eTheses Repository

Learning to predict the behaviour of deformable objects through and for robotic interaction

Arriola Rios, Veronica Esther (2013)
Ph.D. thesis, University of Birmingham.

PDF (5Mb)Accepted Version


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.

Type of Work:Ph.D. thesis.
Supervisor(s):Wyatt, Jeremy and Sloman, Aaron
School/Faculty:Colleges (2008 onwards) > College of Engineering & Physical Sciences
Department:School of Computer Science
Subjects:QA75 Electronic computers. Computer science
Institution:University of Birmingham
ID Code:4636
This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.
Export Reference As : ASCII + BibTeX + Dublin Core + EndNote + HTML + METS + MODS + OpenURL Object + Reference Manager + Refer + RefWorks
Share this item :
QR Code for this page

Repository Staff Only: item control page