Ahead of Indian Prime Minister Narendra Modi's state visit to Washington, the Biden administration is pushing New Delhi to cut through its own red tape and advance a deal for dozens of Unite States-made armed drones, two people familiar with the matter have told Reuters news agency. India has long expressed interest in buying large armed drones from the US. But bureaucratic stumbling blocks have hampered a deal for SeaGuardian drones, which could be worth $2bn to $3bn, for years. US negotiators are counting on Modi's White House visit on June 22 to seal the deal. Since the date for Modi's visit was fixed, the US State Department, Pentagon and White House have asked India to be able to "show" progress on the deal for as many as 30 armable MQ-9B SeaGuardian drones made by General Atomics, two sources told Reuters.

Unmanned aerial vehicles (UAVs) serving as aerial base stations can be deployed to provide wireless connectivity to mobile users, such as vehicles. However, the density of vehicles on roads often varies spatially and temporally primarily due to mobility and traffic situations in a geographical area, making it difficult to provide ubiquitous service. Moreover, as energy-constrained UAVs hover in the sky while serving mobile users, they may be faced with interference from nearby UAV cells or other access points sharing the same frequency band, thereby impacting the system's energy efficiency (EE). Recent multi-agent reinforcement learning (MARL) approaches applied to optimise the users' coverage worked well in reasonably even densities but might not perform as well in uneven users' distribution, i.e., in urban road networks with uneven concentration of vehicles. In this work, we propose a density-aware communication-enabled multi-agent decentralised double deep Q-network (DACEMAD-DDQN) approach that maximises the total system's EE by jointly optimising the trajectory of each UAV, the number of connected users, and the UAVs' energy consumption while keeping track of dense and uneven users' distribution. Our result outperforms state-of-the-art MARL approaches in terms of EE by as much as 65% - 85%.

Propeller failure is a major cause of multirotor Unmanned Aerial Vehicles (UAVs) crashes. While conventional multirotor systems struggle to address this issue due to underactuation, over-actuated platforms can continue flying with appropriate fault-tolerant control (FTC). This paper presents a robust FTC controller for an over-actuated UAV platform composed of quadcopters mounted on passive joints, offering input redundancy at both the high-level vehicle control and the low-level quadcopter control of vectored thrusts. To maximize the benefits of input redundancy during propeller failure, the proposed FTC controller features a hierarchical control architecture with three key components: (i) a low-level adjustment strategy to prevent propeller-level thrust saturation; (ii) a compensation loop for mitigating introduced disturbances; (iii) a nullspace-based control allocation framework to avoid quadcopter-level thrust saturation. Through reallocating actuator inputs in both the low-level and high-level control loops, the low-level quadcopter control can be maintained with up to two failed propellers, ensuring that the whole platform remains stable and avoids crashing. The proposed controller's superior performance is thoroughly examined through simulations and real-world experiments.

Fox News foreign correspondent Greg Palkot, live from Kyiv, reports on Ukraine's counterattack operations and discusses the barriers the country faces with reclaiming territory. Russian President Vladimir Putin admitted Tuesday that Russia is lacking in drones and other equipment in its war against Ukraine. Putin made the remarks during a meeting with war correspondents. The despotic leader told state media that things were missing in Russia's "special military operation." Russian President Vladimir Putin gestures as he speaks during a meeting with Russian war correspondents who cover a special military operation at the Kremlin in Moscow, Russia, Tuesday, June 13, 2023.

We introduce a method that simultaneously learns to explore new large environments and to reconstruct them in 3D from color images only. This is closely related to the Next Best View problem (NBV), where one has to identify where to move the camera next to improve the coverage of an unknown scene. However, most of the current NBV methods rely on depth sensors, need 3D supervision and/or do not scale to large scenes. Our method requires only a color camera and no 3D supervision. It simultaneously learns in a self-supervised fashion to predict a "volume occupancy field" from color images and, from this field, to predict the NBV. Thanks to this approach, our method performs well on new scenes as it is not biased towards any training 3D data. We demonstrate this on a recent dataset made of various 3D scenes and show it performs even better than recent methods requiring a depth sensor, which is not a realistic assumption for outdoor scenes captured with a flying drone.

