As the spring planting season arrives in College Station, Texas, certified master gardener Mark Smith is thrilled that peace is in the air. This time last year, a loud buzzing noise began disrupting Smith's morning routine of checking on the peppers, tomatoes, herbs, and shrubs growing in his backyard. Several times an hour, an Amazon Prime Air delivery drone would noisily emerge about 800 feet away, just past a line of trees behind Smith's home. His neighbors began calling the fleet flying chainsaws. Smith, a retired civil engineer, preferred a different comparison: "It was like your neighbor runs their leaf blower all day long," he says.

You'd probably notice if the car that cut you off or pulled up beside you at a light didn't have a driver. In the UK, self-driving cars are still required by law to have a safety driver at the wheel, so it is difficult to notice them. But car companies have been testing automated vehicles on UK roads at least since 2017. Self-driving cars use Artificial Intelligence (AI) technology to steer themselves and navigate around obstacles. This technology is being introduced in many different ways, for example in cameras that detect whether people are speeding or using mobile phones while driving.

They're balloons – but not as we know them. Scientists at New Mexico's Los Alamos National Laboratory are working on a'vacuum balloon' with a hard shell that could eventually carry humans and travel'as fast as a commercial airliner'. Miles Beaux, a physicist at the lab, told DailyMail.com in an exclusive interview that if his experiments are successful the craft could be used for transport, surveillance, and even for parcel delivery drones. Beaux and his chemist colleague Chris Hamilton have been making small, hollow spheres out of a super-lightweight material called aerogel, then sucking the air out of them in an attempt to create a solid ball that is lighter than the surrounding atmosphere – allowing it to hover. The'vacuum balloons' would trump traditional helium or hydrogen balloons, which slowly lose their lift, and could potentially carry objects in the air indefinitely.

Kurt Knutsson talks about an innovative robot that can explore the depths of the ocean and capture stunning photos and videos. Drones are everywhere these days. They can fly, swim, and even transform into different shapes. They can deliver packages, be used to spy, pick fruits, and even explore the ocean depths. Some of them are downright creepy.

When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall \textit{system-level} competence of a robot as it operates in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy.

According to statistics, the healthcare drone industry has witnessed a dramatic surge in the last couple of years. In 2020, the market grew 30% and is expected to grow from $254 million in 2021 to $1,5 billion in 2028. The most common use case for healthcare drones is the delivery of medical supplies and laboratory samples. However, it appears that in 2022, new ways of using drones have become available. Research groups in the USA have completed test drone organ delivery operations and have done so successfully.

China has developed a new drone that functions in air and water. The year 2023 is turning out to be the year of drone delivery. Several startups have been hard at work testing, learning and honing their ability to deploy a network of drones for efficient delivery. Instant gratification in getting a last-minute item, prescription drug and fast food in record time are some of the focuses anticipated to drive initial demand. Food delivery has grown immensely in popularity, especially since the start of the COVID-19 pandemic.

In 2013, Jeff Bezos announced Amazon was developing a drone delivery service. He estimated at the time that air-dropped packages were "four, five years" away. Nearly a decade later, the service is promised to begin by the end of this year – albeit in only two locations in the US. According to David Carbon, an Australian expat and vice-president of the firm's drone delivery division, Amazon wants to deliver 500m packages annually by drone from 2030. Carbon told AAP earlier this month that the firm was planning a wider rollout for air deliveries in the US and potentially Australia.

Amazon has unveiled its newest delivery drone that will soon be dropping packages from 12 feet in the air in two U.S. cities. The retail giant has long wanted to solve the last leg of package delivery, especially since it launched Amazon Prime's Two-Day delivery offering in 2005. Jeff Bezos first announced drone delivery in 2013, but the service only made a single delivery three years after that. The drone, dubbed MK27-2, will start making deliveries in Lockeford, California, and College Station, Texas, by the end of 2022. The autonomous craft is about five-and-a-half feet in diameter, weighs 80 pounds and can only carry packages that weight less than five pounds.

We propose a novel framework for swarm-based drone delivery services with in-flight energy recharging. The framework aims to enhance the delivery time of multiple packages by reducing the number of stops and recharging times at intermediate stations. The proposed framework considers various intrinsic and extrinsic delivery constraints. We propose to use support drones whose sole purpose is to recharge other drones in the swarm during their flight. In this respect, we compute the optimal set of optimal support drones to minimize the probability of delivery services and recharging time at the next stations. We also use two settings to position the support drones in a flight formation for comparative purposes. Two novel energy sharing methods are proposed, namely, Priority-based and Fairness-based methods. A re-ordering method of the delivery drones is presented to facilitate the in-flight energy composition process. An enhanced A* algorithm is implemented to compose the optimal services in terms of delivery time. Experimental results prove the efficiency of our proposed approach.