Learning-Based Path Planning Algorithm in Ocean Currents for Multi-Glider

Wei Lan, Xiang Jin, Xin Chang, Tianlin Wang, Han Zhou and Maia Angelova

Complexity, 2022, vol. 2022, 1-17

Abstract: In practical application studies of glider formations, ocean currents are a major influencing factor in their path planning. The purpose of this paper is to solve the path planning problem of glider formations in time-varying ocean currents and establish gliders, glider formation, and ocean current models based on existing data. The Doc-CNN architecture is tailored to conform to the operation and environment characteristics of gliders in practical application. After experiments, the algorithm of the improved architecture can be used in the path planning task of glider formation. The algorithm architecture is compared and tested on two datasets, grid maps and ocean maps. Doc-CNN has an advantage over architecture without being tailored to glider characteristics and makes full use of known global information and local information collected by gliders themselves. The results show that the path planning problem of glider formation in ocean currents can be solved by using Doc-CNN.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:1715226

DOI: 10.1155/2022/1715226

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