Ukraine has opened a war crime investigation into the alleged killing of four soldiers captured by Russian forces, according to the Ukrainian parliament's human rights commissioner. Dmytro Lubinets wrote on X on Thursday that the four prisoners of war had no weapons as they walked out of a destroyed building with "their hands raised". "They were shot dead on the spot. This is a clear violation of the Geneva Convention and a grave war crime," he added. The alleged killing of the soldiers is believed to have occurred on March 13 in the southern Ukrainian village of Piatykhatky, according to The Associated Press news agency, which verified drone footage of the troops.

The Defense Ministry said Thursday that the Air Self-Defense Force scrambled fighter jets 704 times in response to possible airspace violations in fiscal 2024, up by 35 from the previous year. Of the total, scrambles against Chinese military aircraft accounted for 464, or 65.9%, down by 15. In August, Chinese military airplanes violated Japanese airspace off the Danjo Islands in Nagasaki Prefecture for the first time. The number of Chinese drones detected by the ministry more than tripled to 30, exceeding the 26 detected between fiscal 2013, when the first Chinese drone was spotted, and fiscal 2023. "China may have developed a system to (fully) operate drones, upgrading from trial flights," a ministry official said.

Reinforcement learning (RL) algorithms can enable high-maneuverability in unmanned aerial vehicles (MAVs), but transferring them from simulation to real-world use is challenging. Variable-pitch propeller (VPP) MAVs offer greater agility, yet their complex dynamics complicate the sim-to-real transfer. This paper introduces a novel RL framework to overcome these challenges, enabling VPP MAVs to perform advanced aerial maneuvers in real-world settings. Our approach includes real-to-sim transfer techniques-such as system identification, domain randomization, and curriculum learning to create robust training simulations and a sim-to-real transfer strategy combining a cascade control system with a fast-response low-level controller for reliable deployment. Results demonstrate the effectiveness of this framework in achieving zero-shot deployment, enabling MAVs to perform complex maneuvers such as flips and wall-backtracking.

Russia's territorial gains in Ukraine are slowing down dramatically, two analyses have found, continuing a pattern from 2024 at a time when both nations are trying to project strength in the face of United States-mediated negotiations aimed at ending the war. Britain's Ministry of Defence last week estimated that Russian forces seized 143sq km (55sq miles) of Ukrainian land in March, compared with 196sq km (76sq miles) in February and 326sq km (126sq miles) in January. The Institute for the Study of War, a Washington, DC-based think tank, spotted the same trend, estimating Russian gains at 203sq km (78sq miles) in March, 354sq km (137sq miles) in February and 427sq km (165sq miles) in January. These estimates are based on satellite imagery and geolocated open-source photography rather than claims by either side. Should this trend continue, Russian forces could come to a standstill by early summer, roughly coinciding with US President Donald Trump's self-imposed early deadline for achieving a ceasefire.

YellowScan's Navigator system is designed to map underwater topography in rivers, ponds and coastal areas. Below, its lidar system scans the landscape, mapping both the land and the shallow waters with pinpoint accuracy. This is precisely what YellowScan's new Navigator system is designed to do. Built specifically for mapping underwater topography in rivers, ponds and coastal areas, the Navigator is changing the game for environmental monitoring. With precision where traditional methods struggle, it's giving researchers and conservationists a whole new way to understand our planet's changing waterways.

Drones have become an important tool in a creator's bag of tricks, allowing them to capture aerial footage that elevates their videos. And nowadays, they've become more accessible as video quality and features have dramatically improved while prices have dropped. Recent budget-friendly models include DJI's Neo and Flip drones, along with the HoverAir X1 Pro lineup, all under 500. If you've got more to spend, the options are similarly plentiful with drones like the DJI Mini 4 Pro and HoverAir X1 Pro Max. And for the price of a good mirrorless camera, you can get DJI's Mavic 3 Pro that offers awesome image quality, range and other features.

