Tag Archives: body
#435816 This Light-based Nervous System Helps ...
Last night, way past midnight, I stumbled onto my porch blindly grasping for my keys after a hellish day of international travel. Lights were low, I was half-asleep, yet my hand grabbed the keychain, found the lock, and opened the door.
If you’re rolling your eyes—yeah, it’s not exactly an epic feat for a human. Thanks to the intricate wiring between our brain and millions of sensors dotted on—and inside—our skin, we know exactly where our hand is in space and what it’s touching without needing visual confirmation. But this combined sense of the internal and the external is completely lost to robots, which generally rely on computer vision or surface mechanosensors to track their movements and their interaction with the outside world. It’s not always a winning strategy.
What if, instead, we could give robots an artificial nervous system?
This month, a team led by Dr. Rob Shepard at Cornell University did just that, with a seriously clever twist. Rather than mimicking the electric signals in our nervous system, his team turned to light. By embedding optical fibers inside a 3D printed stretchable material, the team engineered an “optical lace” that can detect changes in pressure less than a fraction of a pound, and pinpoint the location to a spot half the width of a tiny needle.
The invention isn’t just an artificial skin. Instead, the delicate fibers can be distributed both inside a robot and on its surface, giving it both a sense of tactile touch and—most importantly—an idea of its own body position in space. Optical lace isn’t a superficial coating of mechanical sensors; it’s an entire platform that may finally endow robots with nerve-like networks throughout the body.
Eventually, engineers hope to use this fleshy, washable material to coat the sharp, cold metal interior of current robots, transforming C-3PO more into the human-like hosts of Westworld. Robots with a “bodily” sense could act as better caretakers for the elderly, said Shepard, because they can assist fragile people without inadvertently bruising or otherwise harming them. The results were published in Science Robotics.
An Unconventional Marriage
The optical lace is especially creative because it marries two contrasting ideas: one biological-inspired, the other wholly alien.
The overarching idea for optical lace is based on the animal kingdom. Through sight, hearing, smell, taste, touch, and other senses, we’re able to interpret the outside world—something scientists call exteroception. Thanks to our nervous system, we perform these computations subconsciously, allowing us to constantly “perceive” what’s going on around us.
Our other perception is purely internal. Proprioception (sorry, it’s not called “inception” though it should be) is how we know where our body parts are in space without having to look at them, which lets us perform complex tasks when blind. Although less intuitive than exteroception, proprioception also relies on stretching and other deformations within the muscles and tendons and receptors under the skin, which generate electrical currents that shoot up into the brain for further interpretation.
In other words, in theory it’s possible to recreate both perceptions with a single information-carrying system.
Here’s where the alien factor comes in. Rather than using electrical properties, the team turned to light as their data carrier. They had good reason. “Compared with electricity, light carries information faster and with higher data densities,” the team explained. Light can also transmit in multiple directions simultaneously, and is less susceptible to electromagnetic interference. Although optical nervous systems don’t exist in the biological world, the team decided to improve on Mother Nature and give it a shot.
Optical Lace
The construction starts with engineering a “sheath” for the optical nerve fibers. The team first used an elastic polyurethane—a synthetic material used in foam cushioning, for example—to make a lattice structure filled with large pores, somewhat like a lattice pie crust. Thanks to rapid, high-resolution 3D printing, the scaffold can have different stiffness from top to bottom. To increase sensitivity to the outside world, the team made the top of the lattice soft and pliable, to better transfer force to mechanical sensors. In contrast, the “deeper” regions held their structure better, and kept their structure under pressure.
Now the fun part. The team next threaded stretchable “light guides” into the scaffold. These fibers transmit photons, and are illuminated with a blue LED light. One, the input light guide, ran horizontally across the soft top part of the scaffold. Others ran perpendicular to the input in a “U” shape, going from more surface regions to deeper ones. These are the output guides. The architecture loosely resembles the wiring in our skin and flesh.
Normally, the output guides are separated from the input by a small air gap. When pressed down, the input light fiber distorts slightly, and if the pressure is high enough, it contacts one of the output guides. This causes light from the input fiber to “leak” to the output one, so that it lights up—the stronger the pressure, the brighter the output.
“When the structure deforms, you have contact between the input line and the output lines, and the light jumps into these output loops in the structure, so you can tell where the contact is happening,” said study author Patricia Xu. “The intensity of this determines the intensity of the deformation itself.”
