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

Abstract

AbstractMarine 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.