MATLAB简单机器人视觉控制（仿真3）

        1、前记：主要部分没有改变，就是以前MATLAB简单机器人视觉控制（仿真2）中检测红色物体是用矩形框标记的，然后获得的坐标值比较随意。这一篇中是利用检测到红色后用圆来标记对象。

       2、红色圆标记与机器人运动代码如下：

%%
clear ;
clc;
L1=Link([0       0.4      0.025    pi/2      0     ]); 
L2=Link([pi/2    0        0.56     0         0     ]);
L3=Link([0       0        0.035    pi/2      0     ]);
L4=Link([0       0.515    0        pi/2      0     ]);
L5=Link([pi      0        0        pi/2      0     ]);
L6=Link([0       0.08     0        0         0     ]);
t3r=[L1;L2;L3;L4;L5;L6];
bot=SerialLink(t3r,'name','Useless');
%% 
a = imaqhwinfo;
%[camera_name, camera_id, format] = getCameraInfo(a);
f1=figure;
% set (gcf,'Position',[200,200,400,500], 'color','w');
f2=figure;%robot figure
% Capture the video frames using the videoinput function
% You have to replace the resolution & your installed adaptor name.
vid = videoinput('winvideo',1,'YUY2_640x480');
%sls=videoinput('winvideo',1)
% Set the properties of the video object
 
set(vid,'TriggerRepeat',Inf);
vid.TriggerRepeat= Inf;%持续不断获取图像
set(vid, 'ReturnedColorspace', 'rgb')%设置颜色空间为RGB
vid.FrameGrabInterval = 1;%每隔5帧取一幅图像
preview(vid);%预览窗口
n=50;
%%
while(vid.FramesAcquired<=500)
    
    % Get the snapshot of the current frame
    data=getsnapshot(vid);
    data=imresize(data,[400,500]);
    
    % Now to track red objects in real time
    % we have to subtract the red component 
    % from the grayscale image to extract the red components in the image.
    diff_im = imsubtract(data(:,:,1), rgb2gray(data));
    %Use a median filter to filter out noise
    diff_im = medfilt2(diff_im, [3 3]);
    % Convert the resulting grayscale image into a binary image.
    diff_im = im2bw(diff_im,0.18);
    
    % Remove all those pixels less than 300px
    diff_im = bwareaopen(diff_im,600);
    
    % Label all the connected components in the image.
    bw = bwlabel(diff_im, 8);
    
    % Here we do the image blob analysis.
    % We get a set of properties for each labeled region.
    stats = regionprops('table',bw,'Centroid',...
    'MajorAxisLength','MinorAxisLength');
    figure(f1)
    imshow(data)
   centers = stats.Centroid;
   diameters = mean([stats.MajorAxisLength stats.MinorAxisLength],2);
   radii = diameters/2;
    hold on
    viscircles(centers,radii);
    hold off
    %size function will show the detail about the centers.
      b=size(centers)
%     d=size(radii)
    X=centers(1,1)
    Y=centers(1,2)
    
     Px=X;
     Py=Y;
     Pz=940;
        a2 = 650;
        a3 = 0;
        d3 = 190;
        d4 = 600;
         K = (Px^2+Py^2+Pz^2-a2^2-a3^2-d3^2-d4^2)/(2*a2);
 theta1 = (atan2(Py,Px)-atan2(d3,sqrt(Px^2+Py^2-d3^2)));
                c1 = cos(theta1);
                s1 = sin(theta1);
                theta3 = -1.2*(atan2(a3,d4)-atan2(real(K),real(sqrt(a3^2+d4^2-K^2))));
                c3 = cos(theta3);
                s3 = sin(theta3);
                t23 = atan2((-a3-a2*c3)*Pz-(c1*Px+s1*Py)*(d4-a2*s3),(a2*s3-d4)*Pz+(a3+a2*c3)*(c1*Px+s1*Py));
                theta2 = 1.23*(t23 - theta3);
                c2 = cos(theta2);
                s2 = sin(theta2);
                s23 = ((-a3-a2*c3)*Pz+(c1*Px+s1*Py)*(a2*s3-d4))/(Pz^2+(c1*Px+s1*Py)^2);
                c23 = ((a2*s3-d4)*Pz+(a3+a2*c3)*(c1*Px+s1*Py))/(Pz^2+(c1*Px+s1*Py)^2);
                theta4 = 1.1*atan2(s1+c1,c1*c23-s1*c23 + s23);
                c4 = cos(theta4);
                s4 = sin (theta4);
                s5 = -((c1*c23*c4+s1*s4)+(s1*c23*c4-c1*s4)-(s23*c4));
                c5 = (-c1*s23)+(-s1*s23)+(-c23);
                theta5 = atan2(s5,c5);
                s6 = (c1*c23*s4-s1*c4)-(s1*c23*s4+c1*c4)+(s23*s4);
                c6 = ((c1*c23*c4+s1*s4)*c5-c1*s23*s5)+((s1*c23*c4-c1*s4)*c5-s1*s23*s5)-(s23*c4*c5+c23*s5);
                theta6 = atan2(s6,c6);
   q=[theta1 theta2 theta3 theta4 theta5 theta6];   
   q1=theta1
   q2=theta2
   q3=theta3
   q4=theta4
   q5=theta5
   
 figure(f2)
 bot.plot(q);
end

3、效果：