Nano Robots Tutorial

8/2/2019 Nano Robots Tutorial

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NANOROBOTICS CONTROL DESIGN:

A PRACTICAL APPROACH TUTORIALA. 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


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The new era of Nanotechnology is coming



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Fact

What is Nanotechnology

Nanotechnology Challenge

Proposed Approach

Virtual Environment

Nanorobot DesignEnvironment Sensing

Competitive Nanorobotics

Collective Nanorobotics

Contributions

Fact



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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 CHALLENGEa. 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 manipulationii. Computer graphics

as a tool for exploration and design



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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. 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 probabilistic

positions

a. Comprised by:

obstacles

organ 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 position

and 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 NANOROBOTICS

3D Simulation

Nanorobot and nanorobot adversary in action



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8. COLLECTIVE NANOROBOTICS

Cooperative team behavior


3D Simulation


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9 CONTRIBUTIONS


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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 investigate

nanorobotics 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 commercially

nanobioelectronic 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

Just a few quotes


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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 grows

up that is familiar withit.

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 organ

inlets nutritional levels

Control PerformanceCompetitive Nanorobots

Fig.2: Histogram

Competitive reaction: 30 organs nutritional states

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50

60

70

80

90

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time step simulation

level%

agent lowest agent highest

adversary lowest adversary highest

Simulation competitive: 24 time-steps

0

10

20

30

40

50

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

fre

quency

agent adversary

target: 50%

Q. & A.



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Fig.3: Multi-robot cooperative

reaction

Fig.4: Histogram

cooperative reaction

Control PerformanceCollective Nanorobotics

Q. & A.

Simulation collective robotics: 24 time-steps

0

10

2030

40

50

60

70

80

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

percentage nutritional state all 30 organs inlets

fre

quency

cooperative behavior

target: 50%

Collective reaction: 30 organs nutritional states

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40

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50

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60

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124

time step simulation

level%

lowest level highest level



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Control PerformanceNeural Motion Control

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

cost optimization

Q. & A.

Neural complete trajectory optimization

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120

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

Dista

nce

(unitin1

000nm)

route ON route OFF

Nano Robots Tutorial

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