Tag Archives: production

#435621 ANYbotics Introduces Sleek New ANYmal C ...

Posted on September 13, 2019 by Android

Quadrupedal robots are making significant advances lately, and just in the past few months we’ve seen Boston Dynamics’ Spot hauling a truck, IIT’s HyQReal pulling a plane, MIT’s MiniCheetah doing backflips, Unitree Robotics’ Laikago towing a van, and Ghost Robotics’ Vision 60 exploring a mine. Robot makers are betting that their four-legged machines will prove useful in a variety of applications in construction, security, delivery, and even at home.

ANYbotics has been working on such applications for years, testing out their ANYmal robot in places where humans typically don’t want to go (like offshore platforms) as well as places where humans really don’t want to go (like sewers), and they have a better idea than most companies what can make quadruped robots successful.

This week, ANYbotics is announcing a completely new quadruped platform, ANYmal C, a major upgrade from the really quite research-y ANYmal B. The new quadruped has been optimized for ruggedness and reliability in industrial environments, with a streamlined body painted a color that lets you know it means business.

ANYmal C’s physical specs are pretty impressive for a production quadruped. It can move at 1 meter per second, manage 20-degree slopes and 45-degree stairs, cross 25-centimeter gaps, and squeeze through passages just 60 centimeters wide. It’s packed with cameras and 3D sensors, including a lidar for 3D mapping and simultaneous localization and mapping (SLAM). All these sensors (along with the vast volume of gait research that’s been done with ANYmal) make this one of the most reliably autonomous quadrupeds out there, with real-time motion planning and obstacle avoidance.

Image: ANYbotics

ANYmal can autonomously attach itself to a cone-shaped docking station to recharge.

ANYmal C is also one of the ruggedest legged robots in existence. The 50-kilogram robot is IP67 rated, meaning that it’s completely impervious to dust and can withstand being submerged in a meter of water for an hour. If it’s submerged for longer than that, you’re absolutely doing something wrong. The robot will run for over 2 hours on battery power, and if that’s not enough endurance, don’t worry, because ANYmal can autonomously impale itself on a weird cone-shaped docking station to recharge.

Photo: ANYbotics

ANYmal C’s sensor payload includes cameras and a lidar for 3D mapping and SLAM.

As far as what ANYmal C is designed to actually do, it’s mostly remote inspection tasks where you need to move around through a relatively complex environment, but where for whatever reason you’d be better off not sending a human. ANYmal C has a sensor payload that gives it lots of visual options, like thermal imaging, and with the ability to handle a 10-kilogram payload, the robot can be adapted to many different environments.

Over the next few months, we’re hoping to see more examples of ANYmal C being deployed to do useful stuff in real-world environments, but for now, we do have a bit more detail from ANYbotics CTO Christian Gehring.

IEEE Spectrum: Can you tell us about the development process for ANYmal C?

Christian Gehring: We tested the previous generation of ANYmal (B) in a broad range of environments over the last few years and gained a lot of insights. Based on our learnings, it became clear that we would have to re-design the robot to meet the requirements of industrial customers in terms of safety, quality, reliability, and lifetime. There were different prototype stages both for the new drives and for single robot assemblies. Apart from electrical tests, we thoroughly tested the thermal control and ingress protection of various subsystems like the depth cameras and actuators.

What can ANYmal C do that the previous version of ANYmal can’t?

ANYmal C was redesigned with a focus on performance increase regarding actuation (new drives), computational power (new hexacore Intel i7 PCs), locomotion and navigation skills, and autonomy (new depth cameras). The new robot additionally features a docking system for autonomous recharging and an inspection payload as an option. The design of ANYmal C is far more integrated than its predecessor, which increases both performance and reliability.

How much of ANYmal C’s development and design was driven by your experience with commercial or industry customers?

Tests (such as the offshore installation with TenneT) and discussions with industry customers were important to get the necessary design input in terms of performance, safety, quality, reliability, and lifetime. Most customers ask for very similar inspection tasks that can be performed with our standard inspection payload and the required software packages. Some are looking for a robot that can also solve some simple manipulation tasks like pushing a button. Overall, most use cases customers have in mind are realistic and achievable, but some are really tough for the robot, like climbing 50° stairs in hot environments of 50°C.

