Collision detection for virtual environment using particle swarm optimization with adaptive cauchy mutation

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1007/s10586-017-0815-6
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FormatText
TypeArticle
Journal titleCluster Computing
ISSN1386-7857
1573-7543
Subjectcollision detection; hierarchical bounding box; nonlinear optimization; cauchy mutation; particle swarm
Abstract
Rapid and accurate detection of collision between virtual objects is crucial for many virtual reality based applications. In order to ensure a high-level of accuracy and to meet the real-time requirement, a fast collision detection algorithm between soft bodies is proposed. The developed algorithm is a combination of stochastic methods and particle swarm optimization with adaptive Cauchy mutation. The hierarchical bounding box method is used for a rough detection in order to filter out obvious disjoint space, and the problem is converted to a nonlinear optimization problem based on the distance of points. Then particle swarm optimization with adaptive Cauchy mutation is used to find the optimal solution. When it is updated iteratively, keeping some particles experience value and variation of other particles avoids particles trapped in local optimum, which further accelerates the speed of collision detection. The algorithm is implemented and evaluated through experiments and the results confirm the advantages of the developed algorithm.
Publication date
PublisherSpringer
Terms of use
LanguageEnglish
Peer reviewedYes
NPARC number23001618
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Record identifier8dda8636-2aec-4426-91ac-93baf10d1152
Record created2017-03-13
Record modified2017-03-13
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