Alphabet's Wing division has teamed up with DoorDash to deliver some convenience and grocery items -- such as pantry staples, snacks and household essentials -- by drone. Customers can place an order through the "DoorDash Air" section of the DoorDash app and receive their items in as little as 15 minutes. When they check out, users will need to select a delivery spot for the drone to drop off their package. The DoorDash app will ask them to confirm that the drop zone is clear before the user completes the order. The pilot is live in Logan, Australia, where Wing has been testing its services for a few years.

In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several different communities exist, each defined by a unique task to be learned. In this setting, spatially distributed devices belonging to each community collaboratively contribute towards training their community model via wireless links provided by the UAV. Accordingly, the UAV acts as a mobile orchestrator coordinating the transmissions and the learning schedule among the devices in each community, intending to accelerate the learning process of all tasks. We propose a heuristic metric as a proxy for the training performance of the different tasks. Capitalizing on this metric, a surrogate objective is defined which enables us to jointly optimize the UAV trajectory and the scheduling of the devices by employing convex optimization techniques and graph theory. The simulations illustrate the out-performance of our solution when compared to other handpicked static and mobile UAV deployment baselines.

Drones have shown to be useful aerial vehicles for unmanned transport missions such as food and medical supply delivery. This can be leveraged to deliver life-saving nutrition and medicine for people in emergency situations. However, commercial drones can generally only carry 10 % - 30 % of their own mass as payload, which limits the amount of food delivery in a single flight. One novel solution to noticeably increase the food-carrying ratio of a drone, is recreating some structures of a drone, such as the wings, with edible materials. We thus propose a drone, which is no longer only a food transporting aircraft, but itself is partially edible, increasing its food-carrying mass ratio to 50 %, owing to its edible wings. Furthermore, should the edible drone be left behind in the environment after performing its task in an emergency situation, it will be more biodegradable than its non-edible counterpart, leaving less waste in the environment. Here we describe the choice of materials and scalable design of edible wings, and validate the method in a flight-capable prototype that can provide 300 kcal and carry a payload of 80 g of water.

Shocking drone footage shows migrants streaming across the U.S.-Mexico border into Eagle Pass, Texas, virtually unimpeded. The footage, captured by Fox News with thermal imaging cameras, is just one example of the daily mass crossings reporter Bill Melugin has witnessed at the border over the past week. Border crossings have surged under President Biden, and multiple migrants told Fox that "Joe Biden es el major," or "Joe Biden is the best." Melugin says the border crossings happen every morning "with no resistance" on either side of the border. Biden's administration has insisted that the U.S.-Mexico border is "closed" despite overwhelming evidence to the contrary. Illegal immigrants in a single file line after driver is busted by DPS officers.

The delivery drivers of the future may not leave a package at your door. Instead, they'll be sitting several miles or even time zones away in a control room overseeing a fleet of delivery robots or drones. A look at how innovation and technology are transforming the way we live, work and play. Companies are plowing billions of dollars into autonomous technologies they hope will improve efficiency and solve worker shortages. But executives in these industries say true autonomy is many years away–and may never come.

Fox News' senior foreign affairs correspondent Greg Palkot reports from Kyiv on how Ukrainians are coping under Russian drone and missile attacks. KYIV, Ukraine - A "cold" war is building here in Ukraine at the hands of Vladimir Putin. As their progress slows on the battle front, Russian forces have been targeting civilian infrastructure. With winter closing in, and power, heat and water going, it could be Moscow's most dangerous tactic. "Militarily it's absolutely nothing," Ukrainian Member of Parliament Oleksiy Goncharenko explained, "but yes, it can cause a lot of suffering for civilians."

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.

Generating novel and useful concepts is essential during the early design stage to explore a large variety of design opportunities, which usually requires advanced design thinking ability and a wide range of knowledge from designers. Growing works on computer-aided tools have explored the retrieval of knowledge and heuristics from design data. However, they only provide stimuli to inspire designers from limited aspects. This study explores the recent advance of the natural language generation (NLG) technique in the artificial intelligence (AI) field to automate the early-stage design concept generation. Specifically, a novel approach utilizing the generative pre-trained transformer (GPT) is proposed to leverage the knowledge and reasoning from textual data and transform them into new concepts in understandable language. Three concept generation tasks are defined to leverage different knowledge and reasoning: domain knowledge synthesis, problem-driven synthesis, and analogy-driven synthesis. The experiments with both human and data-driven evaluation show good performance in generating novel and useful concepts.

The connectivity-aware path design is crucial in the effective deployment of autonomous Unmanned Aerial Vehicles (UAVs). Recently, Reinforcement Learning (RL) algorithms have become the popular approach to solving this type of complex problem, but RL algorithms suffer slow convergence. In this paper, we propose a Transfer Learning (TL) approach, where we use a teacher policy previously trained in an old domain to boost the path learning of the agent in the new domain. As the exploration processes and the training continue, the agent refines the path design in the new domain based on the subsequent interactions with the environment. We evaluate our approach considering an old domain at sub-6 GHz and a new domain at millimeter Wave (mmWave). The teacher path policy, previously trained at sub-6 GHz path, is the solution to a connectivity-aware path problem that we formulate as a constrained Markov Decision Process (CMDP). We employ a Lyapunov-based model-free Deep Q-Network (DQN) to solve the path design at sub-6 GHz that guarantees connectivity constraint satisfaction. We empirically demonstrate the effectiveness of our approach for different urban environment scenarios. The results demonstrate that our proposed approach is capable of reducing the training time considerably at mmWave.

After weeks of denial, Iran has confirmed it supplied deadly unmanned drones to Russia for use in its ongoing war with Ukraine. On Saturday, Iranian Foreign Minister Hossein Amirabdollahian told IRNA, Iran's state-run news agency, that reports of continued drone shipments were false and that Iran had not sent drones to Russia since before the invasion began in February. "This fuss made by some Western countries that Iran has provided missiles and drones to Russia to help the war in Ukraine - the missile part is completely wrong. The part about drones is correct, we did provide a limited number of drones to Russia in the months before the start of the war in Ukraine," said Amirabdollahian. The admission from Amirabdollahian comes just weeks after Iran's U.N. representative gave a striking denial to past allegations of drone shipments.