US 20080269948A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2008/0269948 A1 Solomon (43) Pub. Date: Oct. 30, 2008

(54) HYBRID CONTROL SYSTEM FOR ?led on Apr. 16, 2007, provisional application No. COLLECTIVES OF EVOLVABLE 60/941,600, ?led on Jun. 1, 2007, provisional applica NANOROBOTS AND MICROROBOTS tion No. 60/958,466, ?led on Jul. 7, 2007.

(75) Inventor: Neal Solomon, Oakland, CA (US) Publication Classi?cation Correspondence Address: (51) Int. Cl. Neal Solomon G06F 19/00 (2006.01) PO Box 21297 Oakland, CA 94620 (US) (52) U.S. Cl...... 700/245; 901/50 (73) Assignee: Solomon Research LLC, Oakland, (57) ABSTRACT CA (US) A system is described for the organization and self-assembly (21) Appl. No.: 11/985,050 of collectives of nanorobots (CNRs) and microrobots using nano evolvable hardware (N -EHW) mechanisms for biologi (22) Filed: Nov. 13, 2007 cal and electronics applications. CNRs combine to organize into complex geometrical structures and reaggregate their Related US. Application Data structural con?gurations in real time as they adapt to the (60) Provisional application No. 60/ 865,605, ?led on Nov. feedback of evolving environmental conditions to solve com 13, 2006, provisional application No. 60/912,133, plex optimization problems.

CNR group organizes in speci?c initial spatial 1000 con?guration

CNR seeks solution to optimization problem 1010

CNR commences process of restructuring its combined 1020 spatial con?guration

CNR continues to adapt to environmental changes as it solves optimization problems 1030 by adapting its geometrical configuration

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CNR group organizes in speci?c initial spatial 1000 con?guration l CNR seeks solution to optimization problem 1010 l CNR commences process of restructuring its combined 1020 spatial con?guration l CNR continues to adapt to environmental changes as it solves optimization problems 1030 by adapting its geometrical configuration l CNR is organized into speci?c geometric configuration . 1040 Patent Application Publication Oct. 30, 2008 Sheet 7 0f 8 US 2008/0269948 A1

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1100 Patent Application Publication Oct. 30, 2008 Sheet 8 0f 8 US 2008/0269948 A1

FIG. 12

Nanorobots in a collective communicate with each other to activate a strategy to 1200 achieve task i Speci?c-function nanorobots are activated to organize into a speci?c 1210 geometric con?guration l Collective of nanorobots autono mously organize to con?gure into 1220 speci?c geometrical structure 1 CNRs are programmed to change positions at speci?c times 1230

FIG. 13_

CNRs access metaheuristics \ 1310 systems \ 1300 1340 1320 1330 I I / | Local Search Swarm intelligence Arti?cial Immune ' Genetic metaheuristics metaheuristics systems Algorithms - Tabu search - Ant colony optimization - Scatter search - Particle swarm - Adaptive memory optimization programming - Stochastic diffusion search

