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[PhD Thesis] Scalable Collision Detection for Distributed Virtual Environments

Lookup NU author(s): Kier Storey

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Abstract

Distributed Virtual Environments (DVEs) provide a mechanism whereby dispersed users can interact with one-another within a shared virtual world. DVEs commonly allow users to interact with one-another in ways analogous to the real-world, e.g. mimicking Newtonian physics. A scalable DVE should enable large numbers of users to participate simultaneously, regardless of the geographical location and hardware configurations of individual users. In addition, these users should perceive a mutually-consistent virtual world in which each user perceives a consistent series of events in real-time. Collision detection and response is a fundamental requirement of most virtual environments and simulations. It is a computationally-expensive operation which must be performed at frequent intervals in all virtual environments which simulate the motion of solid objects. Collision detection has received large amounts of research interest and as a result a number of efficient collision detection algorithms have been proposed. However, these collision detection approaches are designed to detect collisions efficiently in simulations run on a single machine and are not capable of overcoming problems associated with scalability and consistency, which are of paramount importance in DVEs. This thesis presents a new collision detection approach, termed distributed collision detection, which provides high-levels of scalability, consistency and responsiveness. This thesis presents the algorithms and theory which underpin the distributed collision detection approach and provides experimental results demonstrating its scalability and responsiveness.


Publication metadata

Author(s): Storey K

Publication type: Report

Publication status: Published

Series Title:

Year: 2007

Institution: School of Computing Science, University of Newcastle upon Tyne

Place Published: Newcastle upon Tyne

Notes: British Lending Library DSC stock location number: DXN107710


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