The document discusses the concept of using swarm intelligence to control a swarm of nanorobots for medical applications inside the human body. It proposes two types of nanorobots - look-out nanorobots that explore the body and identify target sites, and worker nanorobots that deliver drugs to target sites. The nanorobots would use algorithms inspired by ant colony optimization and particle swarm optimization to coordinate their behavior through chemical signaling and achieve complex functions through simple individual behaviors.

1. PRESENTED BY:
Vidushi Sharma & Sakshi
Srivastava ,
B.Tech(Biotechnology), 3rd year,
Banasthali University,
Tonk, Rajasthan.

2. 1) INTRODUCTION
* Nanorobots
* Swarm Intelligence
2) BACKGROUND AND MOTIVATION
* Ant Colony Optimization
* Particle Swarm Optimization
* Artificial Bee Colony Optimization
3) PHEROMONE BASED ABC
ALGORITHM
4) ARCHITECHTURE OD
NANOROBOTS
5) FUNCTION SPECIFIC MODELS
* Look-out Nanorobots
* Worker Nanorobots
6) FUTURE
7) CONCLUSION

3. Nanorobots are programmable engineering
devices that are employed to manipulate
and convey information at nano or
microscopic scale with precision.
Nanorobots move in human vascular
system maneuvering specific medical task
such as identifying and destroying cancer
cells and so on.
Applications of nanorobots in the human
body cannot be performed by a single
nanorobot. Therefore, a swarm of a large
number of nanorobots is employed for
fulfilling a specific task. For effective
functioning, a well-developed efficient
Swarm Intelligence System needs to be
developed.

4. Co-ordination
Communication
Task Allocation
Reducing Design complexities
 Adaptation
 Managing Traffic in blood vessels
 Avoid loss of any Nanorobot in-vivo
 Complete exploration
 Target Identification
FUCTIONS AND FEATURES OF
SWARM INTELLIGENCE SYSTEMS:
a. Stigmergy
b. Decentralization
c. Self-organization
d. Bifurcations
e. Positive and negative feedbacks

5. Concept of nanomedicine emerged in 1950’s when Richard Feynman
suggested the use of nanorobots as in vivo surgeons. However, Due to scale
problems, nanorobots with complex capabilities are not possible rather
simple ones. Hence, Swarm intelligence need to be employed in order to
obtain complex collective behaviors constructed with simple individual
behaviors .
In QUORUM SENSING, a nanorobot makes use of chemical molecules to
send signals and sense similar signals through sensors. They have target
specific receptors at surface which sense and analyze the concentration of
chemical signals emitted from the target site . Example: E-cadherin in case
of cancer cells. Three sensing and motion techniques have been suggested
Multidirectional sensing
and motion
Mimic Bacterial sensing
and motion
Random Motion

6. Probabilistic technique based on ant
behaviours of ants
Fig : Each ant leaves a trial of
pherome behind as it reaches target
site.
If trial is long, pherome gets
evaporated before any ant follows it .
Hence ,lesser pherome density and
less attractiveness
If trial is short, It is followed before
pherome gets evaporated and hence
it has stronger pherome density due
to larger run time.

7. Discovered by Dr Eberhart and Dr
Kennedy in 1995, inspired by social
behaviour of flocking birds and
schooling fishes.
Fig : Each particle, keeps its track on
coordinates which are best positions
according to it: pbest.
It constantly tracks target site and
position of its swarm particles with
respect to it. If it finds any position
better than its own free of obstacles, it
moves to it with increased velocity.
Global best position is analysed with
respect to entire swarm towards target
site.

8. Algorithm based on Bee colony and
their collaboration. In bee colony, there
are three components: Employed
,onlookers and scouts.
Fig : Few employed particles look for
target site throughout the body, on
finding it, it returns to its swarms and
transfers this information to them.
Onlookers then move to target site and
perform fuction. When they are
reported another site they move to it.
Scouts are particles, whose sites are left
by onlookers and they search for
another site.

