Tag Archives: features

#435716 Watch This Drone Explode Into Maple Seed ...

Posted on September 17, 2019 by Android

As useful as conventional fixed-wing and quadrotor drones have become, they still tend to be relatively complicated, expensive machines that you really want to be able to use more than once. When a one-way trip is all that you have in mind, you want something simple, reliable, and cheap, and we’ve seen a bunch of different designs for drone gliders that more or less fulfill those criteria.

For an even simpler gliding design, you want to minimize both airframe mass and control surfaces, and the maple tree provides some inspiration in the form of samara, those distinctive seed pods that whirl to the ground in the fall. Samara are essentially just an unbalanced wing that spins, and while the natural ones don’t steer, adding an actuated flap to the robotic version and moving it at just the right time results in enough controllability to aim for a specific point on the ground.

Roboticists at the Singapore University of Technology and Design (SUTD) have been experimenting with samara-inspired drones, and in a new paper in IEEE Robotics and Automation Letters they explore what happens if you attach five of the drones together and then separate them in mid air.

Image: Singapore University of Technology and Design

The drone with all five wings attached (top left), and details of the individual wings: (a) smaller 44.9-gram wing for semi-indoor testing; (b) larger 83.4-gram wing able to carry a Pixracer, GPS, and magnetometer for directional control experiments.

Fundamentally, a samara design acts as a decelerator for an aerial payload. You can think of it like a parachute: It makes sure that whatever you toss out of an airplane gets to the ground intact rather than just smashing itself to bits on impact. Steering is possible, but you don’t get a lot of stability or precision control. The RA-L paper describes one solution to this, which is to collaboratively use five drones at once in a configuration that looks a bit like a helicopter rotor.

And once the multi-drone is right where you want it, the five individual samara drones can split off all at once, heading out on their own missions. It's quite a sight:

The concept features a collaborative autorotation in the initial stage of drop whereby several wings are attached to each other to form a rotor hub. The combined form achieves higher rotational energy and a collaborative control strategy is possible. Once closer to the ground, they can exit the collaborative form and continue to descend to unique destinations. A section of each wing forms a flap and a small actuator changes its pitch cyclically. Since all wing-flaps can actuate simultaneously in collaborative mode, better maneuverability is possible, hence higher resistance against environmental conditions. The vertical and horizontal speeds can be controlled to a certain extent, allowing it to navigate towards a target location and land softly.

The samara autorotating wing drones themselves could conceivably carry small payloads like sensors or emergency medical supplies, with these small-scale versions in the video able to handle an extra 30 grams of payload. While they might not have as much capacity as a traditional fixed-wing glider, they have the advantage of being able to descent vertically, and can perform better than a parachute due to their ability to steer. The researchers plan on improving the design of their little drones, with the goal of increasing the rotation speed and improving the control performance of both the individual drones and the multi-wing collaborative version.

“Dynamics and Control of a Collaborative and Separating Descent of Samara Autorotating Wings,” by Shane Kyi Hla Win, Luke Soe Thura Win, Danial Sufiyan, Gim Song Soh, and Shaohui Foong from Singapore University of Technology and Design, appears in the current issue of IEEE Robotics and Automation Letters.
[ SUTD ]

< Back to IEEE Journal Watch Continue reading →

Posted in Human Robots

#435714 Universal Robots Introduces Its ...

Posted on September 17, 2019 by Android

Universal Robots, already the dominant force in collaborative robots, is flexing its muscles in an effort to further expand its reach in the cobots market. The Danish company is introducing today the UR16e, its strongest robotic arm yet, with a payload capability of 16 kilograms (35.3 lbs), reach of 900 millimeters, and repeatability of +/- 0.05 mm.

Universal says the new “heavy duty payload cobot” will allow customers to automate a broader range of processes, including packaging and palletizing, nut and screw driving, and high-payload and CNC machine tending.

In early 2015, Universal introduced the UR3, its smallest robot, which joined the UR5 and the flagship UR10, offering a payload capability of 3, 5, and 10 kg, respectively. Now the company is going in the other direction, announcing a bigger, stronger arm.

“With Universal joining its competitors in extending the reach and payload capacity of its cobots, a new standard of capability is forming,” Rian Whitton, a senior analyst at ABI Research, in London, tweeted.

Like its predecessors, the UR16e is part of Universal’s e-Series platform, which features 6 degrees of freedom and force/torque sensing on the tool flange. The UR family of cobots have stood out from the competition by being versatile in a variety of applications and, most important, easy to deploy and program. Universal didn’t release UR16e’s price, saying only that it is about 10 percent higher than that of the UR10e, which is about $50,000, depending on the configuration.