Hybrid metaheurlstics \\\\__ 1350 US 2008/0269948 A1 Oct. 30, 2008

HYBRID CONTROL SYSTEM FOR ci?c applications. These applications include factory automa COLLECTIVES OF EVOLVABLE tion, reconnaissance, remote sensing, traf?c coordination, NANOROBOTS AND MICROROBOTS security and haZard management. [0007] One Way to organiZe CR systems is to develop a CROSS-REFERENCES TO RELATED hybrid control system. In one example of a hybrid control APPLICATIONS system, central control is combined With elements of behav [0001] The present application claims the bene?t of priority ior-based control. In another example, a multi-agent system under 35 USC. § 119 from US. Provisional Patent Applica (MAS) is integrated With a multi-robotic system (MRS). tion Ser. No. 60/865,605, ?led on Nov. 13, 2006, US. Provi These systems use elements of evolutionary computation in sional Patent Application Ser. No. 60/912,133, ?led Apr. 16, order for the system to autonomously compute the environ 2007, US. Provisional Patent Application Ser. No. 60/941, mental feedback that must be overcome to achieve a goal. 600, ?led Jun. 1, 2007 and US. Provisional Patent Applica [0008] CR systems are examples of advanced hardWare tion No. 60/958,466, ?led Jul. 7, 2007, the disclosures of Which are hereby incorporated by reference in their entirety systems that employ self-organizational capacities analogous to ones in nature. The bio-inspired computing literature has for all purposes. emerged to identify arti?cial methods to emulate, and sur FIELD OF THE INVENTION pass, speci?c naturally occurring biological systems. For example, the protein netWork that alloWs communication [0002] The present invention involves nanotechnology, betWeen living cells, the neural plasticity of the human brain nanoelectromechanical systems (NEMS) and microelectro or the adaptive operation of the human immune system are mechanical systems (MEMS). The invention also deals With examples of biological system capabilities that are emulated collective robotics (CR) on the nano-scale, or collective nano by arti?cial systems in computer science. robotics (CNR) and nano-scale mechatronics control theory. The invention deals With bio-inspired computing systems, [0009] Several metaheuristic computational methods are including immunocomputing. The ?eld of evolvable hard used to guide processes to solve complex combinatorial opti Ware (EHW) is extended from electronics semiconductors, miZation problems. These bio-inspired computing models viZ., FPGAs, to nanotechnology by using aggregation pro include local search (scatter search, tabu search and adaptive cesses of combining collectives of nanorobots. Applications memory programming), sWarm intelligence (ant colony opti of nano-evolvable hardWare (N-EHW) include bio-medical miZation, particle sWarm optimiZation and stochastic diffu and electronics techniques. sion search), genetic algorithms and arti?cial immune sys tems (immunocomputing). Local search is optimally applied BACKGROUND OF THE INVENTION to cellular automata solutions, While sWarm intelligence and AIS are optimally applied to emergent behaviors. [0003] Since 1996, researchers at MIT have developed the concept of “amorphous computing” Which is applicable to [0010] One of the most prominent recent examples of bio nanorobotics collectives. Amorphous computing