9.  Initially, two methods were used to reach the target sites:
a. Random Motion- Nanorobot moves passively along with blood and
reaches the target site following the Brownian Motion in blood plasma.
b. Follow Gradient- Nanorobot senses and monitors concentrations of E-
Cadherin molecules in blood. On recognising it, a nanorobot moves towards
the higher concentrations of E-Cadherin until it reaches the target site.
The above methods have certain issues like: the nanorobots may get lost,
they may not be able to explore the problem space thoroughly, they may
block any artery or they may collide among themselves.
We have combined the basics of ABC and ACO algorithms to reach a more
possible solution.

11. Architecture of nanorobots has been suggested keeping certain points
into considerations:
Non replicating devices
 Small size- for effective movement in vascular system
 Biocompatible: Physically, Chemically, Thrombogenically, Biologically
 Brownian motion and viscous forces are very significant in blood plasma
 Might need to move against blood flow
 Movement should not cause any harm to body
 Prevent collision among themselves and fragmentation
 Movement close to vessel walls
 Viscous forces are significant in blood plasma
 Avoid external pressure
 Viscosity of blood must not get affected due to presence of nanorobots in
blood

12. Spherical or length: breadth ratio near 1
 Fins and Propellers
 Two external electrodes that treat blood as an electrolyte to generate energy
from reactions taking place in blood
 chemo-tactic sensors
 Nanoshells or pores for drug or attractant secretion
 Safire/ Diamond outer covering

13. LOOK-OUT NANOROBOT:
Two switching E-cadherin and Attractant specific chemo-tactic sensors
that are arranged in a GATE circuit such that either of them is functionally
active at a time.
 RF tellementary antenna on its surface
 A control unit that is responsible for sending out RF signals at any time
 This switching of sensors is responsible for exploration, target
identification, laying of trail by LNs and conveying information to the WNs
without intervention of any one function due to another. It ensures that at any
time LN is either searching for a target site or its fellow WNs without getting
completely lost during the exploration.

14. Fig. : GATE circuit for the two-sensor switching system of Look-out Nanorobots

15. INPUT OUTPUT REMARKS
At T1=T
A D E
1 1 1 LN moves forward till C reaches the pres-
set threshold value.
F G H
0 1 0 No action
K J I L
1 0 1 0 No RF signal is transmitted to WN.
At T1=2xT1
A D E
0 1 0 LN stops walking forward since threshold
for C reached.
F G H
1 1 1 LN moves backward till threshold for
sensor B is reached.
K J I L
0 1 1 0 No RF signal is still transmitted.
At T1=3xT1 (Delay time
period)
A D E
1 0 0
G F H
0 1 0 LN stops moving backward because
threshold for sensor B is reached.
K J I L
1 1 1 1 RF signal transmitted for 4ns.

16. WORKER NANOROBOT:
They have simultaneously working Attractant and E-Cadherin
specific chemo-tactic sensors on their surface
 This arrangement ensures that the nanorobot even if separated from
the swarm at any point of time, performs its function of drug delivery to
the target site and later on finds its way back to the swarm with the help
of attractant concentrations.
 A nanoshell filled with Target specific drug, enclosed by DNA based
shutters on nanopore
 For Adherence, small gripping structure can be incorporated
 Larger size
 Only RF receiving antenna

17. With a little modification in the GATE
switching and the sensors used, this algorithm
can be applied in case of other Target specific
problems such as:
 Artery blockage
 Repairing tissues
If in case Attractant reservoir of any
nanorobot, especially LN is exhausted, it can
lead to collapse of entire swarm system.
 Hence, a system can be developed and
incorporated in nanorobot that modifies a
biological molecule that is present in blood in
abundance to generate an attractant.
 This attractant molecule, after a particular
time span, tends to convert back to its original
form by losing the modification caused by the
nanorobot, and hence, losing its attractant
tendency.
 Such a system can further reduce size of a
nanorobot and the risk of any kind of biohazard
caused due to presence of any foreign molecule
as attractant in the body.

18. Apart from switching of sensors, there is not much complexity in design of
this swarm system. Comparatively less amount of energy is required in this
system as only small part of swarm is engaged in exploration. Since all the
nanorobots obtain their sense of direction either from attractants, that lead
to swarm, or E-Cadherin, that lead to target site, tendency of any
nanorobot to get lost is minimized. Trail following can cause the traffic to
move in a line. At no point of time, such a movement can cause artery
blockage or accumulation of nanorobots at a place that might increase their
concentration in blood plasma above a significant level. Complete
exploration can be expected of this system with faster results and
adaptability. No nanorobot is dependent on another for its functioning and
at the same time, all the nanorobots together collaborate to perform their
task.

