vision based motion planning using cellular neural network
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Vision based Motion Planning using Cellular Neural Network
Iraji & Bagheri
Supervisor: Dr. Bagheri
Sharif University of Techology 2
Chua and Yang-CNN 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.
Introduction Network
Topology r-Neighborhood The Basic Cell Space
Invariance State Equation Templates Block Diagram
Sharif University of Techology 3
Network Topology
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)
Introduction Network
Topology r-Neighborhood The Basic Cell Space
Invariance State Equation Templates Block Diagram
Sharif University of Techology 4
r - Neighborhood 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)
Introduction Network
Topology r-Neighborhood The Basic Cell Space
Invariance State Equation Templates Block Diagram
Sharif University of Techology 5
The Basic Cell 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:
Introduction Network
Topology r-Neighborhood The Basic Cell Space
Invariance State Equation Templates Block Diagram
Sharif University of Techology 6
Space Invariance Inner cells.
– same circuit elements and element values– has (2r+1)^2 neighbors – Space invariance.
Boundary cells.
Boundary Cells Inner Cells
Introduction Network
Topology r-Neighborhood The Basic Cell Space
Invariance State Equation Templates Block Diagram
Sharif University of Techology 7
State Equation
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.
Introduction Network
Topology r-Neighborhood The Basic Cell Space
Invariance State Equation Templates Block Diagram
Sharif University of Techology 8
Templates 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.
Introduction Network
Topology r-Neighborhood The Basic Cell Space
Invariance State Equation Templates Block Diagram
Sharif University of Techology 9
Block diagram of one cell
The first-order non-linear differential equation defining the dynamics of a cellular neural network
Introduction Network
Topology r-Neighborhood The Basic Cell Space
Invariance State Equation Templates Block Diagram
Sharif University of Techology 10
ROBOT PATH PLANNING USING 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.
Introduction Network
Topology r-Neighborhood The Basic Cell Space
Invariance State Equation Templates Block Diagram Path Planning
By CNN
Sharif University of Techology 11
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 12
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.
Introduction Network
Topology r-Neighborhood The Basic Cell Space
Invariance State Equation Templates Block Diagram Path Planning
By CNN Flowchart of
Planning Distance
Evaluation
Sharif University of Techology 13
SUCCESSIVE COMPARISONS METHOD
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
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
Sharif University of Techology 14
Motion Planning Methods Global Approaches Basic concepts
Proposed Model (FAPF)
Local Minima Stochastic
Learning Automata
Adaptive planning system (AFAPF)
Conclusions
Randomized Approaches Genetic Algorithms
Local Approaches: Need heuristics, e. g. the estimation of local gradients in a potential field
Decomposition
Road-Map
Retraction Methods
Require a preprocessing stage (a graph structure of the connectivity of the robot’s free space)