Evaluation of Autonomous Surface Vessel Navigational Performance Under Varying Environmental Conditions

The water quality in inland and coastal environments must be monitored and managed to assure it is safe to use. Increased land use, for example, impacts aquatic ecosystems due to increased surface runoff. Contemporary monitoring techniques are laborious and expensive and limit the ability to gather high resolution spatio-temporal water quality data. Solar powered autonomous surface vessels (ASV) may provide a long endurance solution to overcome spatio-temporal drawbacks of conventional sampling and data collection by providing a mobile powered platform for sensors/instrumentation. However, ASV autopilot navigational accuracy may be affected by environmental conditions (wind, current, and waves) that can alter trajectories and negatively affect spatio-temporal resolution of water quality sampling efforts. The goal of this research was to evaluate the utility and navigational performance of a commercially available, solar powered ASV (SeaTrac SP-48) equipped with a multi-sensor payload to operate autonomously under varying environmental forces. The specific objectives were to evaluate the ASV‘s ability, under varying environmental conditions, to: 1) accurately and repeatedly maintain route heading (measured as cross-track-error [XTE]) and 2) hold station (fixed position; measured as off-station-error [OSE]). XTE increased as intensity of environmental forces increased. Mean OSE increased as station size increased. OSE decreased as environmental conditions (wind and waves) became more rigorous in a constant direction, thus reducing ASV through-station-drift (TSD). This work provides a conceptual framework for development of ASV spatio-temporal resolution limitations for real-time monitoring data collection. Future work will focus on integration of water quality sensors for in-situ data collection and monitoring.