Lifeboat Foundation

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.

Obstacle avoidance is a crucial piece of technology for drones, but commercially-available systems just aren’t fast enough for some situations. Now, engineers at the University of Zurich have developed a new system that gives drones such fast reflexes that they can play – and win at – dodgeball.

According to the researchers, most current obstacle avoidance systems take about 20 to 40 milliseconds to process changes in their surroundings. That’s fine for a drone gently approaching a building and finding its way inside, but it’s no match for fast-moving obstacles like birds or other drones. That makes navigation a problem in certain situations, like when there are a lot of drones together or in dynamic environments like disaster zones, or when a drone just needs to move fast.

Continue reading “Drone plays dodgeball to demo fast new obstacle detection system” »

In a preprint paper, Microsoft researchers describe a machine learning system that reasons out the correct actions to take directly from camera images. It’s trained via simulation and learns to independently navigate environments and conditions in the real world, including unseen situations, which makes it a fit for robots deployed in search and rescue missions. Someday, it could help those robots more quickly identify people in need of help.

“We wanted to push current technology to get closer to a human’s ability to interpret environmental cues, adapt to difficult conditions and operate autonomously,” wrote the researchers in a blog post published this week. “We were interested in exploring the question of what it would take to build autonomous systems that achieve similar performance levels.”