Jürgen von Hollen, president of Universal Robots, says the company decided to launch the UR16e after studying the market and talking to customers about their needs. “What came out of that process is we understood payload was a true barrier for a lot of customers,” he tells IEEE Spectrum. The 16 kg payload will be particularly useful for applications that require mounting specialized tools on the arm to perform tasks like screw driving and machine tending, he explains. Customers that could benefit from such applications include manufacturing, material handling, and automotive companies.

“We’ve added the payload, and that will open up that market for us,” von Hollen says.

The difference between Universal and Rethink

Universal has grown by leaps and bounds since its founding in 2008. By 2015, it had sold more than 5,000 robots; that number was close to 40,000 as of last year. During the same period, revenue more than doubled from about $100 million to $234 million. At a time when a string of robot makers have shuttered, including most notably Rethink Robotics, a cobots pioneer and Universal’s biggest rival, Universal finds itself in an enviable position, having amassed a commanding market share, estimated at between 50 to 60 percent.

About Rethink, von Hollen says the Boston-based company was a “good competitor,” helping disseminate the advantages and possibilities of cobots. “When Rethink basically ended it was more of a negative than a positive, from my perspective,” he says. In his view, a major difference between the two companies is that Rethink focused on delivering full-fledged applications to customers, whereas Universal focused on delivering a product to the market and letting the system integrators and sales partners deploy the robots to the customer base.

“We’ve always been very focused on delivering the product, whereas I think Rethink was much more focused on applications, very early on, and they added a level of complexity to their company that made it become very de-focused,” he says.

The collaborative robots market: massive growth

And yet, despite its success, Universal is still tiny when you compare it to the giants of industrial automation, which include companies like ABB, Fanuc, Yaskawa, and Kuka, with revenue in the billions of dollars. Although some of these companies have added cobots to their product portfolios—ABB’s YuMi, for example—that market represents a drop in the bucket when you consider global robot sales: The size of the cobots market was estimated at $700 million in 2018, whereas the global market for industrial robot systems (including software, peripherals, and system engineering) is close to $50 billion.

Von Hollen notes that cobots are expected to go through an impressive growth curve—nearly 50 percent year after year until 2025, when sales will reach between $9 to $12 billion. If Universal can maintain its dominance and capture a big slice of that market, it’ll add up to a nice sum. To get there, Universal is not alone: It is backed by U.S. electronics testing equipment maker Teradyne, which acquired Universal in 2015 for $285 million.

“The amount of resources we invest year over year matches the growth we had on sales,” von Hollen says. Universal currently has more than 650 employees, most based at its headquarters in Odense, Denmark, and the rest scattered in 27 offices in 18 countries. “No other company [in the cobots segment] is so focused on one product.”

[ Universal Robots ] Continue reading →

Posted in Human Robots

#435676 Intel’s Neuromorphic System Hits 8 ...

Posted on September 16, 2019 by Android

At the DARPA Electronics Resurgence Initiative Summit today in Detroit, Intel plans to unveil an 8-million-neuron neuromorphic system comprising 64 Loihi research chips—codenamed Pohoiki Beach. Loihi chips are built with an architecture that more closely matches the way the brain works than do chips designed to do deep learning or other forms of AI. For the set of problems that such “spiking neural networks” are particularly good at, Loihi is about 1,000 times as fast as a CPU and 10,000 times as energy efficient. The new 64-Loihi system represents the equivalent of 8-million neurons, but that’s just a step to a 768-chip, 100-million-neuron system that the company plans for the end of 2019.

Intel and its research partners are just beginning to test what massive neural systems like Pohoiki Beach can do, but so far the evidence points to even greater performance and efficiency, says Mike Davies, director of neuromorphic research at Intel.

“We’re quickly accumulating results and data that there are definite benefits… mostly in the domain of efficiency. Virtually every one that we benchmark…we find significant gains in this architecture,” he says.

Going from a single-Loihi to 64 of them is more of a software issue than a hardware one. “We designed scalability into the Loihi chip from the beginning,” says Davies. “The chip has a hierarchical routing interface…which allows us to scale to up to 16,000 chips. So 64 is just the next step.”