Double Perception
As a proof-of-concept for proprioception, the team made a cylindrical lace with one input and 12 output channels. They varied the stiffness of the scaffold along the cylinder, and by pressing down at different points, were able to calculate how much each part stretched and deformed—a prominent precursor to knowing where different regions of the structure are moving in space. It’s a very rudimentary sort of proprioception, but one that will become more sophisticated with increasing numbers of strategically-placed mechanosensors.
The test for exteroception was a whole lot stranger. Here, the team engineered another optical lace with 15 output channels and turned it into a squishy piano. When pressed down, an Arduino microcontroller translated light output signals into sound based on the position of each touch. The stronger the pressure, the louder the volume. While not a musical masterpiece, the demo proved their point: the optical lace faithfully reported the strength and location of each touch.
A More Efficient Robot
Although remarkably novel, the optical lace isn’t yet ready for prime time. One problem is scalability: because of light loss, the material is limited to a certain size. However, rather than coating an entire robot, it may help to add optical lace to body parts where perception is critical—for example, fingertips and hands.
The team sees plenty of potential to keep developing the artificial flesh. Depending on particular needs, both the light guides and scaffold can be modified for sensitivity, spatial resolution, and accuracy. Multiple optical fibers that measure for different aspects—pressure, pain, temperature—can potentially be embedded in the same region, giving robots a multitude of senses.
In this way, we hope to reduce the number of electronics and combine signals from multiple sensors without losing information, the authors said. By taking inspiration from biological networks, it may even be possible to use various inputs through an optical lace to control how the robot behaves, closing the loop from sensation to action.
Image Credit: Cornell Organic Robotics Lab. A flexible, porous lattice structure is threaded with stretchable optical fibers containing more than a dozen mechanosensors and attached to an LED light. When the lattice structure is pressed, the sensors pinpoint changes in the photon flow. Continue reading →
Posted in Human Robots
#435806 Boston Dynamics’ Spot Robot Dog ...
Boston Dynamics is announcing this morning that Spot, its versatile quadruped robot, is now for sale. The machine’s animal-like behavior regularly electrifies crowds at tech conferences, and like other Boston Dynamics’ robots, Spot is a YouTube sensation whose videos amass millions of views.
Now anyone interested in buying a Spot—or a pack of them—can go to the company’s website and submit an order form. But don’t pull out your credit card just yet. Spot may cost as much as a luxury car, and it is not really available to consumers. The initial sale, described as an “early adopter program,” is targeting businesses. Boston Dynamics wants to find customers in select industries and help them deploy Spots in real-world scenarios.
“What we’re doing is the productization of Spot,” Boston Dynamics CEO Marc Raibert tells IEEE Spectrum. “It’s really a milestone for us going from robots that work in the lab to these that are hardened for work out in the field.”
Boston Dynamics has always been a secretive company, but last month, in preparation for launching Spot (formerly SpotMini), it allowed our photographers into its headquarters in Waltham, Mass., for a special shoot. In that session, we captured Spot and also Atlas—the company’s highly dynamic humanoid—in action, walking, climbing, and jumping.
You can see Spot’s photo interactives on our Robots Guide. (The Atlas interactives will appear in coming weeks.)
Gif: Bob O’Connor/Robots.ieee.org
And if you’re in the market for a robot dog, here’s everything we know about Boston Dynamics’ plans for Spot.
Who can buy a Spot?
If you’re interested in one, you should go to Boston Dynamics’ website and take a look at the information the company requires from potential buyers. Again, the focus is on businesses. Boston Dynamics says it wants to get Spots out to initial customers that “either have a compelling use case or a development team that we believe can do something really interesting with the robot,” says VP of business development Michael Perry. “Just because of the scarcity of the robots that we have, we’re going to have to be selective about which partners we start working together with.”
What can Spot do?
As you’ve probably seen on the YouTube videos, Spot can walk, trot, avoid obstacles, climb stairs, and much more. The robot’s hardware is almost completely custom, with powerful compute boards for control, and five sensor modules located on every side of Spot’s body, allowing it to survey the space around itself from any direction. The legs are powered by 12 custom motors with a reduction, with a top speed of 1.6 meters per second. The robot can operate for 90 minutes on a charge. In addition to the basic configuration, you can integrate up to 14 kilograms of extra hardware to a payload interface. Among the payload packages Boston Dynamics plans to offer are a 6 degrees-of-freedom arm, a version of which can be seen in some of the YouTube videos, and a ring of cameras called SpotCam that could be used to create Street View–type images inside buildings.
