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, 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.
 Norbert Wiener, The mathematics of self-organizing systems. Recent developments in information and decision processes, Macmillan, N. Y., 1962.
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