Lifeboat Foundation

Researchers from North Carolina State University have developed a technique for measuring speed and distance in indoor environments, which could be used to improve navigation technologies for robots, drones—or pedestrians trying to find their way around an airport. The technique uses a novel combination of Wi-Fi signals and accelerometer technology to track devices in near-real time.

“We call our approach Wi-Fi-assisted Inertial Odometry (WIO),” says Raghav Venkatnarayan, co-corresponding author of a paper on the work and a Ph.D. student at NC State. “WIO uses Wi-Fi as a velocity sensor to accurately track how far something has moved. Think of it as sonar, but using radio waves, rather than sound waves.”

Many devices, such as smartphones, incorporate technology called inertial measurement units (IMUs) to calculate how far a device has moved. However, IMUs suffer from large drift errors, meaning that even minor inaccuracies can quickly become exaggerated.

A team of Australian researchers has designed a reliable strategy for testing physical abilities of humanoid robots—robots that resemble the human body shape in their build and design. Using a blend of machine learning methods and algorithms, the research team succeeded in enabling test robots to effectively react to unknown changes in the simulated environment, improving their odds of functioning in the real world.

The findings, which were published in a joint publication of the IEEE and the Chinese Association of Automation Journal of Automatica Sinica in July, have promising implications in the broad use of humanoid robots in fields such as healthcare, education, disaster response and entertainment.

“Humanoid robots have the ability to move around in many ways and thereby imitate human motions to complete complex tasks. In order to be able to do that, their stability is essential, especially under dynamic and unpredictable conditions,” said corresponding author Dacheng Tao, Professor and ARC Laureate Fellow in the School of Computer Science and the Faculty of Engineering at the University of Sydney.

A new generation of swarming robots which can independently learn and evolve new behaviors in the wild is one step closer, thanks to research from the University of Bristol and the University of the West of England (UWE).

The team used artificial evolution to enable the robots to automatically learn swarm behaviors which are understandable to humans. This new advance published today in Advanced Intelligent Systems, could create new robotic possibilities for environmental monitoring, disaster recovery, infrastructure maintenance, logistics and agriculture.

Until now, artificial evolution has typically been run on a computer which is external to the swarm, with the best strategy then copied to the robots. However, this approach is limiting as it requires external infrastructure and a laboratory setting.

Wearing a flower brooch that blooms before your eyes sounds like magic. KAIST researchers have made it real with robotic muscles.

Researchers have developed an ultrathin, artificial muscle for soft robotics. The advancement, recently reported in the journal Science Robotics, was demonstrated with a robotic blooming flower brooch, dancing robotic butterflies and fluttering tree leaves on a kinetic art piece.

The robotic equivalent of a muscle that can move is called an actuator. The actuator expands, contracts or rotates like muscle fibers using a stimulus such as electricity. Engineers around the world are striving to develop more dynamic actuators that respond quickly, can bend without breaking, and are very durable. Soft, robotic muscles could have a wide variety of applications, from wearable electronics to advanced prosthetics.

MOUNTAIN VIEW (KPIX 5) — A Silicon Valley 3D printing company has been awarded a contract with NASA to launch a project creating a satellite that will manufacture and assemble itself in orbit.

A top NASA administrator visited Mountain View’s Ames Research Center Monday and toured state-of-the-art facilities of Made In Space. NASA awarded Made in Space a $73 million contract to launch Archinaut by 2022, an “autonomous robotic manufacturing and assembly platform.”

Jim Bridenstine, the space agency‘s top official, called Ames a “jewel” and praised the work of Made In Space as “impressive.” The manufacturing company 3D prints structures, parts, tools and more while in orbit.

This artificial skin could give robots a sense of touch.

Summary: The ability to train large scale CNNs directly on your cell phone without sending the data round trip to the cloud is the key to next gen AI applications like real time computer vision and safe self-driving cars. Problem is our current GPU AI chips won’t get us there. But neuromorphic chips look like they will.

Researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and Caltech have developed new soft robotic systems that are inspired by origami. These new systems are able to move and change shape in response to external stimuli. The new developments bring us closer to having fully untethered soft robots. The soft robots that we possess today use external power and control. Because of this, they have to be tethered to off-board systems with hard components.

The research was published in Science Robotics. Jennifer A. Lewis, a Hansjorg Wyss Professor of Biologically Inspired Engineering at SEAS and co-lead author of the study, spoke about the new developments.

“The ability to integrate active materials within 3D-printed objects enables the design and fabrication of entirely new classes of soft robotic matter,” she said.

David Lindell, a graduate student in electrical engineering at Stanford University, along with his team, developed a camera that can watch moving objects around corners. When they tested the new technology, Lindell wore a high visibility tracksuit as he moved around an empty room. They had a camera that was aimed at a blank wall away from Lindell, and the team was able to watch all of his movements with the use of a high powered laser. The laser reconstructed the images through the use of single particles of light that were reflected onto the walls around Lindell. The newly developed camera used advanced sensors and a processing algorithm.

Gordon Wetzstein, assistant professor of electrical engineering at Stanford, spoke about the newly developed technology.

“People talk about building a camera that can see as well as humans for applications such as autonomous cats and robots, but we want to build systems that go well beyond that,” he said. “We want to see things in 3D, around corners and beyond the visible light spectrum.”