AUV Adaptive Sampling Methods: A Review
Source abstract
Abstract
Autonomous underwater vehicles (AUVs) are unmanned marine robots that have been used for a broad range of oceanographic missions. They are programmed to perform at various levels of autonomy, including autonomous behaviours and intelligent behaviours. Adaptive sampling is one class of intelligent behaviour that allows the vehicle to autonomously make decisions during a mission in response to environment changes and vehicle state changes. Having a closed-loop control architecture, an AUV can perceive the environment, interpret the data and take follow-up measures. Thus, the mission plan can be modified, sampling criteria can be adjusted, and target features can be traced. This paper presents an overview of existing adaptive sampling techniques. Included are adaptive mission uses and underlying methods for perception, interpretation and reaction to underwater phenomena in AUV operations. The potential for future research in adaptive missions is discussed.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Underwater Vehicles and Communication Systems · Target Tracking and Data Fusion in Sensor Networks · Water Quality Monitoring Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Australian Maritime College, University of Tasmania, Launceston 7250, TAS, Australia
Department of Ocean and Naval Architectural Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada
School of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
References (100+)
Show source-provided references
1. Seto, M.L. (2012). Marine Robot Autonomy, Springer Science & Business Media.10.1007/978-1-4614-5659-9 ↗
2. Hagen, P.E., Midtgaard, O., and Hasvold, O. (2007, January 18–21). Making AUVs Truly Autonomous. Proceedings of the OCEANS 2007, Aberdeen, UK.10.1109/OCEANS.2007.4449405 ↗
3. Mesoscale variations of sea surface temperature and ocean color patterns at the Mid-Atlantic Bight shelfbreak10.1029/2010GL042658 ↗
4. Design and tests of an adaptive triggering method for capturing peak samples in a thin phytoplankton layer by an autonomous underwater vehicle10.1109/JOE.2010.2081031 ↗
5. Feature based adaptive energy management of sensors on autonomous underwater vehicles10.1016/j.oceaneng.2014.11.015 ↗
6. Using an autonomous underwater vehicle to track a coastal upwelling front10.1109/JOE.2012.2197272 ↗
7. Petillo, S. (2015). Autonomous & Adaptive Oceanographic Feature Tracking on Board Autonomous Underwater Vehicles, Woods Hole Oceanographic Institution.10.1575/1912/7129 ↗
8. Cruz, N.A., and Matos, A.C. (2010, January 24–27). Reactive AUV motion for thermocline tracking. Proceedings of the OCEANS 2010 IEEE-Sydney, Sydney, Australia.10.1109/OCEANSSYD.2010.5603883 ↗
9. Cruz, N.A., and Matos, A.C. (2010, January 24–27). Adaptive sampling of thermoclines with autonomous underwater vehicles. Proceedings of the OCEANS 2010 IEEE-Sydney, Sydney, Australia.10.1109/OCEANS.2010.5663903 ↗
10. Clabon, M. (2003). Thermocline Tracking Using an Upgraded Ocean Explorer Autonomous Underwater Vehicle. [Master’s Thesis, Florida Atlantic University].
11. Petillo, S., Balasuriya, A., and Schmidt, H. (2010, January 24–27). Autonomous adaptive environmental assessment and feature tracking via autonomous underwater vehicles. Proceedings of the OCEANS 2010 IEEE-Sydney, Sydney, Australia.10.1109/OCEANSSYD.2010.5603513 ↗
12. Using an autonomous underwater vehicle to track the thermocline based on peak-gradient detection10.1109/JOE.2012.2192340 ↗
13. Woithe, H.C., and Kremer, U. (2009, January 10–15). A programming architecture for smart autonomous underwater vehicles. Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA.10.1109/IROS.2009.5354098 ↗
14. Cruz, N.A. (2016). Adaptive Ocean Sampling with Modular Robotic Platforms. [Ph.D. Thesis, Universidade do Porto].
15. Rajan, K., Py, F., McGann, C., Ryan, J., O’Reilly, T., Maughan, T., and Roman, B. (, January 23–26). Onboard Adaptive Control of AUVs using Automated Planning. Proceedings of the International Symposium on Unmanned Untethered Submersible Technology (UUST), Durham, NH, USA.
