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

Mengxue Hou; Sungjin Cho; Haomin Zhou; Catherine R Edwards; Fumin Zhang

doi:10.3389/frobt.2021.575267

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

Front Robot AI. 2021 Jul 14:8:575267. doi: 10.3389/frobt.2021.575267. eCollection 2021.

Authors

Mengxue Hou¹, Sungjin Cho², Haomin Zhou³, Catherine R Edwards⁴, Fumin Zhang¹

Affiliations

¹ Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
² Department of Guidance and Control, Agency for Defense Development, Daejeon, South Korea.
³ School of Mathematics, Georgia Institute of Technology, Atlanta, GA, United States.
⁴ Skidaway Institute of Oceanography, University of Georgia, Savannah, GA, United States.

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.

Keywords: bounded cost search; graph search method; parameter identification; robotic path planning; underwater vehicle.