• Arroyo, J., 2011: The Automatic Identification System: Then, now and in the future. Proceedings of the Marine Safety and Security Council, No. 1, U.S. Coast Guard, Washington, DC, 51–57.

  • Benjamin, M. R., , Leonard J. J. , , Curcio J. A. , , and Newman P. M. , 2006: A method for protocol-based collision avoidance between autonomous marine surface craft. J. Field Rob., 23, 333346, doi:10.1002/rob.20121.

    • Search Google Scholar
    • Export Citation
  • Codiga, D. L., 2015: A marine autonomous surface craft for long-duration, spatially explicit, multidisciplinary water column sampling in coastal and estuarine systems. J. Atmos. Oceanic Technol., 32, 627641, doi:10.1175/JTECH-D-14-00171.1.

    • Search Google Scholar
    • Export Citation
  • Filimon, M. A., 2013: Site planning and on-board collision avoidance software to optimize autonomous surface craft surveys. M.S. thesis, Dept. of Ocean Engineering, University of Rhode Island, 62 pp.

  • Kuwata, Y., , Wolf M. T. , , Zarzhitsky D. , , and Huntsberger T. L. , 2014: Safe maritime autonomous navigation with COLREGS, using velocity obstacles. IEEE J. Oceanic Eng., 39, 110119, doi:10.1109/JOE.2013.2254214.

    • Search Google Scholar
    • Export Citation
  • Motwani, A., 2012: A survey of uninhabited surface vehicles. Marine and Industrial Dynamic Analysis, School of Marine Science and Engineering, Plymouth University Tech. Rep. MIDAS.SMSE.2012.TR.2001, 54 pp.

  • Savitz, S., and et al. , 2013: U.S. Navy Employment Options for Unmanned Surface Vehicles (USVs). RAND Corporation, 119 pp. [Available online at http://www.rand.org/pubs/research_reports/RR384.html.]

    • Search Google Scholar
    • Export Citation
  • Svec, P., , Schwartz M. , , Thakur A. , , and Gupta S. K. , 2011: Trajectory planning with look-ahead for unmanned sea surface vehicles to handle environmental disturbances. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS’11), San Francisco, CA, IEEE, 1154–1159, doi:10.1109/IROS.2011.6095021.

  • Taylor, C., , and Stein D. , 2011: The Multipurpose Marine Cadastre: A tool for planning and decision making in the marine environment. 30th Annual Int. Submerged Lands Management Conf., Girdwood, AK, Dept. of Natural Resources, 27 pp. [Available online at http://www.mcatoolkit.org/pdf/ISLMC_11/Multipurpose_Marine_Cadastre_Planning_Tool.pdf.]

  • Yang, W.-R., , Chen C.-Y. , , Hsu C.-M. , , Tseng C.-J. , , and Yang W.-C. , 2011: Multifunctional inshore survey platform with unmanned surface vehicles. Int. J. Autom. Smart Technol., 1, 1925, doi:10.5875/ausmt.v1i2.122.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 15 15 2
PDF Downloads 11 11 2

An AIS-Based Site Planning Method to Help Minimize Collision Risk during Marine Autonomous Surface Craft Deployments

View More View Less
  • 1 Ocean Engineering Department, University of Rhode Island, Narragansett, Rhode Island
  • | 2 Graduate School of Oceanography, University of Rhode Island, Narragansett, Rhode Island
© Get Permissions
Restricted access

Abstract

Marine autonomous surface crafts (MASCs), or unmanned surface vehicles as their precursors have traditionally been known, could cost effectively advance spatiotemporal coverage and resolution capabilities for oceanographic sampling in coastal and estuarine settings, if deployed for long durations (from days to weeks). Site planning for such deployments balances scientific goals against operational risk of collision avoidance (CA) maneuvers required by the MASC during encounters with other vessels. A method is developed and demonstrated in this paper, using archived Automatic Identification System (AIS) vessel tracking data, to quantify such potential encounters of a MASC on a repeat-transect survey. The demonstration site is Rhode Island Sound, where average vessel track frequencies are shown to range from about 8 to 0.01 or less per day, from inshore shipping lanes to areas farther offshore, respectively. Encounters per month ranged from 24 to 1 for increasingly offshore locations, based on a MASC repeatedly traversing an 18-km transect at an average speed of 2.5 m s−1 (~5 kt) over a one-month summer period and stopping at each of 10 equispaced stations in one direction to sample for 10 min. Crude estimates of non-AIS vessel traffic suggest total encounters (with vessels equipped by AIS or not) up to 3–4 times higher. The method can be applied anywhere AIS data are available and is generalizable to any survey configuration. It facilitates investigating sensitivity to choices of transect location and sampling configuration parameters, providing crucial information to guide deployment planning for a given level of confidence in CA capabilities of the MASC.

Corresponding author address: Daniel L. Codiga, Graduate School of Oceanography, University of Rhode Island, 215 S. Ferry Rd., Narragansett, RI 02882. E-mail: d.codiga@gso.uri.edu

Abstract

Marine autonomous surface crafts (MASCs), or unmanned surface vehicles as their precursors have traditionally been known, could cost effectively advance spatiotemporal coverage and resolution capabilities for oceanographic sampling in coastal and estuarine settings, if deployed for long durations (from days to weeks). Site planning for such deployments balances scientific goals against operational risk of collision avoidance (CA) maneuvers required by the MASC during encounters with other vessels. A method is developed and demonstrated in this paper, using archived Automatic Identification System (AIS) vessel tracking data, to quantify such potential encounters of a MASC on a repeat-transect survey. The demonstration site is Rhode Island Sound, where average vessel track frequencies are shown to range from about 8 to 0.01 or less per day, from inshore shipping lanes to areas farther offshore, respectively. Encounters per month ranged from 24 to 1 for increasingly offshore locations, based on a MASC repeatedly traversing an 18-km transect at an average speed of 2.5 m s−1 (~5 kt) over a one-month summer period and stopping at each of 10 equispaced stations in one direction to sample for 10 min. Crude estimates of non-AIS vessel traffic suggest total encounters (with vessels equipped by AIS or not) up to 3–4 times higher. The method can be applied anywhere AIS data are available and is generalizable to any survey configuration. It facilitates investigating sensitivity to choices of transect location and sampling configuration parameters, providing crucial information to guide deployment planning for a given level of confidence in CA capabilities of the MASC.

Corresponding author address: Daniel L. Codiga, Graduate School of Oceanography, University of Rhode Island, 215 S. Ferry Rd., Narragansett, RI 02882. E-mail: d.codiga@gso.uri.edu
Save