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feature/R1000-identification #2

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cosmic_power merged 48 commits from feature/R1000-identification into main 2024-12-16 15:25:53 +00:00
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8 changed files with 198 additions and 24 deletions
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2
Identification_main.m
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@ -24,4 +24,4 @@ robot = get_regressor(robot,opt);
% symbol matched % symbol matched
% verify_regressor_R1000; % verify_regressor_R1000;
robot = get_baseParams(robot, opt); robot = get_baseParams(robot, opt);
% robot = estimate_dyn(robot,opt); robot = estimate_dyn(robot,opt);

21
codegen/base_6AxisFT_regressor_R1000_DVT.m Normal file
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@ -0,0 +1,21 @@
function base_regrssor = base_6AxisFT_regressor_R1000_DVT(theta,dtheta,ddtheta,baseQR)
opt.robot_def = 'direct';
opt.KM_method = 'SCREW';
opt.Vel_method = 'Direct';
opt.LD_method = 'Direct';
opt.debug = false;
opt.robotName = 'R1000_DVT';
opt.reGenerate = false;
opt.Isreal = true;
opt.isJointTorqueSensor = false;
opt.isSixAxisFTSensor = true;
file=[];
robot = get_robot_R1000(theta,dtheta,ddtheta,file,opt);
robot = get_Kinematics(robot, opt);
robot = get_velocity(robot, opt);
robot = get_regressor(robot, opt);
% get base params
robot.baseQR = baseQR;
robot.baseQR.regressor = robot.regressor.U*robot.baseQR.permutationMatrix(:,1:robot.baseQR.numberOfBaseParameters);
base_regrssor = robot.baseQR.regressor;

2
codegen/base_regressor_R1000_DVT.m
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@ -7,6 +7,8 @@ opt.debug = false;
opt.robotName = 'R1000_DVT'; opt.robotName = 'R1000_DVT';
opt.reGenerate = false; opt.reGenerate = false;
opt.Isreal = true; opt.Isreal = true;
opt.isJointTorqueSensor = false;
opt.isSixAxisFTSensor = true;
file=[]; file=[];
robot = get_robot_R1000(theta,dtheta,ddtheta,file,opt); robot = get_robot_R1000(theta,dtheta,ddtheta,file,opt);

18
codegen/regressor_R1000_DVT.m Normal file
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@ -0,0 +1,18 @@
function regrssor = regressor_R1000_DVT(theta,dtheta,ddtheta)
opt.robot_def = 'direct';
opt.KM_method = 'SCREW';
opt.Vel_method = 'Direct';
opt.LD_method = 'Direct';
opt.debug = false;
opt.robotName = 'R1000_EVT';
opt.reGenerate = false;
opt.Isreal = true;
opt.isJointTorqueSensor = false;
opt.isSixAxisFTSensor = true;
file=[];
robot = get_robot_R1000(theta,dtheta,ddtheta,file,opt);
robot = get_Kinematics(robot, opt);
robot = get_velocity(robot, opt);
robot = get_regressor(robot, opt);
regrssor = robot.regressor.U;

2
codegen/standard_regressor_R1000_DVT.m
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@ -7,6 +7,8 @@ opt.debug = false;
opt.robotName = 'R1000_EVT'; opt.robotName = 'R1000_EVT';
opt.reGenerate = false; opt.reGenerate = false;
opt.Isreal = true; opt.Isreal = true;
opt.isJointTorqueSensor = false;
opt.isSixAxisFTSensor = true;
file=[]; file=[];
robot = get_robot_R1000(theta,dtheta,ddtheta,file,opt); robot = get_robot_R1000(theta,dtheta,ddtheta,file,opt);