Image: Boston Dynamics
How do you control Spot?
Learning to drive the robot using its gaming-style controller “takes 15 seconds,” says CEO Marc Raibert. He explains that while teleoperating Spot, you may not realize that the robot is doing a lot of the work. “You don’t really see what that is like until you’re operating the joystick and you go over a box and you don’t have to do anything,” he says. “You’re practically just thinking about what you want to do and the robot takes care of everything.” The control methods have evolved significantly since the company’s first quadruped robots, machines like BigDog and LS3. “The control in those days was much more monolithic, and now we have what we call a sequential composition controller,” Raibert says, “which lets the system have control of the dynamics in a much broader variety of situations.” That means that every time one of Spot’s feet touches or doesn’t touch the ground, this different state of the body affects the basic physical behavior of the robot, and the controller adjusts accordingly. “Our controller is designed to understand what that state is and have different controls depending upon the case,” he says.
How much does Spot cost?
Boston Dynamics would not give us specific details about pricing, saying only that potential customers should contact them for a quote and that there is going to be a leasing option. It’s understandable: As with any expensive and complex product, prices can vary on a case by case basis and depend on factors such as configuration, availability, level of support, and so forth. When we pressed the company for at least an approximate base price, Perry answered: “Our general guidance is that the total cost of the early adopter program lease will be less than the price of a car—but how nice a car will depend on the number of Spots leased and how long the customer will be leasing the robot.”
Can Spot do mapping and SLAM out of the box?
The robot’s perception system includes cameras and 3D sensors (there is no lidar), used to avoid obstacles and sense the terrain so it can climb stairs and walk over rubble. It’s also used to create 3D maps. According to Boston Dynamics, the first software release will offer just teleoperation. But a second release, to be available in the next few weeks, will enable more autonomous behaviors. For example, it will be able to do mapping and autonomous navigation—similar to what the company demonstrated in a video last year, showing how you can drive the robot through an environment, create a 3D point cloud of the environment, and then set waypoints within that map for Spot to go out and execute that mission. For customers that have their own autonomy stack and are interested in using those on Spot, Boston Dynamics made it “as plug and play as possible in terms of how third-party software integrates into Spot’s system,” Perry says. This is done mainly via an API.
How does Spot’s API works?
Boston Dynamics built an API so that customers can create application-level products with Spot without having to deal with low-level control processes. “Rather than going and building joint-level kinematic access to the robot,” Perry explains, “we created a high-level API and SDK that allows people who are used to Web app development or development of missions for drones to use that same scope, and they’ll be able to build applications for Spot.”
What applications should we see first?
Boston Dynamics envisions Spot as a platform: a versatile mobile robot that companies can use to build applications based on their needs. What types of applications? The company says the best way to find out is to put Spot in the hands of as many users as possible and let them develop the applications. Some possibilities include performing remote data collection and light manipulation in construction sites; monitoring sensors and infrastructure at oil and gas sites; and carrying out dangerous missions such as bomb disposal and hazmat inspections. There are also other promising areas such as security, package delivery, and even entertainment. “We have some initial guesses about which markets could benefit most from this technology, and we’ve been engaging with customers doing proof-of-concept trials,” Perry says. “But at the end of the day, that value story is really going to be determined by people going out and exploring and pushing the limits of the robot.”
Photo: Bob O'Connor
How many Spots have been produced?
Last June, Boston Dynamics said it was planning to build about a hundred Spots by the end of the year, eventually ramping up production to a thousand units per year by the middle of this year. The company admits that it is not quite there yet. It has built close to a hundred beta units, which it has used to test and refine the final design. This version is now being mass manufactured, but the company is still “in the early tens of robots,” Perry says.
How did Boston Dynamics test Spot?
The company has tested the robots during proof-of-concept trials with customers, and at least one is already using Spot to survey construction sites. The company has also done reliability tests at its facility in Waltham, Mass. “We drive around, not quite day and night, but hundreds of miles a week, so that we can collect reliability data and find bugs,” Raibert says.
What about competitors?
In recent years, there’s been a proliferation of quadruped robots that will compete in the same space as Spot. The most prominent of these is ANYmal, from ANYbotics, a Swiss company that spun out of ETH Zurich. Other quadrupeds include Vision from Ghost Robotics, used by one of the teams in the DARPA Subterranean Challenge; and Laikago and Aliengo from Unitree Robotics, a Chinese startup. Raibert views the competition as a positive thing. “We’re excited to see all these companies out there helping validate the space,” he says. “I think we’re more in competition with finding the right need [that robots can satisfy] than we are with the other people building the robots at this point.”