16. Zhang, Y., Godin, M., Bellingham, J.G., and Ryan, J.P. (2011, January 6–9). Ocean front detection and tracking by an autonomous underwater vehicle. Proceedings of the OCEANS 2011, Santander, Spain.10.23919/OCEANS.2011.6107160 ↗
17. Autonomous Four—Dimensional Mapping and Tracking of a Coastal Upwelling Front by an Autonomous Underwater Vehicle10.1002/rob.21617 ↗
18. Fiorelli, E., Bhatta, P., Leonard, N.E., and Shulman, I. (2003, January 6). Adaptive sampling using feedback control of an autonomous underwater glider fleet. Proceedings of the 13th International Symposium on Unmanned Untethered Submersible Technology (UUST), Durham, NH, USA.
19. Multi-AUV control and adaptive sampling in Monterey Bay10.1109/JOE.2006.880429 ↗
20. Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in Monterey Bay10.1002/rob.20366 ↗
21. Exploiting adaptive and collaborative AUV autonomy for detection and characterization of internal waves10.1109/JOE.2013.2243251 ↗
22. Spectral-feature classification of oceanographic processes using an autonomous underwater vehicle10.1109/48.972115 ↗
23. Cazenave, F.O. (2008). Internal Waves over the Continental Shelf in South Monterey Bay. [Master’s Thesis, San Jose State University].
24. Das, J., Rajany, K., Frolovy, S., Pyy, F., Ryany, J., Caronz, D.A., and Sukhatme, G.S. (2010, January 4–8). Towards marine bloom trajectory prediction for AUV mission planning. Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, USA.10.1109/ROBOT.2010.5509930 ↗
25. Phytoplankton motility and the competition for nutrients in the thermocline10.3354/meps06999 ↗
26. Influences of upwelling and downwelling winds on red tide bloom dynamics in Monterey Bay, California10.1016/j.csr.2008.11.006 ↗
27. Cross-shelf transport into nearshore waters due to shoaling internal tides in San Pedro Bay, CA10.1016/j.csr.2009.04.008 ↗
28. Spatiotemporal development of physical, chemical, and biological characteristics of stormwater plumes in Santa Monica Bay, California (USA)10.1016/j.seares.2009.11.006 ↗
29. Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea10.1016/j.watres.2017.09.026 ↗
30. Zhang, B., Sukhatme, G.S., and Requicha, A.A. (October, January 28). Adaptive sampling for marine microorganism monitoring. Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan.
