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Advancing Autonomy by Developing a Mission Planning Architecture (Case Study: Autonomous Underwater Vehicle)

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Abstract

The subject of autonomy and mission planning have been comprehensively investigated on various frameworks over the past decades. It has been briefly discussed in previews chapters that among attempted scopes of autonomous operations, the underwater exploration remained still restricted to particular tasks with a low-level of autonomy. Underwater robotic platforms arouse more interest and broadly used in maritime approaches to achieve routine and permanent access to the undersea environment. Autonomous underwater operations including high and low-level mission-motion planning for an AUV is targeted as a case study in this book to precisely investigate challenges that a vehicle can face in long-range missions in harsh environment. These classes of autonomous vehicles are largely employed for numerous purposes such as scientific marine exploration, surveys, sampling and monitoring undersea biodiversity, offshore mapping, installations and mining, etc., Sibenac et al in IEEE OCEANS’02 MTS: 2002 [1], Barrett et al in IEEE Oceans Sydney: 2010 [2].

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References

  1. Sibenac M, Kirkwood W, McEwen R, Shane F, Henthorn R, Gashler D, Thomas H (2002) Modular AUV for routine deep water science operations. In: IEEE OCEANS’02 MTS

    Google Scholar 

  2. Barrett NS, Seiler J, Anderson TJ, Williams SB, Nichol SL, Hill NA (2010) Autonomous Underwater Vehicle (AUV) for mapping marine biodiversity in coastal and shelf waters: implications for marine management. In: IEEE Oceans Sydney https://doi.org/10.1109/OCEANSSYD.2010.5603860

  3. Nicholas A, Johnson R, Lane DM (2011) Narrative monologue as a first step towards advanced mission debrief for AUV operator situational awareness. In: The 15th international conference on advanced robotics, Estonia

    Google Scholar 

  4. MahmoudZadeh S, Powers DMW, Atyabi A (2018) UUV’s hierarchical DE-based motion planning in a semi dynamic underwater wireless sensor network. Proc IEEE Trans Cybern 99:1–14. https://doi.org/10.1109/TCYB.2018.2837134

    Article  Google Scholar 

  5. Strutt JE (2006) Report of the inquiry into the loss of Autosub2 under the Fimbulisen. Technical report, Nat’l Oceanography Centre

    Google Scholar 

  6. AUV Systems. www.km.kongsberg.com

  7. Insaurralde CC (2013) Autonomic computing management for unmanned aerial vehicles. In: IEEE/AIAA digital avionics systems conference, Syracuse, USA, pp 1–11

    Google Scholar 

  8. MahmoudZadeh S, Powers DMW, Sammut K, Yazdani A (2016) Biogeography-based combinatorial strategy for efficient AUV motion planning and task-time management. J Mar Sci Appl 15(4):463–477

    Article  Google Scholar 

  9. Furlong ME, Paxton D, Stevenson P, Pebody M, McPhail SD (2012) Autosub long range: a long range deep diving auv for ocean monitoring. IEEE-OES Auton Underw Veh. https://doi.org/10.1109/AUV.2012.6380737

    Article  Google Scholar 

  10. Millard NW, Griffiths G, Finnegan G, McPhail SD, Meldrum DT, Pebody M, Perrett JR, Stevenson P (1998) Versatile autonomous submersibles-the realising and testing of a practical vehicle. Underw Technol 23(1):7–17

    Article  Google Scholar 

  11. Xu WT, He SW, Song R, Chaudhry S (2012) Finding the k shortest paths in a schedule-based transit network. Comput Oper Res 39(8):1812–1826

    Article  MathSciNet  Google Scholar 

  12. Jin W, Chen SP, Jiang H (2013) Finding the k shortest paths in a time schedule network with constraints on arcs. Comput Oper Res 40(12):2975–2982

    Article  MathSciNet  Google Scholar 

  13. Galea AM (1999) Various methods for obtaining the optimal path for a glider vehicle in shallow water and high currents. In: The 11th international symposium on unmanned untethered submersible technology, pp 150–161

    Google Scholar 

  14. Choi H, Kim Y, Kim H (2011) Genetic algorithm based decentralized task assignment for multiple unmanned aerial vehicles in dynamic environments. J Aeronaut Space Sci 12(2):163–174

    Article  Google Scholar 

  15. Mougouei D, Powers D, Moeini A (2017) An integer linear programming model for binary knapsack problem with dependent item values. In: Australasian joint conference on artificial, Springer, pp 144–154

    Google Scholar 

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MahmoudZadeh, S., Powers, D.M.W., Bairam Zadeh, R. (2019). Advancing Autonomy by Developing a Mission Planning Architecture (Case Study: Autonomous Underwater Vehicle). In: Autonomy and Unmanned Vehicles. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-13-2245-7_4

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