Can you describe how much autonomy you expect ANYmal C to have in industrial or commercial operations?

ANYmal C is primarily developed to perform autonomous routine inspections in industrial environments. This autonomy especially adds value for operations that are difficult to access, as human operation is extremely costly. The robot can naturally also be operated via a remote control and we are working on long-distance remote operation as well.

Do you expect that researchers will be interested in ANYmal C? What research applications could it be useful for?

ANYmal C has been designed to also address the needs of the research community. The robot comes with two powerful hexacore Intel i7 computers and can additionally be equipped with an NVIDIA Jetson Xavier graphics card for learning-based applications. Payload interfaces enable users to easily install and test new sensors. By joining our established ANYmal Research community, researchers get access to simulation tools and software APIs, which boosts their research in various areas like control, machine learning, and navigation.

[ ANYmal C ] Continue reading →

Posted in Human Robots

#435583 Soft Self-Healing Materials for Robots ...

Posted on September 11, 2019 by Android

If there’s one thing we know about robots, it’s that they break. They break, like, literally all the time. The software breaks. The hardware breaks. The bits that you think could never, ever, ever possibly break end up breaking just when you need them not to break the most, and then you have to try to explain what happened to your advisor who’s been standing there watching your robot fail and then stay up all night fixing the thing that seriously was not supposed to break.

While most of this is just a fundamental characteristic of robots that can’t be helped, the European Commission is funding a project called SHERO (Self HEaling soft RObotics) to try and solve at least some of those physical robot breaking problems through the use of structural materials that can autonomously heal themselves over and over again.

SHERO is a three year, €3 million collaboration between Vrije Universiteit Brussel, University of Cambridge, École Supérieure de Physique et de Chimie Industrielles de la ville de Paris (ESPCI-Paris), and Swiss Federal Laboratories for Materials Science and Technology (Empa). As the name SHERO suggests, the goal of the project is to develop soft materials that can completely recover from the kinds of damage that robots are likely to suffer in day to day operations, as well as the occasional more extreme accident.

Most materials, especially soft materials, are fixable somehow, whether it’s with super glue or duct tape. But fixing things involves a human first identifying when they’re broken, and then performing a potentially skill, labor, time, and money intensive task. SHERO’s soft materials will, eventually, make this entire process autonomous, allowing robots to self-identify damage and initiate healing on their own.

Photos: SHERO Project

The damaged robot finger [top] can operate normally after healing itself.

How the self-healing material works
What these self-healing materials can do is really pretty amazing. The researchers are actually developing two different types—the first one heals itself when there’s an application of heat, either internally or externally, which gives some control over when and how the healing process starts. For example, if the robot is handling stuff that’s dirty, you’d want to get it cleaned up before healing it so that dirt doesn’t become embedded in the material. This could mean that the robot either takes itself to a heating station, or it could activate some kind of embedded heating mechanism to be more self-sufficient.

The second kind of self-healing material is autonomous, in that it will heal itself at room temperature without any additional input, and is probably more suitable for relatively minor scrapes and cracks. Here are some numbers about how well the healing works:

Autonomous self-healing polymers do not require heat. They can heal damage at room temperature. Developing soft robotic systems from autonomous self-healing polymers excludes the need of additional heating devices… The healing however takes some time. The healing efficiency after 3 days, 7 days and 14 days is respectively 62 percent, 91 percent and 97 percent.

This material was used to develop a healable soft pneumatic hand. Relevant large cuts can be healed entirely without the need of external heat stimulus. Depending on the size of the damage and even more on the location of damage, the healing takes only seconds or up to a week. Damage on locations on the actuator that are subjected to very small stresses during actuation was healed instantaneously. Larger damages, like cutting the actuator completely in half, took 7 days to heal. But even this severe damage could be healed completely without the need of any external stimulus.

Applications of self-healing robots
Both of these materials can be mixed together, and their mechanical properties can be customized so that the structure that they’re a part of can be tuned to move in different ways. The researchers also plan on introducing flexible conductive sensors into the material, which will help sense damage as well as providing position feedback for control systems. A lot of development will happen over the next few years, and for more details, we spoke with Bram Vanderborght at Vrije Universiteit in Brussels.