31. Harmful algal blooms: Causes, impacts and detection10.1007/s10295-003-0074-9 ↗
32. In situ nutrient monitoring: A tool for capturing nutrient variability and the antecedent conditions that support algal blooms10.1016/j.hal.2008.08.013 ↗
33. Fiber-optic microarray for simultaneous detection of multiple harmful algal bloom species10.1128/AEM.00332-06 ↗
34. Applications of flow cytometry to ecotoxicity testing using microalgae10.1016/S0167-7799(01)01924-2 ↗
35. Development of flow cytometry-based algal bioassays for assessing toxicity of copper in natural waters
36. Phytoplankton cell counting by flow cytometry
37. Chlorophyll fluorescence: A probe of photosynthesis in vivo10.1146/annurev.arplant.59.032607.092759 ↗
38. Fluorescent in situ hybridization with rRNA-targeted oligonucleotide probes to identify small phytoplankton by flow cytometry10.1128/aem.61.7.2506-2513.1995 ↗
39. The use of quantitative polymerase chain reaction for the detection and enumeration of the harmful alga Aureococcus anophagefferens in environmental samples along the United States East Coast10.4319/lom.2003.1.92 ↗
40. Applications of real-time polymerase chain reaction for quantification of microorganisms in environmental samples10.1007/s00253-006-0333-6 ↗
41. Recent applications of DNA microarray technology to toxicology and ecotoxicology10.1289/ehp.8194 ↗
42. Molecular approaches to diagnosing nutritional physiology in harmful algae: Implications for studying the effects of eutrophication10.1016/j.hal.2008.08.016 ↗
43. Liquid chromatography–electrospray ionisation-mass spectrometry based method for the simultaneous determination of algal and cyanobacterial toxins in phytoplankton from marine waters and lakes followed by tentative structural elucidation of microcystins10.1016/S0021-9673(03)00485-0 ↗
44. The application of electrospray ionization coupled to ultrahigh resolution mass spectrometry for the molecular characterization of natural organic matter10.1002/jms.1221 ↗
45. Godin, M., Zhang, Y., Ryan, J., Hoover, T., and Bellingham, J. (2011, January 6–9). Phytoplankton bloom patch center localization by the Tethys Autonomous Underwater Vehicle. Proceedings of the OCEANS 2011, Santander, Spain.10.23919/OCEANS.2011.6107161 ↗
46. Das, J., Harvey, J., Py, F., Vathsangam, H., Graham, R., Rajan, K., and Sukhatme, G.S. (2013, January 6–10). Hierarchical probabilistic regression for AUV-based adaptive sampling of marine phenomena. Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.10.1109/ICRA.2013.6631377 ↗
47. Smith, R.N., Chao, Y., Jones, B.H., Caron, D.A., Li, P.P., and Sukhatme, G.S. (2010, January 15–18). Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking. Proceedings of the 2010 Field and Service Robotics, Cambridge, MA, USA.10.1007/978-3-642-13408-1_24 ↗
48. Smith, R.N., Pereira, A., Chao, Y., Li, P.P., Caron, D.A., Jones, B.H., and Sukhatme, G.S. (2010, January 3–8). Autonomous underwater vehicle trajectory design coupled with predictive ocean models: A case study. Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, USA.10.1109/ROBOT.2010.5509240 ↗
49. Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model10.1177/0278364910377243 ↗
50. Persistent ocean monitoring with underwater gliders: Adapting sampling resolution10.1002/rob.20405 ↗
51. Cormen, T.H., Leiserson, C.E., Rivest, R.L., and Stein, C. (2001). Introduction to Algorithms, 2nd, ed., The MIT Press.
52. Data-driven robotic sampling for marine ecosystem monitoring10.1177/0278364915587723 ↗
53. Bateni, M., Hajiaghayi, M., and Zadimoghaddam, M. (2010). Submodular secretary problem and extensions. Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, Springer.10.1007/978-3-642-15369-3_4 ↗
54. Saigol, Z.A. (2011). Automated Planning for Hydrothermal Vent Prospecting Using AUVs, University of Birmingham.
55. Hydrothermal plumes over spreading-center axes: Global distributions and geological inferences
56. Autonomous and adaptive underwater plume detection and tracking with AUVs: Concepts, methods, and available technology10.3182/20120919-3-IT-2046.00040 ↗
57. A novel trigger-based method for hydrothermal vents prospecting using an autonomous underwater robot10.1007/s10514-010-9187-y ↗
58. Contributions of Theodor Wilhelm Engelmann on phototaxis, chemotaxis, and photosynthesis10.1007/s11120-004-6313-8 ↗
59. A comparison of reactive robot chemotaxis algorithms10.1016/S0921-8890(03)00120-9 ↗
60. Camilli, R., Bingham, B., Jakuba, M., Singh, H., and Whelan, J. (2004, January 9–12). Integrating in-situ chemical sampling with AUV control systems. Proceedings of the Oceans 2004, Kobe, Japan.
