Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.583164
Title: Learning to predict the behaviour of deformable objects through and for robotic interaction
Author: Arriola Rios, Veronica Esther
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2013
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Abstract:
Every day environments contain a great variety of deformable objects and it is not possible to program a robot in advance to know about their characteristic behaviours. For this reason, robots have been highly successful in manoeuvring deformable objects mainly in the industrial sector, where the types of interactions are predictable and highly restricted, but research in everyday environments remains largely unexplored. The contributions of this thesis are: i) the application of an elastic/plastic mass-spring method to model and predict the behaviour of deformable objects manipulated by a robot; ii) the automatic calibration of the parameters of the model, using images of real objects as ground truth; iii) the use of piece-wise regression curves to predict the reaction forces, and iv) the use of the output of this force prediction model as input for the mass-spring model which in turn predicts object deformations; v) the use of the obtained models to solve a material classification problem, where the robot must recognise a material based on interaction with it.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.583164  DOI: Not available
Keywords: QA75 Electronic computers. Computer science
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