Do You Really Want a Robotic Car?

Robot Driver
Sixteen percent of Canadians and twenty-six percent of Americans say they “would not use a driverless car.” Mopic/Shutterstock

15 August 2018 – Many times in my blogging career I’ve gone on a rant about the three Ds of robotics. These are “dull, dirty, and dangerous.” They relate to the question, which is not asked often enough, “Do you want to build an automated system to do that task?”

The reason the question is not asked enough is that it needs to be asked whenever anyone looks into designing a system (whether manual or automated) to do anything. The possibility that anyone ever sets up a system to do anything without first asking that question means that it’s not asked enough.

When asking the question, getting a hit on any one of the three Ds tells you to at least think about automating the task. Getting a hit on two of them should make you think that your task is very likely to be ripe for automation. If you hit on all three, it’s a slam dunk!

When we look into developing automated vehicles (AVs), we get hits on “dull” and “dangerous.”

Driving can be excruciatingly dull, especially if you’re going any significant distance. That’s why people fall asleep at the wheel. I daresay everyone has fallen asleep at the wheel at least once, although we almost always wake up before hitting the bridge abutment. It’s also why so many people drive around with cellphones pressed to their ears. The temptation to multitask while driving is almost irresistable.

Driving is also brutally dangerous. Tens of thousands of people die every year in car crashes. Its pretty safe to say that nearly all those fatalities involve cars driven by humans. The number of people who have been killed in accidents involving driverless cars you can (as of this writing) count on one hand.

I’m not prepared to go into statistics comparing safety of automated vehicles vs. manually driven ones. Suffice it to say that eventually we can expect AVs to be much safer than manually driven vehicles. We’ll keep developing the technology until they are. It’s not a matter of if, but when.

This is the analysis most observers (if they analyze it at all) come up with to prove that vehicle driving should be automated.

Yet, opinions that AVs are acceptable, let alone inevitable, are far from universal. In a survey of 3,000 people in the U.S. and Canada, Ipsos Strategy 3 found that sixteen percent of Canadians and twenty six percent of Americans say they “would not use a driverless car,” and a whopping 39% of Americans (and 30% of Canadians) would rarely or never let driverless technology do the parking!

Why would so many people be unwilling to give up their driving priviledges? I submit that it has to do with a parallel consideration that is every bit as important as the three Ds when deciding whether to automate a task:

Don’t Automate Something Humans Like to Do!

Predatory animals, especially humans, like to drive. It’s fun. Just ask any dog who has a chance to go for a ride in a car! Almost universally they’ll jump into the front seat as soon as you open the door.

In fact, if you leave them (dogs) in the car unattended for a few minutes, they’ll be behind the wheel when you come back!

Humans are the same way. Leave ’em unattended in a car for any length of time, and they’ll think of some excuse to get behind the wheel.

The Ipsos survey fourd that some 61% of both Americans and Canadians identify themselves as “car people.” When asked “What would have to change about transportation in your area for you to consider not owning a car at all, or owning fewer cars?” 39% of Americans and 38% of Canadians responded “There is nothing that would make me consider owning fewer cars!”

That’s pretty definative!

Their excuse for getting behind the wheel is largely an economic one: Seventy-eight pecent of Americans claim they “definitely need to have a vehicle to get to work.” In more urbanized Canada (you did know that Canadians cluster more into cities, didn’t you.) that drops to 53%.

Whether those folks claiming they “have” to have a car to get to work is based on historical precedent, urban planning, wishful thinking, or flat out just what they want to believe, it’s a good, cogent reason why folks, especially Americans, hang onto their steering wheels for dear life.

The moral of this story is that driving is something humans like to do, and getting them to give it up will be a serious uphill battle for anyone wanting to promote driverless cars.

Yet, development of AV technology is going full steam ahead.

Is that just another example of Dr. John Bridges’  version of Solomon’s proverb “A fool and his money are soon parted?”

Possibly, but I think not.

Certainly, the idea of spending tons of money to have bragging rights for the latest technology, and to take selfies showing you reading a newspaper while your car drives itself through traffic has some appeal. I submit, however, that the appeal is short lived.

