Work

A global path planning method for a net-cleaning underwater vehicle (NCUV) based on hierarchical collaborative optimization and pollution level classification

Year: 2026

Type: article

Abstract: To meet the industrial demand for cleaning marine aquaculture net cages and to address the low efficiency and ecological impact of current cleaning operations, this paper proposes a path-planning method for a net-cleaning underwater vehicle (NCUV) based on hierarchical cooperative optimization (HCO). Conventional ant colony optimization (ACO) and grey wolf optimizer (GWO) algorithms typically perform optimization only at a single level, and thus struggle to effectively handle redundant inter-region connections and low coverage efficiency within regions. The proposed HCO approach adopts a two-level cooperative mechanism that combines global region sequencing with local path refinement, thereby resolving both issues. At the global level, the traversal order of sub-regions is optimized using the L-SHADE algorithm, whereas at the local level an improved GWO introduces a nonlinear function to control the convergence factor and thereby optimizes the trajectory within each sub-region. Experimental results demonstrate that, in terms of path planning, the HCO method outperforms traditional ACO and GWO algorithms, achieving significant improvements in cleaning efficiency and energy consumption.

Source: Applied Ocean Research

DOI: 10.1016/j.apor.2026.105048

Authors: Heng Liu, Chuhua Jiang, Junhua Chen, Hao Li, Yongqi Chen

Institution: Ningbo University

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Life below water, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,950