Tag Archives: design

#435541 This Giant AI Chip Is the Size of an ...

Posted on August 26, 2019 by Android

People say size doesn’t matter, but when it comes to AI the makers of the largest computer chip ever beg to differ. There are plenty of question marks about the gargantuan processor, but its unconventional design could herald an innovative new era in silicon design.

Computer chips specialized to run deep learning algorithms are a booming area of research as hardware limitations begin to slow progress, and both established players and startups are vying to build the successor to the GPU, the specialized graphics chip that has become the workhorse of the AI industry.

On Monday Californian startup Cerebras came out of stealth mode to unveil an AI-focused processor that turns conventional wisdom on its head. For decades chip makers have been focused on making their products ever-smaller, but the Wafer Scale Engine (WSE) is the size of an iPad and features 1.2 trillion transistors, 400,000 cores, and 18 gigabytes of on-chip memory.

The Cerebras Wafer-Scale Engine (WSE) is the largest chip ever built. It measures 46,225 square millimeters and includes 1.2 trillion transistors. Optimized for artificial intelligence compute, the WSE is shown here for comparison alongside the largest graphics processing unit. Image Credit: Used with permission from Cerebras Systems.
There is a method to the madness, though. Currently, getting enough cores to run really large-scale deep learning applications means connecting banks of GPUs together. But shuffling data between these chips is a major drain on speed and energy efficiency because the wires connecting them are relatively slow.

Building all 400,000 cores into the same chip should get round that bottleneck, but there are reasons it’s not been done before, and Cerebras has had to come up with some clever hacks to get around those obstacles.

Regular computer chips are manufactured using a process called photolithography to etch transistors onto the surface of a wafer of silicon. The wafers are inches across, so multiple chips are built onto them at once and then split up afterwards. But at 8.5 inches across, the WSE uses the entire wafer for a single chip.

The problem is that while for standard chip-making processes any imperfections in manufacturing will at most lead to a few processors out of several hundred having to be ditched, for Cerebras it would mean scrapping the entire wafer. To get around this the company built in redundant circuits so that even if there are a few defects, the chip can route around them.

The other big issue with a giant chip is the enormous amount of heat the processors can kick off—so the company has had to design a proprietary water-cooling system. That, along with the fact that no one makes connections and packaging for giant chips, means the WSE won’t be sold as a stand-alone component, but as part of a pre-packaged server incorporating the cooling technology.

There are no details on costs or performance so far, but some customers have already been testing prototypes, and according to Cerebras results have been promising. CEO and co-founder Andrew Feldman told Fortune that early tests show they are reducing training time from months to minutes.

We’ll have to wait until the first systems ship to customers in September to see if those claims stand up. But Feldman told ZDNet that the design of their chip should help spur greater innovation in the way engineers design neural networks. Many cornerstones of this process—for instance, tackling data in batches rather than individual data points—are guided more by the hardware limitations of GPUs than by machine learning theory, but their chip will do away with many of those obstacles.

Whether that turns out to be the case or not, the WSE might be the first indication of an innovative new era in silicon design. When Google announced it’s AI-focused Tensor Processing Unit in 2016 it was a wake-up call for chipmakers that we need some out-of-the-box thinking to square the slowing of Moore’s Law with skyrocketing demand for computing power.

It’s not just tech giants’ AI server farms driving innovation. At the other end of the spectrum, the desire to embed intelligence in everyday objects and mobile devices is pushing demand for AI chips that can run on tiny amounts of power and squeeze into the smallest form factors.

These trends have spawned renewed interest in everything from brain-inspired neuromorphic chips to optical processors, but the WSE also shows that there might be mileage in simply taking a sideways look at some of the other design decisions chipmakers have made in the past rather than just pumping ever more transistors onto a chip.

This gigantic chip might be the first exhibit in a weird and wonderful new menagerie of exotic, AI-inspired silicon.