19. 1. Ghada Al-Hudhud , ”On Swarming Medical Nanorobots” , International
Journal of Bio-Science and Bio-Technology Vol. 4, No. 1, March, 2012
2. Dervis Karaboga , Bahriye Akay, “A comparative study of Artificial Bee
Colony algorithm”, Applied Mathematics and Computation 214 (2009)
108–132
3. Dervis Karaboga and Celal Ozturk, “Fuzzy clustering with artificial bee
colony algorithm”, Scientific Research and Essays Vol. 5(14), pp. 1899-
1902, 18 July, 2010
4. Pinfa Boonrong, Boonserm Kaewkamnerdpong, “Canonical PSO based
Nanorobot Control for Blood Vessel Repair” , World Academy of
Science, Engineering and Technology 58 2011
5. Nada M. A. Al Salami, “Ant Colony Optimization Algorithm”, UbiCC
Journal, Volume 4, Number 3, August 2009
6. Vittorio Maniezzo, Luca Maria Gambardella, Fabio de Luigi, “Ant Colony
Optimization”, Future & Emerging Technologies unit of the European
Commission through Project BISON (IST-2001-38923).
7. Micael S. Couceiro, Nuno M. F. Ferreira and Rui Rocha , “Multi-Robot
Exploration based on Swarm Optimization Algorithms”, ENOC 2011, 24-
29 July 2011, Rome, Italy
8. Khin Haymar Saw Hla, YoungSik Choi and Jong Sou Park, “Obstacle
Avoidance Algorithm for Collective Movement in Nanorobots”, IJCSNS
International Journal of Computer Science and Network Security, VOL.8
No.11, November 2008

20. 9. Mohammadjavad Abbasi, Muhammad Shafie Abd Latiff, “Mobility Control to Improve
Nanosensor Network Lifetime based on Particle Swarm Optimization” , International
Journal of Computer Applications (0975 – 8887) Volume 30– No.4, September 2011
10. Simon Garnier · Jacques Gautrais · Guy Theraulaz, “The biological principles of swarm
intelligence”, Swarm Intell (2007) 1: 3–31, DOI 10.1007/s11721-007-0004-y
11. Sanchita Paul, Dipti, “Nan robots: Survey on Recent Developments in Medical
Application”, International Journal of Advanced Research in Computer Science and
Software Engineering, Volume 2, Issue 4, April 2012
12. Nantapat, T. ; Kaewkamnerdpong, B. ; Achalakul, T. ; Sirinaovakul, B. , “ Best so Far ABC
Based Nanorobot Swarm”, Intelligent Human Machine System and Cybernetics (IHMSC),
2011, Page 226-229, DOI: 10,1109/IHMSC.2011.61
13. Chandrasekaran, S. ; Hougen, D. F. , “Swarm Intelligence For Cooperation of Bio-
Nanorobots using Quorum Sensing”, Bio Micro and Nanosystems Conference, 2006,
BMN’06, page 104, DOI 10.1109/BMN.2006.330912
14. Tag Hogg, Adriono Cavalcanti, Bijan Shirinzadeh, Hwee C.Liaw, “Nanorobot
Communication Technique: A Comprehensive Tutorial”, IEEE ICARCV, 2006, International
Conference on Control, Automation, Robotics and Vision.
15. N N Sharma, R K Mittal, “Nanorobot Movement: Challenges and Biologically Inspired
Solutions”, International Journal on Smart Sensing and Intelligent Systems, Vol. 1, No 1,
March 2008.
16. Robert A.Freitas Jr, Christopher A.Phoenix, “Vasculoid: A Personal Nanomedicine
Appliance to Replace Human Blood”, Journal of Evolution and Technology, Vol. 11, 2002
17. Robert A.Freitas Jr, “Current Status of Nanomedicine and Medical Nanorobotics”, Journal
of Computational and Theoretical Nanoscience, Vol. 2, 1-25, 2005.