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Ocean Explorations Using Autonomy: Technologies, Strategies and Applications

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Offshore Robotics

Part of the book series: Offshore Robotics ((OR))

Abstract

Ocean exploration has become one of the most important strategies for a sustainable development for our world. To better understand the ocean and make an efficient use of its resources, autonomous marine vehicles (AMVs) including both surface and underwater vehicles play an essential role to extend and accelerate the exploration capabilities. This chapter provides an in-depth review of the key technologies in the development of autonomous surface vehicles (ASVs) and autonomous underwater vehicles (AUVs), which are two main types of AMVs. With the illustration of some typical vehicle prototypes, the control methods and deployment strategies of ASVs and AUVs, especially the collaborative operation of these two types of vehicles, have been discussed to inspire a wide application of marine autonomy in future ocean explorations.

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References

  1. Wang W, Gheneti B, Mateos L, Duarte F, Ratti C, Rus D (2019) Roboat: an autonomous surface vehicle for urban waterways. IEEE Robot Autom Lett

    Google Scholar 

  2. Aguiar AP, Almeida J, Bayat M, Cardeira B, Cunha R, Häusler A, Maurya P, Oliveira A, Pascoal A, Pereira A (2009) Cooperative control of multiple marine vehicles theoretical challenges and practical issues. IFAC Proc Vol 42(18):412–417

    Article  Google Scholar 

  3. Liu Y, Bucknall R (2015) Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment. Ocean Eng 97(6):126–144

    Article  Google Scholar 

  4. Zhao R, Xiang X, Yu C, Jiang Z (2016) Coordinated formation control of autonomous underwater vehicles based on leader–follower strategy. In: OCEANS 2016 MTS/IEEE Monterey, pp 1–5

    Google Scholar 

  5. Li D, Wang P, Du L (2018) Path planning technologies for autonomous underwater vehicles—a review. IEEE Access 7:9745–9768

    Article  Google Scholar 

  6. Sartore C, Simetti E, Wanderlingh F, Casalino G (2019) Autonomous deep sea mining exploration: the EU ROBUST project control framework. In: OCEANS 2019-Marseille, pp 1–8

    Google Scholar 

  7. Benjamin MR, Curcio JA (2004) COLREGS-based navigation of autonomous marine vehicles. In: 2004 IEEE/OES autonomous underwater vehicles (IEEE Cat. No. 04CH37578), pp 32–39

    Google Scholar 

  8. Zadeh SM, Zadeh RB (2019) Efficient deployment and mission timing of autonomous underwater vehicles in large-scale operations. In: International conference on advanced data mining and applications, pp 792–804

    Google Scholar 

  9. Liu Y, Bucknall R (2018) A survey of formation control and motion planning of multiple unmanned vehicles. Robotica 36:1019–1047

    Article  Google Scholar 

  10. Li C, Sapatnekar SS, Hu J (2016) Control synthesis and delay sensor deployment for efficient ASV designs. In: Proceedings of the 35th international conference on computer-aided design, p 64

    Google Scholar 

  11. Hong MJ, Arshad M (2015) Modeling and motion control of a riverine autonomous surface vehicle (ASV) with differential thrust. J Teknol 74(9):137–143

    Google Scholar 

  12. Gat E (1997) ESL: a language for supporting robust plan execution in embedded autonomous agents. In: 1997 IEEE aerospace conference, vol 1, pp 319–324

    Google Scholar 

  13. Simmons R, Apfelbaum D (1998) A task description language for robot control. In: Proceedings. 1998 IEEE/RSJ international conference on intelligent robots and systems. Innovations in theory, practice and applications (Cat. No. 98CH36190), vol 3, pp 1931–1937

    Google Scholar 

  14. Fraser R, Harris C, Mathias L, Rayner N (1991) Implementing task-level mission management for intelligent autonomous vehicles. Eng Appl Artif Intell 4(4):257–268

    Article  Google Scholar 

  15. Jarvis D, Jarvis J, McFarlane D, Lucas A, Ronnquist R (2001) Implementing a multi-agent systems approach to collaborative autonomous manufacturing operations. In: 2001 IEEE aerospace conference proceedings (Cat. No. 01TH8542), vol 6, pp 2803–2811

