Recent research shows that supervised learning can be an effective tool for designing nearoptimal feedback controllers for high-dimensional nonlinear dynamic systems. But the behavior of neural network controllers is still not well understood. In particular, some neural networks with high test accuracy can fail to even locally stabilize the dynamic system. To address this challenge we propose several novel neural network architectures, which we show guarantee local asymptotic stability while retaining the approximation capacity to learn the optimal feedback policy semi-globally. The proposed architectures are compared against standard neural network feedback controllers through numerical simulations of two high-dimensional nonlinear optimal control problems: stabilization of an unstable Burgers-type partial differential equation, and altitude and course tracking for an unmanned aerial vehicle. The simulations demonstrate that standard neural networks can fail to stabilize the dynamics even when trained well, while the proposed architectures are always at least locally stabilizing. Moreover, the proposed controllers are found to be close to optimal in testing.

In a metal workshop in the industrial city of Kryvyi Rih in southern Ukraine, a homemade anti-drone system waits to be mounted on a military pick-up truck. The contraption -- a heavy machine gun welded to steel tubes -- is one of several do-it-yourself weapons that are proving to be valuable additions to the Ukraine war effort. "We have the skills and the equipment, and we don't lack ideas," said Sergey Bondarenko in the workshop near the southern front. The well-built 39-year-old with a long black beard is a local leader of the territorial defence, a unit of the Ukrainian army. The device will be accompanied by shock absorbers, for more stability and precision, Bondarenko told AFP beside the anti-drone prototype.

Zipline has teamed up with a healthcare provider servicing the Intermountain Region in the US to deliver medicine to customers using its drones. The company has started doing drone deliveries to select Intermountain Healthcare patients in the Salt Lake Valley area. For now, it can only do drops for local communities within several miles of its distribution center. Zipline intends to add more centers over the next five years, though, so it can eventually expand beyond Salt Lake Valley and deliver medicine throughout Utah. As TechCrunch notes, Zipline has long been deploying drones for delivery in Africa, and it wasn't until the pandemic that it started doing drops in the US.

An important use case is offered by vehicular ground users are static and have known locations. The same wireless networks, in which UABSs serve as relays authors in [28] extended their previous work by considering between vehicular users and the network, enabling the users multiple UABSs. Unlike these previous works, in this paper, to upload data collected by on-board sensors [5]-[11]. Such we consider traffic conditions characterized by vehicular users user-generated data are collected by the network, and then with a priori unknown locations and we move beyond conventional forwarded to other vehicles by means of BSs or road side meta-RL by accounting for the constraint that simulators units (RSUs). Being able to offer stronger, possibly line-ofsight for previous traffic configurations cannot be revisited. The (LoS), links to vehicles as compared to (static) ground rest of the paper is organized as follows. The system model BSs, UABSs can support demanding vehicle-to-everything and the problem formulation are described in Section II.

Verizon is using a fleet of drones over southwest Florida to provide cellular connectivity to first responders who working around the clock in search and rescue missions to find survivors who may be trapped inside one of the more than 400 buildings destroyed by Hurricane Ian. Tethered drones that can fly for up to 1,000 hours are beaming down 4G and 5G coverage for an approximate radius of five to seven miles. Cory Davis, National Director for Verizon Frontline's Response Team and Public Safety Operations, told DailyMail.com He explained that along with the drones, Verizon is using satellites that beam down internet from low Earth orbit, generators hitched to trailers and recently sent a portable cell site on a barge to Sanibel Island, which has been completely cut off by the hurricane. Ian hit Lee County, home to Fort Myers, the hardest and Verizon, which is calling the county'ground zero,' is using the most assets to provide communications for first responders who have rescued hundreds of people since the monster storm made landfall last week.

What can we learn by looking down from above? To see a city from the sky is to see it as an eagle would. You can fly high up in the sky in breathtaking drone footage to reveal a landscape of hope and rich culture. Drone view is a powerful tool. A new method allows the creation of bird's eye views from a single frontal photo.

Industrial drone technology company Aeronext, trucking company Seino Holdings and others aim to realize a service to deliver goods within 30 minutes of an order. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this support page. We humbly apologize for the inconvenience.

After months of being hammered on Ukraine's battlefields by U.S. drones and longer range rocket systems, Russia is striking back with a new capability if its own -- attacking the southern port city of Odesa almost daily with winged missiles from Iran. That's leading some Ukrainians to begin to flee Odesa again for the first time since before the summer. Russia has hit the port and the military's Southern Command, both in the center of the city of 1 million, plus an ammunition depot and other targets outside the city. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is often of limited availability, incomplete, or only accessible for a substantial fee or through a non-disclosure agreement. Recently, remotely sensed data (e.g., satellite imagery, aerial photography) have emerged as a potentially rich source of energy systems information. However, the use of these data is frequently challenged by its sheer volume and complexity, precluding manual analysis. Recent breakthroughs in machine learning have enabled automated and rapid extraction of useful information from remotely sensed data, facilitating large-scale acquisition of critical energy system variables. Here we present a systematic review of the literature on this emerging topic, providing an in-depth survey and review of papers published within the past two decades. We first taxonomize the existing literature into ten major areas, spanning the energy value chain. Within each research area, we distill and critically discuss major features that are relevant to energy researchers, including, for example, key challenges regarding the accessibility and reliability of the methods. We then synthesize our findings to identify limitations and trends in the literature as a whole, and discuss opportunities for innovation. These include the opportunity to extend the methods beyond electricity to broader energy systems and wider geographic areas; and the ability to expand the use of these methods in research and decision making as satellite data become cheaper and easier to access. We also find that there are persistent challenges: limited standardization and rigor of performance assessments; limited sharing of code, which would improve replicability; and a limited consideration of the ethics and privacy of data.

Package delivery via the UAVs is a promising transport mode to provide efficient and green logistic services, especially in urban areas or complicated topography. However, the energy storage limit of the UAV makes it difficult to perform long-distance delivery tasks. In this paper, we propose a novel multimodal logistics framework, in which the UAVs can call on ground vehicles to provide hitch services to save their own energy and extend their delivery distance. This multimodal logistics framework is formulated as a two-stage model to jointly consider the incentive mechanism design for ground vehicles and path planning for UAVs. In Stage I, to deal with the motivations for ground vehicles to assist UAV delivery, a dynamic pricing scheme is proposed to best balance the vehicle response time and payments to ground vehicles. It shows that a higher price should be decided if the vehicle response time is long to encourage more vehicles to offer a ride. In Stage II, the task allocation and path planning of the UAVs over traffic network is studied based on the vehicle response time obtained in Stage I. To address pathfinding with restrictions and the performance degradation of the pathfinding algorithm due to the rising number of conflicts in multi-agent pathfinding, we propose the suboptimal conflict avoidance-based path search (CABPS) algorithm, which has polynomial time complexity. Finally, we validate our results via simulations. It is shown that our approach is able to increase the success rate of UAV package delivery. Moreover, we estimate the delivery time of the UAV in a pessimistic case, it is still twice as fast as the delivery time of the ground vehicle only.