Image Credit: Used with permission from Cerebras Systems. Continue reading →

Posted in Human Robots

#435494 Driverless Electric Trucks Are Coming, ...

Posted on August 14, 2019 by Android

Self-driving and electric cars just don’t stop making headlines lately. Amazon invested in self-driving startup Aurora earlier this year. Waymo, Daimler, GM, along with startups like Zoox, have all launched or are planning to launch driverless taxis, many of them all-electric. People are even yanking driverless cars from their timeless natural habitat—roads—to try to teach them to navigate forests and deserts.

The future of driving, it would appear, is upon us.

But an equally important vehicle that often gets left out of the conversation is trucks; their relevance to our day-to-day lives may not be as visible as that of cars, but their impact is more profound than most of us realize.

Two recent developments in trucking point to a future of self-driving, electric semis hauling goods across the country, and likely doing so more quickly, cheaply, and safely than trucks do today.

Self-Driving in Texas
Last week, Kodiak Robotics announced it’s beginning its first commercial deliveries using self-driving trucks on a route from Dallas to Houston. The two cities sit about 240 miles apart, connected primarily by interstate 45. Kodiak is aiming to expand its reach far beyond the heart of Texas (if Dallas and Houston can be considered the heart, that is) to the state’s most far-flung cities, including El Paso to the west and Laredo to the south.

If self-driving trucks are going to be constrained to staying within state lines (and given that the laws regulating them differ by state, they will be for the foreseeable future), Texas is a pretty ideal option. It’s huge (thousands of miles of highway run both east-west and north-south), it’s warm (better than cold for driverless tech components like sensors), its proximity to Mexico means constant movement of both raw materials and manufactured goods (basically, you can’t have too many trucks in Texas), and most crucially, it’s lax on laws (driverless vehicles have been permitted there since 2017).

Spoiler, though—the trucks won’t be fully unmanned. They’ll have safety drivers to guide them onto and off of the highway, and to be there in case of any unexpected glitches.

California Goes (Even More) Electric
According to some top executives in the rideshare industry, automation is just one key component of the future of driving. Another is electricity replacing gas, and it’s not just carmakers that are plugging into the trend.

This week, Daimler Trucks North America announced completion of its first electric semis for customers Penske and NFI, to be used in the companies’ southern California operations. Scheduled to start operating later this month, the trucks will essentially be guinea pigs for testing integration of electric trucks into large-scale fleets; intel gleaned from the trucks’ performance will impact the design of later models.

Design-wise, the trucks aren’t much different from any other semi you’ve seen lumbering down the highway recently. Their range is about 250 miles—not bad if you think about how much more weight a semi is pulling than a passenger sedan—and they’ve been dubbed eCascadia, an electrified version of Freightliner’s heavy-duty Cascadia truck.

Batteries have a long way to go before they can store enough energy to make electric trucks truly viable (not to mention setting up a national charging infrastructure), but Daimler’s announcement is an important step towards an electrically-driven future.

Keep on Truckin’
Obviously, it’s more exciting to think about hailing one of those cute little Waymo cars with no steering wheel to shuttle you across town than it is to think about that 12-pack of toilet paper you ordered on Amazon cruising down the highway in a semi while the safety driver takes a snooze. But pushing driverless and electric tech in the trucking industry makes sense for a few big reasons.

Trucks mostly run long routes on interstate highways—with no pedestrians, stoplights, or other city-street obstacles to contend with, highway driving is much easier to automate. What glitches there are to be smoothed out may as well be smoothed out with cargo on board rather than people. And though you wouldn’t know it amid the frantic shouts of ‘a robot could take your job!’, the US is actually in the midst of a massive shortage of truck drivers—60,000 short as of earlier this year, to be exact.

As Todd Spencer, president of the Owner-Operator Independent Drivers Association, put it, “Trucking is an absolutely essential, critical industry to the nation, to everybody in it.” Alas, trucks get far less love than cars, but come on—probably 90 percent of the things you ate, bought, or used today were at some point moved by a truck.

