Bounded Cost Path Planning for Underwater Vehicles Assisted by a Time-Invariant Partitioned Flow Field Model

Abstract

A bounded cost path planning method is developed for underwater vehicles assisted by a data-driven flow modeling method. The modeled flow field is partitioned as a set of cells of piece-wise constant flow speed. A flow partition algorithm and a parameter estimation algorithm are proposed to learn the flow field structure and parameters with justified convergence. A bounded cost path planning algorithm is developed taking advantage of the partitioned flow model. An extended potential search method is proposed to determine the sequence of partitions that the optimal path crosses. The optimal path within each partition is then determined by solving a constrained optimization problem. Theoretical justification is provided for the proposed extended potential search method generating the optimal solution. The path planned has the highest probability to satisfy the bounded cost constraint. The performance of the algorithms is demonstrated with experimental and simulation results, which show that the proposed method is more computationally efficient than some of the existing methods.

Laplacian regularized motion tomography for underwater vehicle flow mapping with sporadic localization measurements

“…Orthogonal decomposition technique has been applied in [7] to compute flow model parameters, with the help from dynamic mode decomposition and the Koopman operator representation [8,9]. [10,11] develops a data assimilation scheme that adaptively constructs the basis functions from the Eulerian flow field prediction model. Parameters of the basis functions are then estimated from an adaptive learning algorithm.…”

Laplacian Regularized Motion Tomography for Underwater Vehicle Flow Mapping with Sporadic Localization Measurements

“…Orthogonal decomposition technique has been applied in [7] to compute flow model parameters, with the help from dynamic mode decomposition and the Koopman operator representation [8,9]. [10,11] develops a data assimilation scheme that adaptively constructs the basis functions from the Eulerian flow field prediction model. Parameters of the basis functions are then estimated from an adaptive learning algorithm.…”