IEEE Spectrum: How easy or difficult or expensive is it to produce these materials? Will they add significant cost to robotic grippers?

Bram Vanderborght: They are definitely more expensive materials, but it’s also a matter of size of production. At the moment, we’ve made a few kilograms of the material (enough to make several demonstrators), and the price already dropped significantly from when we ordered 100 grams of the material in the first phase of the project. So probably the cost of the gripper will be higher [than a regular gripper], but you won’t need to replace the gripper as often as other grippers that need to be replaced due to wear, so it can be an advantage.

Moreover due to the method of 3D printing the material, the surface is smoother and airtight (so no post-processing is required to make it airtight). Also, the smooth surface is better to avoid contamination for food handling, for example.

In commercial or industrial applications, gradual fatigue seems to be a more common issue than more abrupt trauma like cuts. How well does the self-healing work to improve durability over long periods of time?

We did not test for gradual fatigue over very long times. But both macroscopic and microscopic damage can be healed. So hopefully it can provide an answer here as well.

Image: SHERO Project

After developing a self-healing robot gripper, the researchers plan to use similar materials to build parts that can be used as the skeleton of robots, allowing them to repair themselves on a regular basis.

How much does the self-healing capability restrict the material properties? What are the limits for softness or hardness or smoothness or other characteristics of the material?

Typically the mechanical properties of networked polymers are much better than thermoplastics. Our material is a networked polymer but in which the crosslinks are reversible. We can change quite a lot of parameters in the design of the materials. So we can develop very stiff (fracture strain at 1.24 percent) and very elastic materials (fracture strain at 450 percent). The big advantage that our material has is we can mix it to have intermediate properties. Moreover, at the interface of the materials with different mechanical properties, we have the same chemical bonds, so the interface is perfect. While other materials, they may need to glue it, which gives local stresses and a weak spot.

When the material heals itself, is it less structurally sound in that spot? Can it heal damage that happens to the same spot over and over again?

In theory we can heal it an infinite amount of times. When the wound is not perfectly aligned, of course in that spot it will become weaker. Also too high temperatures lead to irreversible bonds, and impurities lead to weak spots.

Besides grippers and skins, what other potential robotics applications would this technology be useful for?

Most of self healing materials available now are used for coatings. What we are developing are structural components, therefore the mechanical properties of the material need to be good for such applications. So maybe part of the skeleton of the robot can be developed with such materials to make it lighter, since can be designed for regular repair. And for exceptional loads, it breaks and can be repaired like our human body.

[ SHERO Project ] Continue reading →

Posted in Human Robots

#435161 Less Like Us: An Alternate Theory of ...

Posted on June 3, 2019 by Android

The question of whether an artificial general intelligence will be developed in the future—and, if so, when it might arrive—is controversial. One (very uncertain) estimate suggests 2070 might be the earliest we could expect to see such technology.

Some futurists point to Moore’s Law and the increasing capacity of machine learning algorithms to suggest that a more general breakthrough is just around the corner. Others suggest that extrapolating exponential improvements in hardware is unwise, and that creating narrow algorithms that can beat humans at specialized tasks brings us no closer to a “general intelligence.”

But evolution has produced minds like the human mind at least once. Surely we could create artificial intelligence simply by copying nature, either by guided evolution of simple algorithms or wholesale emulation of the human brain.

Both of these ideas are far easier to conceive of than they are to achieve. The 302 neurons of the nematode worm’s brain are still an extremely difficult engineering challenge, let alone the 86 billion in a human brain.

Leaving aside these caveats, though, many people are worried about artificial general intelligence. Nick Bostrom’s influential book on superintelligence imagines it will be an agent—an intelligence with a specific goal. Once such an agent reaches a human level of intelligence, it will improve itself—increasingly rapidly as it gets smarter—in pursuit of whatever goal it has, and this “recursive self-improvement” will lead it to become superintelligent.

This “intelligence explosion” could catch humans off guard. If the initial goal is poorly specified or malicious, or if improper safety features are in place, or if the AI decides it would prefer to do something else instead, humans may be unable to control our own creation. Bostrom gives examples of how a seemingly innocuous goal, such as “Make everyone happy,” could be misinterpreted; perhaps the AI decides to drug humanity into a happy stupor, or convert most of the world into computing infrastructure to pursue its goal.