Why is Boston Dynamics selling Spot now?
Boston Dynamics has long been an R&D-centric firm, with most of its early funding coming from military programs, but it says commercializing robots has always been a goal. Productizing its machines probably accelerated when the company was acquired by Google’s parent company, Alphabet, which had an ambitious (and now apparently very dead) robotics program. The commercial focus likely continued after Alphabet sold Boston Dynamics to SoftBank, whose famed CEO, Masayoshi Son, is known for his love of robots—and profits.
Which should I buy, Spot or Aibo?
Don’t laugh. We’ve gotten emails from individuals interested in purchasing a Spot for personal use after seeing our stories on the robot. Alas, Spot is not a bigger, fancier Aibo pet robot. It’s an expensive, industrial-grade machine that requires development and maintenance. If you’re maybe Jeff Bezos you could probably convince Boston Dynamics to sell you one, but otherwise the company will prioritize businesses.
What’s next for Boston Dynamics?
On the commercial side of things, other than Spot, Boston Dynamics is interested in the logistics space. Earlier this year it announced the acquisition of Kinema Systems, a startup that had developed vision sensors and deep-learning software to enable industrial robot arms to locate and move boxes. There’s also Handle, the mobile robot on whegs (wheels + legs), that can pick up and move packages. Boston Dynamics is hiring both in Waltham, Mass., and Mountain View, Calif., where Kinema was located.
Okay, can I watch a cool video now?
During our visit to Boston Dynamics’ headquarters last month, we saw Atlas and Spot performing some cool new tricks that we unfortunately are not allowed to tell you about. We hope that, although the company is putting a lot of energy and resources into its commercial programs, Boston Dynamics will still find plenty of time to improve its robots, build new ones, and of course, keep making videos. [Update: The company has just released a new Spot video, which we’ve embedded at the top of the post.][Update 2: We should have known. Boston Dynamics sure knows how to create buzz for itself: It has just released a second video, this time of Atlas doing some of those tricks we saw during our visit and couldn’t tell you about. Enjoy!]
[ Boston Dynamics ] Continue reading →
Posted in Human Robots
#435793 Tiny Robots Carry Stem Cells Through a ...
Engineers have built microrobots to perform all sorts of tasks in the body, and can now add to that list another key skill: delivering stem cells. In a paper published today in Science Robotics, researchers describe propelling a magnetically-controlled, stem-cell-carrying bot through a live mouse.
Under a rotating magnetic field, the microrobots moved with rolling and corkscrew-style locomotion. The researchers, led by Hongsoo Choi and his team at the Daegu Gyeongbuk Institute of Science & Technology (DGIST), in South Korea, also demonstrated their bot’s moves in slices of mouse brain, in blood vessels isolated from rat brains, and in a multi-organ-on-a chip.
The invention provides an alternative way to deliver stem cells, which are increasingly important in medicine. Such cells can be coaxed into becoming nearly any kind of cell, making them great candidates for treating neurodegenerative disorders such as Alzheimer’s.
But delivering stem cells typically requires an injection with a needle, which lowers the survival rate of the stem cells, and limits their reach in the body. Microrobots, however, have the potential to deliver stem cells to precise, hard-to-reach areas, with less damage to surrounding tissue, and better survival rates, says Jin-young Kim, a principle investigator at DGIST-ETH Microrobotics Research Center, and an author on the paper.
The virtues of microrobots have inspired several research groups to propose and test different designs in simple conditions, such as microfluidic channels and other static environments. A group out of Hong Kong last year described a burr-shaped bot that carried cells through live, transparent zebrafish.
The new research presents a magnetically-actuated microrobot that successfully carried stem cells through a live mouse. In additional experiments, the cells, which had differentiated into brain cells such as astrocytes, oligodendrocytes, and neurons, transferred to microtissues on the multi-organ-on-a-chip. Taken together, the proof-of-concept experiments demonstrate the potential for microrobots to be used in human stem cell therapy, says Kim.
The team fabricated the robots with 3D laser lithography, and designed them in two shapes: spherical and helical. Using a rotating magnetic field, the scientists navigated the spherical-shaped bots with a rolling motion, and the helical bots with a corkscrew motion. These styles of locomotion proved more efficient than that from a simple pulling force, and were more suitable for use in biological fluids, the scientists reported.
