Primitive mobile life forms, and the mobile cells of higher animals,
derive their motivation and direct their navigation by chemical senses. In the
simplest cases these creatures are hard-wired to swim toward nutrient
concentration gradients, and to swim against irritant concentration gradients.
The vestiges in humans of the sometimes extremely sensitive, selective, and
differential chemical senses of primitive forms are taste, our ability to
detect and identify four classes of chemicals in water solution on our tongues,
and smell or olfaction, our ability to detect and identify many gases, vapors,
and complex mixtures in the air passing through our noses.
Primitive "autonomous" mobile robots derive their motivation
from human directive, and direct their navigation largely via artificial visual
and acoustic senses. Their autonomy is thus in the discretion they are allowed
in planning and executing getting from here to there; the heres and the theres generally
are dictated more-or-less explicitly by humans. An evolutionary path for
sophistication of the navigational abilities of mobile robots looks
self-evident, and in fact such evolution is well underway.
On the other hand an evolutionary path along which mobile robots might
even begin to progress toward replacing motivation by human fiat with self
directed motivation is difficult to conceive, at least if the senses that drive
motivation are to remain as complex as vision and hearing.
But self directed motivation of mobile robots based on one or more
chemical senses is easy to imagine, and indeed is probably within the
capability of existing technology. For example, we could build right now a
mobile robot that would meander around a chemical plant, sniffing as it goes
for gas leaks (or the vapors of liquid leaks), navigating toward them while
avoiding hazards visually, yet always motivated as to overall direction (an
undetermined endpoint notwithstanding) by the chemical concentration gradient,
with end-point navigation perhaps directed to the offending pipe fissure or
open valve by acoustic homing toward the source of the hiss, and effecting
simple repairs or summoning human assistance (with appropriate hazard warning)
based on the fusion of chemical,visual, and acoustic sensory information in
context. Similar scenarios can easily be imagined for firefighting robots,
prospecting robots, rescue robots, contraband nterception robots, and others.
Toward this end, my colleagues and I have been studying chemical sensing,
not with the traditional goal of inventing alternative or improved instruments
for doing precise chemical analysis, but rather from the perspective of
exploring alternative approaches to an artificial sense of smell to motivate autonomous
activity.
First, I describe the characteristics of metal oxide semiconductor gas sensors used as gas sensors, particularly for combustibles. Response, as a
change in resistance, depends on combustible gas identity and concentration,
sensor temperature, and also on the concentrations of other gases and vapors
present,i.e.,response to a mixture is not a linear superposition of responses
to the individual components. These sensors inherently respond to broad classes
of compounds, but differential responses conducive to signature identification
can be induced by variation of parameters such as temperature and catalyst
nature and concentration. I then survey the olfactory system, in contrast with
other biological sensory and sensor interpretation systems, in analogy with MOS
gas sensors, and in analogy between its neurological organization and the
organization of simple artificial neural networks.
I then describe the class of large area thick film MOS resistors that we
have constructed with a spatial gradient in relative sensitivity induced by
differential heating. Multiple resistance measurements along the temperature
gradient yield a multi-feature signature that we analyze and interpret using
artificial neural networks. In particular, I contrast the approach that
emphasizes binary or nearly binary unit activities, and thus forces every
mixture into a class distinct from the classes of its single species
components, with an approach that is less rigidly binary, encouraging creation
of only enough classes to account for the individual species present, and
putting mixtures into multiple classes in proportion to the concentration of
the mixture components in each class.
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