Photo: Tim Herman/Intel Corporation

One of Intel’s Nahuku boards, each of which contains 8 to 32 Intel Loihi neuromorphic chips, shown here interfaced to an Intel Arria 10 FPGA development kit. Intel’s latest neuromorphic system, Pohoiki Beach, is made up of multiple Nahuku boards and contains 64 Loihi chips.

Finding algorithms that run well on an 8-million-neuron system and optimizing those algorithms in software is a considerable effort, he says. Still, the payoff could be huge. Neural networks that are more brain-like, such as Loihi, could be immune to some of the artificial intelligence’s—for lack of a better word—dumbness.

For example, today’s neural networks suffer from something called catastrophic forgetting. If you tried to teach a trained neural network to recognize something new—a new road sign, say—by simply exposing the network to the new input, it would disrupt the network so badly that it would become terrible at recognizing anything. To avoid this, you have to completely retrain the network from the ground up. (DARPA’s Lifelong Learning, or L2M, program is dedicated to solving this problem.)

(Here’s my favorite analogy: Say you coached a basketball team, and you raised the net by 30 centimeters while nobody was looking. The players would miss a bunch at first, but they’d figure things out quickly. If those players were like today’s neural networks, you’d have to pull them off the court and teach them the entire game over again—dribbling, passing, everything.)

Loihi can run networks that might be immune to catastrophic forgetting, meaning it learns a bit more like a human. In fact, there’s evidence through a research collaboration with Thomas Cleland’s group at Cornell University, that Loihi can achieve what’s called one-shot learning. That is, learning a new feature after being exposed to it only once. The Cornell group showed this by abstracting a model of the olfactory system so that it would run on Loihi. When exposed to a new virtual scent, the system not only didn't catastrophically forget everything else it had smelled, it learned to recognize the new scent just from the single exposure.

Loihi might also be able to run feature-extraction algorithms that are immune to the kinds of adversarial attacks that befuddle today’s image recognition systems. Traditional neural networks don’t really understand the features they’re extracting from an image in the way our brains do. “They can be fooled with simplistic attacks like changing individual pixels or adding a screen of noise that wouldn’t fool a human in any way,” Davies explains. But the sparse-coding algorithms Loihi can run work more like the human visual system and so wouldn’t fall for such shenanigans. (Disturbingly, humans are not completely immune to such attacks.)

Photo: Tim Herman/Intel Corporation

A close-up shot of Loihi, Intel’s neuromorphic research chip. Intel’s latest neuromorphic system, Pohoiki Beach, will be comprised of 64 of these Loihi chips.

Researchers have also been using Loihi to improve real-time control for robotic systems. For example, last week at the Telluride Neuromorphic Cognition Engineering Workshop—an event Davies called “summer camp for neuromorphics nerds”—researchers were hard at work using a Loihi-based system to control a foosball table. “It strikes people as crazy,” he says. “But it’s a nice illustration of neuromorphic technology. It’s fast, requires quick response, quick planning, and anticipation. These are what neuromorphic chips are good at.” Continue reading →

Posted in Human Robots

#435658 Video Friday: A Two-Armed Robot That ...

Posted on September 15, 2019 by Android

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!):

ICRES 2019 – July 29-30, 2019 – London, U.K.
DARPA SubT Tunnel Circuit – August 15-22, 2019 – Pittsburgh, Pa., USA
IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
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
Let us know if you have suggestions for next week, and enjoy today’s videos.

I’m sure you’ve seen this video already because you read this blog every day, but if you somehow missed it because you were skiing across Antarctica (the only valid excuse we’re accepting today), here’s our video introducing HMI’s Aquanaut transforming robot submarine.

And after you recover from all that frostbite, make sure and read our in-depth feature article here.

[ Aquanaut ]

Last week we complained about not having seen a ballbot with a manipulator, so Roberto from CMU shared a new video of their ballbot, featuring a pair of 7-DoF arms.

We should learn more at Humanoids 2019.

[ CMU ]

Thanks Roberto!

The FAA is making it easier for recreational drone pilots to get near-realtime approval to fly in lightly controlled airspace.

[ LAANC ]

Self-reconfigurable modular robots are usually composed of multiple modules with uniform docking interfaces that can be transformed into different configurations by themselves. The reconfiguration planning problem is finding what sequence of reconfiguration actions are required for one arrangement of modules to transform into another. We present a novel reconfiguration planning algorithm for modular robots. The algorithm compares the initial configuration with the goal configuration efficiently. The reconfiguration actions can be executed in a distributed manner so that each module can efficiently finish its reconfiguration task which results in a global reconfiguration for the system. In the end, the algorithm is demonstrated on real modular robots and some example reconfiguration tasks are provided.

