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Efficient Deployment and Mission Timing of Autonomous Underwater Vehicles in Large-Scale Operations

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Book cover Advanced Data Mining and Applications (ADMA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11888))

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Abstract

This study introduces a connective model of routing- local path planning for Autonomous Underwater Vehicle (AUV) time efficient maneuver in long-range operations. Assuming the vehicle operating in a turbulent underwater environment, the local path planner produces the water-current resilient shortest paths along the existent nodes in the global route. A re-routing procedure is defined to re-organize the order of nodes in a route and compensate any lost time during the mission. The Firefly Optimization Algorithm (FOA) is conducted by both of the planners to validate the model’s performance in mission timing and its robustness against water current variations. Considering the limitation over the battery lifetime, the model offers an accurate mission timing and real-time performance. The routing system and the local path planner operate cooperatively, and this is another reason for model’s real-time performance. The simulation results confirms the model’s capability in fulfilment of the expected criterion and proves its significant robustness against underwater uncertainties and variations of the mission conditions.

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Authors and Affiliations

  1. School of Information Technology, Deakin University, Geelong, VIC, 3220, Australia

    Somaiyeh MahmoudZadeh

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  1. Somaiyeh MahmoudZadeh
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Correspondence to Somaiyeh MahmoudZadeh .

Editor information

Editors and Affiliations

  1. Deakin University, Burwood, VIC, Australia

    Jianxin Li

  2. The University of Queensland, St. Lucia, QLD, Australia

    Sen Wang

  3. Flinders University, Bedford Park, SA, Australia

    Shaowen Qin

  4. Dalian Neusoft University of Information, Dalian, China

    Xue Li

  5. Beijing Institute of Technology, Beijing, China

    Shuliang Wang

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MahmoudZadeh, S. (2019). Efficient Deployment and Mission Timing of Autonomous Underwater Vehicles in Large-Scale Operations. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_58

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  • DOI: https://doi.org/10.1007/978-3-030-35231-8_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35230-1

  • Online ISBN: 978-3-030-35231-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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