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NANOROBOTICS CONTROL FOR BIOMEDICAL APPLICATIONS

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1 NANOROBOTICS 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. Under supervision of Prof. Dr. TAHA ALARIF Professor in Computer Science Department, Faculty of Computer & Information sciences, Ain Shams University. Prof. Dr. SAFAA AMIN Associate Professor in Scientific Computing Department, Faculty of Computer & Information Sciences- Ain Shams University.

2 Presentation 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

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

4 Introduction ( 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.

5 Introduction ( 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

6 Outlines Literature Review Optimization and Learning Algorithms
√ 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

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

8 Features of nanorobots
Size Bio Compatibility Powering Communication Navigation Diffusion Swarms Removing

9 Outlines √ 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

10 Optimization 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.

11 Evolutionary 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)

12 Evolutionary 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) Then i. If (ps>1/5) Then σ[t]= σ[t-n]/c ii. Else If (ps<1/5) Then σ [t]= σ[t-n]·c Else If (ps=1/5) Then σ [t]= σ[t-n]

16 Swarm 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

17 Particle 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

18 Performance 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

19 Particle 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) Fitness function Fitness value S

20 Particle Swarm Optimization(PSO)
Pbest. Gbest. The basic concept of PSO lies in accelerating each particle toward its pbest and the gbest locations.

21 Particle Swarm Optimization(PSO) Algorithm
Basic algorithm of PSO Initialize the swarm form the solution space Evaluate the fitness of each particle Update individual and global bests Update velocity and position of each particle Go to step2, and repeat until termination condition

22 PSO and ES Comparison Differences 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

23 Outlines √ 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

24 Cooperative 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

25 Mutation 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

26 Simulation Results Straight Strategy Swap Strategy
High Probability Strategy

27 Simulation Analysis The final target area The average time

28 Simulation Analysis ( FOLLOW UP )
The average time in the Partial optimization level The average time in the Full optimization level

29 Simulation Analysis ( FOLLOW UP )
The swap strategy is inefficient mutation strategy The straight and high strategies have almost the same number of swarm of nanorobots High strategy is more efficient than straight strategy in the partial and full optimization levels.

30 Outlines √ 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

31 Human 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

32 Blood 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.

33 Outlines √ Introduction √ Literature Review
√ Optimization and Learning Algorithms √ Cooperative Control of Swarm Nanorobot Target Detection √ Human Blood Stream Environment Polar Coordinate Obstacle Avoidance Algorithm Control Design of Swarm Nanorobot in Human Environment Cooperative Control Design for Nanorobots in Drug Delivery Conclusions Contributions and Publications

34 Polar Coordinate Obstacle Avoidance Algorithm
The nanorobot have sensors to detect obstacles (blood cells ) Self organized trajectory planning is required to avoid obstacles .

35 Polar 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 :

36 Outlines √ 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

37 Control 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.

38 Fj (k) = max F j (si where si ∈S, si ∉Tobstacle )
Local 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, si ∉Tobstacle )

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

40 Global 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 )

41 Movement Control Algorithm
The improved PSO algorithm the obstacle avoidance algorithm The improved PSO algorithm.

42 Polar 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 ○ 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 Movement Control Algorithm
Calculate Δd Calculate time to collision Δtc Calculate Δθij Calculate Cartesian coordinates 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

44 Simulation 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.

45 Simulation 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

46 Simulation for Swarm of 10 Nanorobots
Demonstrates that all the nanorobots reach the target area effectively and in seconds

47 Simulation Analysis Coverage conveys the percentage of target cells received by the nanorobots. Percentage of coverage in each time interval Time required for each nanorobot to generate the best value.

48 Comparison between PSO and High Mutation Strategy
ES PSO Using a set of benchmark test problems

49 Comparison between PSO and High Mutation Strategy
ES PSO The full coverage achieved by all of the nanorobots.

50 Outlines √ 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

51 Cooperative 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

52 Existing 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

53 pH 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.

54 High 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.

55 High 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

56 Tumor Microenvironment
We concentrate on low pH value of the target searching method

57 Control 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.

58 Control 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.

59 Representation of tumor pH environment in drug delivery system
Simulator Platform Representation of tumor pH environment in drug delivery system

60 Simulation Results A group of 25 pH sensitive nanorobots in drug delivery system

61 Simulation Analysis

62 Outlines √ 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

63 Conclusion 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

64 Conclusion (Follow up)
The proposed scheme effectively constructs an obstacle free self-organized trajectory. The simulation results constructed an obstacle free self- organized path.

65 Conclusion (Follow up)
We designed control strategies for nanorobots to Trace the gradient of the measured pH values Reach 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.

66 Outlines √ 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

67 Contributions 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.

68 Contributions (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.

69 Contributions (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.

70 References 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).

71 SARA YOUSEF SERRY ELSAYED


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