Lifeboat Foundation

A team of researchers at Delft University of Technology has developed a drone that flies autonomously using neuromorphic image processing and control based on the workings of animal brains. Animal brains use less data and energy compared to current deep neural networks running on GPUs (graphic chips). Neuromorphic processors are therefore very suitable for small drones because they don’t need heavy and large hardware and batteries.

The results are extraordinary: during flight the drone’s deep neural network processes data up to 64 times faster and consumes three times less energy than when running on a GPU. Further developments of this technology may enable the leap for drones to become as small, agile, and smart as flying insects or birds.

Photo of the “neuromorphic drone” flying over a flower pattern. It illustrates the visual inputs the drone receives from the neuromorphic camera in the corners. Red indicates pixels getting darker, green indicates pixels getting brighter. (Image: TU Delft)

SpaceX is working towards the goal of landing both the super heavy booster and Starship on a drone ship in the ocean, which has the potential to revolutionize space travel and support their mission for greater sustainability and reusability Questions to inspire discussion What is SpaceX’s goal for landing the super heavy booster and Starship?

Unmanned aerial vehicles (UAVs), commonly known as drones, are now used to capture images and carry out a wide range of missions in outdoor environments. While there are now several UAV designs with different advantages and characteristics, most conventional aerial robots are underactuated, meaning that they have fewer independent actuators than their degrees of freedom (DoF).

Underactuated systems are often more cost-effective and can be controlled using simpler control strategies than overactuated systems (i.e., robots that have more independent actuators than their DoF). Nonetheless, they are often less reliable and not as capable of precisely controlling their position and orientation.

Researchers at Tecnalia’s Basque Research and Technology Alliance (BRTA) in Spain recently developed a new overactuated aerial robot that can independently control the position and orientation of its main body. This robot, introduced in a paper published in Robotics and Autonomous Systems, has four quadrotors that cooperatively carry its central body.

As the Pentagon makes a push toward scaling production of autonomous systems and weapons, Anduril Industries is accelerating its own manufacturing capabilities through a new software-based production hub called Arsenal.

The California-based defense technology company announced Wednesday it will build the first Arsenal facility in the U.S., using funding from a recent $1.5 billion Series F investment round. Chris Brose, Anduril’s chief strategy officer, told reporters the firm’s goal is to consolidate manufacturing in order to “hyperscale” production across its product lines, including uncrewed combat drones and autonomous underwater vehicles.

“When we say hyperscale, we mean the ability to produce tens of thousands of a given system,” he said in a briefing. “This is the target that we’re setting for ourselves right now.”

Video of when SpaceX landed an orbital class rocket for the 200th time! Credit: Space.com | footage courtesy: SpaceX | edited by Steve Spaleta Music: The Launch by Jon Bjork / courtesy of Epidemic Sound.

FAA approves the first Category 2 drone for flight over people with AVSS PRS-M3DT parachute: drones with DJI dock.

Year 2023 face_with_colon_three

A few of us have had orders dropped on our lawns by drone already. Millions more will be within range of drone deliveries in 2023.

Wings aren’t the only things that make birds so successful. If it weren’t for their feet, how would pelicans skimming the tips of waves be able to suddenly land on a pier piling, or owls grab a mouse at 64 kilometers per hour without missing a beat? Robot birds must be able to do the same—something that has been a challenge, until now.

In a new study, researchers analyzed the anatomy and behavior of a tiny American parrot called a parrotlet and peregrine falcons, two species known for their expert footwork. They then designed “SNAG” (Stereotyped Nature-Inspired Aerial Grasper) — a pair of jointed legs attached to feet with jointed talons that automatically close around any object they encounter, be it a dowel, branch, or tennis ball.

“Compared with other traditional methods, the proposed has lower computational complexity, faster operation speed, weak influence of light, and strong ability to locate dirt,” the research group said. “The improved path planning algorithm used in this study greatly improves the efficiency of UAV inspection, saves time and resources, reduces operation and maintenance costs, and improves the corresponding operation and maintenance level of photovoltaic power generation.”

The novel approach uses mathematical morphologies for image processing, such as image enhancement, sharpening, filtering, and closing operations. It also uses image histogram equalization and edge detection, among other methods, to find the dusted spot. For path optimization, it uses an improved version of the A (A-star) algorithm.