Vision-assisted control for manipulation using virtual fixtures

We present the design and implementation of a vision-based system for cooperative manipulation at millimeter to micrometer scales. The system is based on an admittance control algorithm that implements a broad class of guidance modes called virtual fixtures. A virtual fixture, like a real fixture, l...

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Bibliographic Details
Published in:IEEE transactions on robotics Vol. 20; no. 6; pp. 953 - 966
Main Authors: Bettini, A., Marayong, P., Lang, S., Okamura, A.M., Hager, G.D.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.12.2004
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Admittance
Algorithms
Applied sciences
Artificial intelligence
Automata. Logic controller
Computer science; control theory; systems
Computer vision
Control systems
Control theory
Control theory. Systems
Exact sciences and technology
Feedback
Fixtures
Haptic interfaces
Human-machine systems
Humans
Manipulation
Micrometers
Motion control
Pattern recognition. Digital image processing. Computational geometry
Process control. Computer integrated manufacturing
robot control
Robot sensing systems
Robotics
Robots
Sensor systems
Sensors
System performance
Trajectories
Tubes
virtual fixtures
Virtual reality
visual servoing
Computer vision
Control
Estimation
Robotics
ISSN:1552-3098, 1941-0468
Online Access:Get full text
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Description
Summary:We present the design and implementation of a vision-based system for cooperative manipulation at millimeter to micrometer scales. The system is based on an admittance control algorithm that implements a broad class of guidance modes called virtual fixtures. A virtual fixture, like a real fixture, limits the motion of a tool to a prescribed class or range of motions. We describe how both hard (unyielding) and soft (yielding) virtual fixtures can be implemented in this control framework. We then detail the construction of virtual fixtures for point positioning and curve following as well as extensions of these to tubes, cones, and sequences thereof. We also describe an implemented system using the JHU Steady Hand Robot. The system uses computer vision as a sensor for providing a reference trajectory, and the virtual fixture control algorithm then provides haptic feedback to implemented direct, shared manipulation. We provide extensive experimental results detailing both system performance and the effects of virtual fixtures on human speed and accuracy.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2004.829483