Chinese and Russian companies are developing an attack drone similar to an Iranian model deployed in Ukraine, European officials familiar with the matter said, a sign that Beijing may be edging closer to providing the sort of lethal aid that western officials have warned against. The companies held talks in 2023 about collaborating to replicate Iran's Shahed drone, and started developing and testing a version this year in preparation for shipment to Russia, said the officials, who asked not to be identified to discuss private information. The Chinese drones have yet to be used in Ukraine, they said. Providing Russia a Shahed-like attack drone would mark a deepening of Beijing's support for Russia despite repeated warnings from the U.S. and its allies. Chinese President Xi Jinping has sought to portray China as neutral in the conflict in Ukraine even as western officials say it's provided components and other support for Russian President Vladimir Putin's forces.

NATO Secretary General Jens Stoltenberg to discuss how NATO members have bolstered their spending, the latest on the war in Ukraine and how the alliance can counter aggression from countries like Russia and China. Explosions reverberated across the pre-dawn sky as Ukrainian air defenses fended off a Russian attack on this small city in western Ukraine, home to an important air base and a frequent target of Moscow's strikes. Hours after the assault, the tidy streets of Starokostiantyniv had returned to a semblance of normality. But the June 27 attack was a stark reminder of the challenges Kyiv faces as it rebuilds its depleted air force and deploys the first U.S.-designed F-16s - fighter aircraft that Russia will be determined to ground or destroy. The first planes are expected to arrive this month, and Ukraine hopes they will boost forces struggling to repel a Russian onslaught along the front line, which includes devastating glide bombs that F-16s could potentially disrupt.

Mae Sot, Thailand – Once again, the attack came from the sky. The Kachin resistance fighters barely heard the sound of the propellers as the Myanmar military's two drones released their payload above their heads in northern Kachin State in late April. "I fell down to the ground when the bombs dropped," Aung Nge, a fighter with the Kachin People's Defense Force (PDF), told Al Jazeera from an undisclosed location. I was awake the whole time." The drone attack seriously injured three men who were holed up close to the front line in Kachin State where battles with the armed forces have been escalating since October last year. In critical condition, field medics sent the men to a hidden hospital deep in the jungle where they could be treated by professional doctors. Within a day of receiving treatment, however, one of the soldiers started to show symptoms the doctors could not understand and his condition began to deteriorate rapidly. Another man from the Kachin Independence Army (KIA), who had been injured in a separate drone strike days after the first attack and appeared to be on the mend with no signs of infection, also took a turn for the worse and died in his sleep. Aung Nge, meanwhile, was about to endure ghastly infections that would spread across his entire body. Doctors told Al Jazeera that the men experienced rapid onset necrosis, an effect not normally seen in a blast wound. Necrosis causes the deterioration of most or all of the cells in an organ or tissue due to disease or the failure of the blood supply. While necrosis can be caused by sepsis, which appears rapidly and is usually accompanied by a fever, doctors said they could find no physiological reason for the rapid deterioration in their patients. Toxic substances can also trigger such reactions, they said. "In close examination of the wounds, they are rapidly necrotising, easily decomposed and not associated with metallic foreign bodies," Dr Soe Min, the veteran trauma doctor who treated the suspicious cases, told Al Jazeera. He has been treating combat-related cases since January 2022 and has seen and treated hundreds of blast injuries. These cases were different, he said. "After two days, all the wounds became blackish in colour with foul-smelling discharge.

Distributed learning and inference algorithms have become indispensable for IoT systems, offering benefits such as workload alleviation, data privacy preservation, and reduced latency. This paper introduces an innovative approach that utilizes unmanned aerial vehicles (UAVs) as a coverage extension relay for IoT environmental monitoring in rural areas. Our method integrates a split learning (SL) strategy between edge devices, a UAV and a server to enhance adaptability and performance of inference mechanisms. By employing UAVs as a relay and by incorporating SL, we address connectivity and resource constraints for applications of learning in IoT in remote settings. Our system model accounts for diverse channel conditions to determine the most suitable transmission strategy for optimal system behaviour. Through simulation analysis, the proposed approach demonstrates its robustness and adaptability, even excelling under adverse channel conditions. Integrating UAV relaying and the SL paradigm offers significant flexibility to the server, enabling adaptive strategies that consider various trade-offs beyond simply minimizing overall inference quality.

In this work we demonstrate the possibility of estimating the wind environment of a UAV without specialised sensors, using only the UAV's trajectory, applying a causal machine learning approach. We implement the causal curiosity method which combines machine learning times series classification and clustering with a causal framework. We analyse three distinct wind environments: constant wind, shear wind, and turbulence, and explore different optimisation strategies for optimal UAV manoeuvres to estimate the wind conditions. The proposed approach can be used to design optimal trajectories in challenging weather conditions, and to avoid specialised sensors that add to the UAV's weight and compromise its functionality.

