nanorobotics control for biomedical applications presented by sara yousef serry elsayed m.sc. degree...

1NANOROBOTICS CONTROL FOR

BIOMEDICAL APPLICATIONS

Presented by

SARA YOUSEF SERRY ELSAYED M.Sc. Degree in Scientific Computing Department,

Faculty of Computer & Information sciences , Ain Shams University.

Prof. Dr. TAHA ALARIFProfessor in Computer Science Department, Faculty of Computer & Information sciences, Ain Shams University.

Under supervision of Prof. Dr. SAFAA AMINAssociate Professor in Scientific Computing Department, Faculty of Computer & Information Sciences- Ain Shams University.

2Presentation overview

• Introduction

• Literature Review

• Optimization and Learning Algorithms

• Cooperative Control of Swarm Nanorobot Target Detection

• Human Blood Stream Environment

• Polar Coordinate Obstacle Avoidance Algorithm

• Control Movement Algorithm for Swarm Nanorobot in Human Environment

• Cooperative Control Design for Nanorobots in Drug Delivery

• Conclusions

• Contributions and Publications

3IntroductionCancer therapies are currently limited to surgery, radiation, and chemotherapy. All three methods risk damage to normal tissues or incomplete eradication of the cancer.

4Introduction ( Follow up )

Motivation • The severe toxic side effects of anticancer drugs on healthy tissues.

• Dose reduction, treatment delay, or discontinuance of therapy.

• Limit the side effects of cancer chemotherapy on healthy organs

• Strengthen drug efficiency to cancer

• Eliminate tumors by delivering medications directly to the tumor.

5Introduction ( Follow up )

Objectives are• Destroy the tumor via injecting swarm of nanorobots

• Avoiding the collision with the blood cells. • Implementing a optimization algorithm called (1+1)

Evolution Strategy (ES) with -1/5th success rule algorithm.

• Combine PSO and Polar Coordinate Obstacle avoidance algorithms

• Adopt our proposed movement control algorithm, for pH sensitive nanorobots

6Outlines

√ Introduction• Literature Review• Optimization and Learning Algorithms• Cooperative Control of Swarm Nanorobot Target Detection• Human Blood Stream Environment• Polar Coordinate Obstacle Avoidance Algorithm• Control Movement Algorithm for Swarm Nanorobot in Human

Environment• Cooperative Control Design for Nanorobots in Drug Delivery• Conclusions• Contributions and Publications

7Literature review

Richard Feynman in 1959. NEMS (Nano Electro Mechanical Systems ). One billionth of a meter(10-9) Nanomedicine Nanorobots Architecture

8Features of nanorobots

• Size • Bio Compatibility • Powering • Communication • Navigation• Diffusion

• Swarms

• Removing

9Outlines√ Introduction

√ Literature Review

• Optimization and Learning Algorithms






• Conclusions


10Optimization and Learning Algorithms

• variables definition domain.

• Artificial intelligence (AI).

• Correlation between optimization and learning

• Evolutionary Algorithms (EA).

• They have three main characteristics:• Population-based. • Fitness-oriented. • Variation-driven.

11Evolutionary Algorithms

• Genetic Algorithm (Holland et al., 1960’s)• Bitstrings, mainly crossover, proportionate selection

• Evolution Strategy (Rechenberg et al., 1960’s)• Real values, mainly mutation, truncation selection

• Evolutionary Programming (Fogel et al., 1960’s)• FSMs, mutation only, tournament selection

• Genetic Programming (Koza, 1990)• Trees, mainly crossover, proportionate selection

• Swarm Intelligence (Beni and Wang ,1989 )

(Considered as Advanced EAs)

12Evolutionary Strategy (ES)

• An individual in an ES is represented as a pair of real vectors, v = (x,σ)

•Mutation is performed by replacing x by

xt+1 = xt + N(0, σ)• ( + ), uses parents and creates offspring.• (, ), works by the parents producing offspring• (1 + 1), this took a single parent and produced a single

offspring.

13(1+1) Evolutionary Strategy

• In the (1+1) ES’s, the new individual replaced its parent if it had a higher fitness.

• In addition, (1+1) ES, maintained the same value for σ throughout the duration of the algorithm.

14(1+1) Evolutionary Strategy with 1/5th Success Rule

• Rechenberg has proposed the “1/5 success rule.”

