91 lines
4.0 KiB
Matlab
91 lines
4.0 KiB
Matlab
function robot = get_regressor(robot, opt)
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% Create symbolic generilized coordiates, their first and second deriatives
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ndof = robot.ndof;
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q_sym = sym('q%d',[ndof+1,1],'real');
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qd_sym = sym('qd%d',[ndof+1,1],'real');
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q2d_sym = sym('qdd%d',[ndof+1,1],'real');
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% init regressor
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robot.regressor.m = sym('m%d',[ndof,1],'real');
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robot.regressor.mc_x = sym('mc%d_x',[ndof,1],'real');
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robot.regressor.mc_y = sym('mc%d_y',[ndof,1],'real');
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robot.regressor.mc_z = sym('mc%d_z',[ndof,1],'real');
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robot.regressor.ixx = sym('i%d_xx',[ndof,1],'real');
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robot.regressor.ixy = sym('i%d_xy',[ndof,1],'real');
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robot.regressor.ixz = sym('i%d_xz',[ndof,1],'real');
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robot.regressor.iyy = sym('i%d_yy',[ndof,1],'real');
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robot.regressor.iyz = sym('i%d_yz',[ndof,1],'real');
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robot.regressor.izz = sym('i%d_zz',[ndof,1],'real');
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robot.regressor.im = sym('im%d',[ndof,1],'real');
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for i = 1:ndof
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robot.regressor.pi(:,i) = [robot.regressor.m(i),robot.regressor.mc_x(i),robot.regressor.mc_y(i),...
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robot.regressor.mc_z(i),robot.regressor.ixx(i),robot.regressor.ixy(i),...
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robot.regressor.ixz(i),robot.regressor.iyy(i),robot.regressor.iyz(i),robot.regressor.izz(i)]';
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end
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[nLnkPrms, nLnks] = size(robot.regressor.pi);
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robot.regressor.pi = reshape(robot.regressor.pi, [nLnkPrms*nLnks, 1]);
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% init matrix
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R = robot.kine.R;
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P = robot.kine.t;
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w = robot.vel.w ;
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dw = robot.vel.dw ;
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dv = robot.vel.dv ;
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switch opt.LD_method
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case 'Direct'
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switch opt.KM_method
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case {'MDH' , 'SCREW'}
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for i = 1:ndof
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p_skew(:,:,i) = vec2skewSymMat(P(:,:,i));
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w_skew(:,:,i) = vec2skewSymMat(w(:,i));
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dw_skew(:,:,i) = vec2skewSymMat(dw(:,i));
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dv_skew(:,:,i) = vec2skewSymMat(dv(:,i));
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w_l(:,:,i) = vec2linearSymMat(w(:,i));
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dw_l(:,:,i) = vec2linearSymMat(dw(:,i));
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% size of matrix A is 6*10, need to -1
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robot.regressor.A(:,:,i) = [dv(:,i),dw_skew(:,:,i)+w_skew(:,:,i)*w_skew(:,:,i),zeros(3,6); ...
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zeros(3,1),-dv_skew(:,:,i),dw_l(:,:,i)+w_skew(:,:,i)*w_l(:,:,i)];
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end
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% construct matrix U, size: [6*n,10*n]
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% U_ = sym(zeros([6*ndof,10*ndof]));
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U_ = [];
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for i = 1:ndof
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% tricky
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for j = i:ndof
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if(j == i)
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TT = eye(6,6);
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U_row = TT*robot.regressor.A(:,:,j);
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else
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TT = TT*Adjoint(RpToTrans(R(:,:,j),P(:,:,j)));
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U_row = [U_row,TT*robot.regressor.A(:,:,j)];
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end
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end
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U_ = [U_;zeros(6,(i-1)*10),U_row];
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end
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robot.regressor.U = U_;
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delta_ = zeros([ndof,6*ndof]);
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for i = 1:ndof
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delta_(i,6*i) = 1;
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end
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robot.regressor.K = delta_*robot.regressor.U;
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if(opt.debug)
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sprintf('size of U=%dx%d.',size(robot.regressor.U))
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sprintf('size of K=%dx%d.',size(robot.regressor.K))
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end
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end
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% matlabFunction(robot.regressor.K,'File',sprintf('autogen/standard_regressor_%s',opt.robotName),...
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% 'Vars',{q_sym,qd_sym,q2d_sym});
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if(opt.reGenerate)
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tic
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matlabFunction(robot.regressor.K,'File',sprintf('autogen/standard_regressor_%s',opt.robotName),...
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'Vars',{q_sym});
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compileTime = toc;
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fprintf("The total compile time was: = %d minutes, %d seconds\n", floor(compileTime/60), ceil(rem(compileTime,60)));
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end
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% matlabFunction(Y_f,'File','autogen/standard_regressor_Two_bar',...
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% 'Vars',{q_sym,qd_sym,q2d_sym});
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case 'Lagrange'
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disp('TODO opt.LD_method Lagrange!')
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return;
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otherwise
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disp('Bad opt.KM_method!')
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return;
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end |