This paper proposes a novel quaternion-based approach for tracking the translation (position and linear velocity) and rotation (attitude and angular velocity) trajectories of underactuated Unmanned Aerial Vehicles (UAVs). Quadrotor UAVs are challenging regarding accuracy, singularity, and uncertainties issues. Controllers designed based on unit-quaternion are singularity-free for attitude representation compared to other methods (e.g., Euler angles), which fail to represent the vehicle's attitude at multiple orientations. Quaternion-based Adaptive Backstepping Control (ABC) and Adaptive Fast Terminal Sliding Mode Control (AFTSMC) are proposed to address a set of challenging problems. A quaternion-based ABC, a superior recursive approach, is proposed to generate the necessary thrust handling unknown uncertainties and UAV translation trajectory tracking. Next, a quaternion-based AFTSMC is developed to overcome parametric uncertainties, avoid singularity, and ensure fast convergence in a finite time. Moreover, the proposed AFTSMC is able to significantly minimize control signal chattering, which is the main reason for actuator failure and provide smooth and accurate rotational control input. To ensure the robustness of the proposed approach, the designed control algorithms have been validated considering unknown time-variant parametric uncertainties and significant initialization errors. The proposed techniques has been compared to state-of-the-art control technique. Keywords: Adaptive Backstepping Control (ABC), Adaptive Fast Terminal Sliding Mode Control (AFTSMC), Unit-quaternion, Unmanned Aerial Vehicles, Singularity Free, Pose Control

Humans' trust in AI constitutes a pivotal element in fostering a synergistic relationship between humans and AI. This is particularly significant in the context of systems that leverage AI technology, such as autonomous driving systems and human-robot interaction. Trust facilitates appropriate utilization of these systems, thereby optimizing their potential benefits. If humans over-trust or under-trust an AI, serious problems such as misuse and accidents occur. To prevent over/under-trust, it is necessary to predict trust dynamics. However, trust is an internal state of humans and hard to directly observe. Therefore, we propose a prediction model for trust dynamics using dynamic structure equation modeling, which extends SEM that can handle time-series data. A path diagram, which shows causalities between variables, is developed in an exploratory way and the resultant path diagram is optimized for effective path structures. Over/under-trust was predicted with 90\% accuracy in a drone simulator task,, and it was predicted with 99\% accuracy in an autonomous driving task. These results show that our proposed method outperformed the conventional method including an auto regression family.

One of the core activities of an active observer involves moving to secure a "better" view of the scene, where the definition of "better" is task-dependent. This paper focuses on the task of human pose estimation from videos capturing a person's activity. Self-occlusions within the scene can complicate or even prevent accurate human pose estimation. To address this, relocating the camera to a new vantage point is necessary to clarify the view, thereby improving 2D human pose estimation. This paper formalizes the process of achieving an improved viewpoint. Our proposed solution to this challenge comprises three main components: a NeRF-based Drone-View Data Generation Framework, an On-Drone Network for Camera View Error Estimation, and a Combined Planner for devising a feasible motion plan to reposition the camera based on the predicted errors for camera views. The Data Generation Framework utilizes NeRF-based methods to generate a comprehensive dataset of human poses and activities, enhancing the drone's adaptability in various scenarios. The Camera View Error Estimation Network is designed to evaluate the current human pose and identify the most promising next viewing angles for the drone, ensuring a reliable and precise pose estimation from those angles. Finally, the combined planner incorporates these angles while considering the drone's physical and environmental limitations, employing efficient algorithms to navigate safe and effective flight paths. This system represents a significant advancement in active 2D human pose estimation for an autonomous UAV agent, offering substantial potential for applications in aerial cinematography by improving the performance of autonomous human pose estimation and maintaining the operational safety and efficiency of UAVs.

This paper examines the influence of initial guesses on trajectory planning for Unmanned Aerial Vehicles (UAVs) formulated in terms of Optimal Control Problem (OCP). The OCP is solved numerically using the Pseudospectral collocation method. Our approach leverages a path identified through Lazy Theta* and incorporates known constraints and a model of the UAV's behavior for the initial guess. Our findings indicate that a suitable initial guess has a beneficial influence on the planned trajectory. They also suggest promising directions for future research.

Relative state estimation is crucial for vision-based swarms to estimate and compensate for the unavoidable drift of visual odometry. For autonomous drones equipped with the most compact sensor setting -- a stereo camera that provides a limited field of view (FoV), the demand for mutual observation for relative state estimation conflicts with the demand for environment observation. To balance the two demands for FoV limited swarms by acquiring mutual observations with a safety guarantee, this paper proposes an active localization correction system, which plans camera orientations via a yaw planner during the flight. The yaw planner manages the contradiction by calculating suitable timing and yaw angle commands based on the evaluation of localization uncertainty estimated by the Kalman Filter. Simulation validates the scalability of our algorithm. In real-world experiments, we reduce positioning drift by up to 65% and managed to maintain a given formation in both indoor and outdoor GPS-denied flight, from which the accuracy, efficiency, and robustness of the proposed system are verified.