Many articles have appeared on this site regarding robots that mimic some natural entity. These include robots that are modeled after insects, worms, and tuna.
Since many of these naturally occurring forms have adapted to particular situations over time, this seems like a good way to go. While these systems adapt the shape and movement of natural systems, there is another area in which reference to the natural world is of use. That area is control systems.
Control systems have evolved over time from fixed value controllers to adaptive control systems. A recent article in the Proceedings of the IEEE December 2012, entitled "Cognitive Control" by Haykin, Fatemi, Setoodeh, and Xue, details the methods of cognitive control and gives examples. As with many of the systems modeled on the natural world, they were studied first in areas other than engineering.
For cognitive control that includes neuroscience and psychology, control systems have evolved from open-loop and PID controllers to more adaptive systems. One of the drivers, of course, is the availability of the computing power to realize these systems. Early control systems were implemented in analog devices. I have seen, as a previous generation, spacecraft and simulator controllers that were based on analog technology. While they could be fast, they were not adaptable or easily changed. These weaknesses helped in the push to digital control systems.
As the technology has progressed, providing more computing power and memory, the ability to implement, in a very cost effective way, adaptive and other sophisticated control approaches is now feasible. Adaptive control lets the systems parameters evolve over time, based on actual interaction with the real world. In 1995, I had a car that had an adaptive shifter mechanism. It adjusted the shift points over time based on the driver's style of driving. I never did figure out how to reset it.
With the development of microcontrollers that have extensive processing power and memory, one of the primary requirements of cognitive control can be realized. That is memory. While adaptive control adjusts parameters over time, cognitive control uses memory to implement a reinforcement-learning approach to adapting.
Many microcontrollers today have the memory capabilities to implement this along with signal processing functions implemented in hardware to efficiently implement the learning algorithms. Thus, implementing learning as a part of the control loop is feasible. This allows us to close the information gap, as the article calls it, in a low-power, low-cost controller. This type of capability is becoming possible in areas like the automotive industry yielding much more efficient engines primarily by providing a more sophisticated engine management system.
Thanks for the article, naperlou! Techcruch recently posted an article and video from the folks at the European RoboEarth project describing a system called "Rapyuta". This cloud-based engine collects and stores information that has been gathered by all sorts of robotic sensors which is then shared with other robots and sensors systems -- described as a "Facebook for robots".
Unlink the millions of years of evolution in biological systems, we seem to be on a near-vertical exponential part of the curve when it comes to automated systems.
On your shifting issue, I was told that power cycling the car (disconnect the battery completely for long enough to lose all power everywhere) will make the transmission control lose it's settings. It may shift funny for a while until it re-learns the shift pressures. The shift pressure control valve in those transmissions is a pretty common source of trouble. it is a $50 part that takes $500 worth of time to dig out.
Yes, indeed, I believe we are at a nearly-vertical part of the exponential curve right now, Bill. It's frightening to think of what that will mean for the next century. I don't think we have the cognitive abilities to even imagine that.
Chuck, I share your amazement in our cognitive abilities. We have seen so many examples of our technology extending our ability to create even more advanced technology and that reinforces my optimism. Thomas Edison famously did not select Tungsten as the filament material for his light bulb because we did not have the material processing technology needed to turn this extremely hard refractory metal into a thin filament. The Human Genome project was projected to take 15 years to complete, but due to innovation along the way provided a rough draft in 10 years (exponential yet again).
When I teach physics, we need to review the basics of time, position, motion, force, work, and energy, but it short order are able to have productive discussions of the Large Hadron Collider and the search for the Higgs Boson. Things are definitely arriving at a rapid pace, but thankfully, our mental models are improving right along with them. =]
Thanks for the link, Lou. It's fun to learn more about control systems for the robots I've written about: the tuna, worms and bugs you mention. But robots are getting really sophisticated, and I wonder how long MCUs will be able to keep up.
Ann, one can never be sure, but MCUs are increasing in power as well. The latest trend is to combine MCUs with technologies like FPGAs. This increases their power tremendously by combining the logic processing capability of the MCU with the signal processing capability of the FPGA. The MCUs themselves often have some level of signal processing capability built in as with the ARM M3/4 line. Look for a blog on this topic from me soon.
Are they robots or androids? We're not exactly sure. Each talking, gesturing Geminoid looks exactly like a real individual, starting with their creator, professor Hiroshi Ishiguro of Osaka University in Japan.
Truchard will be presented the award at the 2014 Golden Mousetrap Awards ceremony during the co-located events Pacific Design & Manufacturing, MD&M West, WestPack, PLASTEC West, Electronics West, ATX West, and AeroCon.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.