The big challenge in navigating microbots in a live animal (or human body) is being able to see them in real time. Imaging with fMRI doesn’t work, because the magnetic fields interfere with the system. “To precisely control microbots in vivo, it is important to actually see them as they move,” the authors wrote in their paper.
That wasn’t possible during experiments in a live mouse, so the researchers had to check the location of the microrobots before and after the experiments using an optical tomography system called IVIS. They also had to resort to using a pulling force with a permanent magnet to navigate the microrobots inside the mouse, due to the limitations of the IVIS system.
Kim says he and his colleagues are developing imaging systems that will enable them to view in real time the locomotion of their microrobots in live animals. Continue reading →
Posted in Human Robots
#435773 Video Friday: Roller-Skating Quadruped ...
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):
IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
RoboBusiness 2019 – October 1-3, 2019 – Santa Clara, CA, USA
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today's videos.
We got a sneak peek of a new version of ANYmal equipped with actuated wheels for feet at the DARPA SubT Challenge, where it did surprisingly well at quickly and (mostly) robustly navigating some very tricky terrain. And when you're not expecting it to travel through a muddy, rocky, and dark tunnel, it looks even more capable:
[ Paper ]
Thanks Marko!
In Langley’s makerspace lab, researchers are developing a series of soft robot actuators to investigate the viability of soft robotics in space exploration and assembly. By design, the actuator has chambers, or air bladders, that expand and compress based on the amount of air in them.
[ NASA ]
I’m not normally a fan of the AdultSize RoboCup soccer competition, but NimbRo had a very impressive season.
I don’t know how it managed to not fall over at 45 seconds, but damn.
[ NimbRo ]
This is more AI than robotics, but that’s okay, because it’s totally cool.
I’m wondering whether the hiders ever tried another possibly effective strategy: trapping the seekers in a locked shelter right at the start.
[ OpenAI ]
We haven’t heard much from Piaggio Fast Forward in a while, but evidently they’ve still got a Gita robot going on, designed to be your personal autonomous caddy for absolutely anything that can fit into something the size of a portable cooler.
Available this fall, I guess?
[ Gita ]
This passively triggered robotic hand is startlingly fast, and seems almost predatory when it grabs stuff, especially once they fit it onto a drone.
[ New Dexterity ]
Thanks Fan!
Autonomous vehicles seem like a recent thing, but CMU has been working on them since the mid 1980s.
CMU was also working on drones back before drones were even really a thing:
[ CMU NavLab ] and [ CMU ]
Welcome to the most complicated and expensive robotic ice cream deployment system ever created.
[ Niska ]
Some impressive dexterity from a robot hand equipped with magnetic gears.
[ Ishikawa Senoo Lab ]
The Buddy Arduino social robot kit is now live on Kickstarter, and you can pledge for one of these little dudes for 49 bucks.
[ Kickstarter ]
Thanks Jenny!
Mobile manipulation robots have high potential to support rescue forces in disaster-response missions. Despite the difficulties imposed by real-world scenarios, robots are promising to perform mission tasks from a safe distance. In the CENTAURO project, we developed a disaster-response system which consists of the highly flexible Centauro robot and suitable control interfaces including an immersive telepresence suit and support-operator controls on different levels of autonomy.
[ CENTAURO ]
Thanks Sven!
Determined robots are the cutest robots.
[ Paper ]
The goal of the Dronument project is to create an aerial platform enabling interior and exterior documentation of heritage sites.
It’s got a base station that helps with localization, but still, flying that close to a chandelier in a UNESCO world heritage site makes me nervous.
[ Dronument ]
Thanks Fan!
Avast ye! No hornswaggling, lick-spittlering, or run-rigging over here – Only serious tech for devs. All hands hoay to check out Misty's capabilities and to build your own skills with plenty of heave ho! ARRRRRRRRGH…
International Talk Like a Pirate Day was yesterday, but I'm sure nobody will look at you funny if you keep at it today too.
[ Misty Robotics ]
This video presents an unobtrusive bimanual teleoperation setup with very low weight, consisting of two Vive visual motion trackers and two Myo surface electromyography bracelets. The video demonstrates complex, dexterous teleoperated bimanual daily-living tasks performed by the torque-controlled humanoid robot TORO.
