Robotics & Intelligent Systems FAQ
- What are Robotics and Intelligent Systems?
- How is it relevant to mining engineering?
- Why do we neeed it now?
- Are there potential problems related to this new technology?
courtesy of Automated Mining Systems
1. What is an Intelligent System?
- Why Automate ?
- What is the "AMS, Mine Automation Architecture" ?
- What is Broadband ?
- Why use Coax ?
- What do you mean by...? (a brief glossary of terms)
Many definitions of intelligence exist, but for our purposes we use the following: intelligence is the ability to reach ones objectives.
A system is more intelligent if it reaches its objectives faster and easier. This includes the ability to learn to do this.
The intelligence of a system is a property of its mind. The mind is the functioning of its brain.
A system is part of the universe, with a limited extension in space and time. Stronger or more correlations exist between one
part of the system and another, than between this part of the system and parts outside the system.
An intelligent system is a system that has its own main objective, as well as senses and actuators.
To reach its objective it chooses an action based on its experiences. It can learn by generalizing the experiences
it has stored in its memories. Examples of intelligent systems are: persons, higher animals, robots, extraterrestrials,
a business, a nation.
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2. How is it relevant to mining engineering?
I guess the right question is how is it NOT relevant to mining? Intelligent Systems has various implications throughout the mining industry, the most obvious being
that human beings will no longer have to risk their lives as autonomous machines can begin to do the more dangerous jobs.
Artificial intelligence, in its more developed form, will be able to adapt to an infinite number of situations more quickly than humans
possibly can and therefore will be more effective.
Another notable relevant point in mining is as a rescue robot during a mining collapse. Normally, it would be extremely risky to send
humans down the mine but having a robot go down is a wonderful alternative.
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3. Why do we need it now?
Apart from the fact that intelligent systems will transform the mining industry, there is also a growing trend towards automation
as we enter the machine era. A study carried out by Hatch Associates in Canada for Industry Science & Technology of the Canadian
Federal Government maps out 4 curves showing the evolution of Canadian mining technologies towards Automation & Autonomous Mining.
It's very clear from the graph that during these next few years Autonomous Mining will dramatically increase as the main technology
used in Canadian Mines. Just imagine how much more important is it for companies and individuals to be knowledgeable in Intelligent
Systems in Mining!
For a more in-depth explanation of how Intelligent Systems are and will revolutionize manufacturing,
read this article from the
National Council for Advanced Manufacturing.
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4. Are there potential problems related to artificial technology?
The widespread use of artificial intelligent systems will bring prosperity and wellbeing
to the population of our planet. Intelligent Systems as robots, as intelligent automation and as advisor programs within
computers, will do all the work that we do not wish to do. We will be free of material worries and will be able to enjoy life.
But this is a new "industrial revolution" and the transition from a society based on work to
one based on leisure has to be handled carefully. Widespread unemployment can be avoided by spreading the available
work between all that are willing to work. The method is a reduction of weekly working hours. Finally work per week
will be so low that a different means of income and maintaining purchasing power, has to be found. This may be the
"social dividend". Each citizen would be a shareholder of the state and receive a monthly dividend. The funds for
this would come mainly from the profits of the robotized factories.
Are robots a danger to humanity? A robot with a main objective of pleasing
human beings is of great help, but a robot with a main objective of its own survival is very dangerous.
Since they will be thinking much faster and more accurately than we, they will, for their own purposes
use all available resources and we would be helpless. Such a robot should be illegal and should be
destroyed as soon as detected.
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courtesy of Walter Fritz, New Horizon Press
..:: The New Era of Intelligent Systems ::..
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The following article from NASA is an
excellent introduction into the new era of intelligent systems!
by Edward Rosenfeld
Over the coming decade, robotics will enter more and more into our daily lives and experiences.
Whereas in the past, we might read about robots or see them in news reports, now we trip over robot
dogs and other cyberpets in our homes and use robots to mow the lawn. In the three to five year time
frame, expect to make use of robots that make other robots or simply replicate themselves. Robots are
already searching disaster areas for survivors. One company believes we will soon "wear"
robots. Meantime, Stephen Hawking is but the latest to warn that robots might replace us.
One of the features of the recent International Joint Conference on Artificial Intelligence (IJCAI) was
a competition that pitted teams of robots at the task of searching for human survivors of urban disasters.