61. Seabed AUV offers new platform for high-resolution imaging10.1029/2004EO310002 ↗
62. Farrell, J.A., Pang, S., Li, W., and Arrieta, R. (2003, January 22–26). Chemical plume tracing experimental results with a REMUS AUV. Proceedings of the OCEANS 2003, San Diego, CA, USA.10.1109/OCEANS.2003.178458 ↗
63. IPIECA-IOGP (2016). In-Water Surveillance of Oil Spills at Sea, International Association of Oil & Gas Producers.
64. Chase, C.R., and Van Bibber, S. (2006, January 2–4). Utilization of automated oil spill detection technology for clean water compliance and spill discharge prevention. Proceedings of the Freshwater Spills Symposium (FSS), Portland, OR, USA.
65. Advances in remote sensing for oil spill disaster management: State-of-the-art sensors technology for oil spill surveillance10.3390/s8010236 ↗
66. Seward, A. (2008). Hydrocarbon Sensors for Oil Spill Prevention and Response, Alliance for Coastal Technologies.
67. A peak-capture algorithm used on an autonomous underwater vehicle in the 2010 Gulf of Mexico oil spill response scientific survey10.1002/rob.20399 ↗
68. Jakuba, M.V., Kinsey, J.C., Yoerger, D.R., Camilli, R., Murphy, C.A., Steinberg, D., and Bender, A. (2011, January 25–30). Exploration of the gulf of mexico oil spill with the sentry autonomous underwater vehicle. Proceedings of the International Conference on Intelligent Robots and Systems (IROS) Workshop on Robotics for Environmental Monitoring (WREM), San Francisco, CA, USA.10.1109/IROS.2011.6048700 ↗
69. Rasmussen, C.E. (2004). Gaussian processes in machine learning. Advanced Lectures on Machine Learning, Springer.10.7551/mitpress/3206.001.0001 ↗
70. Chemical plume source localization10.1109/TSMCB.2006.874689 ↗
71. Identifying rhodamine dye plume sources in near-shore oceanic environments by integration of chemical and visual sensors10.3390/s130303776 ↗
72. Cannell, C.J., Gadre, A.S., and Stilwell, D.J. (2006, January 18–22). Boundary tracking and rapid mapping of a thermal plume using an autonomous vehicle. Proceedings of the OCEANS 2006, Boston, MA, USA.10.1109/OCEANS.2006.306807 ↗
73. Fahad, M., Saul, N., Guo, Y., and Bingham, B. (2015, January 26–30). Robotic simulation of dynamic plume tracking by unmanned surface vessels. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.10.1109/ICRA.2015.7139557 ↗
74. Chemical plume tracing via an autonomous underwater vehicle10.1109/JOE.2004.838066 ↗
75. Chemical plume tracing and odour source localisation by autonomous vehicles10.1017/S0373463307004183 ↗
76. Burian, E., Yoerger, D., Bradley, A., and Singh, H. (1996, January 2–6). Gradient search with autonomous underwater vehicles using scalar measurements. Proceedings of the 1996 Symposium on Autonomous Underwater Vehicle Technology, Monterey, CA, USA.10.1575/1912/5670 ↗
77. Ai, X., You, K., and Song, S. (2016, January 13–15). A source-seeking strategy for an autonomous underwater vehicle via on-line field estimation. Proceedings of the 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), Phuket, Thailand.10.1109/ICARCV.2016.7838757 ↗
78. System architecture directions for networked sensors10.1145/384264.379006 ↗
79. Dhariwal, A., Sukhatme, G.S., and Requicha, A.A. (May, January 26). Bacterium-inspired robots for environmental monitoring. Proceedings of the 2004 IEEE International Conference on Robotics and Automation, New Orleans, LA, USA.10.1109/ROBOT.2004.1308026 ↗
80. Berg, H.C. (1993). Random Walks in Biology, Princeton University Press.
81. Biased random walk models for chemotaxis and related diffusion approximations10.1007/BF00275919 ↗
82. Kramer, E. (1997). A tentative intercausal nexus and its computer model on insect orientation in windborne pheromone plumes. Insect Pheromone Research, Springer.10.1007/978-1-4615-6371-6_22 ↗
83. Belanger, J.H., and Willis, M.A. (1998, January 17–17). Biologically-inspired search algorithms for locating unseen odor sources. Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intelligent Systems and Semiotics (ISAS), Gaithersburg, MD, USA.