• The ratio, , of successful mutations to all mutations should be 1/5. • Increase the variance of the mutation operator if is greater than 1/5 . otherwise, decrease it

• Motivation behind 1/5 rule:• Try larger steps • Proceed in smaller steps

15(1+1) ES with 1/5th Success Rule

1. Create a random initial configuration x0

2. Evaluate fitness function f(x0)3. For t=1 to n (number of generations) Do

a. Produce µ mutations of xt-1 using: xij=xi t-1+σ

[t]·Ni(0,1)b. forall i ϵ n, j=1,2,…, µ

i. Generate one child xc by the combination of the m mutations using m=randint(1, m)

ii. xic= xim, forall i to n

c. Evaluate f(xc)d. Apply comparison to select the best individual xt between xt-1

and xc

f. If (t mod n = 0) Theni. If (ps>1/5) Then σ[t]= σ[t-n]/c

ii. ElseIf (ps<1/5) Then σ [t]= σ[t-n]·c

Else If (ps=1/5) Then σ [t]= σ[t-n]

16Swarm Intelligence (SI)

• Beni and Wang in 1989 with their study of cellular robotic systems.

• The concept of SI was expanded by Bonabeau, Dorigo, and Theraulaz in 1999.

Two common SI algorithms :

• Ant Colony Optimization

• Particle Swarm Optimization

17Particle Swarm Optimization(PSO)

• Proposed by James Kennedy & Russell Eberhart in 1995

• Inspired by social behavior of birds and fishes• Combines self-experience with social experience

18Performance of PSO Algorithms

Relies on selecting several parameters correctly

Constriction factor Used to control the convergence properties of a PSO

Inertia weight How much of the velocity should be keeped from previous steps

Cognitive parameter The individual’s “best” success so far

Social parameter Neighbors’ “best” successes so far

Vmax Maximum velocity along any dimension

19Particle Swarm Optimization

• Swarm: a set of particles (S)

• Particle: a potential solution• Position:• Velocity:

• Each particle maintains• Individual best position (PBest)

• Swarm maintains its global best (GBest)

nniiii xxx ),...,,( ,2,1,x

nniiii vvv ),...,,( ,2,1,v

SFitness

functionFitness value

20Particle Swarm Optimization(PSO)

Pbest.

Gbest.

The basic concept of PSO lies in accelerating each particle toward its pbest and the gbest locations.

21Particle Swarm Optimization(PSO) Algorithm

• Basic algorithm of PSO1. Initialize the swarm form the solution space2. Evaluate the fitness of each particle3. Update individual and global bests4. Update velocity and position of each particle5. Go to step2, and repeat until termination condition

22PSO and ES Comparison

Commonalities• Population based optimization.

• Randomly generated population.

• Fitness values• Update the population• Both systems do not guarantee success.

• Differences• PSO does not have genetic

operators • memory• Particles do not die• The information sharing

mechanism • ES population moves together

23Outlines

√ Introduction


√ Optimization and Learning Algorithms






• Conclusions


24Cooperative Control of Swarm Nanorobot Target Detection

• Communication Between Nanorobots• This optimization algorithm runs independently on each

nanorobot.

• Each nanorobot optimizes only its own plan.

• Nanorobots maintain a record of movement plans

25Mutation Strategies

Straight Strategy Swap Strategy High Probability Strategy

The nanorobot will set its entire movement plan as a straight line in a random direction

Two randomly chosen vectors in the movement plan will be swapped

A randomly vectors in the movement plan are rotated by a random angle taken from a normal distribution causing the entire path to be rotated

26Simulation Results

Straight Strategy

Swap Strategy

High Probability Strategy

27Simulation Analysis

• The final target area The average time

28Simulation Analysis ( FOLLOW UP )

• The average time in the Partial optimization level

The average time in the Full optimization level

29Simulation Analysis ( FOLLOW UP )

The swap strategy is inefficient mutation strategy The straight and high strategies have almost the same

number of swarm of nanorobotsHigh strategy is more efficient than straight strategy in the

partial and full optimization levels.

30Outlines

√ Introduction



√ Cooperative Control of Swarm Nanorobot Target Detection





• Conclusions


31Human Blood Stream Environment

• In this study we solve the path planning problem of swarm nanorobot

• The blood cells are obstacles in the nanorobot movement• Blood flow, blood viscosity and blood density

32Blood Physical Properties

• Blood velocity in the pipe ~1mm/sec

• Blood flow is the actual volume of blood flowing through a vessel, an organ, or the entire circulation at a given time.

33Outlines

√ Introduction




√ Human Blood Stream Environment


• Control Design of Swarm Nanorobot in Human Environment


• Conclusions


34Polar Coordinate Obstacle Avoidance Algorithm

• The nanorobot have sensors to detect obstacles (blood cells )

• Self organized trajectory planning is required to avoid obstacles .

