Institute for Control Engineering of Machine Tools and Manufacturing Units

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71 to 80 of 2,450 Results

nominal_params.tab May 28, 2025 - Replication Data for: A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots Tabular Data - 584 B - 3 Variables, 33 Observations - UNF:6:a38Phj/zcJNdcQ4UoQ1d9Q== additionally contains gearbox ratios
particleswarm_bounds.tab May 28, 2025 - Replication Data for: A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots Tabular Data - 357 B - 4 Variables, 12 Observations - UNF:6:RVVkr37b16CbLZJkrX7qAg== Lower and upper bounds for particleswarm optimization
regression_identified_parameters_IC.tab May 28, 2025 - Replication Data for: A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots Tabular Data - 3.6 KB - 8 Variables, 37 Observations - UNF:6:5/4ZOEzZ83GUpVNorbB0nA== Nominal values, IC's and identifeid parameters
regression_set.tab May 28, 2025 - Replication Data for: A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots Tabular Data - 314.8 KB - 18 Variables, 999 Observations - UNF:6:NyhOBCwJV9EwEavFylCpAA== Figure 3. Regression data set. Lasertracker data: cartesian position of the Target (T) in the lasertracker's coordinate system (LT) x_LT_T: x value [m] y_LT_T: y value [m] z_LT_T: z value [m] Transformation in the robot's coordinate system (0): x_0_T: x value [m] y_0_T: y value [m] z_0_T: z value [m] Disclaimer: x_0_T, y_0_T, and z_0_T were calc...
validation_set.tab May 28, 2025 - Replication Data for: A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots Tabular Data - 123.3 KB - 18 Variables, 391 Observations - UNF:6:0XBMsErN972oRRSt9ehQpg== Figure 11.. Validation data set. Lasertracker data: cartesian position of the Target (T) in the lasertracker's coordinate system (LT) x_LT_T: x value [m] y_LT_T: y value [m] z_LT_T: z value [m] Transformation in the robot's coordinate system (0): x_0_T: x value [m] y_0_T: y value [m] z_0_T: z value [m] Disclaimer: x_0_T, y_0_T, and z_0_T were ca...
Replication Data for: Adaptive Preload Control of Cable-Driven Parallel Robots for Handling Task May 27, 2025 - 2022_DFG_IKEPa Reichenbach, Thomas; Clar, Johannes; Pott, Andreas; Verl, Alexander, 2025, "Replication Data for: Adaptive Preload Control of Cable-Driven Parallel Robots for Handling Task", https://doi.org/10.18419/DARUS-4075, DaRUS, V1 Measurement data and analysis scripts to investigate a novel adaptive preload control (APC) for cable-driven parallel robots. The measurement data is provided as csv files and consists of two sets: "experiment_std.csv" is the measurement data for the performed motion in STD operation and "experiment_apc.csv" is the data of the APC method. Additiona...
analyze.py May 27, 2025 - Replication Data for: Adaptive Preload Control of Cable-Driven Parallel Robots for Handling Task Python Source Code - 1.5 KB - MD5: e45f9fb29e57021570e898478b4495be python functions to import csv files
apc.py May 27, 2025 - Replication Data for: Adaptive Preload Control of Cable-Driven Parallel Robots for Handling Task Python Source Code - 1.7 KB - MD5: a56b742555a89c56f080b3ca83076db9 python class for simulation study of adaptive preload control (apc)
COPacabana.xml May 27, 2025 - Replication Data for: Adaptive Preload Control of Cable-Driven Parallel Robots for Handling Task XML - 11.6 KB - MD5: 65ef377004e7c8270a6cbdf1ad1be155 geometry, control, and platform parameters of the cable robot COPacabana at ISW
experiment_apc.csv May 27, 2025 - Replication Data for: Adaptive Preload Control of Cable-Driven Parallel Robots for Handling Task Comma Separated Values - 654.8 KB - MD5: fbee282713e58d9a336e35ce10ff0eec measurement data of the apc experiment

nominal_params.tab

May 28, 2025 - Replication Data for: A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots

Tabular Data - 584 B - 3 Variables, 33 Observations -

additionally contains gearbox ratios

particleswarm_bounds.tab

May 28, 2025 - Replication Data for: A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots

Tabular Data - 357 B - 4 Variables, 12 Observations -

Lower and upper bounds for particleswarm optimization

regression_identified_parameters_IC.tab

May 28, 2025 - Replication Data for: A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots

Tabular Data - 3.6 KB - 8 Variables, 37 Observations -

Nominal values, IC's and identifeid parameters

regression_set.tab

May 28, 2025 - Replication Data for: A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots

Tabular Data - 314.8 KB - 18 Variables, 999 Observations -

Figure 3. Regression data set. Lasertracker data: cartesian position of the Target (T) in the lasertracker's coordinate system (LT) x_LT_T: x value [m] y_LT_T: y value [m] z_LT_T: z value [m] Transformation in the robot's coordinate system (0): x_0_T: x value [m] y_0_T: y value [m] z_0_T: z value [m] Disclaimer: x_0_T, y_0_T, and z_0_T were calc...

validation_set.tab

May 28, 2025 - Replication Data for: A Nonlinear Elasticity Model and Feedforward Compensation Method to Increase Positioning Accuracy of Industrial Robots

Tabular Data - 123.3 KB - 18 Variables, 391 Observations -

Figure 11.. Validation data set. Lasertracker data: cartesian position of the Target (T) in the lasertracker's coordinate system (LT) x_LT_T: x value [m] y_LT_T: y value [m] z_LT_T: z value [m] Transformation in the robot's coordinate system (0): x_0_T: x value [m] y_0_T: y value [m] z_0_T: z value [m] Disclaimer: x_0_T, y_0_T, and z_0_T were ca...

Replication Data for: Adaptive Preload Control of Cable-Driven Parallel Robots for Handling Task

May 27, 2025 - 2022_DFG_IKEPa

Reichenbach, Thomas; Clar, Johannes; Pott, Andreas; Verl, Alexander, 2025, "Replication Data for: Adaptive Preload Control of Cable-Driven Parallel Robots for Handling Task", https://doi.org/10.18419/DARUS-4075, DaRUS, V1

Measurement data and analysis scripts to investigate a novel adaptive preload control (APC) for cable-driven parallel robots. The measurement data is provided as csv files and consists of two sets: "experiment_std.csv" is the measurement data for the performed motion in STD operation and "experiment_apc.csv" is the data of the APC method. Additiona...

analyze.py

May 27, 2025 - Replication Data for: Adaptive Preload Control of Cable-Driven Parallel Robots for Handling Task

Python Source Code - 1.5 KB -

python functions to import csv files

apc.py

May 27, 2025 - Replication Data for: Adaptive Preload Control of Cable-Driven Parallel Robots for Handling Task

Python Source Code - 1.7 KB -

python class for simulation study of adaptive preload control (apc)

COPacabana.xml

May 27, 2025 - Replication Data for: Adaptive Preload Control of Cable-Driven Parallel Robots for Handling Task

XML - 11.6 KB -

geometry, control, and platform parameters of the cable robot COPacabana at ISW

experiment_apc.csv

May 27, 2025 - Replication Data for: Adaptive Preload Control of Cable-Driven Parallel Robots for Handling Task

Comma Separated Values - 654.8 KB -

measurement data of the apc experiment

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