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Intelligent control of autonomous marine vehicles (AMVs) is one of the essential technologies for exploring marine resources. In the deep sea with a complicated exploration environment, collaboration between heterogeneous AMVs can maximize exploration efficiency by utilizing various functional benefits. Accordingly, this article proposes a method for collaborative path planning for heterogeneous AMVs that employs a fused metaheuristic algorithm for the underwater path planning of an autonomous underwater glider (AUG), and an adaptive surface path planning of an autonomous surface vehicle (ASV), respectively. The fused metaheuristic method balances global and local path explorations for underwater path planning by integrating the gray wolf optimizer (GWO) and equilibrium optimizer (EO), and it reduces the local optimum problem by using a conditional convergence factor; and the adaptive surface path planning approach considers the influence of ocean currents at various locations to guide the ASV collaboratively to track the AUG underwater in the horizontal plane. The fused metaheuristic algorithm has demonstrated superior convergence performance in simulations, which indicates that the proposed method has advantage in terms of underwater path planning for complex marine exploration.
Zhang et al. (2023) studied this question.