Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Published:
Cluster Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Wu, Y., He, F., Zhang, D., Li, X.: Service-oriented feature-based data exchange for cloud-based design and manufacturing. IEEE Trans. Serv. Comput. (2015). doi:10.1109/tsc.2015.2501981

  2. Zachmann, G.: Minimal hierarchical collision detection. In: Proceedings of the ACM Symposium on Virtual Reality Software and Technology, pp. 121-128. ACM, New York (2002)

  3. Teschner, M., Heidelberger, B., Müller, M., Pomeranets, D., Gross, M.: Optimized spatial hashing for collision detection of deformable objects. In: Proceedings of Vision. Modeling, Visualization, pp. 47–54. Aka GmbH, Berlin (2003)

  4. Raghupathi, L., Grisoni, L., Faure, F., Cani, M.P., Chaillou, C.: An intestine surgery simulator: real-time collision processing and visualization. IEEE Trans. Vis. Comput. Graph. 10(6), 708–718 (2004)

    Article  Google Scholar 

  5. Wolff, S., Bucher, C.: Distance fields on unstructured grids: stable interpolation, assumed gradients, collision detection and gap function. Comput. Methods Appl. Mech. Eng. 259(100), 77–92 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  6. Baciu, G., Wong, W.: Image-based techniques in a hybrid collision detector. IEEE Trans. Vis. Comput. Graph. 9(2), 254–271 (2003)

    Article  Google Scholar 

  7. Mattei, T.A.: Nonhomeomorphic topological transformations and the challenge of collision detection in virtual reality simulation in neurosurgery. World Neurosurg. 81(2), 209–213 (2014)

    Article  Google Scholar 

  8. Breuer, M., Almohammed, N.: Modeling and simulation of particle agglomeration in turbulent flows using a hard-sphere model with deterministic collision detection and enhanced structure models. Int. J. Multiph. Flow. 73, 171–206 (2015)

    Article  MathSciNet  Google Scholar 

  9. Kimmerle, S., Nesme, M., Faure, F.: Hierarchy accelerated stochastic collision detection. 9th International Workshop on Vision. Modeling, and Visualization, pp. 307–314. Aka GmbH, Berlin (2004)

  10. Wang, T., Li, W., Wang, Y., Ge, Z., Han, D.: An adaptive stochastic collision detection between deformable objects using particle swarm optimization. Appl. Evol. Comput. 39(7), 450–459 (2006)

    Google Scholar 

  11. Jin, H., Zhao, H., Zhang, X., Guo, Y.: Research on collision detection algorithm between convex polyhedron based on genetic algorithm. J. Central China Norm. Univ. 40(1), 25–28 (2006)

    Google Scholar 

  12. Xia, Y., Zhao, W.: Convex polytope collision detection algorithm based on non-linear programming. J. Changchun Univ. Technol. 29(1), 82–86 (2008)

    MathSciNet  Google Scholar 

  13. Fu, Y., Liang, J., Meng, L., Liu, Q.: Research on fast collision detection using multi-agent particle swarm optimization. J. Syst. Simul. 25(8), 1876–1880 (2013)

    Google Scholar 

  14. Kenney, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the sixth IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE (1995)

  15. Liu, L.L., Li, G.J., Wang, D.W.: Composite particle swarm optimization with nonlinear effect in dynamic environment. Control Theory & Appl. 29(10), 1253–1262 (2012)

    MATH  Google Scholar 

  16. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: from Natural to Artificial Systems. Mathematics & Computer Education, Springer (2003)

  17. Zhang, D.J., He, F.Z., Han, S.H., Li, X.X.: Quantitative optimization of interoperability during feature-based data exchange. Integr. Comput.-Aided Eng. 23(1), 31–50 (2016)

    Article  Google Scholar 

  18. Yuan, C., He, F., Wu, Y., Zhang, D.: Meta-operation conflict resolution for human–human interaction in collaborative feature-based CAD systems. Clust. Comput. 19(1), 237–253 (2016)

    Article  Google Scholar 

  19. Li, K., He, F., Chen, X.: Real-time object tracking via compressive feature selection. Front. Comput. Sci. 10(4), 689–701 (2016)

    Article  Google Scholar 

  20. Sun, J., He, F., Chen, Y., Chen, X.: A multiple template approach for robust tracking of fast motion target. Appl. Math.-A J. Chin. Univ. 31(2), 177–197 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhou, Y., He, F., Qiu, Y.: Optimization of parallel iterated local search algorithms on graphics processing unit. J. Supercomput. 72(6), 2394–2416 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61463031, 61563035, 81660299 and 61503177, and the Science and Technology Department of Jiangxi Province of China under Grants 20121BBE50023 and 20133BCB22002.

Author information

Authors and Affiliations

  1. School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China

    Yanni Zou, Peter X. Liu, Chunsheng Yang, Chunquan Li & Qiangqiang Cheng

  2. Department of System and Computer Engineering, Carleton University, Ottawa, Canada

    Peter X. Liu

Authors
  1. Yanni Zou
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Peter X. Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Chunsheng Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Chunquan Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Qiangqiang Cheng
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Peter X. Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zou, Y., Liu, P.X., Yang, C. et al. Collision detection for virtual environment using particle swarm optimization with adaptive cauchy mutation. Cluster Comput 20, 1765–1774 (2017). https://doi.org/10.1007/s10586-017-0815-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-0815-6

Keywords