Many of the contestants showed real progress in robotic abilities.
Such systems have been under development at a number of institutions, most notably at Carnegie-Mellon
University's Robotics Institute. Robotic search-and-rescue systems developed there have been used in the
past at the site of other kinds of disasters. One example was in a mining collapse, where it was unsafe
for human rescue teams.
As we create robots who can learn and adapt to situations, it is imperative that a higher-level of thinking comes into play.
No longer must something be either black or white (in this case binary which is 0 or 1) but there must also be a set of
values in between. This is where Fuzzy Logic comes into play. Here is an excellent summary of Fuzzy Logic, an essential
aspect of Intelligent Systems:
FUZZY LOGIC - AN INTRODUCTION
by Steven D. Kaehler
1. Where Did Fuzzy Logic Come From?
- Where Did Fuzzy Logic Come From?
- What is Fuzzy Logic?
- How is Fuzzy Logic Different from Conventional Control?
- How Does Fuzzy Logic Work?
The concept of Fuzzy Logic (FL) was conceived by Lotfi Zadeh,
a professor at the University of California at Berkley, and
presented not as a control methodology, but as a way of
processing data by allowing partial set membership rather than
crisp set membership or non-membership. This approach to set
theory was not applied to control systems until the 70's due to
insufficient small-computer capability prior to that time.
Professor Zadeh reasoned that people do not require precise,
numerical information input, and yet they are capable of highly
adaptive control. If feedback controllers could be programmed to
accept noisy, imprecise input, they would be much more effective
and perhaps easier to implement. Unfortunately, U.S.
manufacturers have not been so quick to embrace this technology
while the Europeans and Japanese have been aggressively building
real products around it.
2. What is Fuzzy Logic?
In this context, FL is a problem-solving control system
methodology that lends itself to implementation in systems
ranging from simple, small, embedded micro-controllers to large,
networked, multi-channel PC or workstation-based data acquisition
and control systems. It can be implemented in hardware, software,
or a combination of both. FL provides a simple way to arrive at a
definite conclusion based upon vague, ambiguous, imprecise,
noisy, or missing input information. FL's approach to control
problems mimics how a person would make decisions, only much
3. How is Fuzzy Logic Different from Conventional Control?
FL incorporates a simple, rule-based IF X AND Y THEN Z
approach to a solving control problem rather than attempting to
model a system mathematically. The FL model is empirically-based,
relying on an operator's experience rather than their technical
understanding of the system. For example, rather than dealing
with temperature control in terms such as "SP =500F",
"T <1000F", or "210C <TEMP <220C",
terms like "IF (process is too cool) AND (process is getting
colder) THEN (add heat to the process)" or "IF (process
is too hot) AND (process is heating rapidly) THEN (cool the
process quickly)" are used. These terms are imprecise and
yet very descriptive of what must actually happen. Consider what
you do in the shower if the temperature is too cold: you will
make the water comfortable very quickly with little trouble. FL
is capable of mimicking this type of behavior but at very high
4. How Does Fuzzy Logic Work?
FL requires some numerical parameters in order to operate such
as what is considered significant error and significant
rate-of-change-of-error, but exact values of these numbers are
usually not critical unless very responsive performance is
required in which case empirical tuning would determine them. For
example, a simple temperature control system could use a single
temperature feedback sensor whose data is subtracted from the
command signal to compute "error" and then
time-differentiated to yield the error slope or
rate-of-change-of-error, hereafter called "error-dot".
Error might have units of degs F and a small error considered to
be 2F while a large error is 5F. The "error-dot" might
then have units of degs/min with a small error-dot being 5F/min
and a large one being 15F/min. These values don't have to be
symmetrical and can be "tweaked" once the system is
operating in order to optimize performance. Generally, FL is so
forgiving that the system will probably work the first time
without any tweaking.
FL was conceived as a better method for sorting and handling
data but has proven to be a excellent choice for many control
system applications since it mimics human control logic. It can
be built into anything from small, hand-held products to large
computerized process control systems. It uses an imprecise but
very descriptive language to deal with input data more like a
human operator. It is very robust and forgiving of operator and
data input and often works when first implemented with little or
back to fuzzy logic
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courtesy of Steven D. Kaehler, Seattle Robotics