architec inspired computing lies in the ?eld of immunocomputing. tures involve large numbers of identical parallel computer Computer systems are organiZed to emulate the humoral and processors that have local environmental interactions. This adaptive human immune system operations. In the case of the netWork computing architecture uses sWarm intelligence humoral immune system, a cascade of proteins is emulated in algorithms (particle sWarm optimiZation, ant colony optimi order to accomplish a speci?c task. In the case of the adaptive Zation and stochastic diffusion search) to coordinate the immune system, a novel pathogen Will stimulate a reaction by behaviors of equivalent computational entities to achieve a speci?c antibodies Which Will attack the pathogen and learn goal. While amorphous computing borroWs from grid com to attack similar future pathogens. This process provides a puting models, it is limited to programmable, not reprogram learning and adaptive component that is useful in computa mable, functions. Further, the model only uses identical com tional processes that deal With accomplishing goals in the puting devices, much like ants or bees in colonies or hives. context of feedback from uncertain and indetermini stic envi Finally, the system only uses local control to interact With the ronments. nearest neighbors. [0011] In the development of collective robotics at the nano [0004] Researchers at the Institute for Robotics and Intel scale, hoWever, there are distinctive features that distinguish ligent Systems at USC have developed a system for collective the system from the macro scale. For example, collective microrobots by organiZing robots to cooperate using local nanorobotics (CNR) has substantial resource constraints, rules by using computer simulations. including computation and communications resource limita [0005] Since 2001, researchers at Carnegie Mellon Univer tions. In order for a CNR to exhibit self-organization capa sity have developed a system for “synthetic reality” called bilities, the system must demonstrate arti?cial intelligence “claytronics” Which uses “programmable matter” to self-or for autonomous behaviors. Hence it is necessary to develop a ganiZe into different shapes. This novel system develops novel system for e?icient AI that optimiZes computation novel hardWare and softWare to organiZe three dimensional hardWare and softWare resources. This research stream is still shapes. Claytronics uses components called “catoms” (clay evolving. tronic atoms) that adhere to each other and interact in three [0012] The ?eld of evolvable hardWare (EHW) is divided dimensions. The claytronics system combines ideas from into tWo areas: electronics and robotics. In electronics, the amorphous computing and recon?gurable robotics. HoWever, main uses of EHW are in ?eld programmable gate arrays to date, the goal of organiZing millions of micro-robotic enti (FPGAs). In robotics, EHW is applied to robots that trans ties has not been achieved. form their physical structure by adding or transforming parts. [0006] The ?eld of collective robotics (CR) has a literature In the context of extending EHW to the nanoscale, there are that involves organiZed systems of groups of robots for spe numerous problems to overcome. Particularly in applications US 2008/0269948 A1 Oct. 30, 2008

