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Is Chicago the violent crime capital of the US? How did India-US relations decline so fast? A Ukrainian drone attack killed two women in the village of Golovchino in Russia's Belgorod region, Russia's state TASS news agency reports. A man who was seriously injured in a Ukrainian drone attack in Russia's Belgorod region in April has died in hospital, TASS reports. TASS also reported that Russian forces shot down 82 Ukrainian drones in a 24-hour period.

We address the problem of agile 1v1 quadrotor pursuit-evasion, where a pursuer and an evader learn to outmaneuver each other through reinforcement learning (RL). Such settings face two major challenges: non-stationarity, since each agent's evolving policy alters the environment dynamics and destabilizes training, and catastrophic forgetting, where a policy overfits to the current adversary and loses effectiveness against previously encountered strategies. To tackle these issues, we propose an Asynchronous Multi-Stage Population-Based (AMSPB) algorithm. At each stage, the pursuer and evader are trained asynchronously against a frozen pool of opponents sampled from a growing population of past and current policies, stabilizing training and ensuring exposure to diverse behaviors. Within this framework, we train neural network controllers that output either velocity commands or body rates with collective thrust. Experiments in a high-fidelity simulator show that: (i) AMSPB-trained RL policies outperform RL and geometric baselines; (ii) body-rate-and-thrust controllers achieve more agile flight than velocity-based controllers, leading to better pursuit-evasion performance; (iii) AMSPB yields stable, monotonic gains across stages; and (iv) trained policies in one arena size generalize fairly well to other sizes without retraining.

Reinforcement learning (RL) has shown promise in a large number of robotic control tasks. Nevertheless, its deployment on unmanned aerial vehicles (UAVs) remains challenging, mainly because of reliance on accurate dynamic models and platform-specific sensing, which hinders cross-platform transfer. This paper presents the CORB-Planner (Corridor-as-Observations for RL B-spline planner), a real-time, RL-based trajectory planning framework for high-speed autonomous UAV flight across heterogeneous platforms. The key idea is to combine B-spline trajectory generation with the RL policy producing successive control points with a compact safe flight corridor (SFC) representation obtained via heuristic search. The SFC abstracts obstacle information in a low-dimensional form, mitigating overfitting to platform-specific details and reducing sensitivity to model inaccuracies. To narrow the sim-to-real gap, we adopt an easy-to-hard progressive training pipeline in simulation. A value-based soft decomposed-critic Q (SDCQ) algorithm is used to learn effective policies within approximately ten minutes of training. Benchmarks in simulation and real-world tests demonstrate real-time planning on lightweight onboard hardware and support maximum flight speeds up to 8.2m/s in dense, cluttered environments without external positioning. Compatibility with various UAV configurations (quadrotors, hexarotors) and modest onboard compute underlines the generality and robustness of CORB-Planner for practical deployment.

Abstract--This paper presents an agent-based simulation model for coordinating battery recharging in drone swarms, focusing on applications in Internet of Things (IoT) and Industry 4.0 environments. The proposed model includes a detailed description of the simulation methodology, system architecture, and implementation. One practical use case is explored: Smart Farming, highlighting how autonomous coordination strategies can optimize battery usage and mission efficiency in large-scale drone deployments. This work uses a machine learning technique to analyze the agent-based simulation sensitivity analysis output results. Drones have become important tools within the Internet of Things, and can be used in agribusiness, disaster response, logistics, and other usages.

This survey offers a comprehensive overview of recent advancements in LiDAR-based autonomous Unmanned Aerial Vehicles (UAVs), covering their design, perception, planning, and control strategies. Over the past decade, LiDAR technology has become a crucial enabler for high-speed, agile, and reliable UAV navigation, especially in GPS-denied environments. The paper begins by examining the evolution of LiDAR sensors, emphasizing their unique advantages such as high accuracy, long-range depth measurements, and robust performance under various lighting conditions, making them particularly well-suited for UAV applications. The integration of LiDAR with UAVs has significantly enhanced their autonomy, enabling complex missions in diverse and challenging environments. Subsequently, we explore essential software components, including perception technologies for state estimation and mapping, as well as trajectory planning and control methodologies, and discuss their adoption in LiDAR-based UAVs. Additionally, we analyze various practical applications of the LiDAR-based UAVs, ranging from industrial operations to supporting different aerial platforms and UAV swarm deployments. The survey concludes by discussing existing challenges and proposing future research directions to advance LiDAR-based UAVs and enhance multi-UAV collaboration. By synthesizing recent developments, this paper aims to provide a valuable resource for researchers and practitioners working to push the boundaries of LiDAR-based UAV systems.

We present a VR-based teleoperation system for multirotor flight that renders a third-person view (TPV) of the vehicle together with a live 3D reconstruction of its surroundings. The system runs on an embedded GPU (Jetson Orin NX) with ROS2-WebXR integration and streams geometry and video to a headset for closed-loop control in previously unmapped spaces. We implement a first-person video (FPV) baseline and perform matched trials with two pilots in unmapped indoor spaces. Quantitative metrics are reported from repeated trials with one pilot (N=8). TPV achieved task time comparable to FPV while improving proximal obstacle awareness (minimum obstacle distance +0.20m) and reducing contacts. These results indicate that TPV can preserve control quality while exposing hazards less visible in FPV, supporting safer teleoperation in unknown environments.

Fighter jets from the UK will join Nato allies in defending Polish airspace after last week's incursion of Russian drones, the defence secretary has confirmed. RAF Typhoon jets will fly air defence missions over Poland as part of the military alliance's mission to bolster the eastern flank. Other allies including Denmark, Germany and France are already taking part - a jet from the latter was scrambled earlier on Monday in response to another potential incursion by Russian drones. Nato said that alert was quickly over. Tensions have risen across Europe since Poland accused Russia of the incident, which saw 19 drones enter its territory.

Russian military exercises near NATO borders follow the recent incursion of Russian drones into the airspace of Poland and Romania, further stoking tensions with the West. On Sunday, Russia released images of its launch of a 3M22 Zircon hypersonic missile from a frigate in the Barents Sea, in the Arctic Ocean, near NATO borders. The launch comes against a backdrop of rising tensions with the West, just days after several Russian drones violated the airspace of North Atlantic Treaty Organization member countries Poland and Romania. The Zircon test is part of the Zapad 2025 joint maneuvers with Belarus, a week of military exercises aimed at assessing defensive and coordination capabilities between the two allied countries. It also serves to show that Russia's military force has not lost its strength, despite heavy losses more than three years after the start of the invasion of Ukraine .

Belarus and Russia's show of firepower appears to be a message to Europe In a large field 45 miles (72km) from Belarus' capital Minsk, a battle is raging. There are giant explosions as Sukhoi-34 bombers drop guided bombs. Helicopter gunships join the attack, while surveillance drones sweep overhead to view the damage. Together with other international media we've been brought to the Borisovsky training ground where Belarusian and Russian forces are taking part in joint manoeuvres. Military attachés, too, from a variety of embassies are observing the drill from a viewing platform.