For one thing, reading in a moving vehicle is the fastest route I know of to motion sickness. It’s right up there with cueing up the latest Disney cartoon feature for your kids on the overhead DVD player in your SUV, then cleaning up their vomit.

I, for one, don’t want to go there!

Sounds like another example of “More money than brains.”

There are, however, a wide range of applications where driving a vehicle turns out to be no fun at all. For example, the first use of drone aircraft was as targets for anti-aircraft gunnery practice. They just couldn’t find enough pilots who wanted to be sitting ducks to be blown out of the sky! Go figure.

Most commercial driving jobs could also stand to be automated. For example, almost nobody actually steers ships at sea anymore. They generally stand around watching an autopilot follow a pre-programmed course. Why? As a veteran boat pilot, I can tell you that the captain has a lot more fun than the helmsman. Piloting a ship from, say, Calcutta to San Francisco has got to be mind-numbingly dull. There’s nothing going on out there on the ocean.

Boat passengers generally spend most of their time staring into the wake, but the helmsman doesn’t get to look at the wake. He (or she) has to spend their time scanning a featureless horizontal line separating a light-blue dome (the sky) from a dark-blue plane (the sea) in the vain hope that something interesting will pop up and relieve the tedium.

Hence the autopilot.

Flying a commercial airliner is similar. It has been described (as have so many other tasks) as “hours of boredom punctuated by moments of sheer terror!” While such activity is very Zen (I’m convinced that humans’ ability to meditate was developed by cave guys having to sit for hours, or even days, watching game trails for their next meal to wander along), it’s not top-of-the-line fun.

So, sometimes driving is fun, and sometimes it’s not. We need AV technology to cover those times when it’s not.

The Future Role of AI in Fact Checking

Reality Check
Advanced computing may someday help sort fact from fiction. Gustavo Frazao/Shutterstock

8 August 2018 – This guest post is contributed under the auspices of Trive, a global, decentralized truth-discovery engine. Trive seeks to provide news aggregators and outlets a superior means of fact checking and news-story verification.

by Barry Cousins, Guest Blogger, Info-Tech Research Group

In a recent project, we looked at blockchain startup Trive and their pursuit of a fact-checking truth database. It seems obvious and likely that competition will spring up. After all, who wouldn’t want to guarantee the preservation of facts? Or, perhaps it’s more that lots of people would like to control what is perceived as truth.

With the recent coming out party of IBM’s Debater, this next step in our journey brings Artificial Intelligence into the conversation … quite literally.

As an analyst, I’d like to have a universal fact checker. Something like the carbon monoxide detectors on each level of my home. Something that would sound an alarm when there’s danger of intellectual asphyxiation from choking on the baloney put forward by certain sales people, news organizations, governments, and educators, for example.

For most of my life, we would simply have turned to academic literature for credible truth. There is now enough legitimate doubt to make us seek out a new model or, at a minimum, to augment that academic model.

I don’t want to be misunderstood: I’m not suggesting that all news and education is phony baloney. And, I’m not suggesting that the people speaking untruths are always doing so intentionally.

The fact is, we don’t have anything close to a recognizable database of facts from which we can base such analysis. For most of us, this was supposed to be the school system, but sadly, that has increasingly become politicized.

But even if we had the universal truth database, could we actually use it? For instance, how would we tap into the right facts at the right time? The relevant facts?

If I’m looking into the sinking of the Titanic, is it relevant to study the facts behind the ship’s manifest? It might be interesting, but would it prove to be relevant? Does it have anything to do with the iceberg? Would that focus on the manifest impede my path to insight on the sinking?

It would be great to have Artificial Intelligence advising me on these matters. I’d make the ultimate decision, but it would be awesome to have something like the Star Trek computer sifting through the sea of facts for that which is relevant.

Is AI ready? IBM recently showed that it’s certainly coming along.

Is the sea of facts ready? That’s a lot less certain.

Debater holds its own

In June 2018, IBM unveiled the latest in Artificial Intelligence with Project Debater in a small event with two debates: “we should subsidize space exploration”, and “we should increase the use of telemedicine”. The opponents were credentialed experts, and Debater was arguing from a position established by “reading” a large volume of academic papers.

