MATLAB简单机器人视觉控制（仿真3）

2019-04-14 17:39发布

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        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 {MOD}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 {MOD}后用圆来标记对象。        2、红 {MOD}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 {MOD}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：

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