Russia released a video reportedly showing Viktor Sokolov, commander of Russia's Black Sea Fleet in Crimea, at a meeting with Defence Minister Sergei Shoigu and other military top brass a day after Ukrainian special forces claimed he was among dozens of officers killed in an attack on the fleet's Sevastopol naval base. Ukraine said it was clarifying information regarding Sokolov. The United Kingdom's defence ministry said "a dynamic, deep strike battle" was under way in the Black Sea after the Russian Black Sea Fleet suffered a series of major attacks. Kyiv said its air defences destroyed 26 of 38 Russian drones fired overnight but that some of the drones hit the Danube River port of Izmail, damaging more than 30 vehicles and injuring two drivers during a two-hour attack. The drone barrage also prompted the temporary suspension of ferry services to Romania.

Nearly 1,500 US police departments operate drones but only about a dozen routinely dispatch them in response to 911 calls, according to ACLU research. Drone maker Skydio aims to see that change, with a new model launched last week called the X10. The goal, cofounder and CEO Adam Bry said during a launch event last week in San Francisco, is to "get drones everywhere they can be useful in public safety." The new drone is capable of flying at speeds of 45 miles per hour and is small enough to fit into the trunk of a police car. It has infrared sensors that can be used to track people and fly autonomously in the dark.

Bahrain's military command has accused Yemen's Houthi fighters of killing two Bahraini soldiers in a drone attack on Saudi Arabia's southern border with Yemen. A number of Bahraini soldiers were also wounded in the attack, Bahrain's military said in a statement, which was carried by the official Bahrain News Agency on Monday. The exact number of soldiers wounded was not released. "This terrorist attack was carried out by the Houthis, who sent aircraft targeting the position of the Bahraini guards on the southern border of the kingdom of Saudi Arabia despite the halt of military operations between the warring sides in Yemen," the Bahraini military statement said. The island nation of Bahrain is a close ally of Saudi Arabia, which has been at war with Iran-aligned Houthi fighters in Yemen for several years.

In many massive IoT communication scenarios, the IoT devices require coverage from dynamic units that can move close to the IoT devices and reduce the uplink energy consumption. A robust solution is to deploy a large number of UAVs (UAV swarm) to provide coverage and a better line of sight (LoS) for the IoT network. However, the study of these massive IoT scenarios with a massive number of serving units leads to high dimensional problems with high complexity. In this paper, we apply multi-agent deep reinforcement learning to address the high-dimensional problem that results from deploying a swarm of UAVs to collect fresh information from IoT devices. The target is to minimize the overall age of information in the IoT network. The results reveal that both cooperative and partially cooperative multi-agent deep reinforcement learning approaches are able to outperform the high-complexity centralized deep reinforcement learning approach, which stands helpless in large-scale networks.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Bahrain's military command says one of its officers and a soldier were killed in a drone attack by Yemeni rebels while patrolling the Yemeni-Saudi border. The statement, carried by the official Bahrain News Agency, says "a number" of soldiers were wounded in the attack early Monday. Bahrain is a close ally of Saudi Arabia, which has been at war with the Iran-aligned Houthi rebels in Yemen for several years.

Last year, DJI showed what was possible in a small drone with the Mini 3 Pro by fitting tons of technology and a high-quality camera into a sub-250 gram drone. Following that up was never going to be easy, but now (after numerous leaks) it's unveiled the Mini 4 Pro with a long list of new features. Aside from one improvement, the camera is largely the same. However, it has new omnidirectional obstacle sensors that eliminate the blind spots on the Mini 3 Pro. It also comes with a new feature called ActiveTrack 360 that lets you program camera moves when tracking a subject.