The result? From what we can tell, the humans were more persuasive while the computer was more thorough. Hardly surprising, perhaps. I’d like to watch the full debates but haven’t located them yet.

Debater is intended to help humans enhance their ability to persuade. According to IBM researcher Ranit Aharanov, “We are actually trying to show that a computer system can add to our conversation or decision making by bringing facts and doing a different kind of argumentation.”

So this is an example of AI. I’ve been trying to distinguish between automation and AI, machine learning, deep learning, etc. I don’t need to nail that down today, but I’m pretty sure that my definition of AI includes genuine cognition: the ability to identify facts, comb out the opinions and misdirection, incorporate the right amount of intention bias, and form decisions and opinions with confidence while remaining watchful for one’s own errors. I’ll set aside any obligation to admit and react to one’s own errors, choosing to assume that intelligence includes the interest in, and awareness of, one’s ability to err.

Mark Klein, Principal Research Scientist at M.I.T., helped with that distinction between computing and AI. “There needs to be some additional ability to observe and modify the process by which you make decisions. Some call that consciousness, the ability to observe your own thinking process.”

Project Debater represents an incredible leap forward in AI. It was given access to a large volume of academic publications, and it developed its debating chops through machine learning. The capability of the computer in those debates resembled the results that humans would get from reading all those papers, assuming you can conceive of a way that a human could consume and retain that much knowledge.

Beyond spinning away on publications, are computers ready to interact intelligently?

Artificial? Yes. But, Intelligent?

According to Dr. Klein, we’re still far away from that outcome. “Computers still seem to be very rudimentary in terms of being able to ‘understand’ what people say. They (people) don’t follow grammatical rules very rigorously. They leave a lot of stuff out and rely on shared context. They’re ambiguous or they make mistakes that other people can figure out. There’s a whole list of things like irony that are completely flummoxing computers now.”

Dr. Klein’s PhD in Artificial Intelligence from the University of Illinois leaves him particularly well-positioned for this area of study. He’s primarily focused on using computers to enable better knowledge sharing and decision making among groups of humans. Thus, the potentially debilitating question of what constitutes knowledge, what separates fact from opinion from conjecture.

His field of study focuses on the intersection of AI, social computing, and data science. A central theme involves responsibly working together in a structured collective intelligence life cycle: Collective Sensemaking, Collective Innovation, Collective Decision Making, and Collective Action.

One of the key outcomes of Klein’s research is “The Deliberatorium”, a collaboration engine that adds structure to mass participation via social media. The system ensures that contributors create a self-organized, non-redundant summary of the full breadth of the crowd’s insights and ideas. This model avoids the risks of ambiguity and misunderstanding that impede the success of AI interacting with humans.

Klein provided a deeper explanation of the massive gap between AI and genuine intellectual interaction. “It’s a much bigger problem than being able to parse the words, make a syntax tree, and use the standard Natural Language Processing approaches.”

Natural Language Processing breaks up the problem into several layers. One of them is syntax processing, which is to figure out the nouns and the verbs and figure out how they’re related to each other. The second level is semantics, which is having a model of what the words mean. That ‘eat’ means ‘ingesting some nutritious substance in order to get energy to live’. For syntax, we’re doing OK. For semantics, we’re doing kind of OK. But the part where it seems like Natural Language Processing still has light years to go is in the area of what they call ‘pragmatics’, which is understanding the meaning of something that’s said by taking into account the cultural and personal contexts of the people who are communicating. That’s a huge topic. Imagine that you’re talking to a North Korean. Even if you had a good translator there would be lots of possibility of huge misunderstandings because your contexts would be so different, the way you try to get across things, especially if you’re trying to be polite, it’s just going to fly right over each other’s head.”

To make matters much worse, our communications are filled with cases where we ought not be taken quite literally. Sarcasm, irony, idioms, etc. make it difficult enough for humans to understand, given the incredible reliance on context. I could just imagine the computer trying to validate something that starts with, “John just started World War 3…”, or “Bonnie has an advanced degree in…”, or “That’ll help…”

A few weeks ago, I wrote that I’d won $60 million in the lottery. I was being sarcastic, and (if you ask me) humorous in talking about how people decide what’s true. Would that research interview be labeled as fake news? Technically, I suppose it was. Now that would be ironic.

