Persistent Mission Planning of an Energy-Harvesting Autonomous Underwater Vehicle for Gulf Stream Characterization

Characterizing evolving ocean environments is important to scientific, renewable energy, and military applications. However, performing meaningful characterizations of these resources is complicated by their spatiotemporal evolution and partial observability. In this work, we specifically consider the use of an autonomous underwater vehicle (AUV) with a deployable energy-harvesting kite that enables persistent missions. When the AUV parks itself on the seabed, the kite can deploy, harvesting significant amounts of energy through periodic figure-8 flight. Focusing on a Gulf Stream observational mission, we present a persistent planning algorithm that fuses Gaussian process (GP) modeling with model predictive control (MPC) to optimize AUV charging times to maximize the informativeness of the mission. Based on simulation studies using a mid-Atlantic bight, south Atlantic bight regional ocean model (MAB-SAB-ROM), we demonstrate a 20% reduction in the time required to traverse a given section of the Gulf Stream, which leads to a significant reduction in prediction error.