35Polar Coordinate Obstacle Avoidance

• The new position of obstacle (xj,yj) within time Δt can be calculated :

x j = xi + v f xi * Δt ; y j = yi + v f yi *Δt

• The distance Δd can be calculated by :

36Outlines

√ Introduction





√ Polar Coordinate Obstacle Avoidance Algorithm



• Conclusions


37Control Movement Algorithm for Swarm Nanorobots

• Global path and local path are considered for nanorobot’s movement path planning.

• Global path is carried with some modifications in PSO algorithm .

• When obstacles are encountered, the local path planning is found out for obstacle avoidance.

38Local Path Planning

• The goodness of the position can be computed by using the fitness function Fj.

• Fitness function for each nanorobot at kth iteration is represented by:

Fj (k) = max F j (si where si S, s∈ i T∉ obstacle )

39Global Path Planning

• Total area covered =

• The pbest Fi will be the best fitness value obtained by a nanorobot at a selected time. Fi = E[Dij]

• The gbest Fg will be the global fitness value of a swarm of neighbor nanorobots at the selected time .

Fg = max (Fi (N(si (k))))

40Global Path Planning

• The velocity is updated in the kth iteration by using :

Vi(k+1) = R+ wivi (k)+ c1 * r1 * (Fi (k) – si(k)) + c 2 * r 2 * (Fj (k) – si(k))

• The velocity v of the nanorobot decides where it moves next by using the following equation.

si (k+1)=si (k)+vi( k+1 )

41Movement Control Algorithm

The improved PSO

algorithm

the obstacle avoidance algorithm

The improved PSO

algorithm.

42Polar Coordinate Obstacle Avoidance Algorithm

Calculate Δd,

distance between

nanorobot si and

obstacle

Calculate time to collision

Δtc based on Δd

If Δd < threshold

θ = θ + 180○

If Δd > threshold and the

target area are in the positive y-

axis direction

Δθij = Δθij + 90○

If Δd > threshold and the

target area are in the

negative y-axis

direction

Δθij = Δθij -90○

Calculate Cartesian

coordinate’s xij,yij from

polar coordinates

where xij is r*Cos(Δθij); yij is r* Sin(Δθij); r is the

radius of the obstacle (0< Δθij<180)

Move nanorobot si from xi1,yi1

to xij,yij

43

Calculate Δd

Calculate time to

collision Δtc

Calculate Δθij

Calculate Cartesian coordinate

s

Move nanorobot si from xi1,yi1

to xij,yij

Calculate coverage of

range si to its neighbors

N(si)

If coverage

value>current

optimum

Current optimum

target=current selected

target

Move nanorobot si to new

best position

End

Movement Control Algorithm

44Simulation Schema

C programming environment.Both nanorobot and obstacle flow with same fluid velocity.Nanorobot and obstacle has same radium. Nanorobot has Re ≈ 10−3 .We consider a constant velocity and ignore some

stochastic factors for simplicity.

45Simulation Parameters

Nanorobot radius 3 µm

Nanorobot radius 10 µm

Red cell radius 7 µm

White cell radius 12 µm

Blood viscosity 10-2 g/cm.s

Blood velocity 100 μm /s

Blood density 1 g/cm3

Tfree 40 s

46Simulation for Swarm of 10 Nanorobots

• Demonstrates that all the nanorobots reach the target area effectively and in 48.2189 seconds


Percentage of coverage in each time interval

Time required for each nanorobot to generate the best value.

Coverage conveys the percentage of target cells received by the nanorobots.

48Comparison between PSO and High Mutation Strategy

ES PSO

Using a set of benchmark test problems

49Comparison between PSO and High Mutation Strategy

ES PSO

The full coverage achieved by all of the nanorobots.

50Outlines√ Introduction






√ Control Movement Algorithm for Swarm Nanorobot in Human Environment


• Conclusions


51Cooperative Control Design for Nanorobots in Drug Delivery

• Existing Control Strategies :-• Ishida , design behavior-based source

• Goodman ,the method is extended to scenarios with a group of nanorobots.

• Gazi , give a control law by combining a potential field control law and a gradient based control law.

• Zhang ,use extremum-seeking control theories

52Existing Control Strategies :- ( Follow up )

• Ogren solved the problem by Least square method

• Bachmayer two strategies, the 1st for a single robot with historical data and the 2nd uses a group of robots with projected gradient estimation.

• Michael , a stochastic gradient-ascent algorithm

• Grzybowski , notes that cancers are more acidic than the rest of the body

• Low pH value

53pH Sensitive Nanorobots

• In a tumor microenvironment, the pH distribution is measurable,

• Chemical Sensors are used to measure changes in volume, concentration, displacement and velocity.