Adding driverless and electric tech into that equation, then, should yield positive outcomes on all sides, whether we’re talking about cheaper 12-packs of toilet paper, fewer traffic fatalities due to human error, a less-strained labor force, a stronger economy… or something pretty cool to see as you cruise down the highway in your (driverless, electric, futuristic) car.

Image Credit: Vitpho / Shutterstock.com Continue reading →

Posted in Human Robots

#435474 Watch China’s New Hybrid AI Chip Power ...

Posted on August 7, 2019 by Android

When I lived in Beijing back in the 90s, a man walking his bike was nothing to look at. But today, I did a serious double-take at a video of a bike walking his man.

No kidding.

The bike itself looks overloaded but otherwise completely normal. Underneath its simplicity, however, is a hybrid computer chip that combines brain-inspired circuits with machine learning processes into a computing behemoth. Thanks to its smart chip, the bike self-balances as it gingerly rolls down a paved track before smoothly gaining speed into a jogging pace while navigating dexterously around obstacles. It can even respond to simple voice commands such as “speed up,” “left,” or “straight.”

Far from a circus trick, the bike is a real-world demo of the AI community’s latest attempt at fashioning specialized hardware to keep up with the challenges of machine learning algorithms. The Tianjic (天机*) chip isn’t just your standard neuromorphic chip. Rather, it has the architecture of a brain-like chip, but can also run deep learning algorithms—a match made in heaven that basically mashes together neuro-inspired hardware and software.

The study shows that China is readily nipping at the heels of Google, Facebook, NVIDIA, and other tech behemoths investing in developing new AI chip designs—hell, with billions in government investment it may have already had a head start. A sweeping AI plan from 2017 looks to catch up with the US on AI technology and application by 2020. By 2030, China’s aiming to be the global leader—and a champion for building general AI that matches humans in intellectual competence.

The country’s ambition is reflected in the team’s parting words.

“Our study is expected to stimulate AGI [artificial general intelligence] development by paving the way to more generalized hardware platforms,” said the authors, led by Dr. Luping Shi at Tsinghua University.

A Hardware Conundrum
Shi’s autonomous bike isn’t the first robotic two-wheeler. Back in 2015, the famed research nonprofit SRI International in Menlo Park, California teamed up with Yamaha to engineer MOTOBOT, a humanoid robot capable of driving a motorcycle. Powered by state-of-the-art robotic hardware and machine learning, MOTOBOT eventually raced MotoGPTM world champion Valentino Rossi in a nail-biting match-off.

However, the technological core of MOTOBOT and Shi’s bike vastly differ, and that difference reflects two pathways towards more powerful AI. One, exemplified by MOTOBOT, is software—developing brain-like algorithms with increasingly efficient architecture, efficacy, and speed. That sounds great, but deep neural nets demand so many computational resources that general-purpose chips can’t keep up.

As Shi told China Science Daily: “CPUs and other chips are driven by miniaturization technologies based on physics. Transistors might shrink to nanoscale-level in 10, 20 years. But what then?” As more transistors are squeezed onto these chips, efficient cooling becomes a limiting factor in computational speed. Tax them too much, and they melt.

For AI processes to continue, we need better hardware. An increasingly popular idea is to build neuromorphic chips, which resemble the brain from the ground up. IBM’s TrueNorth, for example, contains a massively parallel architecture nothing like the traditional Von Neumann structure of classic CPUs and GPUs. Similar to biological brains, TrueNorth’s memory is stored within “synapses” between physical “neurons” etched onto the chip, which dramatically cuts down on energy consumption.

But even these chips are limited. Because computation is tethered to hardware architecture, most chips resemble just one specific type of brain-inspired network called spiking neural networks (SNNs). Without doubt, neuromorphic chips are highly efficient setups with dynamics similar to biological networks. They also don’t play nicely with deep learning and other software-based AI.

