A Ukrainian drone attack on an airbase for bombers in southern Russia in December left three people dead, Moscow said. The Ukrainian military did not officially admit to the attack, but air force spokesman Yuriy Ihnat said the explosions were the result of what Russia was doing on Ukrainian soil.

Fox News chief national security correspondent Jennifer Griffin discusses the war in Ukraine and the growing divide over the United States' role on'Sunday Night In America.' Russian President Vladimir Putin ordered officials on Tuesday to tighten up Russia's borders after the country saw a series of overnight drone strikes that allegedly targeted oil depots. In what appeared to be three separate attacks, Russia saw at least two strikes in its southern regions north of Georgia, as well as outside Moscow. Russia's RIA reported that one drone "crashed" near a gas distribution facility roughly 60 miles outside of Moscow. While nothing was hit and no injuries were reported following the incident, the regional governor said a "civilian infrastructure facility" was likely the target. Russian President Vladimir Putin chairs a meeting of the Supervisory Board of the Agency for Strategic Initiatives to promote new projects in Moscow Feb. 9, 2023.

Russian President Vladimir Putin has ordered officials to tighten control of the border with Ukraine after a spate of drone attacks that Russian authorities blamed on Kyiv delivered a new challenge to Moscow a year after its full-scale invasion of its neighbour. One drone crashed on Tuesday just 100km (60 miles) southeast of Moscow in an alarming development for Russian defences. While Putin didn't refer to any specific attacks in a speech in the Russian capital, he stepped up border controls hours after drone attacks targeted several areas in southern and western Russia and authorities closed the airspace over St Petersburg in response to what some reports said was a drone. Also on Tuesday, several Russian television stations aired a missile attack warning that officials blamed on hacking. The drone attacks caused no casualties but provoked a security stir after the war in Ukraine marked its first anniversary last week.

Accurate self and relative state estimation are the critical preconditions for completing swarm tasks, e.g., collaborative autonomous exploration, target tracking, search and rescue. This paper proposes Swarm-LIO: a fully decentralized state estimation method for aerial swarm systems, in which each drone performs precise ego-state estimation, exchanges ego-state and mutual observation information by wireless communication, and estimates relative state with respect to (w.r.t.) the rest of UAVs, all in real-time and only based on LiDAR-inertial measurements. A novel 3D LiDAR-based drone detection, identification and tracking method is proposed to obtain observations of teammate drones. The mutual observation measurements are then tightly-coupled with IMU and LiDAR measurements to perform real-time and accurate estimation of ego-state and relative state jointly. Extensive real-world experiments show the broad adaptability to complicated scenarios, including GPS-denied scenes, degenerate scenes for camera (dark night) or LiDAR (facing a single wall). Compared with ground-truth provided by motion capture system, the result shows the centimeter-level localization accuracy which outperforms other state-of-the-art LiDAR-inertial odometry for single UAV system.

Landing safety is a challenge heavily engaging the research community recently, due to the increasing interest in applications availed by aerial vehicles. In this paper, we propose a landing safety pipeline based on state of the art object detectors and OctoMap. First, a point cloud of surface obstacles is generated, which is then inserted in an OctoMap. The unoccupied areas are identified, thus resulting to a list of safe landing points. Due to the low inference time achieved by state of the art object detectors and the efficient point cloud manipulation using OctoMap, it is feasible for our approach to deploy on low-weight embedded systems. The proposed pipeline has been evaluated in many simulation scenarios, varying in people density, number, and movement. Simulations were executed with an Nvidia Jetson Nano in the loop to confirm the pipeline's performance and robustness in a low computing power hardware. The experiments yielded promising results with a 95% success rate.