    Google Scholar 

  16. McLurkin J, Yamins D (2005) Dynamic task assignment in robot swarms. In: Proceedings of robotics: science and systems, vol 8, pp 129–136

    Google Scholar 

  17. Schwertfeger JN, Jenkins OC (2007) Multi-robot belief propagation for distributed robot allocation. In: 2007 IEEE 6th international conference on development and learning, pp 193–198

    Google Scholar 

  18. Botelho SC, Alami R (1999) M+: a scheme for multi-robot cooperation through negotiated task allocation and achievement. In: Proceedings 1999 IEEE international conference on robotics and automation (Cat. No. 99CH36288C), vol 2, pp 1234–1239

    Google Scholar 

  19. Raboin E, Švec P, Nau D, Gupta SK (2013) Model-predictive target defense by team of unmanned surface vehicles operating in uncertain environments. In: 2013 IEEE international conference on robotics and automation, pp 3517–3522

    Google Scholar 

  20. Ulam PD, Endo Y, Wagner A, Arkin RC (2007) Integrated mission specification and task allocation for robot teams-testing and evaluation. Georgia Institute of Technology

    Google Scholar 

  21. Sasamura H, Ohta R, Saito T (2002) A simple learning algorithm for growing ring SOM and its application to TSP. In: Proceedings of the 9th international conference on neural information processing, 2002, ICONIP'02, vol 3, pp 1287–1290

    Google Scholar 

  22. Kleinhans ML, Martín DV (2006) Using self-organizing maps tosolve travelling salesman problem. Høgskoleringen: Norges teknisk-naturvitenskapelige universitet. https://github.com/DiegoVicen/ntnu-som. Accessed 18 March 2019

  23. Muhammadi M (2019) Kohonen SOM for learning the traveling salesman problem (TSP). Open-sourced code. https://github.com/mmg63/SOM-TSP-Machine-Learning. Accessed 9 April 2019

  24. Liu Y, Bucknall R (2018) Efficient multi-task allocation and path planning for unmanned surface vehicle in support of ocean operations. Neurocomputing 275:1550–1566

    Article  Google Scholar 

  25. Zhu D-Q, Qu Y, Yang SX (2019) Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment. Front Inf Technol Electron Eng 20(3):330–341

    Article  Google Scholar 

  26. Cunningham CT, Roberts RS (2001) An adaptive path planning algorithm for cooperating unmanned air vehicles. In: Proceedings 2001 ICRA. IEEE international conference on robotics and automation (Cat. No. 01CH37164), vol 4, pp 3981–3986

    Google Scholar 

  27. Elango M, Kanagaraj G, Ponnambalam S (2013) Sandholm algorithm with K-means clustering approach for multi-robot task allocation. In: International conference on swarm, evolutionary, and memetic computing, pp 14–22

    Google Scholar 

  28. Elango M, Nachiappan S, Tiwari MK (2011) Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms. Expert Syst Appl 38(6):6486–6491

    Article  Google Scholar 

  29. Wang N, Sun J-C, Er MJ, Liu Y-C (2015) A novel extreme learning control framework of unmanned surface vehicles. IEEE Trans Cybern 46(5):1106–1117

    Article  Google Scholar 

  30. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501

    Article  Google Scholar 

  31. Anonymous (2010) Machine learning—artificial intelligence; SIS Wins DARPA contract for autonomous anti-submarine tracking. Defense & aerospace week, p 138. http://search.proquest.com/docview/762054567/. Accessed 2 June 2019

  32. Lemaire IP (1988) NOSC and remotely operated vehicles (ROVs) and autonomous unmanned vehicles (AUVs). San Diego, USA

    Google Scholar 

  33. Capocci R, Dooly G, Omerdić E, Coleman J, Newe T, Toal D (2017) Inspection-class remotely operated vehicles: a review. J Mar Sci Eng 5(1):13

