Nanorobots Tutorial

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NANOROBOTICS CONTROL DESIGN:A PRACTICAL APPROACH TUTORIAL

A. Cavalcanti, R.A. Freitas Jr., L.C. Kretly

CAN Center for Automation in Nanobiotech IMM Institute for Molecular Manufacturing

UNICAMP University of Campinas

ASME 28th Biennial Mechanisms and Robotics ConferenceASME DETC - Salt Lake City, Utah, USA

September 28 to October 2, 2004

www.n a n o r o b o t d e s i g n.com www.c a n b i o t e c h n e m s.com


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The new era of Nanotechnology is coming



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FactWhat is Nanotechnology

Nanotechnology ChallengeProposed Approach

Virtual Environment

Nanorobot DesignEnvironment Sensing

Competitive NanoroboticsCollective Nanorobotics

ContributionsFact



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FACT


d. Efforts to bring new nanoproducts:IBM, Motorola, Philips Electronics, Xerox/PARC,Hewlett-Packard, Bell Laboratories, and Intel Corp., etc

a. The governments and industries all around the globe:investing for a fast development of nanotechnology

b. The U.S. National Science Foundation launched

a program in Scientific Visualization c. 2003 Investiments in Nanobiotech:

Europe 500 Million, USA 700 Million, Japan 800 Million


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1. WHAT IS NANOTECHNOLOGY ?

a. Interdisciplinary new technology

(Engineering, Computer, Physics, Chemistry and Biology)b. To build

up-to-down and bottom-up strategies

NEMS(nanometer-sized electromechanical structures):

c. The key technology: the new device and theory to explore the nano world

d. Virtual reality / automated planning to assist nanotechnology

- assisting chemical and biological assembly analyses- judgments about manufacturing feasibility



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2. NANOTECHNOLOGY CHALLENGE

a. Main goal of nanotechnology at nanoscale:

- development of molecular nanomachine & systemsPossible applications:- Nanoassembly automation- Health and environmental care

b. An acceptable approach i. Agents as assemblers

sensory feedback intelligent control

is indispensable for micro/nano manipulationii. Computer graphicsas a tool for exploration and design



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3. PROPOSED APPROACH a. Mobile nanorobot control design

- Perform molecular assembly manipulation

- Applied to Nanomedicine b. Nanorobot aims

- Molecules transport, assembly and delivery- Control organ inlets nutritional levels

(ranging from 20 to 80%) target* 50%c. The delivery positions

- Represent organ inlets requiring proteins- Located in known positions

d. Macro-transponder for positional location


e. Nanorobots sensors identify: obstacles / molecule / organ inlets / another nanorobot


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4. VIRTUAL ENVIRONMENT

Top camera view

in the virtual environmentc. Human body:- simplified 3D workspace a lower computational effort- is valuable approach to study

nanorobotics control behaviors for nanomedicinewww.n a n o r o b o t d e s i g n.com www.c a n b i o t e c h n e m s.com

b. The obstacles located inunknown probabilisticpositions

a. Comprised by:

obstaclesorgan inlets,molecules,nanorobots,


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e. Kinetics assumptions:nanoworld dominated by

- Friction, adhesion, and viscous forces are paramount- Gravitational forces are of little or no importance

Top camera view

in the virtual environment

4. VIRTUAL ENVIRONMENT


d. Molecule trajectories:

probabilistic positionand motion acceleration


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5. NANOROBOT DESIGN

b. Nanorobot navigation:

- Uses plane surfaces (three fins total)- Propulsion by bi-directional propellers:

two simultaneously counter-rotating screw drives -navigational acoustic sensors

a. Nanoassembly Manipulation

is taken into the nanorobotwith robotic arm(telescoping manipulator)

nanorobot design



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6. ENVIRONMENT SENSING

a. Decision planning

Directed molecule-capture and delivery

Organs inlets

nanorobot biomolecules

To verify

To attend



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b. Behavior activation

Sensor-based-control loop

6. ENVIRONMENT SENSING



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7. COMPETITIVE NANOROBOTICS3D Simulation

Nanorobot and nanorobot adversary in actionwww.n a n o r o b o t d e s i g n.com www.c a n b i o t e c h n e m s.com


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8. COLLECTIVE NANOROBOTICS

Cooperative team behaviorwww.n a n o r o b o t d e s i g n.com www.c a n b i o t e c h n e m s.com

3D Simulation


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9 CONTRIBUTIONS


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9. CONTRIBUTIONS

b. Rapid Evaluation of Various Control Algorithms

Further biomedical investigationsFUTURE WORKS

with more detailed simulator parameters


c. Show a practical approach to investigatenanorobotics control design

a. Real-time graphics simulation as a valuable tool

for the better investigation of kinematics in nano world


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FACT


a. A first series of commerciallynanobioelectronic products are expected to 2007

c. Company DisplaySearch: rapid market grow

from US$ 84 million today to $ 1.6 Billion in 2007

b. Next 5-10 years: first nanorobots

to medicine and environmental applications

d. Devices and systems based on Nanotechnology:US$ 1 trillion market for 2015

J t f t


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Just a few quotes

There is nothing permanent except change. Heraclitus of Ephesus (ca. 525-475 B.C.)

A scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents

eventually die and a new generation growsup that is familiar with it.

Max Plank (1858-1947)

A pessimist sees the difficulty in every opportunity; An optimist sees the opportunity in every difficulty.

Winston Churchill, (1874-1965)



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Fig.1: Highest/lowest organinlets nutritional levels

Control Performance Competitive Nanorobots

Fig.2: Histogram

Competitive reaction: 30 organs nutritional states

102030

405060708090

1 2 4

time step simulation

l e v e

l %

agent lowest agent highestadversary lowest adversary highest

Simulation competitive: 24 time-step

0

10

20

30

40

50

1 8 15 22 29 36 43 50 57 64 71 78 8percentage nutritional state all 30 orga

f r e

q u e n c y

agent adversary

target: 50%

Q. & A.



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Fig.3: Multi-robot cooperative reaction

Fig.4: Histogram cooperative reaction

Control Performance Collective Nanorobotics

Q. & A.

Simulation collective robotics: 24 time-steps

0102030

4050607080

1 8 15 22 29 36 43 50 57 64 71 78 85 92

percentage nutritional state all 30 organs inlets

f r e q u e n c y

cooperative behavior

target: 50%

Collective reaction: 30 organs nutritional states

35

40

45

50

55

60

65

1 2 4

time step simulation

l e v e

l %

lowest level highest level



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Control Performance Neural Motion Control

Fig.5: Neural motion planning Fig.6:Nanorobot motion cost optimization

Q. & A.

Neural complete trajectory optimization

40

50

607080

90

100

110

120

1 2 3 4 5 6 7 8 9 10 11 12solutions for a time-step simulation

D i s t a

n c e

( u n

i t i n 1 0 0 0 n m

)

route ON route OFF

Nanorobots Tutorial

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