Since the motion of autonomous underwater vehicles is affected by ambient flow, knowledge of an environmental flow field can be used to improve the navigation of autonomous underwater vehicles. Due to imperfect knowledge of flow, the actual trajectory of an autonomous underwater vehicle deviates from the predicted trajectory. The difference between the actual and predicted trajectories is referred to as the motion-integration error, providing information of flow along the vehicle trajectory. Inspired by computerized tomography, this paper proposes motion tomography, a tomographic method for creating a fine-grid spatial map of flow based on the motion-integration error. While typical computerized tomography is a linear problem, motion tomography is a nonlinear problem because of unknown nonlinear trajectories of autonomous underwater vehicles and the dependency of the trajectories on the flow field. Therefore, motion tomography employs an iterative process consisting of two alternating steps: Trajectory tracing and flow field estimation. Starting from an initial guess of the flow field, in the trajectory tracing step, unknown nonlinear vehicle trajectories are estimated. Then, using the estimated vehicle trajectories, a spatial map of flow is constructed through either the non-parametric or parametric flow field estimation. The error bound for trajectory tracing is computed and the convergence of both the non-parametric and parametric flow field estimation algorithms is proved. Simulation and experimental data are analyzed to evaluate the performance of motion tomography when subject to changing vehicle speed and flow variability.

In recent years, collecting scientific data from ocean environments has been increasingly undertaken by underwater gliders. For better navigation performance, the influence of flow on the navigation of underwater gliders may be significantly reduced by estimating flow velocity. However, methods for estimating flow do not always account for spatial and temporal changes in the flow field, leading to poor navigation in complex ocean environments. To improve navigation accuracy in such environmental conditions, this paper studies an approach for the real-time guidance of underwater gliders assisted by predictive ocean models. This study is motivated by glider deployments conducted from January to April 2012 and in February 2013 in Long Bay, South Carolina, where the ocean currents are characterized by strong tides and a stronger alongshore current, the Gulf Stream. The flow speed here often exceeds the forward speed of the glider. To deal with such a challenge, a computationally efficient method of depth-averaged ocean current modeling was developed. The method adjusts the ocean model based on the most recent ocean observations from gliders as feedback, and flow predictions from the model are incorporated into path planning, which produces waypoints. The entire process of flow prediction, path planning, and waypoint computation is performed off-board the gliders in real time by the glider navigation support system, the Glider-Environment Network Information System (GENIoS). This paper presents the setup and method for the glider navigation strategy applied to the Long Bay deployments. For demonstration, the performance of the method described here is compared to that of the default method implemented in the built-in glider navigation system.

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This paper investigates the problem of energyoptimal control for autonomous underwater vehicles (AUVs). To improve the endurance of AUVs, we propose a novel energyoptimal control scheme based on the economic model predictive control (MPC) framework. We first formulate a cost function that computes the energy spent for vehicle operation over a finite-time prediction horizon. Then, to account for the energy consumption beyond the prediction horizon, a terminal cost that approximates the energy to reach the goal (energy-to-go) is incorporated into the MPC cost function. To characterize the energy-to-go, a thorough analysis has been conducted on the globally optimized vehicle trajectory computed using the direct collocation (DC) method for our test-bed AUV, DROP-Sphere. Based on the two operation modes observed from our analysis, the energy-to-go is decomposed into two components: (i) dynamic and (ii) static costs. This breakdown facilitates the estimation of the energy-to-go, improving the AUV energy efficiency. Simulation is conducted using a six-degreesof-freedom dynamic model identified from DROP-Sphere. The proposed method for AUV control results in a near-optimal energy consumption with considerably less computation time compared to the DC method and substantial energy saving compared to a line-of-sight based MPC method. arXiv:1906.08719v1 [eess.SY]