[ DLR RMC ]
Lex Fridman interviews iRobot’s Colin Angle on the Artificial Intelligence Podcast.
Colin Angle is the CEO and co-founder of iRobot, a robotics company that for 29 years has been creating robots that operate successfully in the real world, not as a demo or on a scale of dozens, but on a scale of thousands and millions. As of this year, iRobot has sold more than 25 million robots to consumers, including the Roomba vacuum cleaning robot, the Braava floor mopping robot, and soon the Terra lawn mowing robot. 25 million robots successfully operating autonomously in people's homes to me is an incredible accomplishment of science, engineering, logistics, and all kinds of entrepreneurial innovation.
[ AI Podcast ]
This week’s CMU RI Seminar comes from CMU’s own Sarah Bergbreiter, on Microsystems-Inspired Robotics.
The ability to manufacture micro-scale sensors and actuators has inspired the robotics community for over 30 years. There have been huge success stories; MEMS inertial sensors have enabled an entire market of low-cost, small UAVs. However, the promise of ant-scale robots has largely failed. Ants can move high speeds on surfaces from picnic tables to front lawns, but the few legged microrobots that have walked have done so at slow speeds (< 1 body length/sec) on smooth silicon wafers. In addition, the vision of large numbers of microfabricated sensors interacting directly with the environment has suffered in part due to the brittle materials used in micro-fabrication. This talk will present our progress in the design of sensors, mechanisms, and actuators that utilize new microfabrication processes to incorporate materials with widely varying moduli and functionality to achieve more robustness, dynamic range, and complexity in smaller packages.
[ CMU RI ] Continue reading →
Posted in Human Robots
#435765 The Four Converging Technologies Giving ...
How each of us sees the world is about to change dramatically.
For all of human history, the experience of looking at the world was roughly the same for everyone. But boundaries between the digital and physical are beginning to fade.
The world around us is gaining layer upon layer of digitized, virtually overlaid information—making it rich, meaningful, and interactive. As a result, our respective experiences of the same environment are becoming vastly different, personalized to our goals, dreams, and desires.
Welcome to Web 3.0, or the Spatial Web. In version 1.0, static documents and read-only interactions limited the internet to one-way exchanges. Web 2.0 provided quite an upgrade, introducing multimedia content, interactive web pages, and participatory social media. Yet, all this was still mediated by two-dimensional screens.
Today, we are witnessing the rise of Web 3.0, riding the convergence of high-bandwidth 5G connectivity, rapidly evolving AR eyewear, an emerging trillion-sensor economy, and powerful artificial intelligence.
As a result, we will soon be able to superimpose digital information atop any physical surrounding—freeing our eyes from the tyranny of the screen, immersing us in smart environments, and making our world endlessly dynamic.
In the third post of our five-part series on augmented reality, we will explore the convergence of AR, AI, sensors, and blockchain and dive into the implications through a key use case in manufacturing.
A Tale of Convergence
Let’s deconstruct everything beneath the sleek AR display.
It all begins with graphics processing units (GPUs)—electric circuits that perform rapid calculations to render images. (GPUs can be found in mobile phones, game consoles, and computers.)
However, because AR requires such extensive computing power, single GPUs will not suffice. Instead, blockchain can now enable distributed GPU processing power, and blockchains specifically dedicated to AR holographic processing are on the rise.
Next up, cameras and sensors will aggregate real-time data from any environment to seamlessly integrate physical and virtual worlds. Meanwhile, body-tracking sensors are critical for aligning a user’s self-rendering in AR with a virtually enhanced environment. Depth sensors then provide data for 3D spatial maps, while cameras absorb more surface-level, detailed visual input. In some cases, sensors might even collect biometric data, such as heart rate and brain activity, to incorporate health-related feedback in our everyday AR interfaces and personal recommendation engines.
The next step in the pipeline involves none other than AI. Processing enormous volumes of data instantaneously, embedded AI algorithms will power customized AR experiences in everything from artistic virtual overlays to personalized dietary annotations.
In retail, AIs will use your purchasing history, current closet inventory, and possibly even mood indicators to display digitally rendered items most suitable for your wardrobe, tailored to your measurements.
In healthcare, smart AR glasses will provide physicians with immediately accessible and maximally relevant information (parsed from the entirety of a patient’s medical records and current research) to aid in accurate diagnoses and treatments, freeing doctors to engage in the more human-centric tasks of establishing trust, educating patients and demonstrating empathy.
Image Credit: PHD Ventures.