[ CKbot ]

A nice design of a gripper that uses a passive thumb of sorts to pick up flat objects from flat surfaces.

[ Paper ] via [ Laval University ]

I like this video of a palletizing robot from Kawasaki because in the background you can see a human doing the exact same job and obviously not enjoying it.

[ Kawasaki ]

This robot cleans and “brings joy and laughter.” What else do we need?

I do appreciate that all the robots are named Leo, and that they’re also all female.

[ LionsBot ]

This is less of a dishwashing robot and more of a dishsorting robot, but we’ll forgive it because it doesn’t drop a single dish.

[ TechMagic ]

Thanks Ryosuke!

A slight warning here that the robot in the following video (which costs something like $180,000) appears “naked” in some scenes, none of which are strictly objectionable, we hope.

Beautifully slim and delicate motion life-size motion figures are ideal avatars for expressing emotions to customers in various arts, content and businesses. We can provide a system that integrates not only motion figures but all moving devices.

[ Speecys ]

The best way to operate a Husky with a pair of manipulators on it is to become the robot.

[ UT Austin ]

The FlyJacket drone control system from EPFL has been upgraded so that it can yank you around a little bit.

In several fields of human-machine interaction, haptic guidance has proven to be an effective training tool for enhancing user performance. This work presents the results of psychophysical and motor learning studies that were carried out with human participant to assess the effect of cable-driven haptic guidance for a task involving aerial robotic teleoperation. The guidance system was integrated into an exosuit, called the FlyJacket, that was developed to control drones with torso movements. Results for the Just Noticeable Difference (JND) and from the Stevens Power Law suggest that the perception of force on the users’ torso scales linearly with the amplitude of the force exerted through the cables and the perceived force is close to the magnitude of the stimulus. Motor learning studies reveal that this form of haptic guidance improves user performance in training, but this improvement is not retained when participants are evaluated without guidance.

[ EPFL ]

The SAND Challenge is an opportunity for small businesses to compete in an autonomous unmanned aerial vehicle (UAV) competition to help NASA address safety-critical risks associated with flying UAVs in the national airspace. Set in a post-natural disaster scenario, SAND will push the envelope of aviation.

[ NASA ]

Legged robots have the potential to traverse diverse and rugged terrain. To find a safe and efficient navigation path and to carefully select individual footholds, it is useful to predict properties of the terrain ahead of the robot. In this work, we propose a method to collect data from robot-terrain interaction and associate it to images, to then train a neural network to predict terrain properties from images.

[ RSL ]

Misty wants to be your new receptionist.

[ Misty Robotics ]

For years, we’ve been pointing out that while new Roombas have lots of great features, older Roombas still do a totally decent job of cleaning your floors. This video is a performance comparison between the newest Roomba (the S9+) and the original 2002 Roomba (!), and the results will surprise you. Or maybe they won’t.

[ Vacuum Wars ]

Lex Fridman from MIT interviews Chris Urmson, who was involved in some of the earliest autonomous vehicle projects, Google’s original self-driving car among them, and is currently CEO of Aurora Innovation.

Chris Urmson was the CTO of the Google Self-Driving Car team, a key engineer and leader behind the Carnegie Mellon autonomous vehicle entries in the DARPA grand challenges and the winner of the DARPA urban challenge. Today he is the CEO of Aurora Innovation, an autonomous vehicle software company he started with Sterling Anderson, who was the former director of Tesla Autopilot, and Drew Bagnell, Uber’s former autonomy and perception lead.

[ AI Podcast ]

In this week’s episode of Robots in Depth, Per speaks with Lael Odhner from RightHand Robotics.

Lael Odhner is a co-founder of RightHand Robotics, that is developing a gripper based on the combination of control and soft, compliant parts to get better grasping of objects. Their work focuses on grasping and manipulating everyday human objects in everyday environments.This mimics how human hands combine control and flexibility to grasp objects with great dexterity.

The combination of control and compliance makes the RightHand robotics gripper very light-weight and affordable. The compliance makes it easier to grasp objects of unknown shape and differs from the way industrial robots usually grip. The compliance also helps in a more unstructured environment where contact with the object and its surroundings cannot be exactly predicted.

[ RightHand Robotics ] via [ Robots in Depth ] Continue reading →

Posted in Human Robots

#435648 Surprisingly Speedy Soft Robot Survives ...