This paper tackles the fundamental passive eavesdropping problem in modern wireless communications in which the location and the channel state information (CSI) of the attackers are unknown. In this regard, we propose deploying an unmanned aerial vehicle (UAV) that serves as a mobile aerial relay (AR) to help ground base station (GBS) support a subset of vulnerable users. More precisely, our solution (1) clusters the single-antenna users in two groups to be either served by the GBS directly or via the AR, (2) employs optimal multi-user beamforming to the directly served users, and (3) optimizes the AR's 3D position, its multi-user beamforming matrix and transmit powers by combining closed-form solutions with machine learning techniques. Specifically, we design a plain beamforming and power optimization combined with a deep reinforcement learning (DRL) algorithm for an AR to optimize its trajectory for the security maximization of the served users. Numerical results show that the multi-user multiple input, single output (MU-MISO) system split between a GBS and an AR with optimized transmission parameters without knowledge of the eavesdropping channels achieves high secrecy capacities that scale well with increasing the number of users.

This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV) platform called the Multi-robot Systems (MRS) Drone that can be used in a large range of indoor and outdoor applications. The MRS Drone features unique modularity with respect to changes in actuators, frames, and sensory configuration. As the name suggests, the platform is specially tailored for deployment within a MRS group. The MRS Drone contributes to the state-of-the-art of UAV platforms by allowing smooth real-world deployment of multiple aerial robots, as well as by outperforming other platforms with its modularity. For real-world multi-robot deployment in various applications, the platform is easy to both assemble and modify. Moreover, it is accompanied by a realistic simulator to enable safe pre-flight testing and a smooth transition to complex real-world experiments. In this manuscript, we present mechanical and electrical designs, software architecture, and technical specifications to build a fully autonomous multi UAV system. Finally, we demonstrate the full capabilities and the unique modularity of the MRS Drone in various real-world applications that required a diverse range of platform configurations.

Line coverage is the task of servicing a given set of one-dimensional features in an environment. It is important for the inspection of linear infrastructure such as road networks, power lines, and oil and gas pipelines. This paper addresses the single robot line coverage problem for aerial and ground robots by modeling it as an optimization problem on a graph. The problem belongs to the broad class of arc routing problems and is closely related to the rural postman problem (RPP) on asymmetric graphs. The paper presents an integer linear programming formulation with proofs of correctness. Using the minimum cost flow problem, we develop approximation algorithms with guarantees on the solution quality. These guarantees also improve the existing results for the asymmetric RPP. The main algorithm partitions the problem into three cases based on the structure of the required graph, i.e., the graph induced by the features that require servicing. We evaluate our algorithms on road networks from the 50 most populous cities in the world, consisting of up to 730 road segments. The algorithms, augmented with improvement heuristics, run within 3s and generate solutions that are within 10% of the optimum. We experimentally demonstrate our algorithms with commercial UAVs.

With the increasing utilization of Internet of Things (IoT)- enabled drones in diverse applications like photography, delivery, and surveillance, concerns regarding privacy and security have become more prominent. Drones have the ability to capture sensitive information, compromise privacy, and pose security risks. As a result, the demand for advanced technology to automate drone detection has become crucial. This paper presents a project on a transfer-based drone detection scheme, which forms an integral part of a computer vision-based module and leverages transfer learning to enhance performance. By harnessing the knowledge of pre-trained models from a related domain, transfer learning enables improved results even with limited training data. To evaluate the scheme's performance, we conducted tests on benchmark datasets, including the Drone-vs-Bird Dataset and the UAVDT dataset. Notably, the scheme's effectiveness is highlighted by its IOU-based validation results, demonstrating the potential of deep learning-based technology in automating drone detection in critical areas such as airports, military bases, and other high-security zones.

The United States has accused the Iranian government of helping Russia to build a drone manufacturing plant near Moscow, in an escalation of their defence cooperation. In a statement on Friday, White House National Security Council spokesman John Kirby cited US intelligence findings that indicated Iran had provided material support for the plant, which could be operational by early next year. US officials also double-downed on claims that Iran has sent hundreds of drones -- or unmanned aerial vehicles (UAVs) -- to Russia for use in Ukraine, where a full-scale invasion was launched in 2022. "Russia has been using Iranian UAVs in recent weeks to strike Kyiv and terrorize the Ukrainian population, and the Russia-Iran military partnership appears to be deepening," Kirby said in Friday's statement. "We are also concerned that Russia is working with Iran to produce Iranian UAVs from inside Russia."