Brain-AI Hybrid Core
Shi’s new Tianjic chip brought the two incompatibilities together onto a single piece of brainy hardware.

First was to bridge the deep learning and SNN divide. The two have very different computation philosophies and memory organizations, the team said. The biggest difference, however, is that artificial neural networks transform multidimensional data—image pixels, for example—into a single, continuous, multi-bit 0 and 1 stream. In contrast, neurons in SNNs activate using something called “binary spikes” that code for specific activation events in time.

Confused? Yeah, it’s hard to wrap my head around it too. That’s because SNNs act very similarly to our neural networks and nothing like computers. A particular neuron needs to generate an electrical signal (a “spike”) large enough to transfer down to the next one; little blips in signals don’t count. The way they transmit data also heavily depends on how they’re connected, or the network topology. The takeaway: SNNs work pretty differently than deep learning.

Shi’s team first recreated this firing quirk in the language of computers—0s and 1s—so that the coding mechanism would become compatible with deep learning algorithms. They then carefully aligned the step-by-step building blocks of the two models, which allowed them to tease out similarities into a common ground to further build on. “On the basis of this unified abstraction, we built a cross-paradigm neuron scheme,” they said.

In general, the design allowed both computational approaches to share the synapses, where neurons connect and store data, and the dendrites, the outgoing branches of the neurons. In contrast, the neuron body, where signals integrate, was left reconfigurable for each type of computation, as were the input branches. Each building block was combined into a single unified functional core (FCore), which acts like a deep learning/SNN converter depending on its specific setup. Translation: the chip can do both types of previously incompatible computation.

The Chip
Using nanoscale fabrication, the team arranged 156 FCores, containing roughly 40,000 neurons and 10 million synapses, onto a chip less than a fifth of an inch in length and width. Initial tests showcased the chip’s versatility, in that it can run both SNNs and deep learning algorithms such as the popular convolutional neural network (CNNs) often used in machine vision.

Compared to IBM TrueNorth, the density of Tianjic’s cores increased by 20 percent, speeding up performance ten times and increasing bandwidth at least 100-fold, the team said. When pitted against GPUs, the current hardware darling of machine learning, the chip increased processing throughput up to 100 times, while using just a sliver (1/10,000) of energy.

Although these stats are great, real-life performance is even better as a demo. Here’s where the authors gave their Tianjic brain a body. The team combined one chip with multiple specialized networks to process vision, balance, voice commands, and decision-making in real time. Object detection and target tracking, for example, relied on a deep neural net CNN, whereas voice commands and balance data were recognized using an SNN. The inputs were then integrated inside a neural state machine, which churned out decisions to downstream output modules—for example, controlling the handle bar to turn left.

Thanks to the chip’s brain-like architecture and bilingual ability, Tianjic “allowed all of the neural network models to operate in parallel and realized seamless communication across the models,” the team said. The result is an autonomous bike that rolls after its human, balances across speed bumps, avoids crashing into roadblocks, and answers to voice commands.

General AI?
“It’s a wonderful demonstration and quite impressive,” said the editorial team at Nature, which published the study on its cover last week.

However, they cautioned, when comparing Tianjic with state-of-the-art chips designed for a single problem toe-to-toe on that particular problem, Tianjic falls behind. But building these jack-of-all-trades hybrid chips is definitely worth the effort. Compared to today’s limited AI, what people really want is artificial general intelligence, which will require new architectures that aren’t designed to solve one particular problem.

Until people start to explore, innovate, and play around with different designs, it’s not clear how we can further progress in the pursuit of general AI. A self-driving bike might not be much to look at, but its hybrid brain is a pretty neat place to start.

*The name, in Chinese, means “heavenly machine,” “unknowable mystery of nature,” or “confidentiality.” Go figure.

Image Credit: Alexander Ryabintsev / Shutterstock.com Continue reading →

Posted in Human Robots

#435308 Brain-Machine Interfaces Are Getting ...

