A multiple autonomous underwater vehicles hazard decision method based on information fusion

  • Zhang Lanyong
  • Liu LeiEmail author
  • Zhang Lei
Short Paper


Autonomous underwater vehicle (AUV) plays an important role in ocean research. Compared with single AUV system, multi-AUV system has higher stability, robustness and high efficiency. Multiple AUVs give greater credibility than a single AUV in decision making. In this paper, the problem of multiple AUV making dangerous decisions in dangerous environments is studied. Single AUV can not make an accurate decision when facing some complex situations. We propose to fuse multiple AUV data to obtain more accurate dangerous decisions. The multi-AUV system adopts a distributed multi-robot structure, each AUV is an individual. A transferable information model and Dempster-Shafer evidence theory are used. A new multi-AUV hazard discrimination model is proposed for the final hazard decisionmaking. Verification by calculation, new decision-making method can effectively improve the discriminant ability of multi-AUV system when facing danger. This new hazard decision method improves the survivability of multiple AUVs in actual ocean exploration.


Multi-AUV Evidence theory Information model Decision making 



  1. Wang, Z., Niu, Y.: Niu Ying. Robot obstacle detection based on multi-sensor information fusion, China Test (2017)Google Scholar
  2. Chongzhao, H., Hongyan, Z., Victory, D.: Multi-source information fusion. Tsinghua University Press, Beijing (2010)Google Scholar
  3. Chaozhong, W., Bei, X.: Research on information fusion technology. Inf. Technol. (1), 165–171 (2008)Google Scholar
  4. Sultana, M., Paul, P.P., Gavrilova, M.L.: Social behavioral information fusion in multimodal biometrics. IEEE Trans. Syst. Man. Cybern. Syst. 19(99), 1–2 (2018)Google Scholar
  5. Chen, M., Liu, W., Li, K.: Rail crack recognition based on adaptive weighting multi-classifier fusion decision. Measurement. 123, 102–114 (2018)CrossRefGoogle Scholar
  6. Kuncheva, L.I., Bezdek, J.C., Duin, R.P.: Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognit. 34(2), 299–314 (2001)CrossRefzbMATHGoogle Scholar
  7. Sinha, A., Chen, H., Danu, D.G.: Estimation and decision fusion: a survey. Neurocomputing 71(13), 2650–2656 (2008)CrossRefGoogle Scholar
  8. Blasch, E.P., Breton, R., Valin, P.: Information fusion measures of effectiveness (MOE) for decision support. Proc. Spie Int. Soc. 8050, 805011 (2011)CrossRefGoogle Scholar
  9. Thomopoulos, S.C.A., Viswanathan, R., Bougoulias, D.K.: Optimal distributed decision fusion. IEEE Trans. Aerospace Electronic Syst. 25(5), 761–765 (1989)CrossRefGoogle Scholar
  10. Wang, M.: Intelligent Robot Technology. National Defense Industry Press, Bejing (2015)Google Scholar
  11. Park, S.: Intelligent Robot. Harbin University of Technology Press, Harbin (2011)Google Scholar
  12. Waltz, E., Lirms, J.: Multisensor data fusion. Artech House, Boston (1990)Google Scholar
  13. Han, H., Han, C.: Search the red-faced, multi-target tracking based on multi-sensor information fusion of fuzzy reasoning. Control Decision-Making. (6), 164–170 (2004)Google Scholar
  14. Singh, R.N.P., Bailey, W.H.: Fuzzy logic applications to multisensorr -multitarget correlation. IEEE Trans. Aerospace Electronic Syst. 33(3), 752–769 (1997)CrossRefGoogle Scholar
  15. Murphy, C.K.: Combining belief functions when evidence conflicts. Decision Support Syst. 29(1), 1–9 (2000)CrossRefGoogle Scholar
  16. Chen, S.: Deviation registration in multi-sensor information fusion. Nanjing University of Technology, Nanjing (2010)Google Scholar
  17. Hong, Y.: Research on registration algorithms for multisensor systems [D]. Chin. Acad. Eng. Phys. (10), 45–50 (2014)Google Scholar
  18. Quan, H.,Yumei, H., Lanhua.: Research progress of information fusion theory: joint optimization based on variational bayes. J. Autom. (12), 265–271 (2018)Google Scholar
  19. Bin, Z., Qing, W., Quanhengheng.: Non-threat assessment method of sonar target based on multi-source information fusion. J. Unman. Underw. Syst. (11), 132–138 (2018)Google Scholar
  20. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefzbMATHGoogle Scholar
  21. Pawlak, Z.: Rough sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Boston (1991)CrossRefzbMATHGoogle Scholar
  22. Hall, D.L.: Mathematical Techniques in Multisensor Data Fusion. Artech House, Norwood (1992)Google Scholar
  23. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  24. Laengle, T., Seyfried, J., Rembold, U.: Distributed Control of Microrobots for Different applications. In: Bolles, R.C., Bunke, H., Noltemeier, H. (eds.). Intelligent robots: sensing, modeling and planning. World Scientific Press, Singapore, pp. 322–339 (1997)CrossRefGoogle Scholar
  25. Bo, Fan, Quan, Pan, Hongcai, Zhang: A distributed decision-making method based on transitive confidence model. J. Syst. Simul. 16(11), 2622–2625 (2004)Google Scholar
  26. Rogova, G., Nimier, V.: Reliability in information fusion: literature survey. In: Proceedings of International Conference on Information Fusion, Mountain View, pp. 1158–1165 (2004)Google Scholar
  27. Rogova, G., Kasturi, J.: Reinforcement learning neural network for distributed decision making. In: Proceedings of the Fusion. 2001-Forth Conference on Multisource-Multisensor Information Fusion, Montreal, 2001, TuA2, pp 15–20Google Scholar
  28. Smets, P., Kennes, R.: The transferable belief model. Artif. Intell. 66(2), 191–234 (1994)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of AutomationHarbin Engineering UniversityHarbinChina

Personalised recommendations