nano robotics toutorial

NANOROBOTICS CONTROL DESIGN: A PRACTICAL APPROACH TUTORIAL

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

CAN Center for Automation in NanobiotechIMM 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

The new era of Nanotechnology is coming


FactWhat is Nanotechnology

Nanotechnology ChallengeProposed Approach

Virtual EnvironmentNanorobot Design

Environment SensingCompetitive Nanorobotics

Collective NanoroboticsContributions

Fact

P r e s e n t a t i o n o u t l i n e :


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 launcheda program in “Scientific Visualization”

c. 2003 Investiments in Nanobiotech:Europe 500 Million, USA 700 Million, Japan 800 Million

1. WHAT IS NANOTECHNOLOGY ?

a. Interdisciplinary new technology (Engineering, Computer, Physics, Chemistry and Biology)

b. To build

up-to-down and bottom-up strategiesNEMS(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


2. NANOTECHNOLOGY CHALLENGE

a. Main goal of nanotechnology at nanoscale: - development of molecular nanomachine & systems

Possible applications: - Nanoassembly automation- Health and environmental care

b. An acceptable approachi. Agents as assemblers

sensory feedback intelligent control is indispensable for micro/nano manipulation

ii. Computer graphics as a tool for exploration and design


3. PROPOSED APPROACHa. 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. Nanorobot’s sensors identify:obstacles / molecule / organ inlets / another nanorobot

4. VIRTUAL ENVIRONMENT

Top camera view in the virtual environment

c. Human body:- simplified 3D workspace a lower computational effort- is valuable approach to study

nanorobotics control behaviors for nanomedicine 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

b. The obstacles located in unknown probabilisticpositions

a. Comprised by:

obstaclesorgan inlets, molecules,nanorobots,

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

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 nanorobot with robotic arm(telescoping manipulator)

nanorobot design


6. ENVIRONMENT SENSING

a. Decision planning

Directed molecule-capture and delivery

Organs inlets

nanorobot biomolecules

To verify

To attend


b. Behavior activation

Sensor-based-control loop

6. ENVIRONMENT SENSING


7. COMPETITIVE NANOROBOTICS 3D Simulation

Nanorobot and nanorobot adversary in action 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

8. COLLECTIVE NANOROBOTICS

Cooperative team behavior 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

3D Simulation

9. CONTRIBUTIONS

b. Rapid Evaluation of Various Control Algorithms

Further biomedical investigations

FUTURE WORKS

with more detailed simulator parameters


c. Show a practical approach to investigatenanorobotics control design

a. Real-time graphics simulation as a valuable toolfor the better investigation of kinematics in nano world

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 nanorobotsto medicine and environmental applications

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

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 grows up 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)


Fig.1: Highest/lowest organ inlets nutritional levels

Control Performance – Competitive Nanorobots

Fig.2: Histogram

Competitive reaction: 30 organs nutritional states

102030405060708090

1

24time step simulation

leve

l %

agent lowest agent highestadversary lowest adversary highest

Simulation competitive: 24 time-steps

0

10

20

30

40

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1 8 15 22 29 36 43 50 57 64 71 78 85 92 99percentage nutritional state all 30 organs inlets

freq

uenc

yagent adversary

target: 50%

Q. & A.


Fig.3: Multi-robot cooperative reaction

Fig.4: Histogram cooperative reaction

Control Performance – Collective Nanorobotics

Q. & A.

Simulation collective robotics: 24 time-steps

01020304050607080

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99

percentage nutritional state all 30 organs inlets

freq

uenc

ycooperative behavior

target: 50%

Collective reaction: 30 organs nutritional states

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1

24time step simulation

leve

l %

lowest level highest level


Control Performance – Neural Motion Control

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

Q. & A.

Neural complete trajectory optimization

40

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

Dis

tanc

e (u

nit i

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m)

route ON route OFF


nano robotics toutorial

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