Posted on July 26, 2019 by Android

Elon Musk grabbed a lot of attention with his July 16 announcement that his company Neuralink plans to implant electrodes into the brains of people with paralysis by next year. Their first goal is to create assistive technology to help people who can’t move or are unable to communicate.

If you haven’t been paying attention, brain-machine interfaces (BMIs) that allow people to control robotic arms with their thoughts might sound like science fiction. But science and engineering efforts have already turned it into reality.

In a few research labs around the world, scientists and physicians have been implanting devices into the brains of people who have lost the ability to control their arms or hands for over a decade. In our own research group at the University of Pittsburgh, we’ve enabled people with paralyzed arms and hands to control robotic arms that allow them to grasp and move objects with relative ease. They can even experience touch-like sensations from their own hand when the robot grasps objects.

At its core, a BMI is pretty straightforward. In your brain, microscopic cells called neurons are sending signals back and forth to each other all the time. Everything you think, do and feel as you interact with the world around you is the result of the activity of these 80 billion or so neurons.

If you implant a tiny wire very close to one of these neurons, you can record the electrical activity it generates and send it to a computer. Record enough of these signals from the right area of the brain and it becomes possible to control computers, robots, or anything else you might want, simply by thinking about moving. But doing this comes with tremendous technical challenges, especially if you want to record from hundreds or thousands of neurons.

What Neuralink Is Bringing to the Table
Elon Musk founded Neuralink in 2017, aiming to address these challenges and raise the bar for implanted neural interfaces.

Perhaps the most impressive aspect of Neuralink’s system is the breadth and depth of their approach. Building a BMI is inherently interdisciplinary, requiring expertise in electrode design and microfabrication, implantable materials, surgical methods, electronics, packaging, neuroscience, algorithms, medicine, regulatory issues, and more. Neuralink has created a team that spans most, if not all, of these areas.

With all of this expertise, Neuralink is undoubtedly moving the field forward, and improving their technology rapidly. Individually, many of the components of their system represent significant progress along predictable paths. For example, their electrodes, that they call threads, are very small and flexible; many researchers have tried to harness those properties to minimize the chance the brain’s immune response would reject the electrodes after insertion. Neuralink has also developed high-performance miniature electronics, another focus area for labs working on BMIs.

Often overlooked in academic settings, however, is how an entire system would be efficiently implanted in a brain.

Neuralink’s BMI requires brain surgery. This is because implanted electrodes that are in intimate contact with neurons will always outperform non-invasive electrodes where neurons are far away from the electrodes sitting outside the skull. So, a critical question becomes how to minimize the surgical challenges around getting the device into a brain.

Maybe the most impressive aspect of Neuralink’s announcement was that they created a 3,000-electrode neural interface where electrodes could be implanted at a rate of between 30 and 200 per minute. Each thread of electrodes is implanted by a sophisticated surgical robot that essentially acts like a sewing machine. This all happens while specifically avoiding blood vessels that blanket the surface of the brain. The robotics and imaging that enable this feat, with tight integration to the entire device, is striking.

Neuralink has thought through the challenge of developing a clinically viable BMI from beginning to end in a way that few groups have done, though they acknowledge that many challenges remain as they work towards getting this technology into human patients in the clinic.

Figuring Out What More Electrodes Gets You
The quest for implantable devices with thousands of electrodes is not only the domain of private companies. DARPA, the NIH BRAIN Initiative, and international consortiums are working on neurotechnologies for recording and stimulating in the brain with goals of tens of thousands of electrodes. But what might scientists do with the information from 1,000, 3,000, or maybe even 100,000 neurons?

At some level, devices with more electrodes might not actually be necessary to have a meaningful impact in people’s lives. Effective control of computers for access and communication, of robotic limbs to grasp and move objects as well as of paralyzed muscles is already happening—in people. And it has been for a number of years.

