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Immobile Robots
Roland Piquepaille writes "Wade Roush wrote a long and well-documented article for the Technology Review about this new concept, the immobot, short for "immobile robot." He gives different industrial examples, from NASA to the water utility in Porto Alegre, and from Toyota cars to some new Xerox photocopiers. And he looks at the programming model behind the immobots. No "heuristic" programs here, but model-based programs instead. Check this column for details." The original article has more information.
What was Gutenberg's press? A presentient nonmobile robot?
C'mon people...
about 8 or so years ago i had a photocopier which was able to diagnose its own problems.
isn't this the same thing?
Imobile robots ... sounds alot like some of the computer obsessed people i know. Doesn't thinkgeek have a fridge for your desk now? I mean, we don't even have to get up to get beer anymore...
I think most of the posters so far are missing the big point. Even having a computer control a function at a water plant still requires a fairly large degree of human supervision. Someone has to make sure the computer is working properly, make sure the control programs don't screw up, and if the programs do screw up, they must fix them. The idea behind immobots seems to take it a bit further than that by actually reducing the amount of control neccessary. I suppose you could say it's just a computer, but then couldn't all things effectivly be reduced to computers (our brains, for exmaple) which certain things in common and certain things different.
The point of this article was that the immobots require almost no human control, whereas a computer still requires a human. Or something like that. Just my 1/2000000000000th of Bill's (estimated) fortune worth.
Posting as directed.
Using its engineering knowledge, the robot tried to repair the switch by toggling it on and off.
Isn't this like saying "Using my engineering knowledge, I tried to repair the toilet by jiggling the handle." I'd hardly call brute-force "engineering knowledge."
As you can probably see, a lot of these things don't even need IC's to do their job. This article, IMHO, is like saying the wheel was a great invention.
I'm the Devil the Windows users warned you about.
Definition of Robot: noun: 1. A mechanical device that sometimes resembles a human being and is capable of performing a variety of often complex human tasks on command or by being programmed in advance. 2. A machine or device that operates automatically or by remote control. 3. A person who works mechanically without original thought, especially one who responds automatically to the commands of others. ETYMOLOGY: Czech., from robota, drudgery. Compulsory labors. * The foundation of the term robot as it is used today can be found in a science fiction book and a play written by Karel Capek. The book is titled Valka s Mloky (War with the Newts) and the play is titled R.U.R (Rozuma Univerzalni Roboti) [rozum means wisdom] (Rozum's Universal Robots).
:)
Technically anything that is compulsory labour is a robot, as long as it is artifical. So is a clone a robot? Anyhow, photocopies come under definition 2. Just though i'd drop the definition in
--+> Life, is there any?
Still, we're talking about a basically good idea that needs to be pried out of the hands of the control theorists and used more. As a discipline, control theory has a theorist problem - too many of the people in the field develop pretty theory unusable in the real world. (A friend of mine on the Harvard faculty says that control theorists are failed mathematicians.) As a rule of thumb, if you pick up a book on control theory and it starts out by proving theorems, it's useless. If it starts out with "and here's how we did it in the F-15", things are looking good. Linear control theory leads to beautiful mathematics in the frequency and phase domains, based on Laplace and Z-transforms. Unfortunately, that approach doesn't get you very far for nonlinear systems or systems with multiple states. It's useful mostly for systems that maintain some steady state, for which simple PID controllers tend to be good enough.
Conceptually, model-based control is simple and elegant. You have some mathematical model of the system, which includes some number of inputs, some of which you can set (the control variables), and some of which you can only observe (the disturbance variables). You also have some number of outputs which you wish to control. The idea is to use the model in reverse to figure out useful values for the control variables, given the desired outputs and the disturbance inputs.
For models made up of certain standard components (linear elements like integrators, summers, multipliers, and certain filters) there's an analytical method for solving the model for its inputs. All the heavy work is done offline; the control system itself only has the canned solution to the problem, and a small microcontroller can do the job.
For more complex models, the analytical solution breaks down. Now, the entire solution has to be computed in real time, and the complexity of the controller software becomes much greater. It requires at least a nonlinear equation solver, and if the system has discrite states, some means of exploring the state space as well. These are non-trivial pieces of software.
This isn't a happy thing if you're running something important. The stability behavior of controllers created by offline analysis is predictable. Getting well-behaved controllers for systems that can't be analyzed analytically is tougher.
One thing you have going for you is that you can run the model whenever you want, and use it to check the proposed control solution. Model-based control lets you can ask "what if" of before you do it. You can have a dumb system that does sanity checks on the complex system. As long as you have an backup control strategy (often an emergency shutdown) for when the complex solver isn't generating good answers, there's hope of building safe systems this way.
Model based control can be made "adaptive". You build a model with some tuning parameters, and let it watch the "plant" (the system being controlled) for a while, with some other (either simpler, manual, or an older model) controller running things. The tuner then tries to tweak the model to make it match the plant. Only when the model is tracking the plant reliably is it given control. AI people call this "training"; control engineers, less inclined towards anthromorphism, call it "self-tuning".
Any time the model in control isn't tracking the plant's outputs, that's an indication that something has gone wrong. So you have a clear indication of when things are broken. Model based control is thus able to detect fairly early when it isn't working, unlike most simpler control systems.
So that's model-based control. It works for some problems. Black boxes in avionics bays of high-performance aircraft use it in flight every day. It's about to filter down to the masses, probably accompanied by the sort of hype that came with "fuzzy logic control".
Control theory is more of a traditional engineering discipline, studied by electrical, mechanical, and industrial engineers. It takes a strong math background: calculus, linear and nonlinear equations, tensors, Laplace and Z transforms. There isn't yet "Control Systems for Dummies", although some friends of mine are trying to change that by writing a controls curriculum, accompanied by a parts kit, for bright high-school students.
The path to low-level AI (moving around, not bumping into stuff, not falling down) may lie in the region between model-based control and machine learning. That region is now open for business, due to cheap compute power. Control systems used to be powered by computers with well under 1 MIPS; most of them still are. With cheap gigaflops available, approaches that were once far out of reach can be used. Real-time stereo vision finally works, and is about to get cheap. Stability enhancement systems for cars are quite impressive today. Self-balancing machines, from the Segway to the Asimo, are showing up as products.
Mobile robots, which have been sluggish machines for decades, typically have rather low-performance control systems. The DARPA LA to Las Vegas robot race may change that.