Application of ant colony-potential field algorithm in underwater gravity matching navigation track planning

doi:10.11947/j.AGCS.2020.20190194

Abstract

Abstract: The underwater submersible track in the adaptation area where the gravity characteristics change obviously can ensure the effective implementation of gravity-assisted navigation. Therefore, in the gravity matching navigation stage, the path planning of the submersible is very important. In this paper, the adaptive submarine navigation area is adaptively divided according to the gravity statistical characteristic parameters, and the adaptive and non-adaptive area labels are given. Then the artificial potential field algorithm is introduced based on the ant colony algorithm for the path planning. Reconstructing the heuristic function avoids the local optimal problem of the ant colony algorithm. At the same time, the maximum-minimum ant colony system is used to improve the pheromone update rule, which prevents the “premature” phenomenon. The simulation results show that the proposed ant colony-potential field algorithm can effectively solve the path optimization problem of underwater submersible in gravity-assisted navigation and improve the feasibility of the solution.

Key words: optimal track planning, ant colony algorithm, artificial potential field method, gravity-assisted navigation

CLC Number:

P229.2

ZHANG Chi, LI Shanshan, SHI Yanjun, XING Zhibin, FAN Diao. Application of ant colony-potential field algorithm in underwater gravity matching navigation track planning[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(7): 865-873.

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