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 CNR is organized into speci?c geometric configuration . 1040 Patent Application Publication Oct. 30, 2008 Sheet 1 0f 8 US 2008/0269948 A1 F|G. 1 120/0 O Q Q 7\7\ ‘I, I, 110 / < ________ _. V 100 H6. 2 | | 200 . | \ l : \21o | | 220 Patent Application Publication Oct. 30, 2008 Sheet 2 0f 8 US 2008/0269948 A1 FIG. 3 320 @ Q / //, 330 FIG. 4 400 475 Patent Application Publication Oct. 30, 2008 Sheet 3 0f 8 US 2008/0269948 A1 FIG. 5 570 ® 550 530 / \\ 630 620 640 Patent Application Publication Oct. 30, 2008 Sheet 4 0f 8 US 2008/0269948 A1 FIG. 7 o </ O O O 0 ° 0 m 7 20 o O 1 730 \E/ \\ o O O /// o O o o (--—) O 0 ° 0 o o O 0 0O 0 OO 0 FIG. 8 aoo\ O o 0 /,,.,a10 @ o a - ° / O O O 0 ° 0 ® 840\\ 0 ° O 0 o / //°38i°0 o o 830 O O 0 © 860\ o o W 0 ///° 0 850 ° 0 O o @ 0 /'//° o o Patent Application Publication Oct. 30, 2008 Sheet 5 0f 8 US 2008/0269948 A1 FIG. 9 // 950 Patent Application Publication Oct. 30, 2008 Sheet 6 0f 8 US 2008/0269948 A1 FIG. 10 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 FIG. 1 1 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.