Drexler and Comprehensive AI Services
These are increasingly familiar concerns for an AI that behaves like an agent, seeking to achieve its goal. There are dissenters to this picture of how artificial general intelligence might arise. One notable alternative point of view comes from Eric Drexler, famous for his work on molecular nanotechnology and Engines of Creation, the book that popularized it.

With respect to AI, Drexler believes our view of an artificial intelligence as a single “agent” that acts to maximize a specific goal is too narrow, almost anthropomorphizing AI, or modeling it as a more realistic route towards general intelligence. Instead, he proposes “Comprehensive AI Services” (CAIS) as an alternative route to artificial general intelligence.

What does this mean? Drexler’s argument is that we should look more closely at how machine learning and AI algorithms are actually being developed in the real world. The optimization effort is going into producing algorithms that can provide services and perform tasks like translation, music recommendations, classification, medical diagnoses, and so forth.

AI-driven improvements in technology, argues Drexler, will lead to a proliferation of different algorithms: technology and software improvement, which can automate increasingly more complicated tasks. Recursive improvement in this regime is already occurring—take the newer versions of AlphaGo, which can learn to improve themselves by playing against previous versions.

Many Smart Arms, No Smart Brain
Instead of relying on some unforeseen breakthrough, the CAIS model of AI just assumes that specialized, narrow AI will continue to improve at performing each of its tasks, and the range of tasks that machine learning algorithms will be able to perform will become wider. Ultimately, once a sufficient number of tasks have been automated, the services that an AI will provide will be so comprehensive that they will resemble a general intelligence.

One could then imagine a “general” intelligence as simply an algorithm that is extremely good at matching the task you ask it to perform to the specialized service algorithm that can perform that task. Rather than acting like a single brain that strives to achieve a particular goal, the central AI would be more like a search engine, looking through the tasks it can perform to find the closest match and calling upon a series of subroutines to achieve the goal.

For Drexler, this is inherently a safety feature. Rather than Bostrom’s single, impenetrable, conscious and superintelligent brain (which we must try to psychoanalyze in advance without really knowing what it will look like), we have a network of capabilities. If you don’t want your system to perform certain tasks, you can simply cut it off from access to those services. There is no superintelligent consciousness to outwit or “trap”: more like an extremely high-level programming language that can respond to complicated commands by calling upon one of the myriad specialized algorithms that have been developed by different groups.

This skirts the complex problem of consciousness and all of the sticky moral quandaries that arise in making minds that might be like ours. After all, if you could simulate a human mind, you could simulate it experiencing unimaginable pain. Black Mirror-esque dystopias where emulated minds have no rights and are regularly “erased” or forced to labor in dull and repetitive tasks, hove into view.

Drexler argues that, in this world, there is no need to ever build a conscious algorithm. Yet it seems likely that, at some point, humans will attempt to simulate our own brains, if only in the vain attempt to pursue immortality. This model cannot hold forever. Yet its proponents argue that any world in which we could develop general AI would probably also have developed superintelligent capabilities in a huge range of different tasks, such as computer programming, natural language understanding, and so on. In other words, CAIS arrives first.

The Future In Our Hands?
Drexler argues that his model already incorporates many of the ideas from general AI development. In the marketplace, algorithms compete all the time to perform these services: they undergo the same evolutionary pressures that lead to “higher intelligence,” but the behavior that’s considered superior is chosen by humans, and the nature of the “general intelligence” is far more shaped by human decision-making and human programmers. Development in AI services could still be rapid and disruptive.

But in Drexler’s case, the research and development capacity comes from humans and organizations driven by the desire to improve algorithms that are performing individualized and useful tasks, rather than from a conscious AI recursively reprogramming and improving itself.

In other words, this vision does not absolve us of the responsibility of making our AI safe; if anything, it gives us a greater degree of responsibility. As more and more complex “services” are automated, performing what used to be human jobs at superhuman speed, the economic disruption will be severe.

Equally, as machine learning is trusted to carry out more complex decisions, avoiding algorithmic bias becomes crucial. Shaping each of these individual decision-makers—and trying to predict the complex ways they might interact with each other—is no less daunting a task than specifying the goal for a hypothetical, superintelligent, God-like AI. Arguably, the consequences of the “misalignment” of these services algorithms are already multiplying around us.