    Article  Google Scholar 

  34. Ecorsys, MRAG, Arendal (2014) Study to investigate the state of knowledge of deep-sea mining, Rotterdam, Netherlands

    Google Scholar 

  35. Heffernan O (2019) Deep-Sea Dilemma. Nature 571:465–469

    Article  Google Scholar 

  36. García-Valdovinos LG, Salgado-Jiménez T, Bandala-Sánchez M, Nava-Balanzar L, Hernández-Alvarado R, Cruz-Ledesma JA (2014) Modelling, design and robust control of a remotely operated underwater vehicle. Int J Adv Robot Syst 11(1)

    Google Scholar 

  37. Le KD, Nguyen HD, Ranmuthugala D, Forrest A (2015) A heading observer for ROVs under roll and pitch oscillations and acceleration disturbances using low-cost sensors. Ocean Eng 110:152–162

    Article  Google Scholar 

  38. Soylu S, Proctor AA, Podhorodeski RP, Bradley C, Buckham BJ (2016) Precise trajectory control for an inspection class ROV. Ocean Eng 111:508–523

    Article  Google Scholar 

  39. Sivčev S, Coleman J, Omerdić E, Dooly G, Toal D (2018) Underwater manipulators: a review. Ocean Eng 163:431–450

    Google Scholar 

  40. Wynn RB et al (2014) Autonomous Underwater Vehicles (AUVs): their past, present and future contributions to the advancement of marine geoscience. Mar Geol 352:451–468

    Article  Google Scholar 

  41. Button RW, Kamp J, Curtin TB, Dryden J (2009) A survey of missions for unmanned undersea vehicles

    Google Scholar 

  42. Allard Y, Shahbazian E, Isenor A (2014) Unmanned Underwater Vehicle (UUV) information study, Montreal

    Google Scholar 

  43. Prestero T (2001) Verification of a six-degree of freedom simulation model for the REMUS autonomous underwater vehicle by in partial fulfillment of the requirements for the degrees of and at the chairperson, committee on graduate students verification of a six-degree of F, MIT and WHOI

    Google Scholar 

  44. McPhail S (2009) Autosub6000: a deep diving long range AUV. J Bionic Eng 6(1):55–62

    Article  Google Scholar 

  45. Phillips AB, Steenson L, Harris C, Rogers E, Turnock SR, Furlong M (2009) Delphin2: an over actuated autonomous underwater vehicle for manoeuvring research. Trans RINA Part A Int J Marit Eng 151

    Google Scholar 

  46. Tanakitkorn K, Wilson PA, Turnock SR, Phillips AB (2018) Sliding mode heading control of an overactuated, hover-capable autonomous underwater vehicle with experimental verification. J Field Robot 35(3):396–415

    Article  Google Scholar 

  47. O'Rourke R (2019) Navy large unmanned surface and undersea vehicles: background and issues for congress

    Google Scholar 

  48. Stommel H (1989) The Slocum Mission. Oceanography 2(1):22–25

    Article  Google Scholar 

  49. Eriksen CC et al (2001) Seaglider: a long-range autonomous underwater vehicle for oceanographic research. IEEE J Ocean Eng 26(4):424–436

    Article  Google Scholar 

  50. Sherman J, Davis RE, Owens WB, Valdes J (2001) The autonomous underwater glider ‘spray’

    Google Scholar 

  51. Webb DC, Simonetti PJ, Jones CP SLOCUM: an underwater glider propelled by environmental energy IEEE J Ocean Eng

    Google Scholar 

  52. Claustre H, Beguery L, Patrice PLA (2014) SeaExplorer glider breaks two world records. Sea Technol

    Google Scholar 

  53. Yu JC, Zhang AQ, Jin WM, Chen Q, Tian Y, Liu CJ (2011) Development and experiments of the sea-wing underwater glider. China Ocean Eng 25(4):721–736

    Google Scholar 

  54. Yu J, Zhang F, Zhang A, Jin W, Tian Y (2013) Motion parameter optimization and sensor scheduling for the sea-wing underwater glider. IEEE J Ocean Eng 38(2):243–254

    Article  Google Scholar 

  55. Wood S (2009) Autonomous underwater gliders. In: Inzartsev A (ed) Underwater vehicles. IntechOpen, London, pp 499–524