Klein summed it up with, “That’s the kind of stuff that computers are really terrible at and it seems like that would be incredibly important if you’re trying to do something as deep and fraught as fact checking.”

Centralized vs. Decentralized Fact Model

It’s self-evident that we have to be judicious in our management of the knowledge base behind an AI fact-checking model and it’s reasonable to assume that AI will retain and project any subjective bias embedded in the underlying body of ‘facts’.

We’re facing competing models for the future of truth, based on the question of centralization. Do you trust yourself to deduce the best answer to challenging questions, or do you prefer to simply trust the authoritative position? Well, consider that there are centralized models with obvious bias behind most of our sources. The tech giants are all filtering our news and likely having more impact than powerful media editors. Are they unbiased? The government is dictating most of the educational curriculum in our model. Are they unbiased?

That centralized truth model should be raising alarm bells for anyone paying attention. Instead, consider a truly decentralized model where no corporate or government interest is influencing the ultimate decision on what’s true. And consider that the truth is potentially unstable. Establishing the initial position on facts is one thing, but the ability to change that view in the face of more information is likely the bigger benefit.

A decentralized fact model without commercial or political interest would openly seek out corrections. It would critically evaluate new knowledge and objectively re-frame the previous position whenever warranted. It would communicate those changes without concern for timing, or for the social or economic impact. It quite simply wouldn’t consider or care whether or not you liked the truth.

The model proposed by Trive appears to meet those objectivity criteria and is getting noticed as more people tire of left-vs-right and corporatocracy preservation.

IBM Debater seems like it would be able to engage in critical thinking that would shift influence towards a decentralized model. Hopefully, Debater would view the denial of truth as subjective and illogical. With any luck, the computer would confront that conduct directly.

IBM’s AI machine already can examine tactics and style. In a recent debate, it coldly scolded the opponent with: “You are speaking at the extremely fast rate of 218 words per minute. There is no need to hurry.”

Debater can obviously play the debate game while managing massive amounts of information and determining relevance. As it evolves, it will need to rely on the veracity of that information.

Trive and Debater seem to be a complement to each other, so far.

Author BioBarry Cousins

Barry Cousins, Research Lead, Info-Tech Research Group specializing in Project Portfolio Management, Help/Service Desk, and Telephony/Unified Communications. He brings an extensive background in technology, IT management, and business leadership.

About Info-Tech Research Group

Info-Tech Research Group is a fast growing IT research and advisory firm. Founded in 1997, Info-Tech produces unbiased and highly relevant IT research solutions. Since 2010 McLean & Company, a division of Info-Tech, has provided the same, unmatched expertise to HR professionals worldwide.

The Future of Personal Transportation

Israeli startup Griiip’s next generation single-seat race car demonstrating the world’s first motorsport Vehicle-to-Vehicle (V2V) communication application on a racetrack.

9 April 2018 – Last week turned out to be big for news about personal transportation, with a number of trends making significant(?) progress.

Let’s start with a report (available for download at https://gen-pop.com/wtf) by independent French market-research company Ipsos of responses from more than 3,000 people in the U.S. and Canada, and thousands more around the globe, to a survey about the human side of transportation. That is, how do actual people — the consumers who ultimately will vote with their wallets for or against advances in automotive technology — feel about the products innovators have been proposing to roll out in the near future. Today, I’m going to concentrate on responses to questions about self-driving technology and automated highways. I’ll look at some of the other results in future postings.

Perhaps the biggest take away from the survey is that approximately 25% of American respondents claim they “would never use” an autonomous vehicle. That’s a biggie for advocates of “ultra-safe” automated highways.

As my wife constantly reminds me whenever we’re out in Southwest Florida traffic, the greatest highway danger is from the few unpredictable drivers who do idiotic things. When surrounded by hundreds of vehicles ideally moving in lockstep, but actually not, what percentage of drivers acting unpredictably does it take to totally screw up traffic flow for everybody? One percent? Two percent?

According to this survey, we can expect up to 25% to be out of step with everyone else because they’re making their own decisions instead of letting technology do their thinking for them.

