Attitude dead reckoning in a collaborative virtual environment using cumulative polynomial extrapolation of quaternions

Chui, Yim-Pan; Heng, Pheng‐Ann

doi:10.1002/cpe.873

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…A reduction in network bandwidth requirements over a wide range of error thresholds indicates viable potential for satisfying many spectrums of consistency requirements throughout a broad range of real-time distributed applications. Furthermore, presented computational analysis results indicate the potential of our proposed framework for real-time use in a variety of distributed applications, requiring only a relatively modest amount of additional computational resources [Chui and Heng 2004].…”

Section: Discussionmentioning

confidence: 85%

Multistep-ahead neural-network predictors for network traffic reduction in distributed interactive applications

McCoy

Ward

McLoone

et al. 2007

ACM Trans. Model. Comput. Simul.

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Predictive contract mechanisms such as dead reckoning are widely employed to support scalable remote entity modeling in distributed interactive applications (DIAs). By employing a form of controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the distribution of instantaneous derivative information, dead reckoning trades remote extrapolation accuracy for low computational complexity and ease-of-implementation. In this article, we present a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of instantaneous velocity information with predictive velocity information in order to improve the accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning approach, each controlling host employs a bank of neural network predictors trained to estimate future changes in entity velocity up to and including some maximum prediction horizon. The effect of each estimated change in velocity on the current entity position is simulated to produce an estimate for the likely position of the entity over some short time-span. Upon detecting an error threshold violation, the controlling host transmits a predictive velocity vector that extrapolates through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such an approach succeeds in reducing the spatial error associated with remote extrapolation of entity state. Consequently, a further reduction in network traffic can be achieved. Simulation results conducted using several human users in a highly interactive DIA indicate significant potential for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our proposed neuro-reckoning framework exhibits low computational resource overhead for real-time use and can be seamlessly integrated into many existing dead reckoning mechanisms. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212)

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Section: Discussionmentioning

confidence: 85%

Multistep-ahead neural-network predictors for network traffic reduction in distributed interactive applications

McCoy

Ward

McLoone

et al. 2007

ACM Trans. Model. Comput. Simul.

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show abstract

“…To compensate, a number of adaptive extrapolation schemes have been proposed throughout the literature, aimed at reducing the error associated with remote extrapolation. These schemes can be distinguished according to the nature of computation associated with the adaptive process: [12,13].…”

Section: Related Work A) Predictive Contract Mechanismsmentioning

confidence: 99%

Formalizing a framework for dynamic hybrid strategy models in distributed interactive applications

McCoy¹,

Ward²,

McLoone³

et al. 2006

IET Irish Signals and Systems Conference (ISSC 2006)

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Predictive contract mechanisms such as dead reckoning are widely employed to support scalable remote entity modelling in Distributed Interactive Applications (DIAs). By employing a form of controlled inconsistency, a reduction in network traffic is achieved. Previously, we have proposed the Dynamic Hybrid Strategy Model (DHSM) as an extension to the concept of dead reckoning that adaptively selects extrapolation models based on the use of local performance criteria. In this paper, we formalize the notion of the DHSM as a generalized framework for network traffic reduction in DIAs, alongside a set of consistency metrics for use as local performance criteria.Keywords -distributed interactive applications, predictive contract mechanisms, dead reckoning, consistency, scalability. __________________________________________________________________________________________ I INTRODUCTIONA Distributed Interactive Application (DIA) is a distributed virtual reality system through which individuals can share information via individual and collaborative interaction with each other and their environment [1]. Distributed Interactive Applications offer the realization of simulated virtual worlds that embody a modern extension of communication, encompassing the concepts of shared time, shared space and shared presence [2]. The definition of a DIA encompasses a diverse range of applications that have seen rapid advances in technology and global popularity due to the widespread availability and ease-of-use of the Internet [3].The two primary factors limiting the large-scale deployment of a DIA are network latency and network bandwidth. Network latency refers to the delay in communication between two end-points, while network bandwidth refers to the rate at which data can be communicated per unit time between two end-points. High network latency and low network bandwidth capacity represent the largest contributors to the difficulties faced by DIAs in maintaining and supporting: (a) shared state consistency, (b) potential scalability, and (c) real-time interactivity.In this paper, we are concerned with the use of predictive contract mechanisms for the reduction of network traffic in DIAs, including the well-known dead reckoning algorithms formally defined in the IEEE Standard for Distributed Interactive Simulation (DIS) [4]. Traditional dead reckoning mechanisms often ignore available contextual information that may be influential to the state of an entity, sacrificing remote predictive accuracy in favour of low resource and computational overhead [2]. Previously, we have proposed an extension of dead reckoning, known as the Dynamic Hybrid Strategy Model (DHSM), that builds upon the foundation of the Hybrid Strategy Model (HSM) technique [5,6]. Both the HSM and the DHSM are hybrid predictive contract techniques that dynamically select remote extrapolation models based on local evaluation of current entity dynamics.In this paper, we formalize the concept of the DHSM as a generalized framework for the reduction of network traffic in D...

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Collaborative Simulation of Soft-Tissue Deformation for Virtual Surgery Applications

2009

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Attitude dead reckoning in a collaborative virtual environment using cumulative polynomial extrapolation of quaternions

Cited by 3 publications

References 17 publications

Multistep-ahead neural-network predictors for network traffic reduction in distributed interactive applications

Multistep-ahead neural-network predictors for network traffic reduction in distributed interactive applications

Formalizing a framework for dynamic hybrid strategy models in distributed interactive applications

Collaborative Simulation of Soft-Tissue Deformation for Virtual Surgery Applications

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