The CAIS model bridges the gap between real-world AI, machine learning developments, and real-world safety considerations, as well as the speculative world of superintelligent agents and the safety considerations involved with controlling their behavior. We should keep our minds open as to what form AI and machine learning will take, and how it will influence our societies—and we must take care to ensure that the systems we create don’t end up forcing us all to live in a world of unintended consequences.

Image Credit: MF Production/Shutterstock.com Continue reading →

Posted in Human Robots

#434843 This Week’s Awesome Stories From ...

Posted on April 20, 2019 by Android

ARTIFICIAL INTELLIGENCE
Open AI’s Dota 2 AI Steamrolls World Champion e-Sports Team With Back-to-Back Victories
Nick Statt | The Verge
“…[OpenAI cofounder and CEO, Sam Altman] tells me there probably does not exist a video game out there right now that a system like OpenAI Five can’t eventually master at a level beyond human capability. For the broader AI industry, mastering video games may soon become passé, simple table stakes required to prove your system can learn fast and act in a way required to tackle tougher, real-world tasks with more meaningful benefits.”

ROBOTICS
Boston Dynamics Debuts the Production Version of SpotMini
Brian Heater, Catherine Shu | TechCrunch
“SpotMini is the first commercial robot Boston Dynamics is set to release, but as we learned earlier, it certainly won’t be the last. The company is looking to its wheeled Handle robot in an effort to push into the logistics space. It’s a super-hot category for robotics right now. Notably, Amazon recently acquired Colorado-based start up Canvas to add to its own arm of fulfillment center robots.”

NEUROSCIENCE
Scientists Restore Some Brain Cell Functions in Pigs Four Hours After Death
Joel Achenbach | The Washington Post
“The ethicists say this research can blur the line between life and death, and could complicate the protocols for organ donation, which rely on a clear determination of when a person is dead and beyond resuscitation.”

BIOTECH
How Scientists 3D Printed a Tiny Heart From Human Cells
Yasmin Saplakoglu | Live Science
“Though the heart is much smaller than a human’s (it’s only the size of a rabbit’s), and there’s still a long way to go until it functions like a normal heart, the proof-of-concept experiment could eventually lead to personalized organs or tissues that could be used in the human body…”

SPACE
The Next Clash of Silicon Valley Titans Will Take Place in Space
Luke Dormehl | Digital Trends
“With bold plans that call for thousands of new satellites being put into orbit and astronomical costs, it’s going to be fascinating to observe the next phase of the tech platform battle being fought not on our desktops or mobile devices in our pockets, but outside of Earth’s atmosphere.”

FUTURE HISTORY
The Images That Could Help Rebuild Notre-Dame Cathedral
Alexis C. Madrigal | The Atlantic
“…in 2010, [Andrew] Tallon, an art professor at Vassar, took a Leica ScanStation C10 to Notre-Dame and, with the assistance of Columbia’s Paul Blaer, began to painstakingly scan every piece of the structure, inside and out. …Over five days, they positioned the scanner again and again—50 times in all—to create an unmatched record of the reality of one of the world’s most awe-inspiring buildings, represented as a series of points in space.”

AUGMENTED REALITY
Mapping Our World in 3D Will Let Us Paint Streets With Augmented Reality
Charlotte Jee | MIT Technology Review
“Scape wants to use its location services to become the underlying infrastructure upon which driverless cars, robotics, and augmented-reality services sit. ‘Our end goal is a one-to-one map of the world covering everything,’ says Miller. ‘Our ambition is to be as invisible as GPS is today.’i”

Image Credit: VAlex / Shutterstock.com Continue reading →

Posted in Human Robots

#434827 AI and Robotics Are Transforming ...

Posted on April 12, 2019 by Android

During the past 50 years, the frequency of recorded natural disasters has surged nearly five-fold.

In this blog, I’ll be exploring how converging exponential technologies (AI, robotics, drones, sensors, networks) are transforming the future of disaster relief—how we can prevent them in the first place and get help to victims during that first golden hour wherein immediate relief can save lives.