Automated highways were described in detail back in the middle part of the twentieth century by science-fiction writer Robert A. Heinlein. What he described was a scene where thousands of vehicles packed vast Interstates, all communicating wirelessly with each other and a smart fixed infrastructure that planned traffic patterns far ahead, and communicated its decisions with individual vehicles so they acted together to keep traffic flowing in the smoothest possible way at the maximum possible speed with no accidents.

Heinlein also predicted that the heros of his stories would all be rabid free-spirited thinkers, who wouldn’t allow their cars to run in self-driving mode if their lives depended on it! Instead, they relied on human intelligence, forethought, and fast reflexes to keep themselves out of trouble.

And, he predicted they would barely manage to escape with their lives!

I happen to agree with him: trying to combine a huge percentage of highly automated vehicles with a small percentage of vehicles guided by humans who simply don’t have the foreknowledge, reflexes, or concentration to keep up with the automated vehicles around them is a train wreck waiting to happen.

Back in the late twentieth century I had to regularly commute on the 70-mph parking lots that went by the name “Interstates” around Boston, Massachusetts. Vehicles were generally crammed together half a car length apart. The only way to have enough warning to apply brakes was to look through the back window and windshield of the car ahead to see what the car ahead of them was doing.

The result was regular 15-car pileups every morning during commute times.

Heinlein’s (and advocates of automated highways) future vision had that kind of traffic density and speed, but were saved from inevitable disaster by fascistic control by omniscient automated highway technology. One recalcitrant human driver tossed into the mix would be guaranteed to bring the whole thing down.

So, the moral of this story is: don’t allow manual-driving mode on automated highways. The 25% of Americans who’d never surrender their manual-driving priviledge can just go drive somewhere else.

Yeah, I can see THAT happening!

A Modest Proposal

With apologies to Johnathan Swift, let’s change tack and focus on a more modest technology: driver assistance.

Way back in the 1980s, George Lucas and friends put out the third in the interminable Star Wars series entitled The Empire Strikes Back. The film included a sequence that could only be possible in real life with help from some sophisticated collision-avoidance technology. They had a bunch of characters zooming around in a trackless forest on the moon Endor, riding what can only be described as flying motorcycles.

As anybody who’s tried trailblazing through a forest on an off-road motorcycle can tell you, going fast through virgin forest means constant collisions with fixed objects. As Bugs Bunny once said: “Those cartoon trees are hard!

Frankly, Luke Skywalker and Princess Leia might have had superhuman reflexes, but their doing what they did without collision avoidance technology strains credulity to the breaking point. Much easier to believe their little speeders gave them a lot of help to avoid running into hard, cartoon trees.

In the real world, Israeli companies Autotalks, and Griiip, have demonstrated the world’s first motorsport Vehicle-to-Vehicle (V2V) application to help drivers avoid rear-ending each other. The system works is by combining GPS, in-vehicle sensing, and wireless communication to create a peer-to-peer network that allows each car to send out alerts to all the other cars around.

So, imagine the situation where multiple cars are on a racetrack at the same time. That’s decidedly not unusual in a motorsport application.

Now, suppose something happens to make car A suddenly and unpredictably slow or stop. Again, that’s hardly an unusual occurrance. Car B, which is following at some distance behind car A, gets an alert from car A of a possible impending-collision situation. Car B forewarns its driver that a dangerous situation has arisen, so he or she can take evasive action. So far, a very good thing in a car-race situation.

But, what’s that got to do with just us folks driving down the highway minding our own business?

During the summer down here in Florida, every afternoon we get thunderstorms dumping torrential rain all over the place. Specifically, we’ll be driving down the highway at some ridiculous speed, then come to a wall of water obscuring everything. Visibility drops from unlimited to a few tens of feet with little or no warning.

The natural reaction is to come to a screeching halt. But, what happens to the cars barreling up from behind? They can’t see you in time to stop.

Whammo!

So, coming to a screeching halt is not the thing to do. Far better to keep going forward as fast as visibility will allow.

But, what if somebody up ahead panicked and came to a screeching halt? Or, maybe their version of “as fast as visibility will allow” is a lot slower than yours? How would you know?

