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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.
Using industrial robots for machining applications in flexible manufacturing
processes lacks a high accuracy. The main reason for the deviation is the
flexibility of the gearbox. Secondary Encoders (SE) as an additional, high precision
angle sensor offer a huge potential of detecting gearbox deviations. This paper
aims to use SE to reduce gearbox compliances with a feed forward, adaptive
neural control. The control network is trained with a second network for system
identification. The presented algorithm is capable of online application and optimizes
the robot accuracy in a nonlinear simulation.
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.