Sensorless kinesthetic teaching of robotic manipulators assisted by observer-based force control
Martino Capurso, M. Mahdi Ghazaei Ardakani, Rolf Johansson, Anders Robertsson, Paolo Rocco
Abstract
In modern day industry, robots are indispensable for achieving high production rates and competitiveness. In small and medium scale enterprises, where the production may shift rapidly, it is vital to be able to reprogram robots quickly. Kinesthetic teaching, also known as lead-through programming (LTP), provides a fast approach for teaching a trajectory. In this approach, a trajectory is demonstrated by physical interaction with the robot, i.e., the user manually guides the manipulator. This paper presents a sensorless approach to LTP for redundant robots that eliminates the need for expensive force/torque sensors. The active implementation enhances the passive LTP by an admittance control in joint space based on the external forces applied by the user, estimated with a Kalman filter using the generalized momentum formulation. To improve the quality of the estimation and hence LTP, we use a dithering technique. The active LTP has been implemented on ABB YuMi robot and experimental comparison with an earlier passive LTP is presented.
BibTeX
@inproceedings{icra2017_sensorlesskinest,
title = {Sensorless kinesthetic teaching of robotic manipulators assisted by observer-based force control},
author = {Martino Capurso and M. Mahdi Ghazaei Ardakani and Rolf Johansson and Anders Robertsson and Paolo Rocco},
booktitle = {ICRA 2017},
year = {2017}
}