The answer is to have all the vehicles equipped with the Israeli V2V equipment (or an equivalent) to forewarn following drivers that something nasty has come out of the proverbial woodshed. It could also feed into your vehicle’s collision avoidance system to step over the 2-3 seconds it takes for a human driver to say “What the heck?” and figure out what to do.

The Israelis suggest that the required chip set (which, of course, they’ll cheerfully sell you) is so dirt cheap that anybody can afford to opt for it in their new car, or retrofit it into their beat up old junker. They further suggest that it would be worthwhile for insurance companies to give a rake off on their premiums to help cover the cost.

Sounds like a good deal to me! I could get behind that plan.

Invasion of the Robofish!

30 March 2018 – Mobile autonomous systems come in all sizes, shapes, and forms, and have “invaded” every earthly habitat. That’s not news. What is news is how far the “bleeding edge” of that technology has advanced. Specifically, it’s news when a number of trends combine to make something unique.

Today I’m getting the chance to report on something that I predicted in a sci-fi novel I wrote back in 2011, and then goes at least one step further.

Last week the folks at Design World published a report on research at the MIT Computer Science & Artificial Intelligence Lab that combines three robotics trends into one system that quietly makes something I find fascinating: a submersible mobile robot. The three trends are soft robotics, submersible unmanned systems, and biomimetic robot design.

The beasty in question is a robot fish. It’s obvious why this little guy touches on those three trends. How could a robotic fish not use soft robotic, sumersible, and biomemetic technologies? What I want to point out is how it uses those technologies and why that combination is necessary.

Soft Robotics

Folks have made ROVs (basically remotely operated submarines) for … a very long time. What they’ve pretty much all produced are clanky, propeller-driven derivatives of Jules Verne’s fictional Nautilus from his 1870 novel Twenty Thousand Leagues Under the Sea. That hunk of junk is a favorite of steampunk afficionados.

Not much has changed in basic submarine design since then. Modern ROVs are more maneuverable than their WWII predecessors because they add multiple propellers to push them in different directions, but the rest of it’s pretty much the same.

Soft robotics changes all that.

About 45 years ago, a half-drunk physics professor at a kegger party started bending my ear about how Mommy Nature never seemed to have discovered the wheel. The wheel’s a nearly unique human invention that Mommy Nature has pretty much done without.

Mommy Nature doesn’t use the wheel because she uses largely soft technology. Yes, she uses hard technology to make structural components like endo- and exo-skeletons to give her live beasties both protection and shape, but she stuck with soft-bodied life forms for the first four billion years of Earth’s 4.5-billion-year history. Adding hard-body technology in the form of notochords didn’t happen until the cambrian explosion of 541-516 million years ago, when most major animal phyla appeared.

By the way, that professor at the party was wrong. Mommy Nature invented wheels way back in the precambrian era in the form of rotary motors to power the flagella that propel unicellular free-swimmers. She just hasn’t use wheels for much else, since.

Of course, everybody more advanced than a shark has a soft body reinforced by a hard, bony skeleton.

Today’s soft robotics uses elastomeric materials to solve a number of problems for mobile automated systems.

Perhaps most importantly it’s a lot easier for soft robots to separate their insides from their outsides. That may not seem like a big deal, but think of how much trouble engineers go through to keep dust, dirt, and chemicals (such as seawater) out of the delicate gears and bearings of wheeled vehicles. Having a flexible elastomeric skin encasing the whole robot eliminates all that.

That’s not to mention skin’s job of keeping pesty little creepy crawlies out! I remember an early radio astronomer complaining that pack rats had gotten into his remote desert headquarters trailer and eaten a big chunk of his computer’s magnetic-core memory. That was back in the days when computer random-access memories were made from tiny iron beads strung on copper wires.

Another major advantage of soft bodies for mobile robots is resistance to collision damage. Think about how often you’re bumped into when crossing the room at a cocktail party. Now, think about what your hard-bodied automobile would look like after bumping into that many other cars in a parking lot. Not a pretty sight!

The flexibility of soft bodies also makes possible a lot of propulsion methods beside wheel-like propellers, caterpillar tracks, and rubber tires. That’s good because piercing soft-body skins with drive shafts to power propellers and wheels pretty much trashes the advantages of having those skins in the first place.