Here are the three areas of greatest impact:

AI, predictive mapping, and the power of the crowd
Next-gen robotics and swarm solutions
Aerial drones and immediate aid supply

Let’s dive in!

Artificial Intelligence and Predictive Mapping
When it comes to immediate and high-precision emergency response, data is gold.

Already, the meteoric rise of space-based networks, stratosphere-hovering balloons, and 5G telecommunications infrastructure is in the process of connecting every last individual on the planet.

Aside from democratizing the world’s information, however, this upsurge in connectivity will soon grant anyone the ability to broadcast detailed geo-tagged data, particularly those most vulnerable to natural disasters.

Armed with the power of data broadcasting and the force of the crowd, disaster victims now play a vital role in emergency response, turning a historically one-way blind rescue operation into a two-way dialogue between connected crowds and smart response systems.

With a skyrocketing abundance of data, however, comes a new paradigm: one in which we no longer face a scarcity of answers. Instead, it will be the quality of our questions that matters most.

This is where AI comes in: our mining mechanism.

In the case of emergency response, what if we could strategically map an almost endless amount of incoming data points? Or predict the dynamics of a flood and identify a tsunami’s most vulnerable targets before it even strikes? Or even amplify critical signals to trigger automatic aid by surveillance drones and immediately alert crowdsourced volunteers?

Already, a number of key players are leveraging AI, crowdsourced intelligence, and cutting-edge visualizations to optimize crisis response and multiply relief speeds.

Take One Concern, for instance. Born out of Stanford under the mentorship of leading AI expert Andrew Ng, One Concern leverages AI through analytical disaster assessment and calculated damage estimates.

Partnering with the cities of Los Angeles, San Francisco, and numerous cities in San Mateo County, the platform assigns verified, unique ‘digital fingerprints’ to every element in a city. Building robust models of each system, One Concern’s AI platform can then monitor site-specific impacts of not only climate change but each individual natural disaster, from sweeping thermal shifts to seismic movement.

This data, combined with that of city infrastructure and former disasters, are then used to predict future damage under a range of disaster scenarios, informing prevention methods and structures in need of reinforcement.

Within just four years, One Concern can now make precise predictions with an 85 percent accuracy rate in under 15 minutes.

And as IoT-connected devices and intelligent hardware continue to boom, a blooming trillion-sensor economy will only serve to amplify AI’s predictive capacity, offering us immediate, preventive strategies long before disaster strikes.

Beyond natural disasters, however, crowdsourced intelligence, predictive crisis mapping, and AI-powered responses are just as formidable a triage in humanitarian disasters.

One extraordinary story is that of Ushahidi. When violence broke out after the 2007 Kenyan elections, one local blogger proposed a simple yet powerful question to the web: “Any techies out there willing to do a mashup of where the violence and destruction is occurring and put it on a map?”

Within days, four ‘techies’ heeded the call, building a platform that crowdsourced first-hand reports via SMS, mined the web for answers, and—with over 40,000 verified reports—sent alerts back to locals on the ground and viewers across the world.

Today, Ushahidi has been used in over 150 countries, reaching a total of 20 million people across 100,000+ deployments. Now an open-source crisis-mapping software, its V3 (or “Ushahidi in the Cloud”) is accessible to anyone, mining millions of Tweets, hundreds of thousands of news articles, and geo-tagged, time-stamped data from countless sources.

Aggregating one of the longest-running crisis maps to date, Ushahidi’s Syria Tracker has proved invaluable in the crowdsourcing of witness reports. Providing real-time geographic visualizations of all verified data, Syria Tracker has enabled civilians to report everything from missing people and relief supply needs to civilian casualties and disease outbreaks— all while evading the government’s cell network, keeping identities private, and verifying reports prior to publication.

As mobile connectivity and abundant sensors converge with AI-mined crowd intelligence, real-time awareness will only multiply in speed and scale.

Imagining the Future….

Within the next 10 years, spatial web technology might even allow us to tap into mesh networks.

As I’ve explored in a previous blog on the implications of the spatial web, while traditional networks rely on a limited set of wired access points (or wireless hotspots), a wireless mesh network can connect entire cities via hundreds of dispersed nodes that communicate with each other and share a network connection non-hierarchically.

In short, this means that individual mobile users can together establish a local mesh network using nothing but the computing power in their own devices.

