Name
Abstract | ||
|---|---|---|
With no force sensors, estimating forces for robotic manipulation has gained a lot of attention. However, for industrial robots with harmonic drives (flexible joints), deviations between joint and motor positions inevitably deteriorate the force estimation performance due to the lack of encoders on the joint side. To this end, this letter presents a method to estimate not only joint states but also external forces. The method includes an extended disturbance state observer (DSO) and a proposed task-oriented disturbance modeling (TDM). First, a robust DSO is extended to robots with flexible joints aiming to estimate joint states and disturbances. Then, due to the observed disturbances including (possibly) external forces and also uncertainties such as measurement noises and model errors of the robot dynamics, a learning part is proposed to model the task-oriented disturbances during no-contact motion aiming to improve the effect of uncertainties on the force estimation performance. Finally, when the robot comes in contact with the environment, external forces are estimated as the differences between the modeled (no-contact) disturbances and the real-time observed disturbances. Experimental results, obtained on a six-degrees-of-freedom (6-DOF) industrial robot with flexible joints, show the feasibility and effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LRA.2020.2968058 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | DocType | Volume |
Force and tactile sensing, model learning for control, compliant joint, dynamics, industrial robots | Journal | 5 |
Issue | ISSN | Citations |
2 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |