Paper Info
Title
Fuzzy learning variable admittance control for human-robot cooperation
Abstract
This paper presents a method for variable admittance control in human-robot cooperation tasks, that combines a human-like decision making process and an adaptation algorithm. A Fuzzy Inference System is designed that relies on the measured velocity and the force applied by the operator to modify on-line the damping of the robot admittance, based on expert knowledge for intuitive cooperation. A Fuzzy Model Reference Learning Controller is used to adapt the Fuzzy Inference System according to the minimum jerk trajectory model. To evaluate the performance of the proposed controller a point-to-point cooperation task is conducted with multiple subjects using a KUKA LWR robot.
Year
DOI
Venue
2014
10.1109/IROS.2014.6943240
Intelligent Robots and Systems
Keywords
DocType
ISSN
decision making,fuzzy control,human-robot interaction,trajectory control,KUKA LWR robot,adaptation algorithm,expert knowledge,fuzzy inference system,fuzzy learning variable admittance control,fuzzy model reference learning controller,human-like decision making process,human-robot cooperation tasks,intuitive cooperation,jerk trajectory model,point-to-point cooperation task,robot admittance
Conference
2153-0858
Citations 
PageRank 
References 
6
0.50
7
Authors
2
Name
Order
Citations
PageRank
Fotios Dimeas1396.45
Nikos A. Aspragathos224337.69