    Google Scholar 

  56. Rudnick DL (2016) Ocean research enabled by underwater gliders. Ann Rev Mar Sci 8(1):519–541

    Article  Google Scholar 

  57. Wang S, Sun X, Wang Y, Wu J, Wang X (2011) Dynamic modeling and motion simulation for a winged hybrid-driven underwater glider. China Ocean Eng 25(1):97–112

    Article  Google Scholar 

  58. Cooney L (2016) Expanding the capabilities of the Slocum glider. In: Oceans

    Google Scholar 

  59. Buisson N (2019) BRIDGES project: DXP and UXP gliders bring together research and industry for the development of glider environment services. In: Oceans

    Google Scholar 

  60. Furlong ME, Paxton D, Stevenson P, Pebody M, Mcphail SD, Perrett J (2012) Autosub long range: a long range deep diving AUV for ocean monitoring. In: Autonomous underwater vehicles

    Google Scholar 

  61. Paull L, Saeedi S, Seto M, Li H (2014) AUV navigation and localization: a review. IEEE J Ocean Eng 39(1):131–149

    Article  Google Scholar 

  62. Bahr A, Leonard JJ, Fallon MF (2009) Cooperative localization for autonomous underwater vehicles. Int J Robot Res 28(6):714–728

    Article  Google Scholar 

  63. Li Y, Jiang Y, Cao J, Wang B, Li Y (2015) AUV docking experiments based on vision positioning using two cameras. Ocean Eng 110:163–173

    Article  Google Scholar 

  64. Myint M, Yonemori K, Lwin KN, Yanou A, Minami M (2017) Dual-eyes vision-based docking system for autonomous underwater vehicle: an approach and experiments. J Intell Robot Syst Theory Appl 1–28

    Google Scholar 

  65. Kimball PW et al (2018) The ARTEMIS under-ice AUV docking system. J Field Robot 35(2):299–308

    Article  Google Scholar 

  66. Palomeras N et al (2018) AUV homing and docking for remote operations. Ocean Eng 154:106–120

    Article  Google Scholar 

  67. Matsuda T, Maki T, Masuda K, Sakamaki T (2019) Resident autonomous underwater vehicle: underwater system for prolonged and continuous monitoring based at a seafloor station. Robot Auton Syst 120:103231

    Google Scholar 

  68. Zhong L, Li D, Lin M, Lin R, Yang C (2019) A fast binocular localisation method for AUV docking. Sensors (Switzerland) 19(7)

    Google Scholar 

  69. Testor P et al (2019) OceanGliders: a component of the integrated GOOS. Front Mar Sci 6

    Google Scholar 

  70. Jung YS, Lee KW, Lee SY, Choi MH, Lee BH (2009) An efficient underwater coverage method for multi-AUV with sea current disturbances. Int J Control Autom Syst 7(4):615–629

    Article  Google Scholar 

  71. Xiang X, Jouvencel B, Parodi O (2010) Coordinated formation control of multiple autonomous underwater vehicles for pipeline inspection. Int J Adv Robot Syst 7(1)

    Google Scholar 

  72. Yoon S, Qiao C (2012) Cooperative search and survey using Autonomous Underwater Vehicles (AUVs). IEEE Trans Parallel Distrib Syst 23(3):364–379

    Article  Google Scholar 

  73. Yao P, Qi SB (2019) Obstacle-avoiding path planning for multiple autonomous underwater vehicles with simultaneous arrival. Sci China Technol Sci 62(1):121–132

    Article  Google Scholar 

  74. Keeter M et al (2012) Cooperative search with autonomous vehicles in a 3D aquatic testbed

    Google Scholar 

  75. Sousa J, Cruz N, Matos A, Pereira FL (1997) Mutliple AUVs for coastal oceanography. In: Oceans

    Google Scholar 

  76. Matsuda T, Maki T, Sato Y, Sakamaki T, Ura T (2018) Alternating landmark navigation of multiple AUVs for wide seafloor survey: field experiment and performance verification. J Field Robot 35(3):359–395

    Article  Google Scholar 

  77. Belbachir A (2011) A cooperative architecture for target localization using underwater vehicles. Institut National Polytechnique de Toulouse