This research aims to evaluate the performance of the rotors and study the behavior of the human driver in interacting with the rotors. In recent years, rotors have been increasingly used between countries due to their safety, capacity, and environmental advantages, and because they provide safe and fluid flows of vehicles for transit and integration. It turns out that roundabouts can significantly reduce speed at twisting intersections, entry speed and the resulting effect on speed depends on the rating of road users. In our research, (bus, car, truck) drivers were given special attention and their behavior was categorized into (conservative, normal, aggressive). Anticipating and recognizing driver behavior is an important challenge. Therefore, the aim of this research is to study the effect of roundabouts on these classifiers and to develop a method for predicting the behavior of road users at roundabout intersections. Safety is primarily due to two inherent features of the rotor. First, by comparing the data collected and processed in order to classify and evaluate drivers' behavior, and comparing the speeds of the drivers (bus, car and truck), the speed of motorists at crossing the roundabout was more fit than that of buses and trucks. We looked because the car is smaller and all parts of the rotor are visible to it. So drivers coming from all directions have to slow down, giving them more time to react and mitigating the consequences in the event of an accident. Second, with fewer conflicting flows (and points of conflict), drivers only need to look to their left (in right-hand traffic) for other vehicles, making their job of crossing the roundabout easier as there is less need to split attention between different directions.

We study the problem of learning a function that maps context observations (input) to parameters of a submodular function (output). Our motivating case study is a specific type of vehicle routing problem, in which a team of Unmanned Ground Vehicles (UGVs) can serve as mobile charging stations to recharge a team of Unmanned Ground Vehicles (UAVs) that execute persistent monitoring tasks. {We want to learn the mapping from observations of UAV task routes and wind field to the parameters of a submodular objective function, which describes the distribution of landing positions of the UAVs .} Traditionally, such a learning problem is solved independently as a prediction phase without considering the downstream task optimization phase. However, the loss function used in prediction may be misaligned with our final goal, i.e., a good routing decision. Good performance in the isolated prediction phase does not necessarily lead to good decisions in the downstream routing task. In this paper, we propose a framework that incorporates task optimization as a differentiable layer in the prediction phase. Our framework allows end-to-end training of the prediction model without using engineered intermediate loss that is targeted only at the prediction performance. In the proposed framework, task optimization (submodular maximization) is made differentiable by introducing stochastic perturbations into deterministic algorithms (i.e., stochastic smoothing). We demonstrate the efficacy of the proposed framework using synthetic data. Experimental results of the mobile charging station routing problem show that the proposed framework can result in better routing decisions, e.g. the average number of UAVs recharged increases, compared to the prediction-optimization separate approach.

Global navigation satellite systems (GNSS) denied environments/conditions require unmanned aerial vehicles (UAVs) to energy-efficiently and reliably fly. To this end, this study presents perception-and-energy-aware motion planning for UAVs in GNSS-denied environments. The proposed planner solves the trajectory planning problem by optimizing a cost function consisting of two indices: the total energy consumption of a UAV and the perception quality of light detection and ranging (LiDAR) sensor mounted on the UAV. Before online navigation, a high-fidelity simulator acquires a flight dataset to learn energy consumption for the UAV and heteroscedastic uncertainty associated with LiDAR measurements, both as functions of the horizontal velocity of the UAV. The learned models enable the online planner to estimate energy consumption and perception quality, reducing UAV battery usage and localization errors. Simulation experiments in a photorealistic environment confirm that the proposed planner can address the trade-off between energy efficiency and perception quality under heteroscedastic uncertainty. The open-source code is released at https://gitlab.com/ReI08/perception-energy-planner.

The design and control of winged aircraft and drones is an iterative process aimed at identifying a compromise of mission-specific costs and constraints. When agility is required, shape-shifting (morphing) drones represent an efficient solution. However, morphing drones require the addition of actuated joints that increase the topology and control coupling, making the design process more complex. We propose a co-design optimisation method that assists the engineers by proposing a morphing drone's conceptual design that includes topology, actuation, morphing strategy, and controller parameters. The method consists of applying multi-objective constraint-based optimisation to a multi-body winged drone with trajectory optimisation to solve the motion intelligence problem under diverse flight mission requirements. We show that co-designed morphing drones outperform fixed-winged drones in terms of energy efficiency and agility, suggesting that the proposed co-design method could be a useful addition to the aircraft engineering toolbox.