Take this a step further, and a local population of strangers could collectively broadcast countless 360-degree feeds across a local mesh network.

Imagine a scenario in which armed attacks break out across disjointed urban districts, each cluster of eye witnesses and at-risk civilians broadcasting an aggregate of 360-degree videos, all fed through photogrammetry AIs that build out a live hologram in real time, giving family members and first responders complete information.

Or take a coastal community in the throes of torrential rainfall and failing infrastructure. Now empowered by a collective live feed, verification of data reports takes a matter of seconds, and richly-layered data informs first responders and AI platforms with unbelievable accuracy and specificity of relief needs.

By linking all the right technological pieces, we might even see the rise of automated drone deliveries. Imagine: crowdsourced intelligence is first cross-referenced with sensor data and verified algorithmically. AI is then leveraged to determine the specific needs and degree of urgency at ultra-precise coordinates. Within minutes, once approved by personnel, swarm robots rush to collect the requisite supplies, equipping size-appropriate drones with the right aid for rapid-fire delivery.

This brings us to a second critical convergence: robots and drones.

While cutting-edge drone technology revolutionizes the way we deliver aid, new breakthroughs in AI-geared robotics are paving the way for superhuman emergency responses in some of today’s most dangerous environments.

Let’s explore a few of the most disruptive examples to reach the testing phase.

First up….

Autonomous Robots and Swarm Solutions
As hardware advancements converge with exploding AI capabilities, disaster relief robots are graduating from assistance roles to fully autonomous responders at a breakneck pace.

Born out of MIT’s Biomimetic Robotics Lab, the Cheetah III is but one of many robots that may form our first line of defense in everything from earthquake search-and-rescue missions to high-risk ops in dangerous radiation zones.

Now capable of running at 6.4 meters per second, Cheetah III can even leap up to a height of 60 centimeters, autonomously determining how to avoid obstacles and jump over hurdles as they arise.

Initially designed to perform spectral inspection tasks in hazardous settings (think: nuclear plants or chemical factories), the Cheetah’s various iterations have focused on increasing its payload capacity, range of motion, and even a gripping function with enhanced dexterity.

Cheetah III and future versions are aimed at saving lives in almost any environment.

And the Cheetah III is not alone. Just this February, Tokyo’s Electric Power Company (TEPCO) has put one of its own robots to the test. For the first time since Japan’s devastating 2011 tsunami, which led to three nuclear meltdowns in the nation’s Fukushima nuclear power plant, a robot has successfully examined the reactor’s fuel.

Broadcasting the process with its built-in camera, the robot was able to retrieve small chunks of radioactive fuel at five of the six test sites, offering tremendous promise for long-term plans to clean up the still-deadly interior.

Also out of Japan, Mitsubishi Heavy Industries (MHi) is even using robots to fight fires with full autonomy. In a remarkable new feat, MHi’s Water Cannon Bot can now put out blazes in difficult-to-access or highly dangerous fire sites.

Delivering foam or water at 4,000 liters per minute and 1 megapascal (MPa) of pressure, the Cannon Bot and its accompanying Hose Extension Bot even form part of a greater AI-geared system to conduct reconnaissance and surveillance on larger transport vehicles.

As wildfires grow ever more untameable, high-volume production of such bots could prove a true lifesaver. Paired with predictive AI forest fire mapping and autonomous hauling vehicles, not only will solutions like MHi’s Cannon Bot save numerous lives, but avoid population displacement and paralyzing damage to our natural environment before disaster has the chance to spread.

But even in cases where emergency shelter is needed, groundbreaking (literally) robotics solutions are fast to the rescue.

After multiple iterations by Fastbrick Robotics, the Hadrian X end-to-end bricklaying robot can now autonomously build a fully livable, 180-square-meter home in under three days. Using a laser-guided robotic attachment, the all-in-one brick-loaded truck simply drives to a construction site and directs blocks through its robotic arm in accordance with a 3D model.

Meeting verified building standards, Hadrian and similar solutions hold massive promise in the long-term, deployable across post-conflict refugee sites and regions recovering from natural catastrophes.

But what if we need to build emergency shelters from local soil at hand? Marking an extraordinary convergence between robotics and 3D printing, the Institute for Advanced Architecture of Catalonia (IAAC) is already working on a solution.

