NDLI: Control methods for guidance virtual fixtures in compliant human-machine interfaces

Content Provider	IEEE Xplore Digital Library
Author	Marayong, P. Hager, G.D. Okamura, A.M.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Johns Hopkins Univ., Baltimore, MD (Marayong, P.; Hager, G.D.; Okamura, A.M.)
Abstract	This work focuses on the implementation of a vision-based motion guidance method, called virtual fixtures, on admittance-controlled human-machine cooperative robots with compliance. The robot compliance here refers to the structural elastic deformation of the device. The high mechanical stiffness and non-backdrivability of a typical admittance-controlled robot allow for slow and precise motions, making it highly suitable for tasks that require accuracy near human physical limits, such as microsurgery. However, previous experiments have shown that even small robot compliance degraded virtual fixture performance, especially at the micro scale. In this work, control methods to minimize the effect of robot compliance on virtual fixture performance were developed for admittance-controlled cooperative systems. Based on a linear model of the robot dynamics, we applied a Kalman filter to integrate the measurements obtained from the camera and encoders to estimate the robot end-effector position. A partitioned control law was used to achieve end-effector trajectory following on the desired velocity commanded by the admittance and virtual fixture control laws. The effectiveness of the Kalman filter and the controller was validated on a one degree-of-freedom admittance-controlled cooperative testbed.
Starting Page	1166
Ending Page	1172
File Size	1921784
Page Count	7
File Format	PDF
ISBN	9781424420575
DOI	10.1109/IROS.2008.4650838
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robots Fixtures Robot sensing systems Cameras Velocity measurement Kalman filters Robot vision systems
Content Type	Text
Resource Type	Article

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