    Google Scholar 

  78. Cao X, Yu AL (2017) Multi-AUV cooperative target search algorithm in 3-D underwater workspace. J Navig 70(6):1293–1311

    Article  Google Scholar 

  79. Cao X, Sun H, Jan GE (2018) Multi-AUV cooperative target search and tracking in unknown underwater environment. Ocean Eng 150:1–11

    Article  Google Scholar 

  80. Ni J, Yang L, Shi P, Luo C (2018) An improved DSA-based approach for multi-AUV cooperative search. Comput Intell Neurosci 2018

    Google Scholar 

  81. Cao X, Guo L (2019) A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments. Int J Adv Robot Syst 16(4)

    Google Scholar 

  82. Rui G, Chitre M (2010) Cooperative positioning using range-only measurements between two AUVs. In: Oceans

    Google Scholar 

  83. Birk A et al (2012) Cooperative cognitive control for autonomous underwater vehicles (CO3AUVs): overview and progresses in the 3rd project year. IFAC Proc Vol 45(5):361–366

    Article  Google Scholar 

  84. Walls JM, Eustice RM (2013) An exact decentralized cooperative navigation algorithm for acoustically networked underwater vehicles with robustness to faulty communication: theory and experiment. In: Proceedings of the robotics: science and systems

    Google Scholar 

  85. Yao Y (2013) Cooperative navigation system for multiple unmanned underwater vehicles. IFAC Proc Vol (IFAC-PapersOnline) 3(PART 1):719–723

    Google Scholar 

  86. Walls JM, Eustice RM (2014) An origin state method for communication constrained cooperative localization with robustness to packet loss. Int J Robot Res 33(9):1191–1208

    Article  Google Scholar 

  87. Djapic V, Gravelle E, Ouimet M, Martinez S, Cortes J (2017) Cooperative navigation of low cost autonomous underwater vehicles. In: 4th underwater acoustics conference and exhibition cooperative

    Google Scholar 

  88. Li Q, Ben Y, Naqvi SM, Neasham JA, Chambers JA (2018) Robust student’s t-based cooperative navigation for autonomous underwater vehicles. IEEE Trans Instrum Meas 67(8):1762–1777

    Article  Google Scholar 

  89. Seah WKG, Tan H-X, Liu Z, Ang MH (2005) Multiple-UUV approach for enhancing connectivity in underwater Ad-hoc sensor networks. In: Oceans

    Google Scholar 

  90. Allotta B et al (2017) Employment of an autonomous underwater vehicle as mobile bridge among heterogeneous acoustic nodes. IFAC PapersOnLine 50(1):12380–12385

    Article  Google Scholar 

  91. Jawhar I, Mohamed N, Al-Jaroodi J, Zhang S (2019) An architecture for using autonomous underwater vehicles in wireless sensor networks for underwater pipeline monitoring. IEEE Trans Ind Inform 15(3):1329–1340

    Article  Google Scholar 

  92. Hudson K et al (2019) Reevaluating the canyon hypothesis in a biological hotspot in the western Antarctic Peninsula. J Geophys Res Ocean 124(8):6345–6359

    Article  Google Scholar 

  93. Liu S, Xu H, Lin Y, Gao L (2019) Visual navigation for recovering an AUV by another AUV in shallow water. Sensors 19(8)

    Google Scholar 

  94. Rehman FU, Thomas G, Anderlini E (2019) Centralized control system design for underwater transportation using two hovering autonomous underwater vehicles (HAUVs). IFAC PapersOnLine 52(11):13–18

    Article  MathSciNet  Google Scholar 

  95. Ferri G, Djapic V (2013) Robotics and automation (ICRA), 2013 IEEE international conference on: date 6–10 May 2013. In: IEEE international conference on robotics and automation (ICRA), pp 5586–5592

    Google Scholar 

  96. Proctor AA et al (2018) Unlocking the power of combined autonomous operations with underwater and surface vehicles: success with a deep-water survey AUV and USV mothership. In Oceans

    Google Scholar 

  97. Conte G, Scaradozzi D, Mannocchi D, Raspa P, Panebianco L, Screpanti L (2018) Development and experimental tests of a ROS multi-agent structure for autonomous surface vehicles. J Intell Robot Syst Theory Appl 92(3–4):705–718