involving biology or medicine, the application of EHW to [0021] The aggregation and reaggregation of CNRs into nanorobotics presents a range of interesting challenges. speci?c transforming geometric structures resembles evolv able hardWare (EHW). Particularly as the evolving environ Problems that the Present System Solves ment changes, the nano evolvable hardWare (N -EHW) assem bly comprised of CNRs transforms. [0013] There are several classes of problem that the present [0022] The development of an evolutionary system for system addresses. In some cases, combinatorial optimiZation CNRs creates a cognitive system to emulate self-organization problems require the identi?cation of a complex arrangement processes in Which the system autonomously reorganiZes in of nanorobotic parts to be assembled and reassembled in a relation to a changing and uncertain environment in order to particular order. Another class of problems involves environ achieve a goal. In one approach, the nanorobotic collective mental interaction With a CNR system. In order to achieve a facilitates cognitive computing in extensible geometric space goal in an evolving environment, key constraints must be by employing reprogrammable integrated circuits that satis?ed that require identifying environmental change. change their topological structure and alloW nanorobots in the [0014] These complex problems are grouped as multi-ob collective to cooperate to solve optimization problems in jective optimiZation problems (MOOPs) in Which there are order to recon?gure the structure of the collective on-de multiple choices betWeen competing goals. Some of these mand. MOOPs Will take the form of temporal sequences in Which [0023] The CNR hybrid control system uses advanced the solution requires solving a succession of micro goals. The computational and communications resources. The compu realiZation of speci?c thresholds in a process is necessary tational resources are structured into single nanorobot, local prior to pursuing the next goal in the sequence. Such contin netWork and external computer capabilities. The CNR hybrid gent phases in a process are required in order for the CNR control system continuously modulates betWeen the most system to interact at each stage of environmental feedback. e?icient available computer function. By using e?icient AI, [0015] In order to solve critical problems at the molecular softWare and communications systems, the CNRs operate biology scale, methods need to be delineated in Which CNRs robustly. aggregate together to form speci?c evolvable structures. [0024] These processes are applied to speci?c biological [0016] The present system focuses on applications in the and electronics problems. In biology, the CNRs emulate pro biological and medical domains. In the biological domain, teins and initiate or block genetic behaviors, such as the one problem involves producing CNR teams that aggregate functional operation of particular genes. In particular, intra into particular geometric architectures to emulate the func cellular mechanisms of RNA transcription are blocked using tioning of proteins. It is necessary to ?nd Ways to identify, the present system. mask and precisely copy proteins in order to imitate their [0025] There are a number of genetic diseases, including structure and behaviors. Speci?cally, it is necessary to ?nd various neoplasties, metastatic processes, mechanisms of cel Ways to use the CNRs in order to activate or deactivate par lular degeneration and immunological functional operations ticular genes by using the facsimile proteins as keys to induce to Which the present system is targeted. a set of behaviors. In addition, the present system Will use [0026] The present system is also used for electronics CNRs that are organiZed to emulate proteins in order to block applications. In an additional embodiment, the system is DNA functioning as Well as to block enZymes from function applied to micro-scale robotics systems. ing. [0017] Finally, it is necessary to ?nd a Way to identify CNR ADVANTAGES OF THE PRESENT INVENTION locations and activities along a series of pathWays of trajec [0027] There are a number of advantages of the present tories. This method alloWs the coordination and integration of system. The present system provides methods and techniques a collective of nanorobots as they solve problems. to apply CNRs to biological or electronics applications. [0028] The present system provides Ways to develop on SUMMARY OF THE INVENTION demand self-assembly and reaggregation processes at the nano- and micro-scale in order to solve important biological [0018] The present system autonomously organiZes groups or electronics problems. of nanorobot and microrobot collectives for speci?c biologi [0029] In biology, the system identi?es and emulates natu cal or electronics applications. The CNRs take on indepen ral structures, such as proteins or cells, by combining CNRs dent team behaviors similar to a division of labor, With spe into micro-assemblies to achieve goals in an evolving bio cialists performing particular functions at key times to logical environment. accomplish a goal more ef?ciently. [0030] Finally, the present system presents a Way to target [0019] The evolvability and structural transformability of speci?c cellular regions With CNRs in order to solve particu the CNR groups is accomplished via interaction With an lar intracellular problems. evolving environment. The speci?c nanorobots combine in [0031] (I) Nano Evolvable HardWare (N-EHW) order to create structures that meet environmental goals. [0032] Heretofore, evolvable hardWare (EHW) has been [0020] The process of forming into a speci?c structure, like restricted to tWo main types: (a) restructuring semiconductor a protein formed by a combination of amino acids, Will con gates embodied in ?eld programmable gate arrays (FPGAs) tinue to modify the CNRs’ aggregate geometric structure in Which shift from one application speci?c integrated circuit order to transform the con?guration of the CNR group to (ASIC) position to another in order to optimiZe e?iciency, satisfy key evolving environmental constraints. In order to particularly useful in uncertain environments for the purpose solve particular evolving environmental problems, several of rapid prototyping and (b) restructuring, primarily additive CNR teams Will compete to achieve a goal, thereby stimulat robotic equipment, used for manufacturing or mobile sens ing multiple options rather than limiting the process to a ing. A third, related application of EHW can be made to single opportunity to solve a problem. collective robotics in Which multiple robots combine to con US 2008/0269948 A1 Oct. 30, 2008

