to maintain inventories while upgrading quality, the International Institute for Strategic Studies said. Even after the loss of so many tanks - including an estimated 1,120 in the past year - Russia still has about twice as many available for combat as Ukraine, according to the IISS's annual Military Balance, a key research tool for defense analysts. Henry Boyd, the institute's senior fellow for military capability, said Russia had been roughly "breaking even" in terms of replacements.

An Israeli drone strike has targeted two journalists in Muraj, north of Rafah, including Al Jazeera Arabic correspondent, Ismail Abu Omar who doctors say is in a critical condition.

Beverly Hills police drone captures slip and fall. The affluent 90210 zip code is often associated with a hit television show that aired in the 1990s. It is also where an alleged burglar fell off a ladder into a pool. The Beverly Hills Police Department (BHPD) shared drone footage of the incident from Jan. 6 on Instagram with the caption, "Burglar caught in 4K. The video first shows a man crawling out of a home's window before being seen atop a tall ladder over what appears to be a garage.

Two journalists, including an Al Jazeera reporter, have been wounded in an Israeli attack north of Rafah in southern Gaza. The condition of Al Jazeera Arabic correspondent Ismail Abu Omar and his cameraman Ahmad Matar was described as serious and both were transferred to the European Gaza Hospital in Khan Younis for treatment on Tuesday. Abu Omar has had his right leg amputated, but pieces of shrapnel remained in his head and chest. Doctors were trying to save his left leg. He was undergoing surgery after suffering significant blood loss from a possible cut in the femoral artery.

Digital twin (DT) platforms are increasingly regarded as a promising technology for controlling, optimizing, and monitoring complex engineering systems such as next-generation wireless networks. An important challenge in adopting DT solutions is their reliance on data collected offline, lacking direct access to the physical environment. This limitation is particularly severe in multi-agent systems, for which conventional multi-agent reinforcement (MARL) requires online interactions with the environment. A direct application of online MARL schemes to an offline setting would generally fail due to the epistemic uncertainty entailed by the limited availability of data. In this work, we propose an offline MARL scheme for DT-based wireless networks that integrates distributional RL and conservative Q-learning to address the environment's inherent aleatoric uncertainty and the epistemic uncertainty arising from limited data. To further exploit the offline data, we adapt the proposed scheme to the centralized training decentralized execution framework, allowing joint training of the agents' policies. The proposed MARL scheme, referred to as multi-agent conservative quantile regression (MA-CQR) addresses general risk-sensitive design criteria and is applied to the trajectory planning problem in drone networks, showcasing its advantages.

Many existing obstacle avoidance algorithms overlook the crucial balance between safety and agility, especially in environments of varying complexity. In our study, we introduce an obstacle avoidance pipeline based on reinforcement learning. This pipeline enables drones to adapt their flying speed according to the environmental complexity. Moreover, to improve the obstacle avoidance performance in cluttered environments, we propose a novel latent space. The latent space in this representation is explicitly trained to retain memory of previous depth map observations. Our findings confirm that varying speed leads to a superior balance of success rate and agility in cluttered environments. Additionally, our memory-augmented latent representation outperforms the latent representation commonly used in reinforcement learning. Finally, after minimal fine-tuning, we successfully deployed our network on a real drone for enhanced obstacle avoidance.

This work presents a neural network model capable of recognizing small and tiny objects in thermal images collected by unmanned aerial vehicles. Our model consists of three parts, the backbone, the neck, and the prediction head. The backbone is developed based on the structure of YOLOv5 combined with the use of a transformer encoder at the end. The neck includes a BI-FPN block combined with the use of a sliding window and a transformer to increase the information fed into the prediction head. The prediction head carries out the detection by evaluating feature maps with the Sigmoid function. The use of transformers with attention and sliding windows increases recognition accuracy while keeping the model at a reasonable number of parameters and computation requirements for embedded systems. Experiments conducted on public dataset VEDAI and our collected datasets show that our model has a higher accuracy than state-of-the-art methods such as ResNet, Faster RCNN, ComNet, ViT, YOLOv5, SMPNet, and DPNetV3. Experiments on the embedded computer Jetson AGX show that our model achieves a real-time computation speed with a stability rate of over 90%.

This paper presents a new swarm intelligence-based approach to deal with the cooperative path planning problem of unmanned aerial vehicles (UAVs), which is essential for the automatic inspection of infrastructure. The approach uses a 3D model of the structure to generate viewpoints for the UAVs. The calculation of the viewpoints considers the constraints related to the UAV formation model, camera parameters, and requirements for data post-processing. The viewpoints are then used as input to formulate the path planning as an extended traveling salesman problem and the definition of a new cost function. Ant colony optimization is finally used to solve the problem to yield optimal inspection paths. Experiments with 3D models of real structures have been conducted to evaluate the performance of the proposed approach. The results show that our system is not only capable of generating feasible inspection paths for UAVs but also reducing the path length by 29.47\% for complex structures when compared with another heuristic approach. The source code of the algorithm can be found at https://github.com/duynamrcv/aco_3d_ipp.