This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). The selection of UAV relay nodes in IoV employs mechanisms executed either at centralized servers or decentralized nodes, which have two main limitations: 1) the traceability of the selection mechanism execution and 2) the coordination among the selected UAVs, which is currently offered in a centralized manner and is not coupled with the relay selection. Existing UAV coordination methods often rely on optimization methods, which are not adaptable to different environment complexities, or on centralized deep reinforcement learning, which lacks scalability in multi-UAV settings. Overall, there is a need for a comprehensive framework where relay selection and coordination are coupled and executed in a transparent and trusted manner. This work proposes a framework empowered by reinforcement learning and Blockchain for UAV-assisted IoV networks. It consists of three main components: a two-sided UAV relay selection mechanism for UAV-assisted IoV, a decentralized Multi-Agent Deep Reinforcement Learning (MDRL) model for autonomous UAV coordination, and a Blockchain implementation for transparency and traceability in the interactions between vehicles and UAVs. The relay selection considers the two-sided preferences of vehicles and UAVs based on the Quality-of-UAV (QoU) and the Quality-of-Vehicle (QoV). Upon selection of relay UAVs, the decentralized coordination between them is enabled through an MDRL model trained to control their mobility and maintain the network coverage and connectivity using Proximal Policy Optimization (PPO). The evaluation results demonstrate that the proposed selection and coordination mechanisms improve the stability of the selected relays and maximize the coverage and connectivity achieved by the UAVs.

This paper introduces an innovative drone carrier concept that is applied in maritime port security or offshore rescue. This system works with a heterogeneous system consisting of multiple Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) to perform inspection and intervention tasks in GNSS-denied or interrupted environments. The carrier, an electric catamaran measuring 4m by 7m, features a 4m by 6m deck supporting automated takeoff and landing for four DJI M300 drones, along with a 10kg-payload manipulator operable in up to level 3 sea conditions. Utilizing an offshore gimbal camera for navigation, the carrier can autonomously navigate, approach and dock with non-cooperative vessels, guided by an onboard camera, LiDAR, and Doppler Velocity Log (DVL) over a 3 km$^2$ area. UAVs equipped with onboard Ultra-Wideband (UWB) technology execute mapping, detection, and manipulation tasks using a versatile gripper designed for wet, saline conditions. Additionally, two UAVs can coordinate to transport large objects to the manipulator or interact directly with them. These procedures are fully automated and were successfully demonstrated at the Mohammed Bin Zayed International Robotic Competition (MBZIRC2024), where the drone carrier equipped with four UAVS and one manipulator, automatically accomplished the intervention tasks in sea-level-3 (wave height 1.25m) based on the rough target information.

An interactive map of UFO sightings has revealed shockingly new reports of drones in New Jersey and other states, suggesting this bizarre mystery is still unfolding. The map, created by the UAP (Unidentified Aerial Phenomena) tracking website Enigma Labs, shows hundreds of sightings logged as recently as January 7 in multiple Northeastern states. The mysterious drones along the East Coast appeared in November, with 22 people issuing reports to Enigma starting on the 20th. But that number dramatically increased to 347 by December 31. A drone ban was issued from December 18 to January 17, during which Enigma labs said the average number of reported sightings dropped by 43 percent.

The world needs whistleblowers, perhaps now more than ever. But whistleblowing has never been more dangerous. Jennifer Gibson has seen this problem develop up close. As a whistleblower lawyer based in the U.K., she has represented concerned insiders in the national security and tech worlds for more than a decade. She's represented family members of civilians killed by Pentagon drone strikes, and executives from top tech companies who've turned against their billionaire bosses.

Since the ceasefire has gone into force, the skies in Gaza have changed. There is an unusual stillness. We do not hear Israeli fighter jets or helicopters any more. The quadcopters are also gone, but the drones – the "zanana" – remain. The buzzing of Israeli drones is unmistakable.

Russia and Ukraine have continued to exchange barrages of air attacks, despite Donald Trump having said he would end the war within 24 hours of becoming US president. While Trump was inaugurated on Monday afternoon, neither Kyiv nor Moscow have shown signs of de-escalating the drone and missile strikes they have been launching against one another in recent months. Both launched barrages overnight on Tuesday. Russia said it downed 55 Ukrainian drones, more than half of which were intercepted over regions on the border. Kyiv said it struck an oil depot near the town of Liski in the Voronezh region, sparking a blaze at the facility for the second time in less than a week.