Since the 1990s, the Utah Array, which has just 100 electrodes and is manufactured by Blackrock Microsystems, has been a critical device in neuroscience and clinical research. This electrode array is FDA-cleared for temporary neural recording. Several research groups, including our own, have implanted Utah Arrays in people that lasted multiple years.

Currently, the biggest constraints are related to connectors, electronics, and system-level engineering, not the implanted electrode itself—although increasing the electrodes’ lifespan to more than five years would represent a significant advance. As those technical capabilities improve, it might turn out that the ability to accurately control computers and robots is limited more by scientists’ understanding of what the neurons are saying—that is, the neural code—than by the number of electrodes on the device.

Even the most capable implanted system, and maybe the most capable devices researchers can reasonably imagine, might fall short of the goal of actually augmenting skilled human performance. Nevertheless, Neuralink’s goal of creating better BMIs has the potential to improve the lives of people who can’t move or are unable to communicate. Right now, Musk’s vision of using BMIs to meld physical brains and intelligence with artificial ones is no more than a dream.

So, what does the future look like for Neuralink and other groups creating implantable BMIs? Devices with more electrodes that last longer and are connected to smaller and more powerful wireless electronics are essential. Better devices themselves, however, are insufficient. Continued public and private investment in companies and academic research labs, as well as innovative ways for these groups to work together to share technologies and data, will be necessary to truly advance scientists’ understanding of the brain and deliver on the promise of BMIs to improve peoples’ lives.

While researchers need to keep the future societal implications of advanced neurotechnologies in mind—there’s an essential role for ethicists and regulation—BMIs could be truly transformative as they help more people overcome limitations caused by injury or disease in the brain and body.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: UPMC/Pitt Health Sciences, / CC BY-NC-ND Continue reading →

Posted in Human Robots

#435167 A Closer Look at the Robots Helping Us ...

Posted on June 5, 2019 by Android

Buck Rogers had Twiki. Luke Skywalker palled around with C-3PO and R2-D2. And astronauts aboard the International Space Station (ISS) now have their own robotic companions in space—Astrobee.

A pair of the cube-shaped robots were launched to the ISS during an April re-supply mission and are currently being commissioned for use on the space station. The free-flying space robots, dubbed Bumble and Honey, are the latest generation of robotic machines to join the human crew on the ISS.

Exploration of the solar system and beyond will require autonomous machines that can assist humans with numerous tasks—or go where we cannot. NASA has said repeatedly that robots will be instrumental in future space missions to the moon, Mars, and even to the icy moon Europa.

The Astrobee robots will specifically test robotic capabilities in zero gravity, replacing the SPHERES (Synchronized Position Hold, Engage, Reorient, Experimental Satellite) robots that have been on the ISS for more than a decade to test various technologies ranging from communications to navigation.

The 18-sided robots, each about the size of a volleyball or an oversized Dungeons and Dragons die, use CO2-based cold-gas thrusters for movement and a series of ultrasonic beacons for orientation. The Astrobee robots, on the other hand, can propel themselves autonomously around the interior of the ISS using electric fans and six cameras.

The modular design of the Astrobee robots means they are highly plug-and-play, capable of being reconfigured with different hardware modules. The robots’ software is also open-source, encouraging scientists and programmers to develop and test new algorithms and features.

And, yes, the Astrobee robots will be busy as bees once they are fully commissioned this fall, with experiments planned to begin next year. Scientists hope to learn more about how robots can assist space crews and perform caretaking duties on spacecraft.

Robots Working Together
The Astrobee robots are expected to be joined by a familiar “face” on the ISS later this year—the humanoid robot Robonaut.

Robonaut, also known as R2, was the first US-built robot on the ISS. It joined the crew back in 2011 without legs, which were added in 2014. However, the installation never entirely worked, as R2 experienced power failures that eventually led to its return to Earth last year to fix the problem. If all goes as planned, the space station’s first humanoid robot will return to the ISS to lend a hand to the astronauts and the new robotic arrivals.

