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Distance Transform Path Planning Algorithm

January 26, 2014 11:24 am / 2 Comments on Distance Transform Path Planning Algorithm

clc;close all;clear all;

Input Map

RGB = imread('Mapn.png');
figure('Position',[600 0 600 1000],'color','k');
image(RGB);
hold on;
axis off
axis image
hold off;
% Convert to binary image matrix and inverse matrix values
I = rgb2gray(RGB);
binaryImage = im2bw(RGB, 0.3);
binaryImage = 1-binaryImage;
% Inital pose
xs = 540; ys = 750;
% Goal Pose
xg = 150; yg = 150;

dist_01

Distance to Goal

sz  = size(binaryImage);
for i = 1:sz(1)
    for j = 1:sz(2)
       rg(i,j) = sqrt((i-yg)^2 +(j - xg)^2);
      end
end
% Pose Matrix
pose = zeros(sz(1),sz(2));
pose(ys,xs) = 1;
pose(yg,xg) = 1;
Di = 15; % Distance of influence of the obstacles
% Compute for every pixel the distance to the nearest obstacle(non zero
% elements which after inversion corespondent to the obstacles)
[D,IDX] = bwdist(binaryImage);
for i = 1:sz(1)
    for j = 1:sz(2)
        rd = D(i,j);
        if (rd <= Di)
            rg(i,j)  = 1e6;
        end
    end
end

Distance Transform Path Planning Algorithm

figure('Position',[600 0 600 1000],'color','k');
hold on;
image(RGB);
colormap gray
contour(rg,15);
spy(pose,'*r');
axis off
axis image
title('Distance Transform Path Planning Algorithm','color','w');
x = xs; y = ys;
last = rg(y-1,x-1);
while (x ~= xg)||(y ~= yg)
    dis =[ rg(y-1,x-1), rg(y-1,x),rg(y-1,x+1);
        rg(y,x-1), rg(y,x) ,rg(y,x+1);
        rg(y+1,x-1), rg(y+1,x),rg(y+1,x+1)];
     m = min(dis(:));
     [r,c] = find(dis == m);
   rg(y,x) = 1E6;
   y = y-2+r;
   x = x-2+c;
   pose(y,x) = 1;
  end
spy(pose,'.r');
set(gcf, 'InvertHardCopy', 'off');
hold off;

dist_02

Potential Field Path Planning

January 26, 2014 11:11 am / 9 Comments on Potential Field Path Planning

clc;close all;clear all;

Input Map

RGB = imread('Mapn.png');
figure('Position',[600 0 600 1000],'color','k');
image(RGB);
hold on;
axis off
axis image

potential_01

Convert to binary image matrix and inverse matrix values

I = rgb2gray(RGB);
binaryImage = im2bw(RGB, 0.3);
binaryImage = 1-binaryImage;
% Inital pose
xs = 540; ys = 750;
% Goal Pose
xg = 150; yg = 150;

Attractive potential

sz  = size(binaryImage);
for i = 1:sz(1)
    for j = 1:sz(2)
       rg = sqrt((i-yg)^2 +(j - xg)^2);
       U_att(i,j) = 0.5*rg^2;
    end
end
contour(U_att,40);

set(gcf, 'InvertHardCopy', 'off');
% Pose Matrix
pose = zeros(sz(1),sz(2));
pose(ys,xs) = 1;
pose(yg,xg) = 1;
spy(pose,'*r');
title({'Attractive Potential Field';...
   'Circles Center Corespondents to the Goal Position '},'color','w');
hold off;

potential_02

Repulsive potential

Di = 35; % Distance of influence of the object
% Compute for every pixel the distance to the nearest obstacle(non zero
% elements which after inversion corespondent to the obstacles)
[D,IDX] = bwdist(binaryImage);
for i = 1:sz(1)
    for j = 1:sz(2)
        rd = D(i,j);
        if (rd <= Di)
            U_rep(i,j)  = 0.5*(rd - Di)^2;
        else
            U_rep(i,j)  = 0;
        end
    end
end
figure('Position',[600 0 600 1000],'color','k');
hold on;
colormap jet;
contourf(U_rep,5);
spy(pose,'*r');
axis off
axis image
set(gcf, 'InvertHardCopy', 'off');
title('Repulsive Potential Field','color','w');
hold off;

potential_03

Summary Potential Field

k_s  = 2; % Scaling factor
U_sum = U_rep/max(U_rep(:))+k_s*U_att/max(U_att(:));
figure('Position',[600 0 600 1000],'color','k');
hold on;
contourf(U_sum,15);
spy(pose,'*r');
axis off
axis image
title('Summary Potential Field','color','w');
set(gcf, 'InvertHardCopy', 'off');
hold off;

potential_04

Potential Field Path Planning

figure('Position',[600 0 600 1000],'color','k');
hold on;
contourf(U_sum,15);
spy(pose,'*r');
axis off
axis image
title('Potential Field Path Planning,*Note the Potential Trap','color','w');
x = xs; y = ys;
last = U_sum(y-1,x-1);
while (x ~= xg)||(y ~= yg)
    dis =[ U_sum(y-1,x-1), U_sum(y-1,x),U_sum(y-1,x+1);
        U_sum(y,x-1), U_sum(y,x) ,U_sum(y,x+1);
        U_sum(y+1,x-1), U_sum(y+1,x),U_sum(y+1,x+1)];
     m = min(dis(:));
     [r,c] = find(dis == m);
   U_sum(y,x) = inf;
   y = y-2+r;
   x = x-2+c;
   pose(y,x) = 1;
  end
spy(pose,'.r');
set(gcf, 'InvertHardCopy', 'off');
hold off;

potential_05

 

Computing a Skeleton of a Map for Mobile Robots,Path Planning

January 13, 2014 4:28 pm / Leave a comment

clc;close all;clear all;

Input Map

RGB = imread('Map1.png');
figure('Position',[0 0 800 800]);
imshow(RGB);


Robot Map1


Convert to binary image matrix and inverse matrix values


I = rgb2gray(RGB);
binaryImage = im2bw(RGB, 0.3);
binaryImage = 1-binaryImage;
figure('Position',[0 0 800 800]);
imshow(binaryImage);


Warning: Image is too big to fit on screen; displaying at
50%


Robot Map 1


Compute for every pixel the distance to the nearest obstacle(non zero elements which after inversion corespondent to the obstacles)


[D,IDX] = bwdist(binaryImage);
figure('Position',[0 0 800 800],'color','k')
colormap bone
image(D);
axis off          % Remove axis ticks and numbers
axis image
set(gcf, 'InvertHardCopy', 'off');


Robot Map 3


Taking the Laplacian of the distance transform. Play with the treshold value 0.15 to remove or add more futures in skeleton


lap = del2(D);
sz  = size(lap);
for i = 1:sz(1)
    for j = 1:sz(2)
         if( abs(lap(i,j)) < 0.15)
       lap(i,j) = 0;
    end
    end
end
lap = flipdim(lap ,1);           %# vertical flip
figure('Position',[0 0 800 800],'color','k');
colormap winter;
contour(lap,'DisplayName','lap');
axis off
axis image
set(gcf, 'InvertHardCopy', 'off');


Robot Skeleton




 






	




			
		


		




	

		

	

				
		

		
		



	
	


			
		
		
	
				





	
	

		

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