Vision based Motion Planning using Cellular Neural Network

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

Vision based Motion Planning using Cellular Neural Network. Iraji &amp; 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

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

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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.

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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

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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.

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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