Convergence in Manufacturing
One of the nearest-term use cases of AR is manufacturing, as large producers begin dedicating capital to enterprise AR headsets. And over the next ten years, AR will converge with AI, sensors, and blockchain to multiply manufacturer productivity and employee experience.
(1) Convergence with AI
In initial application, digital guides superimposed on production tables will vastly improve employee accuracy and speed, while minimizing error rates.
Already, the International Air Transport Association (IATA) — whose airlines supply 82 percent of air travel — recently implemented industrial tech company Atheer’s AR headsets in cargo management. And with barely any delay, IATA reported a whopping 30 percent improvement in cargo handling speed and no less than a 90 percent reduction in errors.
With similar success rates, Boeing brought Skylight’s smart AR glasses to the runway, now used in the manufacturing of hundreds of airplanes. Sure enough—the aerospace giant has now seen a 25 percent drop in production time and near-zero error rates.
Beyond cargo management and air travel, however, smart AR headsets will also enable on-the-job training without reducing the productivity of other workers or sacrificing hardware. Jaguar Land Rover, for instance, implemented Bosch’s Re’flekt One AR solution to gear technicians with “x-ray” vision: allowing them to visualize the insides of Range Rover Sport vehicles without removing any dashboards.
And as enterprise capabilities continue to soar, AIs will soon become the go-to experts, offering support to manufacturers in need of assembly assistance. Instant guidance and real-time feedback will dramatically reduce production downtime, boost overall output, and even help customers struggling with DIY assembly at home.
Perhaps one of the most profitable business opportunities, AR guidance through centralized AI systems will also serve to mitigate supply chain inefficiencies at extraordinary scale. Coordinating moving parts, eliminating the need for manned scanners at each checkpoint, and directing traffic within warehouses, joint AI-AR systems will vastly improve workflow while overseeing quality assurance.
After its initial implementation of AR “vision picking” in 2015, leading courier company DHL recently announced it would continue to use Google’s newest smart lens in warehouses across the world. Motivated by the initial group’s reported 15 percent jump in productivity, DHL’s decision is part of the logistics giant’s $300 million investment in new technologies.
And as direct-to-consumer e-commerce fundamentally transforms the retail sector, supply chain optimization will only grow increasingly vital. AR could very well prove the definitive step for gaining a competitive edge in delivery speeds.
As explained by Vital Enterprises CEO Ash Eldritch, “All these technologies that are coming together around artificial intelligence are going to augment the capabilities of the worker and that’s very powerful. I call it Augmented Intelligence. The idea is that you can take someone of a certain skill level and by augmenting them with artificial intelligence via augmented reality and the Internet of Things, you can elevate the skill level of that worker.”
Already, large producers like Goodyear, thyssenkrupp, and Johnson Controls are using the Microsoft HoloLens 2—priced at $3,500 per headset—for manufacturing and design purposes.
Perhaps the most heartening outcome of the AI-AR convergence is that, rather than replacing humans in manufacturing, AR is an ideal interface for human collaboration with AI. And as AI merges with human capital, prepare to see exponential improvements in productivity, professional training, and product quality.
(2) Convergence with Sensors
On the hardware front, these AI-AR systems will require a mass proliferation of sensors to detect the external environment and apply computer vision in AI decision-making.
To measure depth, for instance, some scanning depth sensors project a structured pattern of infrared light dots onto a scene, detecting and analyzing reflected light to generate 3D maps of the environment. Stereoscopic imaging, using two lenses, has also been commonly used for depth measurements. But leading technology like Microsoft’s HoloLens 2 and Intel’s RealSense 400-series camera implement a new method called “phased time-of-flight” (ToF).
In ToF sensing, the HoloLens 2 uses numerous lasers, each with 100 milliwatts (mW) of power, in quick bursts. The distance between nearby objects and the headset wearer is then measured by the amount of light in the return beam that has shifted from the original signal. Finally, the phase difference reveals the location of each object within the field of view, which enables accurate hand-tracking and surface reconstruction.
With a far lower computing power requirement, the phased ToF sensor is also more durable than stereoscopic sensing, which relies on the precise alignment of two prisms. The phased ToF sensor’s silicon base also makes it easily mass-produced, rendering the HoloLens 2 a far better candidate for widespread consumer adoption.
