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System Reliable Probability for Multi-AUV Cooperative Systems under the Influence of Current

Published online by Cambridge University Press:  05 July 2019

Qingwei Liang*
Affiliation:
(School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China)
Tianyuan Sun
Affiliation:
(The 32nd Research Institute of China Electronic Technology Group Corporation, Shanghai, China)
Junlin Ou
Affiliation:
(School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China)

Abstract

Real multi-Autonomous Underwater Vehicle (AUV) cooperative systems operate in complicated marine environments. The interaction between a multi-AUV cooperative system and its marine environment will affect the reliability of the system. Current is an important influencing factor of multi-AUV cooperative systems. A reliability index of multi-AUV cooperative systems known as System Reliable Probability (SRP) is proposed in this study. A method to calculate SRP is introduced, and the influence of current on SRP is discussed in detail. Current is considered an attack source, and the degree of its influence on SRP is calculated. As an example, the performance of this method is shown on two multi-AUV cooperative systems. Results show that the influence of the same current environment on different structures of the multi-AUV cooperative systems differs. This result provides a reference for the structure selection of multi-AUV systems. This study provides a practical method to estimate the reliability of multi-AUV cooperative systems.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2019 

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References

REFERENCES

Albert, R., Jeong, H. and Barabasi, A.L. (2000). Error and Attack Tolerance of Complex Networks. Nature, 406, 378382.Google Scholar
Andrews, J.D., Prescot, D.Rt. and Remenyte, P.R. (2008). A systems reliability approach to decision making in autonomous multi-platform systems operating a phased mission. Reliability and Maintainability Symposium, RAMS 2008.Google Scholar
Carlesi, N., Michel, F., Jouvencel, B. and Ferber, J. (2011). Generic Architecture for Multi-AUV Cooperation Based on a Multi-Agent Reactive Organizational Approach. IEEE//RSJ International Conference on Intelligent Robots and Systems. San Francisco, CA.Google Scholar
Chen, J.G. and Zhang, Y.J. (2006). Study on evaluation algorithm for topology survivability of communication network. Radio Communications Technology, 32, 67.Google Scholar
Deng, H.Z., Wu, J., Lv, Y., Li, X. and Tan, Y.J. (2008). Influence of complex network topologic structure on system invulnerability. Systems Engineering and Electronics, 30, 24252428.Google Scholar
Gerkey, B., Vaughan, R.T. and Howard, A. (2003). The player/stage project: Tools for multi-robot and distributed sensor systems. Proceedings of the 11th international conference on advanced robotics, Coimbra, Portugal.Google Scholar
Gupta, M.P., Behnam, A., Lian, F., Estrada, D., Pop, E. and Kumar, S. (2013). High Field Breakdown Characteristics of Carbon Nanotube Thin Film Transistors. Nanotechnology, 24, 405204–405204.Google Scholar
Li, Y., Pang, Y.J., Zhang, L. and Zhang, H. H. (2012). Semi-physical simulation of AUV pipeline tracking. Journal of Central South University, 19, 24682476.Google Scholar
Liang, Q.W., Sun, T.Y. and Wang, D.D. (2017). Reliability indexes for multi-AUV cooperative systems. Journal of Systems Engineering and Electronics, 28, 179186.Google Scholar
Liang, Q.W., Sun, T.Y. and Shi, L. (2016). Reliability analysis for mutative topology structure multi-AUV cooperative system based on interactive Markov chains model. Robotica, 35, 17611772Google Scholar
Liang, X. L. (2013). Precise underwater localization based on ocean current information. Shanghai: Shanghai Jiaotong University.Google Scholar
Liu, B.S. and Lei, J.Y. (2010). Principles of hydroacoustics. Harbin Engineering University Press, 264268.Google Scholar
Liu, B., Chen, Z.Y. and Zhang, Z.B. (2006). The method research of network reliability modeling and evaluation for armada. Ship Science and Technology, 28, 9698.Google Scholar
Liu, M.L., Wu, X.F. and Huang, Q. (2010). Vulnerability of the UUV formation network for the coordinated detection. Ship Electronic Engineering, 30, 8284.Google Scholar
Ma, C. and Lu, Z. (2009). Non-probabilistic reliability analysis method for implicit limit state function. Journal of Mechanical Strength, 31, 4550.Google Scholar
Maurelli, F., Saigol, Z., Insaurralde, C.C., Petillot, Y.R. and Lane, D.M. (2012). Marine world representation and acoustic communication: challenges for multi-robot collaboration. IEEE Auv, Southampton, Uk, 134, 1–6.Google Scholar
Paull, L., Saeedi, S., Seto, M. and Howard, L. (2014). AUV Navigation and Localization: A Review. IEEE Journal of Oceanic Engineering, 39, 131149.Google Scholar
Rabbath, C.A. and Léchevin, N. (2010). Safety and reliability in cooperating unmanned aerial systems. Singapore: World Scientific.Google Scholar
Robson, C. (2002). Real world research: a resource for social scientists and practitioner-researchers. Oxford: Blackwell.Google Scholar
Schillewaert, N., Langerak, F. and Duhamel, T. (1998). Non probability sampling for WWW surveys: a comparison of methods. Journal of the Market Research Society, 40, 307322.Google Scholar
Schneider, K., Rainwater, C., Pohl, E., Hernandez, I. and R-Marquez, J.E. (2013). Social network analysis via multi-state reliability and conditional influence models. Reliability Engineering & System Safety, 109, 99109.Google Scholar
Seyyedmohsen, A. (2010). Cooperative Fault Estimation and Accommodation in Formation Flight of Unmanned Vehicles. Concordia University, Canada.Google Scholar
Sun, L.N. and Wang, X.W. (2013). Optimization algorithm aimulation of complex network communication anti-damage nodes. Computer Simulation, 30, 218221.Google Scholar
Sun, W., Xu, A.G. and Gao, Y. (2013a). Strapdown gyrocompass algorithm for AUV attitude determination using a digital filter. Measurement, 46, 815822.Google Scholar
Sun, X.J., Liu, X.Y. and Ding, R.H. (2013b). Improvement routing strategy based on the shortest path and load dynamic. Journal of Naval Aeronautical and Astronautical, 28, 95100.Google Scholar
Waldmann, C., Kausche, A., Iversen, M. and Pototzky, A. (2014). MOTH-An underwater glider design study carried out as part of the HGF alliance ROBEX. 2014 IEEE/OES Autonomous Underwater Vehicles (AUV). MS, USAGoogle Scholar
Wang, N., Su, Sh.F., Pan, X.X., Yu, X. and Xie, G.M. (2018a). Yaw-Guided trajectory tracking control of an asymmetric underactuated surface vehicle. IEEE Transactions on Industrial Informatics. DOI:10.1109/TII.2018.2877046.Google Scholar
Wang, N., Xie, G.M., Pan, X.X. and Su, S.F. (2018b). Full-state regulation control of asymmetric underactuated surface vehicles. IEEE Transactions on Intelligent Vehicles. DOI:10.1109/TIE.2018.2890500.Google Scholar
Xu, Y. and Zou, Z.H. (2009). Evaluation for marine environment impacting on efficiency of shipborne torpedo. Ship Electronic Engineering, 29, 177179.Google Scholar
Zuev, K.M., Wu, S. and Beck, J.L. (2015). General network reliability problem and its efficient solution by Subset Simulation. Probabilistic Engineering Mechanics, 40, 2535.Google Scholar