Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

51 to 60 of 2,475 Results
Tabular Data - 553.4 KB - 15 Variables, 2500 Observations - UNF:6:kotS25lCQAlDiB0QyIgMkw==
Simulated error between the solely kinematic model (dhh) and the elastokinematic model. The dhh model was virtually calibrated to x = 1.9m and z =0.85m by optimizing for offset angles of q2 and q3. Then a grid of 250x250 samples in q2 and q3 was evaluated. The absolute error is the L2 norm between the x and z coordinates of the dhh and elastokinema...
Tabular Data - 72.1 KB - 4 Variables, 999 Observations - UNF:6:RWgQ1bKuhN+BvGvSHqztzA==
Rigid body estimation in q (joint angles) of the regression data set. [x_LT_T_dhh_q, y_LT_T_dhh_q, z_LT_T_dhh_q]^\top can be calculated with eq. 23 by applying the parameters estimated with eq. 24. x_LT_T_dhh_q: x value [m] y_LT_T_dhh_q: y value [m] z_LT_T_dhh_q: z value [m] e_22_norm_dhh_q: euclidean distance between the above stated quantities a...
Tabular Data - 28.2 KB - 4 Variables, 391 Observations - UNF:6:iMy2vTIzSCypmh51xOmPmQ==
Rigid body estimation in q (joint angles) of the validation data set. [x_LT_T_dhh_q, y_LT_T_dhh_q, z_LT_T_dhh_q]^\top can be calculated with eq. 23 by applying the parameters estimated with eq. 24; {}^{LT}_H_0 and {}^6_H_T were obtained through regression, as the positioning of the laser tracker and the target presumably changed. x_LT_T_dhh_q: x...
Tabular Data - 72.1 KB - 4 Variables, 999 Observations - UNF:6:M4B4w7UhtKmM2YC6rz+USw==
Rigid body estimation in theta' (motor encoder values mapped to the output shaft) of the regression data set. [x_LT_T_dhh_theta, y_LT_T_dhh_theta, z_LT_T_dhh_theta]^\top can be calculated with eq. 9 by applying the parameters estimated with eq. 10. x_LT_T_dhh_theta: x value [m] y_LT_T_dhh_theta: y value [m] z_LT_T_dhh_theta: z value [m] e_2norm_dh...
Tabular Data - 27.9 KB - 4 Variables, 391 Observations - UNF:6:7UDwLlQHHV01YKXBZM3Qcw==
Rigid body estimation in theta' (motor encoder values mapped to the output shaft) of the validation data set. [x_LT_T_dhh_theta, y_LT_T_dhh_theta, z_LT_T_dhh_theta]^\top can be calculated with eq. 9 by applying the parameters estimated with eq. 10. {}^{LT}_H_0 and {}^6_H_T were obtained through regression, as the positioning of the laser tracker...
Tabular Data - 200.9 KB - 10 Variables, 999 Observations - UNF:6:kGzF/rBCaRvvaqk1Z3mqkA==
Estimation of the elastokinematic compliance, including gearbox and link deflections. tau_g2_theta: \tau_{g,2}(\theta') joint torque, calculated with \theta' instead of q [Nm] tau_g3_theta: \tau_{g,3}(\theta') joint torque, calculated with \theta' instead of q [Nm] Delta_phi2_elastokin: \Delta\varphi_2 Elastokinematic model (eq. 31) [rad] Delta_ph...
Tabular Data - 72.0 KB - 4 Variables, 999 Observations - UNF:6:QMCDnGyWlNJsgKXDsmbBig==
Elastokinematic estimation in theta' (motor encoder values mapped to the output shaft) of the regression data set. [x_LT_T_elastokin, y_LT_T_elastokin, z_LT_T_elastokin]^\top can be calculated with eq. 32 by applying the parameters estimated with eq. 33. x_LT_T_elastokin: x value [m] y_LT_T_elastokin: y value [m] z_LT_T_elastokin: z value [m] e_2n...
Tabular Data - 28.2 KB - 4 Variables, 391 Observations - UNF:6:bZxgphx/37PhcDkls6wXFw==
Elastokinematic estimation in theta' (motor encoder values mapped to the output shaft) of the validation data set. [x_LT_T_elastokin, y_LT_T_elastokin, z_LT_T_elastokin]^\top can be calculated with eq. 32 by applying the parameters estimated with eq. 33. {}^{LT}_H_0 and {}^6_H_T were obtained through regression, as the positioning of the laser tr...
Tabular Data - 6.2 KB - 4 Variables, 91 Observations - UNF:6:sIeNYR85EAp9jV/qEINfqg==
Friction experiment to show the local dependency; described in Sec III.C q2: q_2 [rad] vel: constant velocity of the third joint fric_meas: right hand side of (21a), mapped to the outpus shaft (*gearbox ratio) fric_model: LSQ fit (eq 21c), mapped to the output shaft (*gearbox ratio) Used in figure 6
Tabular Data - 128.7 KB - 17 Variables, 412 Observations - UNF:6:GaNzld3EG0Md1R5TNT/PRw==
Gearbox stiffness identification, using SE (Sec III.C) g_2: gravitational vector of q2 [Nm] tau_gc: \tau_{gc}: gravity compensation torque [Nm] tau_g3: = g_3; joint torque/gravitation vector of q3 [Nm] deltaPhi2_meas: \Delta\varphi_2, measured, shifted + 0.02 rad [rad] deltaPhi2_model: \Delta\varphi_2, model, shifted + 0.02 rad [rad] deltaPhi3_meas...
Add Data

Log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.