In this article, we present a centralized approach for the control of multiple unmanned aerial vehicles (UAVs) for landing on moving unmanned ground vehicles (UGVs) using control barrier functions (CBFs). The proposed control framework employs two kinds of CBFs to impose safety constraints on the UAVs' motion. The first class of CBFs (LCBF) is a three-dimensional exponentially decaying function centered above the landing platform, designed to safely and precisely land UAVs on the UGVs. The second set is a spherical CBF (SCBF), defined between every pair of UAVs, which avoids collisions between them. The LCBF is time-varying and adapts to the motions of the UGVs. In the proposed CBF approach, the control input from the UAV's nominal tracking controller designed to reach the landing platform is filtered to choose a minimally-deviating control input that ensures safety (as defined by the CBFs). As the control inputs of every UAV are shared in establishing multiple CBF constraints, we prove that the control inputs are shared without conflict in rendering the safe sets forward invariant. The performance of the control framework is validated through a simulated scenario involving three UAVs landing on three moving targets.

The Autonomy of Unmanned Aerial Vehicles (UAVs) in indoor environments poses significant challenges due to the lack of reliable GPS signals in enclosed spaces such as warehouses, factories, and indoor facilities. Micro Aerial Vehicles (MAVs) are preferred for navigating in these complex, GPS-denied scenarios because of their agility, low power consumption, and limited computational capabilities. In this paper, we propose a Reinforcement Learning based Deep-Proximal Policy Optimization (D-PPO) algorithm to enhance realtime navigation through improving the computation efficiency. The end-to-end network is trained in 3D realistic meta-environments created using the Unreal Engine. With these trained meta-weights, the MAV system underwent extensive experimental trials in real-world indoor environments. The results indicate that the proposed method reduces computational latency by 91\% during training period without significant degradation in performance. The algorithm was tested on a DJI Tello drone, yielding similar results.

A.2024.0146 1 Rolling Horizon Coverage Control with Collaborative Autonomous Agents Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou and Marios M. Polycarpou Abstract This work proposes a coverage controller that enables an aerial team of distributed autonomous agents to collaboratively generate non-myopic coverage plans over a rolling finite horizon, aiming to cover specific points on the surface area of a 3D object of interest. The collaborative coverage problem, formulated, as a distributed model predictive control problem, optimizes the agents' motion and camera control inputs, while considering inter-agent constraints aiming at reducing work redundancy. The proposed coverage controller integrates constraints based on light-path propagation techniques to predict the parts of the object's surface that are visible with regard to the agents' future anticipated states. This work also demonstrates how complex, non-linear visibility assessment constraints can be converted into logical expressions that are embedded as binary constraints into a mixed-integer optimization framework. The proposed approach has been demonstrated through simulations and practical applications for inspecting buildings with unmanned aerial vehicles (UA Vs). I NTRODUCTION The interest in swarm systems such as systems utilizing multiple autonomous unmanned aerial vehicles (UA Vs) has skyrocketed over the last few decades. Rapid advancements in robotics, automation and artificial intelligence coupled with the decreasing costs of electronic components have fuelled a remarkable surge in interest towards the technologies and applications of swarming systems. This work addresses the challenge of coverage planning and control using multiple collaborative intelligent autonomous agents, specifically autonomous UA Vs. Coverage planning [1] is crucial in several application domains including search and rescue operations and critical infrastructure inspections. It is one of the essential functionalities that can notably enhance the autonomy of existing swarming systems enabling them to execute fully automated missions in the aforementioned scenarios. In coverage planning our objective is to design trajectories that allow a team of autonomous mobile agents to comprehensively cover a designated area or points of interest. Concurrently we aim to optimize a specific mission goal such as minimizing the mission's duration and energy consumption of the agents. This work introduces a coverage control framework that optimizes both the kinematic and camera control inputs of multiple UA V agents simultaneously.

Considering the widespread integration of aerial robots in inspection, search and rescue, and monitoring tasks, there is a growing demand to design intuitive human-drone interfaces. These aim to streamline and enhance the user interaction and collaboration process during drone navigation, ultimately expediting mission success and accommodating users' inputs. In this paper, we present a novel human-drone mixed reality interface that aims to (a) increase human-drone spatial awareness by sharing relevant spatial information and representations between the human equipped with a Head Mounted Display (HMD) and the robot and (b) enable safer and intuitive human-drone interactive and collaborative navigation in unknown environments beyond the simple command and control or teleoperation paradigm. We validate our framework through extensive user studies and experiments in a simulated post-disaster scenario, comparing its performance against a traditional First-Person View (FPV) control systems. Furthermore, multiple tests on several users underscore the advantages of the proposed solution, which offers intuitive and natural interaction with the system. This demonstrates the solution's ability to assist humans during a drone navigation mission, ensuring its safe and effective execution.