Title
Human-Robot Collaborative Object Transfer Using Human Motion Prediction Based On Dynamic Movement Primitives
Abstract
This work focuses on the prediction of the human's motion in a collaborative human-robot object transfer with the aim of assisting the human and minimizing his/her effort. The desired pattern of motion is learned from a human demonstration and is encoded with a DMP (Dynamic Movement Primitive). During the object transfer to unknown targets, a model reference with a DMP-based control input and an EKF-based (Extended Kalman Filter) observer for predicting the target and temporal scaling is used. Global boundedness under the emergence of bounded forces with bounded energy is proved. The object dynamics are assumed known. The validation of the proposed approach is performed through experiments using a Kuka LWR4+ robot equipped with an ATI sensor at its end-effector.
Year
DOI
Venue
2019
10.23919/ECC.2019.8796249
2019 18TH EUROPEAN CONTROL CONFERENCE (ECC)
Field
DocType
Citations 
Object dynamics,Computer vision,Extended Kalman filter,Computer science,Human motion,Artificial intelligence,Robot,Observer (quantum physics),Scaling,Human–robot interaction,Bounded function
Conference
1
PageRank 
References 
Authors
0.37
0
3
Name
Order
Citations
PageRank
Antonis Sidiropoulos138027.27
Yiannis Karayiannidis216222.05
Zoe Doulgeri333247.11