Sooner or later, much of the research of systems
theory and complexity arrives at the topic of self-organization, the
spontaneous local interaction between elements of an initially disordered
system as analyzed by Ilya Prigogine. This is so for a variety
of reasons: first off, self-organization, if statistically significant and
meaningfully predictable, may be superior to organization by command because,
aside from factors influencing its design, it does not require
instruction, supervision or enforcement – the organizer may spare himself
critical components of due diligence (and therefore potentially resulting liability)
which, in and of itself, can amount to a rather significant difference in cost-efficiency
for any purpose-oriented organization.
Self-organization has been receiving much
attention since the dawn of intelligent observation of swarms of fish, birds,
anthills, beehives and – with increasingly obvious similarities – human
behavior in cities. Later, a thermodynamic view of the phenomenon prevailed
over the initial cybernetics approach. Based on initial observations on the
mathematics of self-organizing systems by Norbert Wiener,[1] they follow
algorithms relying on sensor data, interacting with neighbors, looking for
patterns. Such pattern-oriented behavior makes the swarm as a whole much more
resilient in self-assembling structures that, given a certain numerical
threshold and degree of complexity, may not be able to be destroyed by almost
any influence.
This is also where robotics approximates nature
and the observations of aspects of “swarm intelligence” in cells,
social insects
and higher-developed species of social animals like birds and fish. Swarm intelligence is the collective behavior of decentralized, self-organized
systems.
Collective behavior comprises both spontaneous
social processes and events not reflective of existing social structure. It is a phenomenon of almost
every complex adaptive system.
Swarm robotics research typically operates “in silico” rather than “in vivo,” because digital simulation of
robots saves manufacturing costs but incurs the costs and vagaries of flaws in
the simulation design. Only in a few cases has the robot count in physical
experiments reached three digits. Still, at a cost of $14 apiece, a swarm of 1024, even with some spares, does not even reach
$15,000.
A team around Radhika Nagpal at Harvard’s Self-Organizing Systems Research Group of the Wyss Biologically Inspired Engineering Institute recently conducted an unusual
experiment with one of the largest physically congregated swarms of robots they
call “kilobots” because they made, well, “a kilo” or 1024 of them.
Kilobots communicate by infrared signal and move around by vibrating
metal leg according to a set of relatively simple algorithmic rules based on
merely three capabilities and three supplemental strategies.
The striking similarity of forms and patterns created through
self-assembly suggests that nature relies on concepts that are very similar and
also very robust, as the videoclip of self-organizing robots shows,
because interaction with their neighbors plus detection of patterns of sensor
data renders the swarm overall considerably more resilient.
Publishing
their results in Science, the
authors note that the ability of a virtually unlimited number of elements to
self-assemble into a broad array of forms solely by means of local interaction
motivates exploration of further advanced collective algorithms “capable of
detecting malfunctioning robots and recovering from large-scale external
damages, as well as new robot designs that, like army ants, can physically
attach to each other to form stable self-assemblages.”
The
evolution of nanorobotics and molecular robotics, on the other hand, is
practically unimaginable without a very robust and advanced foundation of
self-organization and complex collective behavior. It has been suggested that
nanorobotics be based on the concept of Open Technology, similar to Open Source in software development, as a
common heritage of mankind for future generations, especially in light of the
fact that a “nanorobotics race” is beginning to evolve along the patterns established previously in
the space race or nuclear arms race, and a large number of patents have been
granted recently on nanorobots that can be characterized as pre-emptive patent trolling in a quest for monopolistic dominance of emerging technologies
by large corporations especially in nanomedicine, but also in banking and
finance.
Therefore it seems that, while nature itself
continues not to be patentable as a matter of principle, our understanding of
it has conclusively entered a very slippery slope toward full-scale
commercialization that probably can be halted only by a breakthrough of concepts
represented by Open Technology in the public interest. Just as no one has
suggested that Open Source would terminate the profitability of investments in
software development overall, nor that all software be developed on an Open
Source basis, the significance for the speed of progress of Open Technology
taken as a whole in advanced emerging technologies appears to be very difficult
to overestimate.
[1] Norbert Wiener, The mathematics of self-organizing systems.
Recent developments in information and decision processes, Macmillan, N.
Y., 1962.
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