65 lines
2.6 KiB
Plaintext
65 lines
2.6 KiB
Plaintext
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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('q2d%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.com = sym('com%d',[ndof,1],'real');
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robot.regressor.I = sym('I%d',[ndof,1],'real');
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robot.regressor.I_vec = inertiaMatrix2Vector(robot.regressor.I);
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robot.regressor.mc = robot.regressor.m.*robot.regressor.com;
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robot.regressor.pi = [robot.I_vec(:,i); robot.mc(:,i); robot.m(i)];
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% init matrix
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R = robot.R;
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P = robot.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'
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for i = 2:ndof+1
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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-1) = [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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if(opt.debug)
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sprintf('size of U_=%dx%d.',size(robot.regressor.U));
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end
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robot.regressor.K = [zeros(1,3),Z0]*;
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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
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