stitute a system of collective biodynotics whereby the aggre optimization problems in order to identify the precise loca gated robotic entities emulate biologically evolved entities, tions of CNRs in a self-assembled, and evolving, apparatus. such as insects, in order to solve problems in an evolving [0045] Examples of speci?c niche applications that use environment. N-EHW are (a) molecular biology, (b) micro-electronics and [0033] HoWever, an additional category of EHW is devel nano-electronics systems and (c) sensors in control systems. oped in the present invention, one that focuses on nano- and [0046] An example of the use of N-EHW in molecular micro-scale robotic devices. N-EHW devices combine mul biology is to perform speci?c functions in intracellular sys tiple CNRs into a speci?c geometrical structure on-demand in tems in vitro or in vivo. Speci?cally, the system is useful in order to solve problems in an evolving micro-environment order to create arti?cial synthetic assemblies to combine arti such as intra-cellular behaviors. ?cial amino acid or peptide chains on-demand into pre-or [0034] (l) Self-Assembly of Nano Evolvable HardWare dered proteins. HoWever, the system also recon?gures its (N-EHW) assembly structure into neW proteins by using the same arti [0035] Collectives of nano-robots (CNRs) are nano-scale ?cial amino acid parts. robots that contain nano-scale computation and communica [0047] In one embodiment, the arti?cial amino acid parts tions capability. These CNRs Work together to perform spe are pre-ordered to easily assemble into a particular pre-ar ci?c goals and to solve problems in micro-scale environments ranged protein structure. In another embodiment, a typology such as in vivo or in vitro molecular biology or in electronics of protein structures are organiZed by protein families in order applications. The CNRs Work together by using softWare for the N-EHW comprised of CNRs to restructure into pre agents Which exchange information, model and present solu ordered proteins on-demand as the environment changes. tions to problems and employ speci?c functional capabilities. [0048] (3) Collective Nanorobotic System With Intelligent [0036] The present invention goes a step beyond the main Reaggregation Process for Adaptive Geometric Con?gura CNR system by providing methods for the CNRs to combine tion and produce a form of nano evolvable hardWare (N-EHW). [0049] Because the system has social intelligence, it is able These self-con?guring hardWare apparatuses are nano-scale to continuously reorganiZe the geometric con?guration of the intelligent robotic devices that aggregate into speci?c geo CNR. This process of continuous reorganization is a form of metric shapes in non-deterministic environments. reaggregation. The combined spatial con?guration of groups [0037] The present invention thus presents an arti?cial syn of nanorobots is reordered contingent on environmental thetic molecular self-assembly mechanism. stimuli and feedback mechanisms. As the CNR seeks to solve [0038] (2) Reaggregation Process of Nano Evolvable Hard a problem, it engages in a process of continuous restructuring of its extensible geometric structure in order to continually Ware (N -EHW) adapt to its changing environmental situation. [0039] The CNRs Work together cooperatively on teams in [0050] After automatically structuring into a particular ini order to form into speci?c structures; these structures are tial spatial con?guration, the CNR shifts its binding mecha either pre-determined or organiZed on-demand. After the ini nism to reassemble into neW con?gurations at further stages tial con?guration of the N-EHW is completed, the process in the process of solving a problem. In a case Where protein Will continue in an evolving environment in Which the CNR function is emulated, for example, the CNR discovers that it cooperative addresses demands to recon?gure into neW geo needs to change its geometrical shape to accomplish a goal, metric shapes in order to solve complex problems. The multi and then activates the structural transformation. In the context phasal progression of transformation of N-EHWs illustrates a of a biological environment, one situation may call for opera form of self-organization. By restructuring their con?gura tion of the CNR by mimicking a speci?c protein structure tions, the CNRs activate novel functions in order to solve With one unique functional operation in one event and a evolving problems. At each successive stage of the progress change to another structure of protein With another function of the N-EHW process, the system obtains and assimilates in another event. By developing a system for continually neW information about the changing situation in the environ recon?gurable protein patterns, the present invention creates ment and adapts to the changes. a novel “smart protein” mechanism that Will modify shapes to [0040] In order to adapt to the changing environment, the solve problems in various real-time situations. N-EHW apparatus modulates the supply of CNR robot com [0051] (4) Collective Nanobiodynotics for Macro Geomet ponents at precise phases in the transformational process. ric Nanorobotic Transformation [0041] In one embodiment, the CNRs Will “pre-make” or [0052] The ?eld of biodynotics seeks to organiZe individual organiZe a speci?c N-EHW structure en-route to solve a par structural entities into complex transformable con?gurations ticular problem as the N-EHW assembly receives environ to emulate biological entities. Collective biodynotics is the mental feedback and then continue to restructure at the loca ?eld of organiZing groups of robotic entities into robotic tion of the problem in order to continue to solve it. shapes or combinations of robotic shapes to emulate biologi [0042] Reaggregation behaviors of N-EHW engage in a cal entities. continuous transformation process contingent on environ [0053] The present invention creates the ?eld of collective mental feedback and change. Speci?cally, the N-EHW col nanobiodynotics by providing methods to combine groups of lection of reaggregating nanorobots interacts With, and adapts nano-scale robotic entities into functional entities that emu to, their evolving environment. late micro-biological behaviors. [0043] In order to perform these complex dynamic proce [0054] The system of collective nanobiodynotics coordi dures, the N-EHW system comprised of CNRs generates or nates multiple CNR teams in order to organiZe into speci?c adds parts to the main evolving structures in order to add device con?gurations in real time. These teams are organiZed utility. to Work together cooperatively to accomplish a task, for [0044] The reaggregation process of the N-EHW system is instance, by using functional specialists in a division of labor, constantly solving geometrically oriented combinatorial or organiZed to compete in order to achieve a goal. US 2008/0269948 A1 Oct. 30, 2008

