%% R1000
N=9;
ndof = N;
% traj
time = 0:0.01:1;
f=1;    
q_J = sin(2*pi*f*time);
qd_J = (2*pi*f)*cos(2*pi*f*time);
qdd_J = -(2*pi*f)^2*sin(2*pi*f*time);
zero_ = zeros(1,length(q_J));

thetalist = [q_J;zero_;zero_;zero_;zero_;zero_;zero_;zero_;zero_]';
dthetalist = [qd_J;zero_;zero_;zero_;zero_;zero_;zero_;zero_;zero_]';
ddthetalist = [qdd_J;zero_;zero_;zero_;zero_;zero_;zero_;zero_;zero_]';
% Dynamics parameters
link_mass = robot.m;
com_pos = robot.com;
link_inertia = robot.I;


% init regressor
robot.regressor.m = link_mass;
robot.regressor.mc_x = com_pos(1,:);
robot.regressor.mc_y = com_pos(2,:);
robot.regressor.mc_z = com_pos(3,:);
robot.regressor.ixx = link_inertia(1,1,:);
robot.regressor.ixy = link_inertia(1,2,:);
robot.regressor.ixz = link_inertia(1,3,:);
robot.regressor.iyy = link_inertia(2,2,:);
robot.regressor.iyz = link_inertia(2,3,:);
robot.regressor.izz = link_inertia(3,3,:);
robot.regressor.im = sym('im%d',[ndof,1],'real');
for i = 1:ndof
    robot.regressor.pi(:,i) = [robot.regressor.m(i),robot.regressor.mc_x(i),robot.regressor.mc_y(i),...
        robot.regressor.mc_z(i),robot.regressor.ixx(i),robot.regressor.ixy(i),...
        robot.regressor.ixz(i),robot.regressor.iyy(i),robot.regressor.iyz(i),robot.regressor.izz(i)]';
end

[nLnkPrms, nLnks] = size(robot.regressor.pi);
robot.regressor.pi = reshape(robot.regressor.pi, [nLnkPrms*nLnks, 1]);
% init matrix
R = robot.R;
P = robot.t;
w = robot.vel.w ;
dw = robot.vel.dw ;
dv = robot.vel.dv ;
switch opt.LD_method
    case 'Direct'
        switch opt.KM_method
            case 'MDH'
                for i = 1:ndof
                    p_skew(:,:,i) = vec2skewSymMat(P(:,:,i));
                    w_skew(:,:,i) = vec2skewSymMat(w(:,i));
                    dw_skew(:,:,i) = vec2skewSymMat(dw(:,i));
                    dv_skew(:,:,i) = vec2skewSymMat(dv(:,i));
                    w_l(:,:,i) = vec2linearSymMat(w(:,i));
                    dw_l(:,:,i) = vec2linearSymMat(dw(:,i));
                    % size of matrix A is 6*10, need to -1
                    robot.regressor.A(:,:,i) = [dv(:,i),dw_skew(:,:,i)+w_skew(:,:,i)*w_skew(:,:,i),zeros(3,6); ...
                        zeros(3,1),-dv_skew(:,:,i),dw_l(:,:,i)+w_skew(:,:,i)*w_l(:,:,i)];
                end
                % construct matrix U, size: [6*n,10*n]
                % U_ = sym(zeros([6*ndof,10*ndof]));
                U_ = [];
                for i = 1:ndof
                    % tricky
                    for j = i:ndof
                        if(j == i)
                            TT = eye(6,6);
                            U_row = TT*robot.regressor.A(:,:,j);
                        else
                            TT = TT*Adjoint(RpToTrans(R(:,:,j),P(:,:,j)));
                            U_row = [U_row,TT*robot.regressor.A(:,:,j)];
                        end
                    end
                    U_ = [U_;zeros(6,(i-1)*10),U_row];
                end
                robot.regressor.U = U_;
                delta_ = zeros([ndof,6*ndof]);
                for i = 1:ndof
                    delta_(i,6*i) = 1;
                end
                robot.regressor.K = delta_*robot.regressor.U;
                if(opt.debug)
                    sprintf('size of U=%dx%d.',size(robot.regressor.U));
                    sprintf('size of K=%dx%d.',size(robot.regressor.K));
                end
        end

    case 'Lagrange'
        disp('TODO opt.LD_method Lagrange!')
        return;
    otherwise
        disp('Bad opt.KM_method!')
        return;
end