Zhengxin Wang’s research while affiliated with Hohai University and other places

Ocean exploration is one of the fundamental issues for the sustainable development of human society, which is also the basis for realizing the concept of the Internet of Underwater Things (IoUT) applications, such as the smart ocean city. The collaboration of heterogeneous autonomous marine vehicles (AMVs) based on underwater wireless communication is known as a practical approach to ocean exploration, typically with the autonomous surface vehicle (ASV) and the autonomous underwater glider (AUG). However, the difference in their specifications and movements makes the following problems for collaborative work. First, when an AUG floats to a certain depth, and an ASV interacts via underwater wireless communication, the interaction has a certain time limit and their movements to an interaction position have to be synchronized; secondly, in the case where multiple AUGs are exploring underwater, the ASV needs to plan the sequence of surface interactions to ensure timely and efficient data collection. Accordingly, this paper proposes a heuristic surface path planning method for data collection with heterogeneous AMVs (HSPP-HA). The HSPP-HA optimizes the interaction schedule between ASV and multiple AUGs through a modified shuffled frog-leaping algorithm (SFLA). It applies a spatial-temporal k-means clustering in initializing the memeplex group of SFLA to adapt time-sensitive interactions by weighting their spatial and temporal proximities and adopts an adaptive convergence factor which varies by algorithm iterations to balance the local and global searches and to minimize the potential local optimum problem in each local search. Through simulations, the proposed HSPP-HA shows advantages in terms of access rate, path length and data collection rate compared to recent and classic path planning methods.

Intelligent control of autonomous marine vehicles (AMV) 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, the paper 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 grey 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.