[0055] Nanorobots are combined into unique geometrical the appended claims. All publications, patents, and patent shapes. This feature of CNRs creates a transformable hard applications cited herein are hereby incorporated by refer Ware system that emulates virtually any extensible spatial ence for all purposes in their entirety. con?guration. By using collectives of nanorobots, this system is organized autonomously and the structural transformation DESCRIPTION OF THE DRAWINGS process continuously self-organizes. The use of CNRs for this [0063] FIG. 1 is a diagram shoWing groups of nanorobots. process makes it possible for structural conversion to activate [0064] FIG. 2 is a schematic diagram shoWing the process a functional conversion. This nanobiodynotics system has of combining tWo sets of nanorobots. numerous applications. [0065] FIG. 3 is a diagram shoWing several phases in a [0056] Underlying the collective nanobiodynotics system process of changing con?gurations of nanorobots. is the notion that smaller individual nanorobotic units Work [0066] FIG. 4 is a diagram shoWing the addition of nano together to change the shape of a larger extensible unit on robots to an assembly of nanorobots. demand. Since aggregate CNR function is integrated With the [0067] FIG. 5 is a schematic diagram shoWing the changing process of structural transformation, the utility of the trans con?guration of overlapping assemblies of nanorobotic col formable structures vary With each sequence of change of the lectives in tWo phases. process. In this sense, individual nanorobots are a form of [0068] FIG. 6 is a schematic diagram illustrating the arti?cial “synthetic stem cells” that are used to form various changed con?guration of a collective of nanorobots as it speci?c topological structures on demand. The biodynotics interacts With a changing environment. CNR system may be used on the surfaces of larger inert [0069] FIG. 7 is a schematic diagram shoWing the integra objects to change their structure. tion of tWo groups of nanorobots over three phases. [0057] Conceive of this process as a set of tiny Lego’s that [0070] FIG. 8 is a schematic diagram shoWing the process ?t together and, upon speci?c stimuli, disassemble and reas of the removal of nanorobots in one group by the nanorobots semble into neW con?gurations of pieces. Unlike the arti?cial in another group over several phases. neural netWork hardWare system, Which has speci?c func [0071] FIG. 9 is a diagram shoWing the recon?guration of tionality, this system is primarily extensible and emphasiZes arti?cial proteins using collectives of nanorobots over three the transformation of physical structure. phases. [0058] To add an analogy from nature, this process is simi [0072] FIG. 10 is a How chart describing the process of lar to the folding and malleability of peptides to create spe recon?guration of a collective of nanorobots. ci?c protein functions. By using a database of combinatorial [0073] FIG. 11 is a diagram shoWing the common core of a chemistry and a catalogue of protein structures, researchers device that has changing con?gurations of nanorobotic col are able to identify the purposes of speci?c classes of proteins lectives in three phases. as Well as their optimum range of conditions. Similarly, col lective nanobiodynotics systems Will be used for creating [0074] FIG. 12 is a How chart describing the recon?gura tion process of a nanorobotic collective. transformable geometric structures to enter an object, trans form to perform speci?c functions, and then retransform in [0075] FIG. 13 is a chart that shoWs the use of metaheuristic order to exit the object. These complex synthetic processes systems by collectives of nanorobots. emulate natural transformable functions. The numerous applications of this system Will be discussed beloW in the DETAILED DESCRIPTION OF THE DRAWINGS context of biology, medicine and security. [0076] One of the chief attributes of evolvable hardWare [0059] One of the advantages of con?guring collectives of composed of collectives of nanorobots or microrobots is the nanorobots in biodynotics is to facilitate a novel concept of ability to aggregate into speci?c con?gurations and then to intelligent self-assembly. For instance, sets of individual reaggregate into different con?gurations. This ability to nanoparts may be alloWed to ?t into constricted space and reaggregate physical structure on-demand provides a neW then automatically self-con?gure into usable Whole entities application of social intelligence. In the case of nanorobotics With speci?c functionality in order to solve a problem. This is and microrobotics, each robot has intelligence capabilities a nano-scale version of the analogy of getting big parts because of on-board integrated circuitry that computationally through a narroW doorWay and then reassembling them in a processes solutions to problems. When linked to other nano bigger room. robotic or microrobotic entities, the collectives of nanorobots [0060] Another advantage of collective nanobiodynotics is and microrobots display active social intelligence that modi the use of redundancies in CNR netWorks in order to emulate ?es extensible spatial position of the combined agents. biological processes such as the immune system. This model [0077] The folloWing description of the draWings illus provides a failsafe process to accomplish a task. trates the organiZation and reorganization processes of col [0061] Reference to the remaining portions of the speci? lectives of nanorobots. The draWings also apply to microro cation, including the draWings and claims, Will realiZe other bots. features and advantages of the present invention. Further [0078] FIG. 1 shoWs a collective of nanorobots (110) that features and advantages of the present invention, as Well as moves location to combine into a neW structure at a neW the structure and operation of various embodiments of the location (100) and then to disaggregate into neW separate present invention, are described in detail beloW With respect structures (120) at another location. to accompanying draWings. [0079] FIG. 2 shoWs tWo triangular groups of nanorobots [0062] It is understood that the examples and embodiments (200 and 210) that combine to form one contiguous assembly described herein are for illustrative purposes only and that (220). FIG. 3 shoWs four phases of the recon?guration of various modi?cations or changes in light thereof Will be sug group of nanorobots from A to B, from B to C and from C to gested to persons skilled in the art and are to be included D. This changed composition of the group of nanorobots Within the spirit and purvieW of this application and scope of resembles a collection of Legos that shift the angles of the US 2008/0269948 A1 Oct. 30, 2008

