Long - duration Environmentally - adaptive Autonomous Rigorous

Specific Objectives:  Derive, implement and evaluate rigorous and efficient Bayesian smoothing theory and schemes that respect nonlinear dynamics and capture non-Gaussian statistics, for robust persistent inference and learning, integrating information backward and forward in time over long durations in largedimensional multiscale fluid and ocean dynamics.  Derive and develop adaptive sampling theories and methods that predict the types and locations of the observations to be collected that maximize information about the ocean system studied (e.g. about its model state variables, parameters and/or formulations)  Merge and refine our reduced-order DO stochastic equations with our path planning methods, to obtain new stochastic schemes for time-, coordination-, energy-, dynamicsand swarmoptimal path planning that efficiently account for ocean forecast uncertainties.  Develop efficient onboard routing and high-level adaptation schemes that utilize observations collected by vehicles to autonomously adapt optimal plans (e.g. for paths, sampling strategies, collaboration or decision making process).  Apply these schemes to simulated fluid and ocean dynamics, from idealized to realistic settings, and integrate these schemes for real sea exercises of opportunity involving distributed computations across components of the autonomous naval sensing systems.