Ukraine's Air Force claimed it shot down 93 of 141 drones Russia launched in attacks overnight. The Air Force also said that 47 of the drones were "lost" while two returned to Russia. Russia said it destroyed 31 Ukrainian drones which had primarily targeted industrial sites in Russia's Tatarstan region, located about 1,000km (about 600 miles) from the Ukrainian border. No victims or damage have been reported. The governor of Russia's Bryansk region, Alexander Bogomaz, said 14 Ukrainian drones were neutralised in the region, which borders Ukraine.

For many tasks, multi-robot teams often provide greater efficiency, robustness, and resiliency. However, multi-robot collaboration in real-world scenarios poses a number of major challenges, especially when dynamic robots must balance competing objectives like formation control and obstacle avoidance in the presence of stochastic dynamics and sensor uncertainty. In this paper, we propose a distributed, multi-agent receding-horizon feedback motion planning approach using Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC) that is able to reason about both model and measurement uncertainty to achieve robust multi-agent formation control while navigating cluttered obstacle fields and avoiding inter-robot collisions. Our approach relies not only on the underlying PAC-NMPC algorithm but also on a terminal cost-function derived from gyroscopic obstacle avoidance. Through numerical simulation, we show that our distributed approach performs on par with a centralized formulation, that it offers improved performance in the case of significant measurement noise, and that it can scale to more complex dynamical systems.

Multi-UAV air combat is a complex task involving multiple autonomous UAVs, an evolving field in both aerospace and artificial intelligence. This paper aims to enhance adversarial performance through collaborative strategies. Previous approaches predominantly discretize the action space into predefined actions, limiting UAV maneuverability and complex strategy implementation. Others simplify the problem to 1v1 combat, neglecting the cooperative dynamics among multiple UAVs. To address the high-dimensional challenges inherent in six-degree-of-freedom space and improve cooperation, we propose a hierarchical framework utilizing the Leader-Follower Multi-Agent Proximal Policy Optimization (LFMAPPO) strategy. Specifically, the framework is structured into three levels. The top level conducts a macro-level assessment of the environment and guides execution policy. The middle level determines the angle of the desired action. The bottom level generates precise action commands for the high-dimensional action space. Moreover, we optimize the state-value functions by assigning distinct roles with the leader-follower strategy to train the top-level policy, followers estimate the leader's utility, promoting effective cooperation among agents. Additionally, the incorporation of a target selector, aligned with the UAVs' posture, assesses the threat level of targets. Finally, simulation experiments validate the effectiveness of our proposed method.

Drones are increasingly used in forestry to capture high-resolution remote sensing data. While operations above the forest canopy are already highly automated, flying inside forests remains challenging, primarily relying on manual piloting. Inside dense forests, reliance on the Global Navigation Satellite System (GNSS) for localization is not feasible. Additionally, the drone must autonomously adjust its flight path to avoid collisions. Recently, advancements in robotics have enabled autonomous drone flights in GNSS-denied obstacle-rich areas. In this article, a step towards autonomous forest data collection is taken by building a prototype of a robotic under-canopy drone utilizing state-of-the-art open-source methods and validating its performance for data collection inside forests. The autonomous flight capability was evaluated through multiple test flights in two boreal forest test sites. The tree parameter estimation capability was studied by conducting diameter at breast height (DBH) estimation using onboard stereo camera data and photogrammetric methods. The prototype conducted flights in selected challenging forest environments, and the experiments showed excellent performance in forest reconstruction with a miniaturized stereoscopic photogrammetric system. The stem detection algorithm managed to identify 79.31 % of the stems. The DBH estimation had a root mean square error (RMSE) of 3.33 cm (12.79 %) and a bias of 1.01 cm (3.87 %) across all trees. For trees with a DBH less than 30 cm, the RMSE was 1.16 cm (5.74 %), and the bias was 0.13 cm (0.64 %). When considering the overall performance in terms of DBH accuracy, autonomy, and forest complexity, the proposed approach was superior compared to methods proposed in the scientific literature. Results provided valuable insights into autonomous forest reconstruction using drones, and several further development topics were proposed.