connections as new units are added or removed. This drawing ratus (1120) has not changed position. Similarly at phase C, shows the self-assembly process of a collective of nanoro the nanorobotic collective (1150) has changed its con?gura bots. tion on the surface of the apparatus (1140), which retained its [0080] FIG. 4 shows three phases of the addition of new position. nanorobots to new positions to the right of the initial group of [0088] FIG. 12 describes the process of organiZing a col three nanorobots (400, 410 and 420) at A. The connection lective nanobiodynotic algorithm. After the nanorobots in a (460) is added at phase B, while the additional connection collective communicate with each other to activate a strategy (490) is added at phase C. This drawing shows the building to achieve a task (1200), the speci?c-function nanorobots are out process as a nanorobotic collective changes composition activated to organiZe into a speci?c geometric con?guration by adding nanorobots to a speci?c assembly. (1210). The collective of nanorobots then autonomously [0081] FIG. 5 shows two sets of overlapping circles. In the organiZe to con?gure into speci?c geometrical structure initial position of phaseA, the central position (570) is shown, (1220). The CNRs are programmed to change their geometric while as the circles change positions to move closer together, positions at speci?c times (1230). the central position (580) at phase B is more compressed. [0089] In FIG. 13, a table shows the different main meta [0082] FIG. 6 shows the process of changing con?guration heuristics systems that are employed by the nanorobotics of a collective of nanorobots as it interacts with its environ system. The main metaheuristics models are local search, ment. At phase A the nanorobotic collective (600) receives swarm intelligence, arti?cial immune systems and genetic feedback from the environment (610) to which it reacts by algorithms. These metaheuristics systems each have several reorganiZing its physical structure (620) at phase B. The main types and hybrids, which are selectively chosen by the collective of nanorobots continues to interact with its envi collective of nanorobots in order to perform social behaviors. ronment (630), which has itself changed. This interaction has caused the collective of nanorobots (640) to change its physi What is claimed is: cal con?guration again at phase C as it interacts with a 1. A system for managing aggregation of a collective of changed environment (650). This process continues until the nanorobots (CNRs), comprising: collective of nanorobots has achieved its mission. A plurality of nanorobots, each nanorobot including pro [0083] In FIG. 7, the interaction process of groups of nano gram code con?gured to communicate and exchange robots in shown. At phase A, the smaller nanorobots (700) information with other nanorobots; interact with the larger nanorobots (710). At phase B, the two A plurality of sets of nanorobots; groups (720 and 730) of nanorobots are attracted to move Control logic con?gured to control formation of the plu closer to each other. Finally, at phase C, the two groups rality of nanorobots into a plurality of con?gurations in intermingle (740). This process of integration is critical to the response to external stimulus; self-assembly process. It resembles the integration of chemi Wherein each set of nanorobots in a CNR is organiZed into cal structures in a chemical reaction as compatible chemicals speci?c spatial con?guration by cooperating nanorobot reorganiZe their structures. behaviors; [0084] FIG. 8 shows several phases in which a collective of Wherein