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Science of Autonomy: Time-Optimal Path Planning and Adaptive Sampling for Swarms of Ocean Vehicles

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Springer Handbook of Ocean Engineering

Part of the book series: Springer Handbooks ((SHB))

Abstract

The science of autonomy is the systematic development of fundamental knowledge about autonomous decision making and task completing in the form of testable autonomous methods, models and systems. In ocean applications, it involves varied disciplines that are not often connected. However, marine autonomy applications are rapidly growing, both in numbers and in complexity. This new paradigm in ocean science and operations motivates the need to carry out interdisciplinary research in the science of autonomy. This chapter reviews some recent results and research directions in time-optimal path planning and optimal adaptive sampling. The aim is to set a basis for a large number of vehicles forming heterogeneous and collaborative underwater swarms that are smart, i. e., knowledgeable about the predicted environment and their uncertainties, and about the predicted effects of autonomous sensing on future operations. The methodologies are generic and applicable to any swarm that moves and senses dynamic environmental fields. However, our focus is underwater path planning and adaptive sampling with a range of vehicles such as autonomous underwater vehicles (GlossaryTerm

AUV

s), gliders, ships or remote sensing platforms.

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Abbreviations

1-D:

one-dimensional

2-D:

two-dimensional

3-D:

three-dimensional

4-D:

four-dimensional

AUV:

autonomous underwater vehicle

AWACS:

autonomous wide aperture cluster for surveillance

CTD:

conductivity, temperature and depth

ESSE:

error subspace statistical estimation

GA:

genetic algorithm

GMM:

Gaussian Mixture Model

MILP:

mixed integer linear programming

MIP:

mixed integer programming

MSEAS:

multidisciplinary simulation, estimation and assimilation system

PDE:

partial differential equation

PDF:

probability density function

POMDP:

partially observable Markov decision process

REMUS:

remote environmental monitoring units

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Author information

Authors and Affiliations

  1. Dep. Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, MA 02139-43071, Cambridge, Massachusetts, USA

    Pierre F.J. Lermusiaux, Tapovan Lolla & Patrick J. Haley Jr.

  2. Dep. Aerospace Engineering, University of Illinois at Urbana Champaign, 104S Wright Street, IL 61801, Urbana, Illinois, USA

    Konuralp Yigit

  3. Creare, 16 Great Hollow Road, NH 30755, Hanover, New Hampshire, USA

    Mattheus P. Ueckermann

  4. TrackMan A/S, Stubbeled 2, 2950, Vedbæk, Denmark

    Thomas Sondergaard

  5. American Meteorological Society, 45 Beacon Street, MA 02108-3693, Boston, Massachusetts, USA

    Wayne G. Leslie

Authors
  1. Pierre F.J. Lermusiaux
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  2. Tapovan Lolla
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  3. Patrick J. Haley Jr.
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  4. Konuralp Yigit
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  5. Mattheus P. Ueckermann
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  6. Thomas Sondergaard
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  7. Wayne G. Leslie
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Corresponding author

Correspondence to Pierre F.J. Lermusiaux .

Editor information

Editors and Affiliations

  1. The Inst. for Ocean and Systems Engineering – SeaTech, Florida Atlantic University, 101 North Beach Road, FL 33004, Dania Beach, Florida, USA

    Manhar R. Dhanak

  2. School of Naval Architecture & Marine Engineering, University of New Orleans, 2000 Lakeshore Drive, LA 70148, New Orleans, Louisiana, USA

    Nikolaos I. Xiros

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Lermusiaux, P.F. et al. (2016). Science of Autonomy: Time-Optimal Path Planning and Adaptive Sampling for Swarms of Ocean Vehicles. In: Dhanak, M.R., Xiros, N.I. (eds) Springer Handbook of Ocean Engineering. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-16649-0_21

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  • DOI: https://doi.org/10.1007/978-3-319-16649-0_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16648-3

  • Online ISBN: 978-3-319-16649-0

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