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UBC Theses and Dissertations
Statistical modeling of contact interaction for telerobotics and haptics Shi, Yunling
Abstract
Force signals provide essential information for manipulation. This thesis focuses on monitoring the contact state of a robot arm (or a haptic device) with the environment. We describe a procedure to segment and interpret force signals by using a statistical, model-based approach. This idea will be useful for high level robot programming, as force signals are not compatible between different devices (robot arm, or haptic device), and costly to transmit. To relate the force data stream to the parameters of interest, we address the criteria of dividing tasks into subtasks by detecting the changes of the observations based on a specific force signal input device, each of the subtasks corresponding to. an auto-regression model. Each hypothesized contact model has an estimator. The observations of position, velocity, and force are input into a collection of estimators. The estimators output the measure of match as well as the residual process to be fed back to the state change detector. So we can detect a subtask and select a model from a set of candidate models to determine the state of contact. In this thesis, we simplify and improve the traditional approach of change detection and estimation to make it suitable for manipulation tasks. The context is also a fundamental to manipulation. The sequence of subtasks determines the task structure, and thus the goal of the operator. The Markov process encodes the subtasks and prior knowledge with each subtask state.
Item Metadata
Title |
Statistical modeling of contact interaction for telerobotics and haptics
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
1997
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Description |
Force signals provide essential information for manipulation. This thesis
focuses on monitoring the contact state of a robot arm (or a haptic device) with
the environment. We describe a procedure to segment and interpret force signals
by using a statistical, model-based approach. This idea will be useful for high level
robot programming, as force signals are not compatible between different devices
(robot arm, or haptic device), and costly to transmit.
To relate the force data stream to the parameters of interest, we address the
criteria of dividing tasks into subtasks by detecting the changes of the observations
based on a specific force signal input device, each of the subtasks corresponding to.
an auto-regression model. Each hypothesized contact model has an estimator. The
observations of position, velocity, and force are input into a collection of estimators.
The estimators output the measure of match as well as the residual process to be
fed back to the state change detector. So we can detect a subtask and select a model
from a set of candidate models to determine the state of contact. In this thesis, we
simplify and improve the traditional approach of change detection and estimation
to make it suitable for manipulation tasks.
The context is also a fundamental to manipulation. The sequence of subtasks determines the task structure, and thus the goal of the operator. The Markov process
encodes the subtasks and prior knowledge with each subtask state.
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Extent |
3475482 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-03-24
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0051596
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
1997-11
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.