Inertial odometry is an attractive solution to the problem of state estimation for agile quadrotor flight. It is inexpensive, lightweight, and it is not affected by perceptual degradation. However, only relying on the integration of the inertial measurements for state estimation is infeasible. The errors and time-varying biases present in such measurements cause the accumulation of large drift in the pose estimates. Recently, inertial odometry has made significant progress in estimating the motion of pedestrians. State-of-the-art algorithms rely on learning a motion prior that is typical of humans but cannot be transferred to drones. In this work, we propose a learning-based odometry algorithm that uses an inertial measurement unit (IMU) as the only sensor modality for autonomous drone racing tasks. The core idea of our system is to couple a model-based filter, driven by the inertial measurements, with a learning-based module that has access to the thrust measurements. We show that our inertial odometry algorithm is superior to the state-of-the-art filter-based and optimization-based visual-inertial odometry as well as the state-of-the-art learned-inertial odometry in estimating the pose of an autonomous racing drone. Additionally, we show that our system is comparable to a visual-inertial odometry solution that uses a camera and exploits the known gate location and appearance. We believe that the application in autonomous drone racing paves the way for novel research in inertial odometry for agile quadrotor flight.

In this paper, an algorithm for Unmanned Aircraft Systems Traffic Management (UTM) for a finite number of unmanned aerial vehicles (UAVs) is proposed. This algorithm is developed by combining the Rapidly-Exploring Random Trees (RRT) and Velocity Obstacle (VO) algorithms and is referred to as the RRT-VO UTM algorithm. Here, the RRT algorithm works offline to generate obstacle-free waypoints in a given environment with known static obstacles. The VO algorithm, on the other hand, operates online to avoid collisions with other UAVS and known static obstacles. The boundary of the static obstacles are approximated by small circles to facilitate the formulation of VO algorithm. The proposed algorithm's performance is evaluated using numerical simulation and then compared to the well-known artificial potential field (APF) algorithm for collision avoidance. The advantages of the proposed method are clearly shown in terms of lower path length and collision avoidance capabilities for a challenging scenario.

In the skies above Chula Vista, California, where the police department runs a drone program 10 hours a day, seven days a week, it's not uncommon to see an unmanned aerial vehicle darting across the sky. Chula Vista is one of a dozen departments in the US that operate what are called drone-as-first-responder programs, where drones are dispatched by pilots, who are listening to live 911 calls, and often arrive first at the scenes of accidents, emergencies, and crimes, cameras in tow. But many argue that police forces' adoption of drones is happening too quickly. The use of drones as surveillance tools and first responders is a fundamental shift in policing, one without a well-informed public debate around privacy regulations, tactics, and limits. There's also little evidence available of its efficacy, with scant proof that drone policing reduces crime.

Chula Vista was the first police department to be awarded such a waiver. Now roughly 225 departments have them, and a dozen of those, including Chula Vista's, operate what are called drone-as-first-responder programs, where drones are dispatched by pilots, who are listening to live 911 calls, and often arrive first at the scenes of accidents, emergencies, and crimes, cameras in tow. The FAA is widely expected to fully legalize BVLOS within the next few years, which would make it easier for other such programs to launch; the sheriff-elect in Las Vegas, Nevada, already announced plans to pre-position hundreds of drones citywide to respond rapidly to crimes and shootings. New technologies such as autonomous flying, where drones can fly pre-programmed routes or respond to commands without the need for human operators, aren't far away. "This is rapidly escalating," says Matt Sloane, founder of Atlanta-based Skyfire Consulting, which helps train law enforcement agencies on the use of drones.

Maintaining the visibility of the targets is one of the major objectives of aerial tracking applications. This paper proposes QP Chaser, a trajectory planning pipeline that can enhance the visibility of single- and dual-target in both static and dynamic environments. As the name suggests, the proposed planner generates a target-visible trajectory via quadratic programming problems. First, the predictor forecasts the reachable sets of moving objects with a sample-and-check strategy considering obstacles. Subsequently, the trajectory planner reinforces the visibility of targets with consideration of 1) path topology and 2) reachable sets of targets and obstacles. We define a target-visible region (TVR) with topology analysis of not only static obstacles but also dynamic obstacles, and it reflects reachable sets of moving targets and obstacles to maintain the whole body of the target within the camera image robustly and ceaselessly. The online performance of the proposed planner is validated in multiple scenarios, including high-fidelity simulations and real-world experiments.