Neural Adaptive Control of a Robot Joint Using Secondary Encoders

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

Improved vision based pose estimation for industrial robots via sparse regression

In this work amonocular machine vision based pose estimation system is developed for industrial robots and the accuracy of the estimated pose is im-proved via sparse regression. The proposed sparse regressionbased methodis usedimprove the accuracy obtained from the Levenberg-Marquardt (LM) based pose estimation algorithmduring the trajectory tracking of an industrial robot’s end effector. The proposed method utilizes a set of basis functions to sparsely identify the nonlinear relationship between the estimated pose and the true pose provided by a laser tracker.Moreover,a camera target was designed and fitted with fiducial markers,andto prevent ambiguities in pose estimation, the markers are placed in such a way to guarantee the detection of at least two distinct nonparallel markers from a single camera within ± 90° in all directions of the cam-era’s view. The effectiveness of the proposed method is validated by an experi-mental study performed using a KUKA KR240 R2900 ultra robot while follow-ing sixteen distinct trajectories based on ISO 9238. The obtained results show that the proposed method provides parsimonious models which improve the pose estimation accuracy and precision of the vision based system during trajectory tracking of industrial robots' end effector