Posted on September 14, 2019 by Android

Soft robots are getting more and more popular for some very good reasons. Their relative simplicity is one. Their relative low cost is another. And for their simplicity and low cost, they’re generally able to perform very impressively, leveraging the unique features inherent to their design and construction to move themselves and interact with their environment. The other significant reason why soft robots are so appealing is that they’re durable. Without the constraints of rigid parts, they can withstand the sort of abuse that would make any roboticist cringe.

In the current issue of Science Robotics, a group of researchers from Tsinghua University in China and University of California, Berkeley, present a new kind of soft robot that’s both higher performance and much more robust than just about anything we’ve seen before. The deceptively simple robot looks like a bent strip of paper, but it’s able to move at 20 body lengths per second and survive being stomped on by a human wearing tennis shoes. Take that, cockroaches.

This prototype robot measures just 3 centimeters by 1.5 cm. It takes a scanning electron microscope to actually see what the robot is made of—a thermoplastic layer is sandwiched by palladium-gold electrodes, bonded with adhesive silicone to a structural plastic at the bottom. When an AC voltage (as low as 8 volts but typically about 60 volts) is run through the electrodes, the thermoplastic extends and contracts, causing the robot’s back to flex and the little “foot” to shuffle. A complete step cycle takes just 50 milliseconds, yielding a 200 hertz gait. And technically, the robot “runs,” since it does have a brief aerial phase.

Image: Science Robotics

Photos from a high-speed camera show the robot’s gait (A to D) as it contracts and expands its body.

To put the robot’s top speed of 20 body lengths per second in perspective, have a look at this nifty chart, which shows where other animals relative running speeds of some animals and robots versus body mass:

Image: Science Robotics

This chart shows the relative running speeds of some mammals (purple area), arthropods (orange area), and soft robots (blue area) versus body mass. For both mammals and arthropods, relative speeds show a strong negative scaling law with respect to the body mass: speeds increase as body masses decrease. However, for soft robots, the relationship appears to be the opposite: speeds decrease as the body mass decrease. For the little soft robots created by the researchers from Tsinghua University and UC Berkeley (red stars), the scaling law is similar to that of living animals: Higher speed was attained as the body mass decreased.

If you were wondering, like we were, just what that number 39 is on that chart (top left corner), it’s a species of tiny mite that was discovered underneath a rock in California in 1916. The mite is just under 1 mm in size, but it can run at 0.8 kilometer per hour, which is 322 body lengths per second, making it by far (like, by a factor of two at least) the fastest land animal on Earth relative to size. If a human was to run that fast relative to our size, we’d be traveling at a little bit over 2,000 kilometers per hour. It’s not a coincidence that pretty much everything in the upper left of the chart is an insect—speed scales favorably with decreasing mass, since actuators have a proportionally larger effect.

Other notable robots on the chart with impressive speed to mass ratios are number 27, which is this magnetically driven quadruped robot from UMD, and number 86, UC Berkeley’s X2-VelociRoACH.

Anyway, back to this robot. Some other cool things about it:

You can step on it, squishing it flat with a load about 1 million times its own body weight, and it’ll keep on crawling, albeit only half as fast.
Even climbing a slope of 15 degrees, it can still manage to move at 1 body length per second.
It carries peanuts! With a payload of six times its own weight, it moves a sixth as fast, but still, it’s not like you need your peanuts delivered all that quickly anyway, do you?

Image: Science Robotics

The researchers also put together a prototype with two legs instead of one, which was able to demonstrate a potentially faster galloping gait by spending more time in the air. They suggest that robots like these could be used for “environmental exploration, structural inspection, information reconnaissance, and disaster relief,” which are the sorts of things that you suggest that your robot could be used for when you really have no idea what it could be used for. But this work is certainly impressive, with speed and robustness that are largely unmatched by other soft robots. An untethered version seems possible due to the relatively low voltages required to drive the robot, and if they can put some peanut-sized sensors on there as well, practical applications might actually be forthcoming sometime soon.

“Insect-scale Fast Moving and Ultrarobust Soft Robot,” by Yichuan Wu, Justin K. Yim, Jiaming Liang, Zhichun Shao, Mingjing Qi, Junwen Zhong, Zihao Luo, Xiaojun Yan, Min Zhang, Xiaohao Wang, Ronald S. Fearing, Robert J. Full, and Liwei Lin from Tsinghua University and UC Berkeley, is published in Science Robotics. Continue reading →

Posted in Human Robots