% ----------------------------------------------------------------------
% In this file full regressor of the robot as well as load regressor
% are obtained using Lagrange formulation of dynamics.
% ----------------------------------------------------------------------
% Get robot description
run('main_ur.m')

generateLoadRegressorFunction = 0;
generateFullRegressorFunction = 0;

% Symbolic generilized coordiates, their first and second deriatives
q_sym = sym('q%d',[6,1],'real');
qd_sym = sym('qd%d',[6,1],'real');
q2d_sym = sym('q2d%d',[6,1],'real');

% ------------------------------------------------------------------------
% Getting gradient of energy functions, to derive dynamics
% ------------------------------------------------------------------------
T_pk = sym(zeros(4,4,6)); % transformation between links
w_kk(:,1) = sym(zeros(3,1)); % angular velocity k in frame k
v_kk(:,1) = sym(zeros(3,1)); % linear velocity of the origin of frame k in frame k
g_kk(:,1) = sym([0,0,9.81])'; % vector of graviatational accelerations in frame k
p_kk(:,1) = sym(zeros(3,1)); % origin of frame k in frame k
DK(:,1) = sym(zeros(10,1)); % gradient of kinetic energy
DP(:,1) = sym([zeros(1,6),[0,0,9.81],0]'); % gradient of gravitational energy

for i = 1:6
    jnt_axs_k = str2num(ur10.robot.joint{i}.axis.Attributes.xyz)';
% Transformation from parent link frame p to current joint frame
    rpy_k = sym(str2num(ur10.robot.joint{i}.origin.Attributes.rpy));
    R_pj = RPY(rpy_k);
    R_pj(abs(R_pj)<sqrt(eps)) = sym(0); % to avoid numerical errors
    p_pj = str2num(ur10.robot.joint{i}.origin.Attributes.xyz)';
    T_pj = sym([R_pj, p_pj; zeros(1,3), 1]); % to avoid numerical errors
% Tranformation from joint frame of the joint that rotaties body k to
% link frame. The transformation is pure rotation
    R_jk = Rot(q_sym(i),sym(jnt_axs_k));
    p_jk = sym(zeros(3,1));
    T_jk = [R_jk, p_jk; sym(zeros(1,3)),sym(1)];
% Transformation from parent link frame p to current link frame k
    T_pk(:,:,i) = T_pj*T_jk;
    z_kk(:,i) = sym(jnt_axs_k);
        
    w_kk(:,i+1) = T_pk(1:3,1:3,i)'*w_kk(:,i) + sym(jnt_axs_k)*qd_sym(i);
    v_kk(:,i+1) = T_pk(1:3,1:3,i)'*(v_kk(:,i) + cross(w_kk(:,i),sym(p_pj)));
    g_kk(:,i+1) = T_pk(1:3,1:3,i)'*g_kk(:,i);
    p_kk(:,i+1) = T_pk(1:3,1:3,i)'*(p_kk(:,i) + sym(p_pj));
        
    beta_K(i,:) = [sym(0.5)*w2wtlda(w_kk(:,i+1)),...
                   v_kk(:,i+1)'*vec2skewSymMat(w_kk(:,i+1)),...
                   sym(0.5)*v_kk(:,i+1)'*v_kk(:,i+1)];
    beta_P(i,:) = [sym(zeros(1,6)), g_kk(:,i+1)',...
                   g_kk(:,i+1)'*p_kk(:,i+1)];
end


% --------------------------------------------------------------------
% Gradient of the kinetic and potential energy of the load
% --------------------------------------------------------------------
% Transformation from link 6 frame to end-effector frame
rpy_ee = sym(str2num(ur10.robot.joint{7}.origin.Attributes.rpy));
R_6ee = RPY(rpy_ee);
R_6ee(abs(R_6ee)<sqrt(eps)) = sym(0); % to avoid numerical errors
p_6ee = str2num(ur10.robot.joint{7}.origin.Attributes.xyz)';
T_6ee = sym([R_6ee, p_6ee; zeros(1,3), 1]); % to avoid numerical errors

w_eeee = T_6ee(1:3,1:3)'*w_kk(:,7);
v_eeee = T_6ee(1:3,1:3)'*(v_kk(:,7) + cross(w_kk(:,i+1),sym(p_6ee)));
g_eeee = T_6ee(1:3,1:3)'*g_kk(:,7);
p_eeee = T_6ee(1:3,1:3)'*(p_kk(:,7) + sym(p_6ee));

beta_Kl = [sym(0.5)*w2wtlda(w_eeee), v_eeee'*vec2skewSymMat(w_eeee),...
            sym(0.5)*(v_eeee'*v_eeee)];
        
beta_Pl = [sym(zeros(1,6)), g_eeee', g_eeee'*p_eeee];


% ---------------------------------------------------------------------
% Dynamic regressor of the load
% ---------------------------------------------------------------------
Lagrl = beta_Kl - beta_Pl;
dLagrl_dq = jacobian(Lagrl,q_sym)';
dLagrl_dqd = jacobian(Lagrl,qd_sym)';
tl = sym(zeros(6,10));
for i = 1:6
   tl = tl + diff(dLagrl_dqd,q_sym(i))*qd_sym(i)+...
                diff(dLagrl_dqd,qd_sym(i))*q2d_sym(i);
end
Y_l = tl - dLagrl_dq;

if generateLoadRegressorFunction
    matlabFunction(Y_l,'File','autogen/load_regressor_UR10E',...
                   'Vars',{q_sym,qd_sym,q2d_sym});
end


% ---------------------------------------------------------------------
% Dynamic regressor of the full paramters
% ---------------------------------------------------------------------
Lagrf = [beta_K(1,:) - beta_P(1,:), beta_K(2,:) - beta_P(2,:),...
         beta_K(3,:) - beta_P(3,:), beta_K(4,:) - beta_P(4,:),...
         beta_K(5,:) - beta_P(5,:), beta_K(6,:) - beta_P(6,:)];
dLagrf_dq = jacobian(Lagrf,q_sym)';
dLagrf_dqd = jacobian(Lagrf,qd_sym)';
tf = sym(zeros(6,60));
for i = 1:6
   tf = tf + diff(dLagrf_dqd,q_sym(i))*qd_sym(i)+...
                diff(dLagrf_dqd,qd_sym(i))*q2d_sym(i);
end
Y_f = tf - dLagrf_dq;

if generateFullRegressorFunction
    matlabFunction(Y_f,'File','autogen/full_regressor_UR10E',...
                   'Vars',{q_sym,qd_sym,q2d_sym});
end

% -------------------------------------------------------------------
% Dynamics matrices of the robot
% -------------------------------------------------------------------
pi_sndrd_sym = sym('pi%d%d',[60,1],'real');
t1 = [beta_P(1,:), beta_P(2,:), beta_P(3,:), beta_P(4,:),...
      beta_P(5,:), beta_P(6,:)];
G_vctr_sym = jacobian(t1, q_sym)'*pi_sndrd_sym;

matlabFunction(G_vctr_sym, 'File','autogen/G_vctr_fcn2',...
                   'Vars',{q_sym, pi_sndrd_sym}, 'Optimize',false);