# Vision based Motion Planning using Cellular Neural Network - PowerPoint PPT Presentation

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Vision based Motion Planning using Cellular Neural Network. Iraji & Bagheri. Supervisor: Dr. Bagheri. Chua and Yang-CNN . Introduction Network Topology r-Neighborhood The Basic Cell Space Invariance State Equation Templates Block Diagram. Introduced 1988. Image Processing

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Vision based Motion Planning using Cellular Neural Network

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## Vision based Motion Planning using Cellular Neural Network

Iraji & Bagheri

Supervisor: Dr. Bagheri

### Chua and Yang-CNN

• Introduction

• Network Topology

• r-Neighborhood

• The Basic Cell

• Space Invariance

• State Equation

• Templates

• Block Diagram

• Introduced 1988.

• Image Processing

• Multi-disciplinary:

• Robotic

• Biological vision

• Image and video signal processing

• Generation of static and dynamic patterns:

• Chua & Yang-CNN is widely used due to

• Versatility versus simplicity.

• Easiness of implementation.

Sharif University of Techology

### Network Topology

• Introduction

• Network Topology

• r-Neighborhood

• The Basic Cell

• Space Invariance

• State Equation

• Templates

• Block Diagram

• Regular grid , i.e. matrix, of cells.

• In the 2-dimensional case:

• Each cell corresponds to a pixel in the image.

• A Cell is identified by its position in the grid.

• Local connectivity.

• Direct interaction among adjacent cells.

• Propagation effect -> Global interaction.

C(I , J)

Sharif University of Techology

### r - Neighborhood

• Introduction

• Network Topology

• r-Neighborhood

• The Basic Cell

• Space Invariance

• State Equation

• Templates

• Block Diagram

• The set of cells within a certain distance r to cell C(i,j). where r >=0.

• Denoted Nr(i,j).

• Neighborhood size is (2r+1)x(2r+1)

Sharif University of Techology

### The Basic Cell

• Introduction

• Network Topology

• r-Neighborhood

• The Basic Cell

• Space Invariance

• State Equation

• Templates

• Block Diagram

• Cell C(i,j) is a dynamical system

• The state evolves according to prescribed state equation.

• Standard Isolated Cell: contribution of state and input variables is given by using weighting coefficients:

Sharif University of Techology

### Space Invariance

• Introduction

• Network Topology

• r-Neighborhood

• The Basic Cell

• Space Invariance

• State Equation

• Templates

• Block Diagram

• Inner cells.

• same circuit elements and element values

• has (2r+1)^2 neighbors

• Space invariance.

• Boundary cells.

Inner Cells

Boundary Cells

Sharif University of Techology

### State Equation

• Introduction

• Network Topology

• r-Neighborhood

• The Basic Cell

• Space Invariance

• State Equation

• Templates

• Block Diagram

• xij is the state of cell Cij.

• I is an independent bias constant.

• yij(t) = f(xij(t)), where f can be any convenient non-linear function.

• The matrices A(.) and B(.) are known as cloning templates.

• constant external input uij.

Sharif University of Techology

### Templates

• Introduction

• Network Topology

• r-Neighborhood

• The Basic Cell

• Space Invariance

• State Equation

• Templates

• Block Diagram

• The functionality of the CNN array can be controlled by the cloning template A, B, I

• Where A and B are (2r+1) x (2r+1) real matrices

• I is a scalar number in two dimensional cellular neural networks.

Sharif University of Techology

### Block diagram of one cell

• Introduction

• Network Topology

• r-Neighborhood

• The Basic Cell

• Space Invariance

• State Equation

• Templates

• Block Diagram

• The first-order non-linear differential equation defining the dynamics of a cellular neural network

Sharif University of Techology

### ROBOT PATH PLANNING USING CNN

• Introduction

• Network Topology

• r-Neighborhood

• The Basic Cell

• Space Invariance

• State Equation

• Templates

• Block Diagram

• Path Planning By CNN

• Environment with obstacles must be divided into discrete images.

• Representing the workspace in the form of an M×N cells.

• Having the value of the pixel in the interval [-1,1].

• Binary image, that represent obstacle and target and start positions.

Sharif University of Techology

### Flowchart of Motion Planning

• Introduction

• Network Topology

• r-Neighborhood

• The Basic Cell

• Space Invariance

• State Equation

• Templates

• Block Diagram

• Path Planning By CNN

• Flowchart of Planning

CNN Computing

Sharif University of Techology

### Distance Evaluation

• Introduction

• Network Topology

• r-Neighborhood

• The Basic Cell

• Space Invariance

• State Equation

• Templates

• Block Diagram

• Path Planning By CNN

• Flowchart of Planning

• Distance Evaluation

• Distance evaluation between free points from the workspace and the target point.

• Using the template explore.tem

• a is a nonlinear function, and depends on the difference yij-ykl.

Sharif University of Techology

### SUCCESSIVE COMPARISONS METHOD

• Introduction

• Network Topology

• r-Neighborhood

• The Basic Cell

• Space Invariance

• State Equation

• Templates

• Block Diagram

• Path Planning By CNN

• Flowchart of Planning

• Distance Evaluation

• Successive Comparison

• Path planning method through successive comparisons.

• Smallest neighbor cell from eight possible directions N, S, E, V, SE, NE, NV, SV, is chosen.

• Template from the shift.tem family

Sharif University of Techology

### Motion Planning Methods

Decomposition

• Basic concepts

• Proposed Model (FAPF)

• Local Minima

• Stochastic Learning Automata

• Conclusions

• Global Approaches