Lifeboat Foundation

As we enter our third decade in the 21st century, it seems appropriate to reflect on the ways technology developed and note the breakthroughs that were achieved in the last 10 years.

The 2010s saw IBM’s Watson win a game of Jeopardy, ushering in mainstream awareness of machine learning, along with DeepMind’s AlphaGO becoming the world’s Go champion. It was the decade that industrial tools like drones, 3D printers, genetic sequencing, and virtual reality (VR) all became consumer products. And it was a decade in which some alarming trends related to surveillance, targeted misinformation, and deepfakes came online.

For better or worse, the past decade was a breathtaking era in human history in which the idea of exponential growth in information technologies powered by computation became a mainstream concept.

The Air Force Research Laboratory (AFRL), based at Wright-Patterson Air Force Base, has delivered what it says is the Air Force’s first high-energy laser weapon for battlefield use against drones.

AFRL has set up the laser weapon system overseas for a 12-month field assessment. The Air Force Strategic Development Planning & Experimentation (SDPE) Office located at Wright-Patt is leading the project, AFRL said in a statement Monday.

The biggest change worldwide in the last decade was probably the smartphone revolution, but overall, cities themselves still look pretty much the same. In the decade ahead, cities will change a lot more. Most of our regular readers probably think I am referring to how autonomous vehicles networks will start taking over and how owning a car will start to become closer to owning a horse. However, the real answer isn’t just the autonomous vehicles on the roads — they will likely also compete with autonomous eVTOL aircraft carrying people between hubs.

Today, the European Union is moving one step closer to making this second part a reality. Together with Daedalean, an autonomous flight company we have covered in the past, EASA published a new joint report covering “The Learning Assurance for Neural Networks.”

Coronavirus is sending in the drones. In what’s being billed as a “world first,” startup Manna Aero has begun a drone delivery service in Moneygall, Ireland. Delivering medicine to vulnerable people locked in their homes, it provides yet another strong example of how technology is helping the world adjust to life in the shadow of the coronavirus.

Having received authorisation from the Irish Aviation Authority, Manna Aero’s service began last Friday as a pilot in Moneygall, which was previously best known as Barack Obama’s ancestral village. However, if the trial is successful, the service will be rolled out throughout Ireland, and could also be used to deliver food.

The drones will deliver prescription orders for medicine to around a dozen households. As Manna Zero’s founder Bobby Healy told the Irish Independent, the drones ensure “zero human-contact” and can execute deliveries “in ways normal delivery can’t.”

Micro aerial vehicles (MAVs) could have numerous useful applications, for instance, assisting humans in completing warehouse inventories or search and rescue missions. While many companies worldwide have already started producing and using MAVs, some of these flying robots still have considerable limitations.

To work most effectively, MAVs should be supported by an efficient pose estimation system. This is a system or method that can calculate a drone’s position and attitude, which can then be used to control its flight, adjust its speed and aid its navigation while it is operating autonomously and when controlled remotely.

Researchers at Huazhong University of Science and Technology in China have recently developed a new system for the pose estimation of MAVs in indoor environments. Their new approach, outlined in a paper pre-published on arXIv and set to be published in IEEE Transactions on Industrial Electronics, leverages existing WiFi infrastructure to enable more effective navigation in small and agile drones.

A man under Coronavirus lockdown used a drone to walk his dog 🐶.

UPS is working with German startup Wingcopter to develop a new type of delivery drone, to be used for the logistics company’s growing commercial drone delivery efforts both in the U.S. and globally. Wingcopter has already designed an electric vertical takeoff and landing (eVTOL) aircraft that has a range of up to 75 miles and can achieve speeds as high as 150 miles per hour in conditions that include wind speeds of up to 45 miles per hour.

Wingcopter will be working closely with UPS’ Flight Forward subsidiary, the dedicated drone delivery unit that UPS developed last year in July to house its commercial drone delivery program. In October, Flight Forward received Federal Aviation Administration (FAA) approval to effectively operate a full-scale “drone airline” at scale for the purpose of package delivery.

Wingcopter has already demonstrated how its drones could operate in commercial settings, including during a demonstration with Merck earlier this year that saw its autonomous eVTOLs carry small packages between the drug company’s various office locations in Darmstadt in Germany. It also used its aircraft to deliver critical medical supplies and life-saving equipment to hard to reach areas, including through partnerships with UNICEF and other relief organizations.

Perception-Action loops are at the core of most our daily life activities. Subconsciously, our brains use sensory inputs to trigger specific motor actions in real time and this becomes a continuous activity that in all sorts of activities from playing sports to watching TV. In the context of artificial intelligence(AI), perception-action loops are the cornerstone of autonomous systems such as self-driving vehicles. While disciplines such as imitation learning or reinforcement learning have certainly made progress in this area, the current generation of autonomous systems are still nowhere near human skill in making those decisions directly from visual data. Recently, AI researchers from Microsoft published a paper proposing a transfer learning method to learn perception-action policies from in a simulated environment and apply the knowledge to fly an autonomous drone.

The challenge of learning which actions to take based on sensory input is not so much related to theory as to practical implementations. In recent years, methods like reinforcement learning and imitation learning have shown tremendous promise in this area but they remain constrained by the need of large amounts of difficult-to-collect labeled real world data. Simulated data, on the other hand, is easy to generate, but generally does not render safe behaviors in diverse real-life scenarios. Being able to learn policies in simulated environments and extrapolate the knowledge to real world environments remains one of the main challenges of autonomous systems. To advance research in this area, the AI community has created many benchmarks for real world autonomous systems. One of the most challenging is known as first person view drone racing.

In first-person view(FPV) done racing, expert pilots are able to plan and control a quadrotor with high agility using a potentially noisy monocular camera feed, without comprising safety. The Microsoft Research team attempted to build an autonomous agent that can control a drone in FPV racing.

As the COVID-19 pandemic spreads, robots, drones, and AI are helping healthcare organizations respond to worker shortages and the risk of infection.