Sensor Evaluation and Fleet Modeling of Long-Range Low-Cost Autonomous Surface Vehicles

Abstract

This thesis examines the development and assessment of sensor configurations for Long-Range Low-Cost Autonomous Surface Vehicles (ASVs) with a focus on Maritime Domain Awareness (MDA) applications. Utilizing the Platform for Expanding AUV exploRation (PEARL) as a model, the study systematically evaluates various sensor options to identify optimal suites for MDA operations. Through an analysis of 255 sensor combinations, considering factors such as range, power consumption, field of view, resolution, and cost, this research identifies key sensor configurations that maximize operational utility while minimizing cost. The research identified that sensors should include a RADAR, AIS, IR cameras, and visual light cameras, allowing operation in all lighting and weather conditions. The study further explores fleet modeling for two MDA use cases—The Littorals and Open Ocean scenarios—providing insights into the cost-effectiveness and coverage efficiency of deploying fleets of sensor-equipped PEARL units. The fleet modeling demonstrated that these low-cost ASVs can cover approximately 20 times the area of a Saildrone Voyager for about the same capital cost. The findings contribute to the advancement of low-cost ASV technology for enhanced maritime surveillance and data collection, offering scalable solutions to maritime domain challenges.