109
estimate_dyn.asv Normal file
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@ -0,0 +1,109 @@
function robot = estimate_dyn(robot,opt)
% -------------------------------------------------------------------
% Get datas
% ------------------------------------------------------------------------
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);
q=[q_J;q_J;q_J;q_J;q_J;q_J;q_J;q_J;q_J];
qd=[qd_J;qd_J;qd_J;qd_J;qd_J;qd_J;qd_J;qd_J;qd_J];
qdd=[qdd_J;qdd_J;qdd_J;qdd_J;qdd_J;qdd_J;qdd_J;qdd_J;qdd_J];
g = [0; 0; -9.8];
% pi -> [m;mc;I] 10 element
[nLnkPrms, nLnks] = size(robot.pi);
robot_pi = reshape(robot.pi, [nLnkPrms*nLnks, 1]);
if opt.isJointTorqueSensor
tau = zeros([robot.ndof,length(q_J)]);
for i = 1:length(q_J)
% regressor = standard_regressor_Two_bar(q(:,i),qd(:,i),qdd(:,i));
standard_regressor_func = sprintf('standard_regressor_%s',opt.robotName);
regressor = feval(standard_regressor_func,q(:,i),qd(:,i),qdd(:,i));
tau(:,i)=regressor*robot_pi;
end
elseif opt.isSixAxisFTSensor
tau = zeros([6,length(q_J)]);
for i = 1:length(q_J)
regressor_func = sprintf('regressor_%s',opt.robotName);
regressor = feval(regressor_func,q(:,i),qd(:,i),qdd(:,i));
FT = regressor*robot_pi;
joint_idex = robot.ndof-2;
tau(:,i) = FT(6*(joint_idex-1)+1:6*(joint_idex));
end
end
idntfcnTrjctry.t = time;
idntfcnTrjctry.q = q;
idntfcnTrjctry.qd = qd;
idntfcnTrjctry.qdd = qdd;
idntfcnTrjctry.tau = tau;
% -------------------------------------------------------------------
% Generate Regressors based on data
% ------------------------------------------------------------------------
drvGains = [];
baseQR = robot.baseQR;
[Tau, Wb] = buildObservationMatrices(idntfcnTrjctry, baseQR, drvGains, opt);
% ---------------------------------------------------------------------
% Estimate parameters
% ---------------------------------------------------------------------
sol = struct;
method = 'OLS';
if strcmp(method, 'OLS')
% Usual least squares
[sol.pi_b, sol.pi_fr] = ordinaryLeastSquareEstimation(Tau, Wb);
elseif strcmp(method, 'PC-OLS')
% Physically consistent OLS using SDP optimization
[sol.pi_b, sol.pi_fr, sol.pi_s] = physicallyConsistentEstimation(Tau, Wb, baseQR);
else
error("Chosen method for dynamic parameter estimation does not exist");
end
robot.sol = sol;
% Local unctions
function [Tau, Wb] = buildObservationMatrices(idntfcnTrjctry, baseQR, drvGains,opt)
% The function builds observation matrix for UR10E
E1 = baseQR.permutationMatrix(:,1:baseQR.numberOfBaseParameters);
Wb = []; Tau = [];
for i = 1:1:length(idntfcnTrjctry.t)
% Yi = regressorWithMotorDynamics(idntfcnTrjctry.q(i,:)',...
% idntfcnTrjctry.qd(i,:)',...
% idntfcnTrjctry.q2d(i,:)');
% Yfrctni = frictionRegressor(idntfcnTrjctry.qd_fltrd(i,:)');
% Ybi = [Yi*E1, Yfrctni];
if opt.isJointTorqueSensor
base_regressor_func = sprintf('base_regressor_%s',opt.robotName);
Yb = feval(base_regressor_func, idntfcnTrjctry.q(:,i), ...
idntfcnTrjctry.qd(:,i),idntfcnTrjctry.qdd(:,i),baseQR);
Wb = vertcat(Wb, Yb);
% Tau = vertcat(Tau, diag(drvGains)*idntfcnTrjctry.i_fltrd(i,:)');
Tau = vertcat(Tau, idntfcnTrjctry.tau(:,i));
elseif opt.isSixAxisFTSensor
base_6AxisFT_regressor_func = sprintf('base_6AxisFT_regressor_%s',opt.robotName);
Yb = feval(base_6AxisFT_regressor_func, idntfcnTrjctry.q(:,i), ...
idntfcnTrjctry.qd(:,i),idntfcnTrjctry.qdd(:,i),baseQR);
joint_idex = robot.ndof-2;
tau(:,i) = FT(6*(joint_idex-1)+1:6*(joint_idex));
Wb = vertcat(Wb, Yb);
% Tau = vertcat(Tau, diag(drvGains)*idntfcnTrjctry.i_fltrd(i,:)');
Tau = vertcat(Tau, idntfcnTrjctry.tau(:,i));
end
end
end
function [pib_OLS, pifrctn_OLS] = ordinaryLeastSquareEstimation(Tau, Wb)
% Function perfroms ordinary least squares estimation of parameters
% pi_OLS = (Wb'*Wb)\(Wb'*Tau);
% pib_OLS = pi_OLS(1:40); % variables for base paramters
% pifrctn_OLS = pi_OLS(41:end);
pib_OLS=pinv(Wb)*Tau;
pifrctn_OLS = 0;
end
function [pib_OLS, pifrctn_OLS] = MultiLeastSquareEstimation(idntfcnTrjctry, Tau, Wb)
% Function perfroms Multi step ordinary least squares estimation of parameters
pib_OLS=pinv(Wb)*Tau;
pifrctn_OLS = 0;
end
end