    Article  Google Scholar 

  98. Gu H, Meng L, Bai G, Zhang H, Lin Y, Liu S (2018) Automated recovery of the UUV based on the towed system by the USV. In: Oceans

    Google Scholar 

  99. Sarda EI, Dhanak MR (2019) Launch and recovery of an autonomous underwater vehicle from a station-keeping unmanned surface vehicle. IEEE J Ocean Eng 44(2)

    Google Scholar 

  100. Li B, Page BR, Hoffman J, Moridian B, Mahmoudian N (2019) Rendezvous planning for multiple AUVs with mobile charging stations in dynamic currents. IEEE Robot Autom Lett 4(2):1653–1660

    Article  Google Scholar 

  101. Das B, Subudhi B, Pati BB (2016) Cooperative formation control of autonomous underwater vehicles: an overview. Int J Autom Comput 13(3):199–225 (Chinese Academy of Sciences)

    Google Scholar 

  102. Das B, Subudhi B, Pati BB (2016) Co-operative control of a team of autonomous underwater vehicles in an obstacle-rich environment. J Mar Eng Technol 15(3):135–151

    Article  Google Scholar 

  103. Yan ZP, Liu YB, Yu CB, Zhou JJ (2017) Leader-following coordination of multiple UUVs formation under two independent topologies and time-varying delays. J Cent South Univ 24(2):382–393

    Google Scholar 

  104. Wang N, Qian C, Sun J, Liu Y (2016) Adaptive robust finite-time trajectory tracking control of fully actuated marine surface vehicles. IEEE Trans Control Syst Technol 24(4):1454–1462. https://doi.org/10.1109/TCST.2015.2496585

    Article  Google Scholar 

  105. Wang N, Er MJ, Sun J, Liu Y (2016) Adaptive robust online constructive fuzzy control of a complex surface vehicle system. IEEE Trans Cybern 46(7):1511–1523. https://doi.org/10.1109/TCYB.2015.2451116

    Article  Google Scholar 

  106. Wang N, Er MJ (2016) Direct adaptive fuzzy tracking control of marine vehicles with fully unknown parametric dynamics and uncertainties. IEEE Trans Control Syst Technol 24(5):1845–1852

    Article  Google Scholar 

  107. Wang N, Er MJ (2016) Direct adaptive fuzzy tracking control of marine vehicles with fully unknown parametric dynamics and uncertainties. IEEE Trans Control Syst Technol 24(5):1845–1852. https://doi.org/10.1109/TCST.2015.2510587

    Article  Google Scholar 

  108. Wang N, Sun J, Er MJ (2018) Tracking-error-based universal adaptive fuzzy control for output tracking of nonlinear systems with completely unknown dynamics. IEEE Trans Fuzzy Syst 26(2):869–883. https://doi.org/10.1109/TFUZZ.2017.2697399

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, University College London, Torrington Place, London, WC17JE, UK

    Yuanchang Liu, Enrico Anderlini, Shuwu Wang, Song Ma & Zitian Ding

  2. School of Energy and Power Engineering, Wuhan University of Technology, Wuhan, PR China

    Shuwu Wang

  3. Intelligent Transport System Research Centre, Wuhan University of Technology, Wuhan, PR China

    Shuwu Wang

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  1. Yuanchang Liu
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  2. Enrico Anderlini
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  3. Shuwu Wang
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  4. Song Ma
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  5. Zitian Ding
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Correspondence to Yuanchang Liu .

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Editors and Affiliations

  1. Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, China

    Shun-Feng Su

  2. School of Shipbuilding Engineering, Harbin Engineering University, Harbin, Heilongjiang, China

    Ning Wang

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Liu, Y., Anderlini, E., Wang, S., Ma, S., Ding, Z. (2022). Ocean Explorations Using Autonomy: Technologies, Strategies and Applications. In: Su, SF., Wang, N. (eds) Offshore Robotics. Offshore Robotics. Springer, Singapore. https://doi.org/10.1007/978-981-16-2078-2_2

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