Hybrid control system for collectives of evolvable nanorobots and microrobots

• US 20080269948A1
• Filed: 11/13/2007
• Published: 10/30/2008
• Est. Priority Date: 11/13/2006
• Status: Abandoned Application

• Current Assignee: Solomon Research LLC
• Original Assignee: Solomon Research LLC
• Inventors: Solomon, Neal
• Application Number:
  US11/985,050
• Publication Number:
  US 20080269948A1
• Time in Patent Office: Days
• US Class Current: 700/245
• CPC Class Codes: A61B 5/416 the spleen
• CPC Class Codes: A61M 37/00 Other apparatus for introducing media into the body for reproduction or fertilisation A61B17/425; apparatus for iontophoresis or cataphoresis A61N1/30; Percutany, i.e. introducing medicines into the body by diffusion through the skin salt baths A61H33/04
• CPC Class Codes: B82Y 10/00 Nanotechnology for information processing, storage or transmission, e.g. quantum computing or single electron logic
• CPC Class Codes: G06N 3/002 Biomolecular computers, i.e. using biomolecules, proteins, cells using DNA G06N3/123; using neurons G06N3/061
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 70/60 relating to pathologies

Abstract

A system is described for the organization and self-assembly of collectives of nanorobots (CNRs) and microrobots using nano evolvable hardware (N-EHW) mechanisms for biological and electronics applications. CNRs combine to organize into complex geometrical structures and reaggregate their structural configurations in real time as they adapt to the feedback of evolving environmental conditions to solve complex optimization problems.

Claims

1. A system for managing aggregation of a collective of nanorobots (CNRs), comprising:

A plurality of nanorobots, each nanorobot including program code configured to communicate and exchange information with other nanorobots;

A plurality of sets of nanorobots;

Control logic configured to control formation of the plurality of nanorobots into a plurality of configurations in response to external stimulus;

Wherein each set of nanorobots in a CNR is organized into specific spatial configuration by cooperating nanorobot behaviors;

Wherein the specific spatial configuration of each set of nanorobots in the CNR is based on information in the environment.

2. A system for managing a collective of nanorobots (CNRs), comprising:

A plurality of nanorobots that function collectively as a network;

Wherein the network collects information about the environment;

Wherein the information is shared between the nanorobots in the CNR;

Wherein the information is divided between the nanorobots;

Wherein the information is analyzed by the nanorobots;

Wherein the nanorobots cooperate in making a decision and generate instructions on how to proceed;

Wherein the instructions are provided to member nanorobots in the CNR; and

Wherein the nanorobots are organized into specific configurations based on the instructions in order to interact with the environment.

3. A system for managing automated collective nanorobots (CNRs), comprising:

A plurality of nanorobots, each nanorobot including program code configured to communicate and exchange information with other nanorobots;

Wherein the nanorobots in a CNR work together to obtain information from the environment and to cooperatively make decisions on collective behavior;

Wherein the nanorobots configure into specific sets to achieve the strategies specified by the collective;

Wherein the nanorobots receive new information from the environment;

Wherein the nanorobots receive new instructions from an external computer by communications input;

Wherein the nanorobots analyze the new information about the environment and the new instructions and determine a new course of action;

Wherein sets of nanorobots in the CNR restructure their physical geometric structure into new configurations;

Wherein the CNR solves multiple objective combinatorial optimization problems as it continuously reorganizes within a changing non-deterministic environment; and

Wherein the CNR continuously self-assembles into different configurations.