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

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Abstract

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.

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References

  • Wang, Z., Niu, Y.: Niu Ying. Robot obstacle detection based on multi-sensor information fusion, China Test (2017)

    Google Scholar 

  • Chongzhao, H., Hongyan, Z., Victory, D.: Multi-source information fusion. Tsinghua University Press, Beijing (2010)

    Google Scholar 

  • Chaozhong, W., Bei, X.: Research on information fusion technology. Inf. Technol. (1), 165–171 (2008)

  • 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 

  • Chen, M., Liu, W., Li, K.: Rail crack recognition based on adaptive weighting multi-classifier fusion decision. Measurement. 123, 102–114 (2018)

    Article  Google Scholar 

  • 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)

    Article  MATH  Google Scholar 

  • Sinha, A., Chen, H., Danu, D.G.: Estimation and decision fusion: a survey. Neurocomputing 71(13), 2650–2656 (2008)

    Article  Google Scholar 

  • Blasch, E.P., Breton, R., Valin, P.: Information fusion measures of effectiveness (MOE) for decision support. Proc. Spie Int. Soc. 8050, 805011 (2011)

    Article  Google Scholar 

  • Thomopoulos, S.C.A., Viswanathan, R., Bougoulias, D.K.: Optimal distributed decision fusion. IEEE Trans. Aerospace Electronic Syst. 25(5), 761–765 (1989)

    Article  Google Scholar 

  • Wang, M.: Intelligent Robot Technology. National Defense Industry Press, Bejing (2015)

    Google Scholar 

  • Park, S.: Intelligent Robot. Harbin University of Technology Press, Harbin (2011)

    Google Scholar 

  • Waltz, E., Lirms, J.: Multisensor data fusion. Artech House, Boston (1990)

    Google Scholar 

  • 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)

  • Singh, R.N.P., Bailey, W.H.: Fuzzy logic applications to multisensorr -multitarget correlation. IEEE Trans. Aerospace Electronic Syst. 33(3), 752–769 (1997)

    Article  Google Scholar 

  • Murphy, C.K.: Combining belief functions when evidence conflicts. Decision Support Syst. 29(1), 1–9 (2000)

    Article  Google Scholar 

  • Chen, S.: Deviation registration in multi-sensor information fusion. Nanjing University of Technology, Nanjing (2010)

    Google Scholar 

  • Hong, Y.: Research on registration algorithms for multisensor systems [D]. Chin. Acad. Eng. Phys. (10), 45–50 (2014)

  • Quan, H.,Yumei, H., Lanhua.: Research progress of information fusion theory: joint optimization based on variational bayes. J. Autom. (12), 265–271 (2018)

  • 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)

  • Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  • Pawlak, Z.: Rough sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Boston (1991)

    Book  MATH  Google Scholar 

  • Hall, D.L.: Mathematical Techniques in Multisensor Data Fusion. Artech House, Norwood (1992)

    Google Scholar 

  • Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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 

  • Rogova, G., Nimier, V.: Reliability in information fusion: literature survey. In: Proceedings of International Conference on Information Fusion, Mountain View, pp. 1158–1165 (2004)

  • 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 15–20

  • Smets, P., Kennes, R.: The transferable belief model. Artif. Intell. 66(2), 191–234 (1994)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China

    Zhang Lanyong, Liu Lei & Zhang Lei

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  1. Zhang Lanyong
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  2. Liu Lei
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  3. Zhang Lei
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Correspondence to Liu Lei.

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Lanyong, Z., Lei, L. & Lei, Z. A multiple autonomous underwater vehicles hazard decision method based on information fusion. Int J Intell Robot Appl 3, 178–185 (2019). https://doi.org/10.1007/s41315-019-00094-3

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  • DOI: https://doi.org/10.1007/s41315-019-00094-3

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