84. Distributed odor source localization10.1109/JSEN.2002.800682 ↗
85. Moth-inspired chemical plume tracing on an autonomous underwater vehicle10.1109/TRO.2006.870627 ↗
86. Li, W., and Carter, D. (2006, January 18–22). Subsumption architecture for fluid-advected chemical plume tracing with soft obstacle avoidance. Proceedings of the OCEANS 2006, Boston, MA, USA.
87. Grasso, F.W., Basil, J.A., and Atema, J. (1998, January 17–17). Toward the convergence: Robot and lobster perspectives of tracking odors to their source in the turbulent marine environment. Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intelligent Systems and Semiotics (ISAS), Gaithersburg, MD, USA.
88. Integration of flow and chemical sensing for guidance of autonomous marine robots in turbulent flows10.1023/A:1016275516949 ↗
89. Cannell, C.J., and Stilwell, D.J. (2005, January 19–23). A comparison of two approaches for adaptive sampling of environmental processes using autonomous underwater vehicles. Proceedings of the OCEANS 2005 MTS/IEEE, Washington, DC, USA.
90. Autonomous detection and sampling of water types and fronts in a coastal upwelling system by an autonomous underwater vehicle10.4319/lom.2012.10.934 ↗
91. Zhang, Y., Ryan, J., Bellingham, J., Harvey, J., Mcewen, R., Chavez, F., and Scholin, C. (2011, January 5). Classification of water masses and targeted sampling of ocean plankton populations by an autonomous underwater vehicle. Proceedings of the AGU Fall Meeting Abstracts, San Francisco, CA, USA.
92. Evolution of a physical and biological front from upwelling to relaxation10.1016/j.csr.2015.08.005 ↗
93. Lebreton, L.C.-M., and Franz, T. (2018, September 20). Trajectory Analysis of Deep Sea Oil Spill Scenarios in New Zealand Waters. Available online: http://www. greenpeace. org/new-zealand/Global/newzealand.
94. Fahad, M., Guo, Y., Bingham, B., Krasnosky, K., Fitzpatrick, L., and Sanabria, F.A. (2017, January 24–28). Robotic experiments to evaluate ocean plume characteristics and structure. Proceedings of the International Conference on 2017 IEEE/RSJ Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.10.1109/IROS.2017.8206509 ↗
95. A distributed multi-robot adaptive sampling scheme for the estimation of the spatial distribution in widespread fields10.1186/1687-1499-2012-223 ↗
96. Thickness of a submarine lava flow determined from near-bottom magnetic field mapping by autonomous underwater vehicle10.1029/98GL00442 ↗
97. Tracking hydrocarbon plume transport and biodegradation at Deepwater Horizon10.1126/science.1195223 ↗
98. Daxiong, J., Shenzhen, R., Rong, Z., Ruiwen, Y., Hongyu, Z., and Yang, L. (2013, January 23–27). A tracking control method of ASV following AUV. Proceedings of the Oceans 2013, San Diego, CA, USA.
99. A multi-robot hunting algorithm based on dynamic prediction for trajectory of the moving target and hunting points
100. Khoshrou, A., Aguiar, A.P., and Pereira, F.L. (2016). Adaptive sampling using an unsupervised learning of gmms applied to a fleet of auvs with ctd measurements. Robot 2015: Second Iberian Robotics Conference, Springer.10.1007/978-3-319-27146-0_25 ↗