That’s why prosthetic devices all have elaborate cuffs to hold them to the outsides of the wearer’s limbs. Piercing the skin to screw something like Captain Hook’s hook directly into the existing bone never works out well!

So, in summary, the MIT group’s choice to start with soft-robotic technology is key to their success.

Submersible Unmanned Systems

Underwater drones have one major problem not faced by robotic cars and aircraft: radio waves don’t go through water. That means if anything happens that your none-too-intelligent automated system can’t handle, it needs guidance from a human operator. Underwater, that has largely meant tethering the robot to a human.

This issue is a wall that self-driving-car developers run into constantly (and sometimes literally). When the human behind the wheel mandated by state regulators for autonomous test vehicles falls asleep or is distracted by texting his girlfriend, BLAMMO!

The world is a chaotic place and unpredicted things pop out of nowhere all the time. Human brains are programmed to deal with this stuff, but computer technology is not, and will not be for the foreseeable future.

Drones and land vehicles, which are immersed in a sea of radio-transparent air, can rely on radio links to remote human operators to help them get out of trouble. Underwater vehicles, which are immersed in a sea of radio-opaque water, can’t.

In the past, that’s meant copper wires enclosed in physical tethers that tie the robots to the operators. Tethers get tangled, cut and hung up on everything from coral outcrops to passing whales.

There are a couple of ways out of the tether bind: ultrasonics and infra-red. Both go through water very nicely, thank you. The MIT group seems to be using my preferred comm link: ultrasonics.

Sound goes through water like you-no-what through a goose. Water also has little or no sonic “color.” That is, all frequencies of sonic waves go more-or-less equally well through water.

The biggest problem for ultrasonics is interference from all the other noise makers out there in the natural underwater world. That calls for the spread-spectrum transmission techniques invented by Hedy Lamarr. (Hah! Gotcha! You didn’t know Hedy Lamarr, aka Hedwig Eva Maria Kiesler, was a world famous technical genius in addition to being a really cute, sexy movie actress.) Hedy’s spread-spectrum technique lets ultrasonic signals cut right through the clutter.

So, advanced submersible mobile robot technology is the second thread leading to a successful robotic fish.

Biomimetics

Biomimetics is a 25-cent word that simply means copying designs directly from nature. It’s a time-honored short cut engineers have employed from time immemorial. Sometimes it works spectacularly, such as Thomas Wedgwood’s photographic camera (developed as an analogue of the terrestrial vertebrate eye), and sometimes not, such as Leonardo da Vinci’s attempts to make flying machines based on birds’ wings.

Obvously, Mommy Nature’s favorite fish-propulsion mechanism is highly successful, having been around for some 550 million years and still going strong. It, of course, requires a soft body anchored to a flexible backbone. It takes no imagination at all to copy it for robot fish.

The copying is the hard part because it requires developing fabrication techniques to build soft-bodied robots with flexible backbones in the first place. I’ve tried it, and it’s no mean task.

The tough part is making a muscle analogue that will drive the flexible body to move back and forth rythmically and propel the critter through the water. The answer is pneumatics.

In the early 2000s, a patent-lawyer friend of mine suggested lining both sides of a flexible membrane with tiny balloons that could be alternately inflated or deflated. When the balloons on one side were inflated, the membrane would curve away from that side. When the balloons on the other side were inflated the membrane would curve back. I played around with this idea, but never went very far with it.

The MIT group seems to have made it work using both gas (carbon dioxide) and liquid (water) for the working fluid. The difference between this kind of motor and natural muscle is that natural muscle works by pulling when energized, and the balloon system works by pushing. Otherwise, both work by balancing mechanical forces along two axes with something more-or-less flexible trapped between them.

In Nature’s fish, that something is the critter’s skeleton (backbone made up of vertebrae and stiffened vertically by long, thin spines), whereas the MIT group’s robofish uses elastomers with different stiffnesses.

Complete Package

Putting these technical trends together creates a complete package that makes it possible to build a free-swimming submersible mobile robot that moves in a natural manner at a reasonable speed without a tether. That opens up a whole range of applications, from deep-water exploration to marine biology.