Pressure sensor array model for collecting user’s responses to test action in active robot learning

2.50
Hdl Handle:
http://hdl.handle.net/10547/135336
Title:
Pressure sensor array model for collecting user’s responses to test action in active robot learning
Authors:
Zou, Weidong
Abstract:
Active robot learning (ARL) is an approach to the development of beliefs of the robots on their users’ intentions and preferences, which is needed by the robots to facilitate the seamless cooperation with users. Such approach allows the robots to perform tests on its users and to form high-order beliefs according to the users’ responses. This study carried out primary research on designing a pressure sensor array model attached to the robot’s finger tips to collect the user’s responses to test action in the ARL system. A mathematical model and the reference value threshold which decides the pressure distribution were proposed through a benchmark scenario experiment. The robot holds an object and presents it to the user. When the user does not take over the object, the pressure distribution on the robot’s finger tips shown on the pressure sensor array is uneven. When the user takes over the object, the pressure distribution on the robot’s finger tips is even. According to the relationship between the pressure distribution and the user’s responses, the user’s responses to test action can be recognized by the robot. Two cases of the benchmark scenario which is the robot passing an object to the user is simulated in a simulation software, GraspIt, in this study. The simulation results proved the developed pressure sensor array model can successfully collect the user’s responses to test actions in the ARL.
Publisher:
University of Bedfordshire
Issue Date:
Oct-2010
URI:
http://hdl.handle.net/10547/135336
Type:
Thesis
Language:
en
Description:
A thesis submitted to the University of Bedfordshire, in fulfilment of the requirements for the degree of Master of Science by research
Appears in Collections:
Masters e-theses

Full metadata record

DC FieldValue Language
dc.contributor.authorZou, Weidongen
dc.date.accessioned2011-07-05T10:07:31Z-
dc.date.available2011-07-05T10:07:31Z-
dc.date.issued2010-10-
dc.identifier.urihttp://hdl.handle.net/10547/135336-
dc.descriptionA thesis submitted to the University of Bedfordshire, in fulfilment of the requirements for the degree of Master of Science by researchen
dc.description.abstractActive robot learning (ARL) is an approach to the development of beliefs of the robots on their users’ intentions and preferences, which is needed by the robots to facilitate the seamless cooperation with users. Such approach allows the robots to perform tests on its users and to form high-order beliefs according to the users’ responses. This study carried out primary research on designing a pressure sensor array model attached to the robot’s finger tips to collect the user’s responses to test action in the ARL system. A mathematical model and the reference value threshold which decides the pressure distribution were proposed through a benchmark scenario experiment. The robot holds an object and presents it to the user. When the user does not take over the object, the pressure distribution on the robot’s finger tips shown on the pressure sensor array is uneven. When the user takes over the object, the pressure distribution on the robot’s finger tips is even. According to the relationship between the pressure distribution and the user’s responses, the user’s responses to test action can be recognized by the robot. Two cases of the benchmark scenario which is the robot passing an object to the user is simulated in a simulation software, GraspIt, in this study. The simulation results proved the developed pressure sensor array model can successfully collect the user’s responses to test actions in the ARL.en
dc.language.isoenen
dc.publisherUniversity of Bedfordshireen
dc.subjectroboticsen
dc.subjectactive robot learningen
dc.subjectGraspIt,en
dc.subjectpressure sensor arrayen
dc.titlePressure sensor array model for collecting user’s responses to test action in active robot learningen
dc.typeThesisen
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