feature/R1000-identification #2
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@ -22,4 +22,4 @@ robot = get_regressor(robot,opt);
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% symbol matched
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% verify_regressor_R1000;
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robot = get_baseParams(robot, opt);
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robot = estimate_dyn(robot,opt);
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% robot = estimate_dyn(robot,opt);
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@ -14,7 +14,7 @@ q2d_max = 6*pi*ones(ndof,1);
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% Find relation between independent columns and dependent columns
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% -----------------------------------------------------------------------
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% Get observation matrix of identifiable paramters
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W = [];
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W = []; isSixAxisFTSensor =1; isJointTorqueSensor =0;
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for i = 1:25
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q_rnd = q_min + (q_max - q_min).*rand(ndof,1);
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qd_rnd = -qd_max + 2*qd_max.*rand(ndof,1);
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@ -22,11 +22,16 @@ for i = 1:25
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if includeMotorDynamics
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% Y = regressorWithMotorDynamics(q_rnd,qd_rnd,q2d_rnd);
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else
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elseif isJointTorqueSensor
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standard_regressor_func = sprintf('standard_regressor_%s',opt.robotName);
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Y = feval(standard_regressor_func, q_rnd,qd_rnd,q2d_rnd);
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%FIXME: better compute standard_regressor
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% Y = standard_regressor_func(q_rnd,qd_rnd,q2d_rnd);
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elseif isSixAxisFTSensor
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standard_regressor_func = sprintf('standard_regressor_%s',opt.robotName);
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Y = feval(standard_regressor_func, q_rnd,qd_rnd,q2d_rnd);
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% FIXME hack here
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Zeros_ = zeros(size(Y));
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Zeros_(ndof-3,:) = Y(ndof-3,:);
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Y = Zeros_;
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end
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W = vertcat(W,Y);
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end
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