50
estimate_dyn.m
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@ -11,15 +11,26 @@ q=[q_J;q_J;q_J;q_J;q_J;q_J;q_J;q_J;q_J];
qd=[qd_J;qd_J;qd_J;qd_J;qd_J;qd_J;qd_J;qd_J;qd_J]; qd=[qd_J;qd_J;qd_J;qd_J;qd_J;qd_J;qd_J;qd_J;qd_J];
qdd=[qdd_J;qdd_J;qdd_J;qdd_J;qdd_J;qdd_J;qdd_J;qdd_J;qdd_J]; qdd=[qdd_J;qdd_J;qdd_J;qdd_J;qdd_J;qdd_J;qdd_J;qdd_J;qdd_J];
g = [0; 0; -9.8]; g = [0; 0; -9.8];
tau = zeros([robot.ndof,length(q_J)]);
% pi -> [m;mc;I] 10 element % pi -> [m;mc;I] 10 element
[nLnkPrms, nLnks] = size(robot.pi); [nLnkPrms, nLnks] = size(robot.pi);
robot_pi = reshape(robot.pi, [nLnkPrms*nLnks, 1]); robot_pi = reshape(robot.pi, [nLnkPrms*nLnks, 1]);
for i = 1:length(q_J) if opt.isJointTorqueSensor
% regressor = standard_regressor_Two_bar(q(:,i),qd(:,i),qdd(:,i)); tau = zeros([robot.ndof,length(q_J)]);
standard_regressor_func = sprintf('standard_regressor_%s',opt.robotName); for i = 1:length(q_J)
regressor = feval(standard_regressor_func,q(:,i),qd(:,i),qdd(:,i)); % regressor = standard_regressor_Two_bar(q(:,i),qd(:,i),qdd(:,i));
tau(:,i)=regressor*robot_pi; standard_regressor_func = sprintf('standard_regressor_%s',opt.robotName);
regressor = feval(standard_regressor_func,q(:,i),qd(:,i),qdd(:,i));
tau(:,i)=regressor*robot_pi;
end
elseif opt.isSixAxisFTSensor
tau = zeros([6,length(q_J)]);
for i = 1:length(q_J)
regressor_func = sprintf('regressor_%s',opt.robotName);
regressor = feval(regressor_func,q(:,i),qd(:,i),qdd(:,i));
FT = regressor*robot_pi;
joint_idex = robot.ndof-2;
tau(:,i) = FT(6*(joint_idex-1)+1:6*(joint_idex));
end
end end
idntfcnTrjctry.t = time; idntfcnTrjctry.t = time;
idntfcnTrjctry.q = q; idntfcnTrjctry.q = q;
@ -31,7 +42,7 @@ idntfcnTrjctry.tau = tau;
% ------------------------------------------------------------------------ % ------------------------------------------------------------------------
drvGains = []; drvGains = [];
baseQR = robot.baseQR; baseQR = robot.baseQR;
[Tau, Wb] = buildObservationMatrices(idntfcnTrjctry, baseQR, drvGains); [Tau, Wb] = buildObservationMatrices(idntfcnTrjctry, baseQR, drvGains, opt);
% --------------------------------------------------------------------- % ---------------------------------------------------------------------
% Estimate parameters % Estimate parameters
@ -49,7 +60,7 @@ else
end end
robot.sol = sol; robot.sol = sol;
% Local unctions % Local unctions
function [Tau, Wb] = buildObservationMatrices(idntfcnTrjctry, baseQR, drvGains) function [Tau, Wb] = buildObservationMatrices(idntfcnTrjctry, baseQR, drvGains,opt)
% The function builds observation matrix for UR10E % The function builds observation matrix for UR10E
E1 = baseQR.permutationMatrix(:,1:baseQR.numberOfBaseParameters); E1 = baseQR.permutationMatrix(:,1:baseQR.numberOfBaseParameters);
@ -60,12 +71,23 @@ robot.sol = sol;
% idntfcnTrjctry.q2d(i,:)'); % idntfcnTrjctry.q2d(i,:)');
% Yfrctni = frictionRegressor(idntfcnTrjctry.qd_fltrd(i,:)'); % Yfrctni = frictionRegressor(idntfcnTrjctry.qd_fltrd(i,:)');
% Ybi = [Yi*E1, Yfrctni]; % Ybi = [Yi*E1, Yfrctni];
base_regressor_func = sprintf('base_regressor_%s',opt.robotName); if opt.isJointTorqueSensor
Yb = feval(base_regressor_func, idntfcnTrjctry.q(:,i), ... base_regressor_func = sprintf('base_regressor_%s',opt.robotName);
idntfcnTrjctry.qd(:,i),idntfcnTrjctry.qdd(:,i),baseQR); Yb = feval(base_regressor_func, idntfcnTrjctry.q(:,i), ...
Wb = vertcat(Wb, Yb); idntfcnTrjctry.qd(:,i),idntfcnTrjctry.qdd(:,i),baseQR);
% Tau = vertcat(Tau, diag(drvGains)*idntfcnTrjctry.i_fltrd(i,:)'); Wb = vertcat(Wb, Yb);
Tau = vertcat(Tau, idntfcnTrjctry.tau(:,i)); % Tau = vertcat(Tau, diag(drvGains)*idntfcnTrjctry.i_fltrd(i,:)');
Tau = vertcat(Tau, idntfcnTrjctry.tau(:,i));
elseif opt.isSixAxisFTSensor
base_6AxisFT_regressor_func = sprintf('base_6AxisFT_regressor_%s',opt.robotName);
Yb = feval(base_6AxisFT_regressor_func, idntfcnTrjctry.q(:,i), ...
idntfcnTrjctry.qd(:,i),idntfcnTrjctry.qdd(:,i),baseQR);
joint_idex = robot.ndof-2;
Yb = Yb(6*(joint_idex-1)+1:6*(joint_idex),:);
Wb = vertcat(Wb, Yb);
% Tau = vertcat(Tau, diag(drvGains)*idntfcnTrjctry.i_fltrd(i,:)');
Tau = vertcat(Tau, idntfcnTrjctry.tau(:,i));
end
end end
end end

18
get_baseParams.m
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@ -26,16 +26,16 @@ for i = 1:25
standard_regressor_func = sprintf('standard_regressor_%s',opt.robotName); standard_regressor_func = sprintf('standard_regressor_%s',opt.robotName);
Y = feval(standard_regressor_func, q_rnd,qd_rnd,q2d_rnd); Y = feval(standard_regressor_func, q_rnd,qd_rnd,q2d_rnd);
elseif isSixAxisFTSensor elseif isSixAxisFTSensor
% regressor_func = sprintf('regressor_%s',opt.robotName); regressor_func = sprintf('regressor_%s',opt.robotName);
% Y = feval(regressor_func, q_rnd,qd_rnd,q2d_rnd); Y = feval(regressor_func, q_rnd,qd_rnd,q2d_rnd);
% joint_idex = ndof-2; joint_idex = ndof-2;
% Y = Y(6*(joint_idex-1)+1:6*(joint_idex),:); Y = Y(6*(joint_idex-1)+1:6*(joint_idex),:);
% FIXME hack here % FIXME hack here
standard_regressor_func = sprintf('standard_regressor_%s',opt.robotName); % standard_regressor_func = sprintf('standard_regressor_%s',opt.robotName);
Y = feval(standard_regressor_func, q_rnd,qd_rnd,q2d_rnd); % Y = feval(standard_regressor_func, q_rnd,qd_rnd,q2d_rnd);
Zeros_ = zeros(size(Y)); % Zeros_ = zeros(size(Y));
Zeros_(ndof-2,:) = Y(ndof-2,:); % Zeros_(ndof-2,:) = Y(ndof-2,:);
Y = Zeros_; % Y = Zeros_;
end end
W = vertcat(W,Y); W = vertcat(W,Y);
end end