the speci?c spatial con?guration of each set of nanorobots attacks and destroys nano-objects. In phase A, the nanorobots in the CNR is based on information in the collective of nanorobots (800) moves towards the nano-ob environment. jects (810). The ?rst nano-object that is identi?ed (840) is 2. A system for managing a collective of nanorobots surrounded by the collective of nanorobots (830) at phase B. (CNRs), comprising: At phase C, another nano-object (860) is identi?ed and sur A plurality of nanorobots that function collectively as a rounded by the nanorobotic collective (850). This step is network; repeated until all of the nano-objects are destroyed. This Wherein the network collects information about the envi process is useful to defeat pathogens. It is also for groups of ronment; nanorobots to attack and defeat other groups of nanorobots by employing complex game theoretic strategies. Wherein the information is shared between the nanorobots in the CNR; [0085] FIG. 9 shows the several phases of the recon?gura Wherein the information is divided between the nanoro tion of an arti?cial protein. At phase A, the strands of the arti?cial protein, which are constructed of assemblies of bots; nanorobots, are organiZed into a speci?c structure. This initial Wherein the information is analyZed by the nanorobots; structure transforms at phase B and again at phase C. Wherein the nanorobots cooperate in making a decision [0086] The process of reaggregation of collectives of nano and generate instructions on how to proceed; robots is described in FIG. 10.After the CNR group organiZes Wherein the instructions are provided to member nanoro in a speci?c initial spatial con?guration (1000), it seeks a bots in the CNR; and solution to an optimiZation problem (1010). The CNR com Wherein the nanorobots are organiZed into speci?c con mences the process of restructuring its combined spatial con ?gurations based on the instructions in order to interact ?guration (1020) and continues to adapt to environmental with the environment. changes as it solves optimiZation problems (1030) by adapt 3. A system for managing automated collective nanorobots ing its geometrical con?guration. The CNR is then organiZed (CNRs), comprising: into a speci?c geometrical con?guration. A plurality of nanorobots, each nanorobot including pro [0087] FIG. 11 shows three phases in the changing process gram code con?gured to communicate and exchange of CNRs. In this drawing, at phase A, the core (1100) has a information with other nanorobots; collective of nanorobots (1110) on its surface in a speci?c Wherein the nanorobots in a CNR work together to obtain con?guration. The con?guration of the nanorobots (1130) information from the environment and to cooperatively changes it position at phase B, while the common core appa make decisions on collective behavior; US 2008/0269948 A1 Oct. 30, 2008

Wherein the nanorobots con?gure into speci?c sets to Wherein sets of nanorobots in the CNR restructure their achieve the strategies speci?ed by the collective; physical geometric structure into neW con?gurations; Wherein the nanorobots receive neW information from the Wherein the CNR solves multiple objective combinatorial environment; optimization problems as it continuously reorganiZes Wherein the nanorobots receive neW instructions from an Within a changing non-deterministic environment; and external computer by communications input; Wherein the CNR continuously self-assembles into differ Wherein the nanorobots analyZe the neW information ent con?gurations. about the environment and the neW instructions and determine a neW course of action;