Name
Title | ||
|---|---|---|
Human-robot collaborative object transfer using human motion prediction based on Cartesian pose Dynamic Movement Primitives |
Abstract | ||
|---|---|---|
In this work, the problem of human-robot collaborative object transfer to unknown target poses is addressed. The desired pattern of the end-effector pose trajectory to a known target pose is encoded using DMPs (Dynamic Movement Primitives). During transportation of the object to new unknown targets, a DMP-based reference model and an EKF (Extended Kalman Filter) for estimating the target pose and time duration of the human's intended motion is proposed. A stability analysis of the overall scheme is provided. Experiments using a Kuka LWR4+ robot equipped with an ATI sensor at its end-effector validate its efficacy with respect to the required human effort and compare it with an admittance control scheme. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICRA48506.2021.9562035 | 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) |
DocType | Volume | Issue |
Conference | 2021 | 1 |
ISSN | Citations | PageRank |
1050-4729 | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonis Sidiropoulos | 1 | 0 | 0.34 |
| Yiannis Karayiannidis | 2 | 162 | 22.05 |
| Zoe Doulgeri | 3 | 332 | 47.11 |