To apply inertial measurement—typically used in airplanes and spacecraft—the HoloLens 2 additionally uses a built-in accelerometer, gyroscope, and magnetometer. Further equipped with four “environment understanding cameras” that track head movements, the headset also uses a 2.4MP HD photographic video camera and ambient light sensor that work in concert to enable advanced computer vision.
For natural viewing experiences, sensor-supplied gaze tracking increasingly creates depth in digital displays. Nvidia’s work on Foveated AR Display, for instance, brings the primary foveal area into focus, while peripheral regions fall into a softer background— mimicking natural visual perception and concentrating computing power on the area that needs it most.
Gaze tracking sensors are also slated to grant users control over their (now immersive) screens without any hand gestures. Conducting simple visual cues, even staring at an object for more than three seconds, will activate commands instantaneously.
And our manufacturing example above is not the only one. Stacked convergence of blockchain, sensors, AI and AR will disrupt almost every major industry.
Take healthcare, for example, wherein biometric sensors will soon customize users’ AR experiences. Already, MIT Media Lab’s Deep Reality group has created an underwater VR relaxation experience that responds to real-time brain activity detected by a modified version of the Muse EEG. The experience even adapts to users’ biometric data, from heart rate to electro dermal activity (inputted from an Empatica E4 wristband).
Now rapidly dematerializing, sensors will converge with AR to improve physical-digital surface integration, intuitive hand and eye controls, and an increasingly personalized augmented world. Keep an eye on companies like MicroVision, now making tremendous leaps in sensor technology.
While I’ll be doing a deep dive into sensor applications across each industry in our next blog, it’s critical to first discuss how we might power sensor- and AI-driven augmented worlds.
(3) Convergence with Blockchain
Because AR requires much more compute power than typical 2D experiences, centralized GPUs and cloud computing systems are hard at work to provide the necessary infrastructure. Nonetheless, the workload is taxing and blockchain may prove the best solution.
A major player in this pursuit, Otoy aims to create the largest distributed GPU network in the world, called the Render Network RNDR. Built specifically on the Ethereum blockchain for holographic media, and undergoing Beta testing, this network is set to revolutionize AR deployment accessibility.
Alphabet Chairman Eric Schmidt (an investor in Otoy’s network), has even said, “I predicted that 90% of computing would eventually reside in the web based cloud… Otoy has created a remarkable technology which moves that last 10%—high-end graphics processing—entirely to the cloud. This is a disruptive and important achievement. In my view, it marks the tipping point where the web replaces the PC as the dominant computing platform of the future.”
Leveraging the crowd, RNDR allows anyone with a GPU to contribute their power to the network for a commission of up to $300 a month in RNDR tokens. These can then be redeemed in cash or used to create users’ own AR content.
In a double win, Otoy’s blockchain network and similar iterations not only allow designers to profit when not using their GPUs, but also democratize the experience for newer artists in the field.
And beyond these networks’ power suppliers, distributing GPU processing power will allow more manufacturing companies to access AR design tools and customize learning experiences. By further dispersing content creation across a broad network of individuals, blockchain also has the valuable potential to boost AR hardware investment across a number of industry beneficiaries.
On the consumer side, startups like Scanetchain are also entering the blockchain-AR space for a different reason. Allowing users to scan items with their smartphone, Scanetchain’s app provides access to a trove of information, from manufacturer and price, to origin and shipping details.
Based on NEM (a peer-to-peer cryptocurrency that implements a blockchain consensus algorithm), the app aims to make information far more accessible and, in the process, create a social network of purchasing behavior. Users earn tokens by watching ads, and all transactions are hashed into blocks and securely recorded.
The writing is on the wall—our future of brick-and-mortar retail will largely lean on blockchain to create the necessary digital links.
Final Thoughts
Integrating AI into AR creates an “auto-magical” manufacturing pipeline that will fundamentally transform the industry, cutting down on marginal costs, reducing inefficiencies and waste, and maximizing employee productivity.
Bolstering the AI-AR convergence, sensor technology is already blurring the boundaries between our augmented and physical worlds, soon to be near-undetectable. While intuitive hand and eye motions dictate commands in a hands-free interface, biometric data is poised to customize each AR experience to be far more in touch with our mental and physical health.
And underpinning it all, distributed computing power with blockchain networks like RNDR will democratize AR, boosting global consumer adoption at plummeting price points.
As AR soars in importance—whether in retail, manufacturing, entertainment, or beyond—the stacked convergence discussed above merits significant investment over the next decade. The augmented world is only just getting started.
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This article originally appeared on Diamandis.com
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