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Path Planning for the Autonomous Underwater Vehicle

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Book cover Swarm, Evolutionary, and Memetic Computing (SEMCCO 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8298))

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Abstract

This paper introduces a novel method to find the optimal path for Autonomous Underwater Vehicles (AUVs). AUVs have gained importance over the last few years as service and research tools in a variety of applications. Path planning is one of the challenging tasks when dynamic obstacles are encountered. The Dijkstra’s algorithm is modified suitably to account for static as well as dynamic obstacles by adding an Additional Part (AP). In addition, the proposed algorithm takes into account the dynamics of the water flow and corrects the path suitably. Only two-dimensional routes are considered in the applications. The numerical results show that the proposed algorithm is effective in finding optimal paths.

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Author information

Authors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, 2600, Australia

    Andrey Kirsanov, Sreenatha G. Anavatti & Tapabrata Ray

Authors
  1. Andrey Kirsanov
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  2. Sreenatha G. Anavatti
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  3. Tapabrata Ray
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Editor information

Editors and Affiliations

  1. Electrical Engineering Dept, IIT Delhi, Hauz Khas, 110016, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, 700108, Kolkata, India

    Swagatam Das

  4. SRM Engineering College, Department of Electrical and Electronics Engineering, SRM University, Potheri, Kattankulathur, 603203, Kancheepuram, Tamil Nadu, India

    Shubhransu Sekhar Dash

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© 2013 Springer International Publishing Switzerland

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Kirsanov, A., Anavatti, S.G., Ray, T. (2013). Path Planning for the Autonomous Underwater Vehicle. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_43

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  • DOI: https://doi.org/10.1007/978-3-319-03756-1_43

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03755-4

  • Online ISBN: 978-3-319-03756-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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