Dataset and Baseline for an Industrial Robot Identification Benchmar
Creator Names: Jonas Weigand, Julian Götz, Jonas Ulmen, Martin Ruskowski
Affiliation: TU Kaiserslautern
Publisher: TU Kaiserslautern
Year: 2022
Resource Type: dataset
Rights: MIT License, Creative Commons Attribution Share Alike 4.0 International
Abstract: We present an identification benchmark data set for a full robot movement with an KUKA KR300 R2500 ultra SE industrial robot. It is a robot with a nominal payload capacity of 300 kg, a weight of 1120 kg and a reach of 2500mm. It exhibits 12 states accounting for position and velocity for each of the 6 joints. The robot encounters backlash in all joints, pose-dependent inertia, pose-dependent gravitational loads, pose-dependent hydraulic forces, pose- and velocity dependent centripetal and Coriolis forces as well as a nonlinear friction, which is temperature dependent and therefore potentially time varying. We supply the prepared dataset for black-box identification of the forward or the inverse robot dynamics. Additional to the data for black-box modelling, we supply high-frequency raw data and videos of each experiment. A baseline and figures of merit are defined to make results compareable across different identification methods.
Related Identifiers: https://doi.org/10.1016/j.rcim.2020.102039, DOI:10.1109/IROS45743.2020.9341518
Robot_Identification_Benchmark_Description.pdf.zip
Robot_Identification_Benchmark_Description.pdf.zip.md5
Robot_Identification_Benchmark_Videos.rar.zip
Robot_Identification_Benchmark_Videos.rar.zip.md5
Robot_Identification_Benchmark_Without_Raw_Data.rar.zip
Robot_Identification_Benchmark_Without_Raw_Data.rar.zip.md5