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Reliability analysis for mutative topology structure multi-AUV cooperative system based on interactive Markov chains model

Published online by Cambridge University Press:  17 August 2016

Qingwei Liang ,
Tianyuan Sun  and
Liang Shi
Qingwei Liang*
Affiliation:
College of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, P. R. China. E-mail: 309250030@qq.com
Tianyuan Sun
Affiliation:
The 32th Research Institute of China Electronic Technology Group Corporation, Shanghai, 200233, P. R. China. E-mail: t.y.sun@163.com
Liang Shi
Affiliation:
College of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, P. R. China. E-mail: 309250030@qq.com
*
*Corresponding author. E-mail: liangqingwei@nwpu.edu.cn.
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Summary

In the real practice of multi-AUV (Autonomous Underwater Vehicle) cooperative systems, tasks or malfunctions will change the topology. The process of mutative topology structure will affect the reliability of multi-AUV cooperative system. The interactive Markov chains model, which is an intercurrent model of functional action and capability index, is selected to reflect the reliability of topology-changed multi-AUV cooperative systems. In this model, multi-AUV cooperative systems are described by the conception—“Action”. The concept of “action transfer” is used to describe the topology-changed multi-AUV cooperative system, and model checking is used to solve the interactive Markov chains, giving the probability of reliability within a certain time for the system. The result shows that the method proposed in this paper has a practical value.

Keywords

Multi-AUV cooperative systemReliabilityMutative topology structureInteractive Markov chains model
Type
Articles
Information
Robotica , Volume 35 , Issue 8 , August 2017 , pp. 1761 - 1772
Copyright
Copyright © Cambridge University Press 2016 

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References

1. Conti, R., Meli, E., Ridolfi, A. and Allotta, B., “An innovative decentralized strategy for I-AUVs cooperative manipulation tasks,” Robot. Auton. Syst. 72 (10), 261–276 (2015).Google Scholar
2. Allotta, B., Costanzi, R., Meli, E., Pugi, L., Ridolfi, A. and Vettori, G., “Cooperative localization of a team of AUVs by a tetrahedral configuration,” Robot. Auton. Syst. 62 (8), 1228–1237 (2014).CrossRefGoogle Scholar
3. Wang, J. N. and Xin, M., “Distributed optimal cooperative tracking control of multiple autonomous robots,” Robot. Auton. Syst. 60 (4), 572–583 (2012).Google Scholar
4. Howe, B. M. and Mcginnis, T., “Sensor Networks for Cabled Ocean Observatories,” International Symposium on Underwater Technology, Taipei, China (Jun. 2004) pp. 113–120.Google Scholar
5. Carlesi, N., Michel, F., Jouvencel, B. and Ferber, J., “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, USA (Sept. 2011) pp. 5041–5047.CrossRefGoogle Scholar
6. Bellingham, J. G., “New oceanographic uses of autonomous underwater vehicles,” Mar. Technol. Soc. J. 31 (3), 34–47 (1997).Google Scholar
7. Sehmidt, H., Bellingham, J. G. and Elisseeff, P., “Acoustically Focused Oceanographic Sampling in Coastal Environments,” Proceedings of the Conference on Rapid Environmental Assessmen, Italy, (Mar. 1997) pp. 145–151.Google Scholar
8. Xu, H. G., Research on Formation Control and Coordination of Multiple Underwater Robots (Harbin Engineering University, China, 2005).Google Scholar
9. Gerkey, B., Vaughan, R. T. and Howard, A., “The Player/stage Project: Tools for Multi-robot and Distributed Sensor Systems,” Proceedings of the 11th International Conference on Advanced Robotics, Portugal, (Jun. 2003) pp. 317–323.Google Scholar
10. Andrews, J. D., Prescott, D. R. and Remenyte, P. R., “A Systems Reliability Approach to Decision Making in Autonomous Multi-platform Systems Operating a Phased Mission,” Reliability and Maintainability Symposium, RAMS 2008. (2008) pp. 8–14.Google Scholar
11. Rabbath, C. A. and Léchevin, N., Safety and Reliability in Cooperating Unmanned Aerial Systems (World Scientific, Singapore, 2010).CrossRefGoogle Scholar
12. Liu, M. L., Wu, X. F. and Huang, Q., “Vulnerability of the UUV formation network for the coordinated detection,” Ship Electron. Eng. 30 (11), 82–84 (2010).Google Scholar
13. Liu, B., Chen, Z. Y. and Zhang, Z. B., “The method research of network reliability modeling and evaluation for armada,” Ship Sci. Technol. 28 (3), 96–98 (2006).Google Scholar
14. Fiorelli, E., Bhatta, P., Leonard, N. E. and Shulman, I., “Adaptive Sampling Using Feedback Control of an Autonomous Underwater Glider Fleet,” Proceedings 13th International Symposium on Unmanned Untethered Submersible Technology, Durham NH (2003) pp. 1–16.Google Scholar
15. Fiorelli, E. and Leonard, N. E., “Multi-AUV control and adaptive sampling in monterey bay,” IEEE J. Oceanic Eng. 31 (4), 134–147 (2004).Google Scholar
16. Zhuang, L., Cai, M. and Shen, C. X., “Trusted dynamic measurement based on interactive Markov chains,” J. Comput. Res. Dev. 48 (8), 1464–1472 (2011).Google Scholar
17. Xu, Z. X., Wu, J. Z. and Chen, J. F., “Design and implementation of IMC-based performance checker,” J. Comput. Appl. 30 (1), 215–217 (2010).Google Scholar
18. Fang, X. P. and Yan, W. S., “Formation optimization for cooperative localization based on moving long baseline with two leader AUVs,” Acta Armamentarii. 33 (8), 1020–1024 (2012).Google Scholar
19. Conlisk, J., “Interactive Markov chains,” J. Math. Soc. 4 (2), 157–185 (1976).CrossRefGoogle Scholar
20. Hermanns, H. and Lohrey, M., “Priority and Maximal Progress are Completely Axiomatisable,” 9th International Conference on Concurrency Theory. (Springer, Berlin, Heidelberg, 1998) pp. 237–252.Google Scholar
21. Mashek, D. G., Bornfeldt, K. E. and Coleman, R. A., “Revised nomenclature for the mammalian long-chain acyl-CoA synthetase gene family,” J. Lipid Res. 45 (10), 1958–1961 (2004).Google Scholar
22. Clarke, E. M. and Emerson, E. A., “Design and Synthesis of Synchronization Skeletons using Branching Time Temporal Logic,” Logic of Programs, Workshop (Berlin, 1981) pp. 131, 52–71.Google Scholar
23. Emerson, E. A. and Clarke, E. M., “Using branching time temporal logic to synthesize synchronization skeletons,” Sci. Comput. Program. 2 (3), 241–266 (1982).Google Scholar
24. Queille, J. P. and Sifakis, J., “Specification and Verification of Concurrent Systems in CESAR,” International Symposium on Programming. Springer, Berlin, Heidelberg, (1982) pp. 337–351.Google Scholar
25. Xu, X., Wu, J. Z., Lin, L. N. and Chen, J. F., “Model checking interactive Markov chains,” Comput. Appl. 28 (7), 1868–1871 (2008).Google Scholar