In particular, NASA is interested in how the two different robotic platforms can complement each other, with an eye toward outfitting the agency’s proposed lunar orbital space station with various robots that can supplement a human crew.

“We don’t have definite plans for what would happen on the Gateway yet, but there’s a general recognition that intra-vehicular robots are important for space stations,” Astrobee technical lead Trey Smith in the NASA Intelligent Robotics Group told IEEE Spectrum. “And so, it would not be surprising to see a mobile manipulator like Robonaut, and a free flyer like Astrobee, on the Gateway.”

While the focus on R2 has been to test its capabilities in zero gravity and to use it for mundane or dangerous tasks in space, the technology enabling the humanoid robot has proven to be equally useful on Earth.

For example, R2 has amazing dexterity for a robot, with sensors, actuators, and tendons comparable to the nerves, muscles, and tendons in a human hand. Based on that design, engineers are working on a robotic glove that can help factory workers, for instance, do their jobs better while reducing the risk of repetitive injuries. R2 has also inspired development of a robotic exoskeleton for both astronauts in space and paraplegics on Earth.

Working Hard on Soft Robotics
While innovative and technologically sophisticated, Astrobee and Robonaut are typical robots in that neither one would do well in a limbo contest. In other words, most robots are limited in their flexibility and agility based on current hardware and materials.

A subfield of robotics known as soft robotics involves developing robots with highly pliant materials that mimic biological organisms in how they move. Scientists at NASA’s Langley Research Center are investigating how soft robots could help with future space exploration.

Specifically, the researchers are looking at a series of properties to understand how actuators—components responsible for moving a robotic part, such as Robonaut’s hand—can be built and used in space.

The team first 3D prints a mold and then pours a flexible material like silicone into the mold. Air bladders or chambers in the actuator expand and compress using just air.

Some of the first applications of soft robotics sound more tool-like than R2-D2-like. For example, two soft robots could connect to produce a temporary shelter for astronauts on the moon or serve as an impromptu wind shield during one of Mars’ infamous dust storms.

The idea is to use soft robots in situations that are “dangerous, dirty, or dull,” according to Jack Fitzpatrick, a NASA intern working on the soft robotics project at Langley.

Working on Mars
Of course, space robots aren’t only designed to assist humans. In many instances, they are the only option to explore even relatively close celestial bodies like Mars. Four American-made robotic rovers have been used to investigate the fourth planet from the sun since 1997.

Opportunity is perhaps the most famous, covering about 25 miles of terrain across Mars over 15 years. A dust storm knocked it out of commission last year, with NASA officially ending the mission in February.

However, the biggest and baddest of the Mars rovers, Curiosity, is still crawling across the Martian surface, sending back valuable data since 2012. The car-size robot carries 17 cameras, a laser to vaporize rocks for study, and a drill to collect samples. It is on the hunt for signs of biological life.

The next year or two could see a virtual traffic jam of robots to Mars. NASA’s Mars 2020 Rover is next in line to visit the Red Planet, sporting scientific gadgets like an X-ray fluorescence spectrometer for chemical analyses and ground-penetrating radar to see below the Martian surface.

This diagram shows the instrument payload for the Mars 2020 mission. Image Credit: NASA.
Meanwhile, the Europeans have teamed with the Russians on a rover called Rosalind Franklin, named after a famed British chemist, that will drill down into the Martian ground for evidence of past or present life as soon as 2021.

The Chinese are also preparing to begin searching for life on Mars using robots as soon as next year, as part of the country’s Mars Global Remote Sensing Orbiter and Small Rover program. The mission is scheduled to be the first in a series of launches that would culminate with bringing samples back from Mars to Earth.

Perhaps there is no more famous utterance in the universe of science fiction as “to boldly go where no one has gone before.” However, the fact is that human exploration of the solar system and beyond will only be possible with robots of different sizes, shapes, and sophistication.

Image Credit: NASA. Continue reading →

Posted in Human Robots