• pH sensitive nanorobot is promoted as an alternative treatment for cancer.

54High pH Therapy

• The 'High pH Therapy‘ prevents cancer cells from undergoing mitosis

• Anaerobic metabolism

• Produces lactic acid

• This alters DNA to allow uncontrolled growth.

• Also causes pain.

55High pH Therapy ( Follow up )

• A swarm of pH sensitive nanorobot: increases the intracellular pH of tumor cells.

• Generates alkaline solution.

• Though given as the chloride salt.

• Follows sodium pathway into cells.

• Raises intracellular pH to 8

• The resulting alkaline environment result in cell death.

• Ends pain

56Tumor Microenvironment

• We concentrate on low pH value of the target searching method

57Control Problem

• We define our control objective to be the group of robots reaching the tumor area, which is defined by certain pH value around the tumor area.

58Control Algorithm for Drug Delivery in tumor

The 1th nanorobot didn't detect an obstacle. It moves according to the PSO algorithm The nanorobot receives pH value, position and

velocity of all the other nanorobots by communication using PSO.

Until one or more nanorobots has a measurement of the pH value less than 7.0.

Consequently the high pH therapy will be applied to destroy the tumor.

59Simulator Platform

Representation of tumor pH environment in drug delivery system

60Simulation Results

A group of 25 pH sensitive nanorobots in drug delivery system

62Outlines

√ Introduction







√ Cooperative Control Design for Nanorobots in Drug Delivery

• Conclusions


63Conclusion

In this study, we Developed cooperative control strategies Concluded that the high strategy is more efficient than

the straight strategy Introduced Behavior-based robot navigation methods

64Conclusion (Follow up)

The proposed scheme effectively constructs an obstacle free self-organized trajectory.

The simulation results constructed an obstacle free self-organized path.

65Conclusion (Follow up)

We designed control strategies for nanorobots to Trace the gradient of the measured pH valuesReach the tumor cells with the lowest pH value.

Also, the capability of the control strategy is illustrated through simulating a scenario of drug delivery by a group of nanorobots.

66Outlines

√ Introduction







√ Cooperative Control Design for Nanorobots in Drug Delivery

√ Conclusions


67Contributions

• Improving the (1+1) evolutionary strategy with 1/5th success rule algorithm

• Comparing between the three mutation strategies

• Improving the PSO algorithm for the purpose of communication between nanorobots.

68Contributions (Follow up)

• Modifying the obstacle avoidance algorithm to enable nanorobot to avoid blood cell.

• Studying the effects of the fluid flow of the blood on the motion of nanorobots.

69Contributions (Follow up)

• Combining PSO and obstacle avoidance algorithms to control nanorobots’ behavior.

• Developing a new control algorithm for pH sensitive nanorobots to

• Simulating the pH tumor environment and the process of nanorobots in the drug delivery system.

70References and Publications

] S.Ahmed, S.E. Amin, T. Alarif ,“A Novel Communication Technique for Nanorobots Swarms Based on Evolutionary Strategies”, Proceedings of the UKSim-AMSS 16th International Conference on Computer Modeling and Simulation.

[2] S.Ahmed, S.E. Amin, T. Alarif ,” Simulation for the Motion of Nanorobots in Human Blood Stream Environment”, Proceedings of the ACV-international Conference on Advances in Computer Vision.

[3] S.Ahmed, S.E. Amin, T. Alarif , “Efficient Cooperative Control System for pH Sensitive Nanorobots in Drug Delivery”, International Journal of Computer Applications( IJCA).

[4] S.Ahmed, S.E. Amin, T. Alarif , “Assessment of Applying Path Planning Technique to Nanorobots in a Human Blood Environment” , Proceedings of the 2014 UKSim-AMSS 8th European Modeling Symposium on Mathematical Modeling and Computer Simulation.

[5] S.Ahmed, S.E. Amin, T. Alarif , “Investigation of Mutation Evolutionary Strategies Applied to Nanorobots”, International Journal of Advanced Robotic Systems, (SUBMITTED).

[6] S.Ahmed, S.E. Amin, T. Alarif , “Swarm Nanorobot Path Planning in a Human Blood Environment”, Pattern Recognition Letters (SUBMITTED).

71SARA YOUSEF SERRY ELSAYED

nanorobotics control for biomedical applications presented by sara yousef serry elsayed m.sc. degree...

Documents

optimization algorithm

nanorobotics control

injecting swarm of nanorobots

success rule algorithm

ph sensitive nanorobots

blood cells

publications contributions

drug efficiency