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Please use this identifier to cite or link to this item: http://hdl.handle.net/1993/191

Title: An intuitive and flexible architecture for intelligent mobile robots
Authors: Liu, Xiao-Wen Terry
Supervisor: Baltes, Jacky (Computer Science)
Examining Committee: McNeill, Dean (Elec. & Comp. Engineering) Anderson, John (Computer Science) Baltes, Jacky (Computer Science)
Graduation Date: February 2006
Keywords: Intuitive Flexible Architecture Intelligent Mobile Robots
Issue Date: 6-Jan-2006
Citation: Xiao-Wen Terry Liu and Jacky Baltes. An intuitive and flexible architecture for intelligent mobile robots. In S. C. Mukhopadhyay and G. Sen Gupta, editors, Second International Conference on Autonomous Robots and Agents (ICARA), pages 52-57. Massey University, December 2004.
Abstract: The goal of this thesis is to develop an intuitive, adaptive, and flexible architecture for controlling intelligent mobile robots. This architecture is a hybrid architecture that combines deliberative planning, reactive control, finite state automata, behaviour trees and uses competition for behaviour selection. This behaviour selection is based on a task manager, which selects behaviours based on approximations of their applicability to the current situation and the expected reward value for performing that behaviour. One important feature of this architecture is that it makes important behavioural information explicit using Extensible Markup Language (XML). This explicit representation is an important part in making the architecture easy to debug and extend. The utility, intuitiveness and flexibility of this architecture is shown in an evaluation of this architecture against older control programs that lack such explicit behavioural representation. This evaluation was carried out by developing behaviours for several common robotic tasks and demonstrating common problems that arose during the course of this development.
URI: http://hdl.handle.net/1993/191
Type: Electronic Thesis or Dissertation
Appears in Collection(s):FGS - Electronic Theses & Dissertations (Public)

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