In a major feat for low-cost construction in remote zones, IAAC has found a way to convert almost any soil into a building material with three times the tensile strength of industrial clay. Offering myriad benefits, including natural insulation, low GHG emissions, fire protection, air circulation, and thermal mediation, IAAC’s new 3D printed native soil can build houses on-site for as little as $1,000.

But while cutting-edge robotics unlock extraordinary new frontiers for low-cost, large-scale emergency construction, novel hardware and computing breakthroughs are also enabling robotic scale at the other extreme of the spectrum.

Again, inspired by biological phenomena, robotics specialists across the US have begun to pilot tiny robotic prototypes for locating trapped individuals and assessing infrastructural damage.

Take RoboBees, tiny Harvard-developed bots that use electrostatic adhesion to ‘perch’ on walls and even ceilings, evaluating structural damage in the aftermath of an earthquake.

Or Carnegie Mellon’s prototyped Snakebot, capable of navigating through entry points that would otherwise be completely inaccessible to human responders. Driven by AI, the Snakebot can maneuver through even the most densely-packed rubble to locate survivors, using cameras and microphones for communication.

But when it comes to fast-paced reconnaissance in inaccessible regions, miniature robot swarms have good company.

Next-Generation Drones for Instantaneous Relief Supplies
Particularly in the case of wildfires and conflict zones, autonomous drone technology is fundamentally revolutionizing the way we identify survivors in need and automate relief supply.

Not only are drones enabling high-resolution imagery for real-time mapping and damage assessment, but preliminary research shows that UAVs far outpace ground-based rescue teams in locating isolated survivors.

As presented by a team of electrical engineers from the University of Science and Technology of China, drones could even build out a mobile wireless broadband network in record time using a “drone-assisted multi-hop device-to-device” program.

And as shown during Houston’s Hurricane Harvey, drones can provide scores of predictive intel on everything from future flooding to damage estimates.

Among multiple others, a team led by Texas A&M computer science professor and director of the university’s Center for Robot-Assisted Search and Rescue Dr. Robin Murphy flew a total of 119 drone missions over the city, from small-scale quadcopters to military-grade unmanned planes. Not only were these critical for monitoring levee infrastructure, but also for identifying those left behind by human rescue teams.

But beyond surveillance, UAVs have begun to provide lifesaving supplies across some of the most remote regions of the globe. One of the most inspiring examples to date is Zipline.

Created in 2014, Zipline has completed 12,352 life-saving drone deliveries to date. While drones are designed, tested, and assembled in California, Zipline primarily operates in Rwanda and Tanzania, hiring local operators and providing over 11 million people with instant access to medical supplies.

Providing everything from vaccines and HIV medications to blood and IV tubes, Zipline’s drones far outpace ground-based supply transport, in many instances providing life-critical blood cells, plasma, and platelets in under an hour.

But drone technology is even beginning to transcend the limited scale of medical supplies and food.

Now developing its drones under contracts with DARPA and the US Marine Corps, Logistic Gliders, Inc. has built autonomously-navigating drones capable of carrying 1,800 pounds of cargo over unprecedented long distances.

Built from plywood, Logistic’s gliders are projected to cost as little as a few hundred dollars each, making them perfect candidates for high-volume remote aid deliveries, whether navigated by a pilot or self-flown in accordance with real-time disaster zone mapping.

As hardware continues to advance, autonomous drone technology coupled with real-time mapping algorithms pose no end of abundant opportunities for aid supply, disaster monitoring, and richly layered intel previously unimaginable for humanitarian relief.

Concluding Thoughts
Perhaps one of the most consequential and impactful applications of converging technologies is their transformation of disaster relief methods.

While AI-driven intel platforms crowdsource firsthand experiential data from those on the ground, mobile connectivity and drone-supplied networks are granting newfound narrative power to those most in need.

And as a wave of new hardware advancements gives rise to robotic responders, swarm technology, and aerial drones, we are fast approaching an age of instantaneous and efficiently-distributed responses in the midst of conflict and natural catastrophes alike.

Empowered by these new tools, what might we create when everyone on the planet has the same access to relief supplies and immediate resources? In a new age of prevention and fast recovery, what futures can you envision?

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