A review on path planning and intelligent navigation of deep-sea mining vehicle

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Authors: Fei Sha, Zeqian Li, Xuguang Chen, Yuhang Zuo
Cited by: 1
References: 79

Abstract

Deep-sea polymetallic nodules are characterized by their high grade and enormous reserves, and their commercial development is of great significance in addressing the shortage of terrestrial resources. Locomotion technology of mining vehicles is a core component of mining systems, and it directly determines the efficiency and productivity of collection operations. The feasibility verification stage remains in most existing research and technologies, and a large-scale, intelligent, and highly reliable locomotion technology system has not yet been established. Based on the basic dynamic characteristics of mining vehicles, this review systematically consolidates the research on structural optimization of vehicles, motion control, and dynamic coupling with trajectory control systems, and provides an integrated perspective connecting these domains with recent advances in path planning and intelligent navigation for deep-sea polymetallic nodule collection. It further examines path planning and navigation control in extreme environments by analyzing the inherent mechanisms of traction failure, along with existing prediction models and classical algorithms. The study identified theoretical gaps in the multibody dynamics analysis of deep-sea mining vehicles, outlined emerging trends in trajectory control, and clarified the challenges related to reliability and intelligence under extreme operating conditions. This integrative approach highlights the systemic interactions overlooked in previous studies, and proposes a novel framework for the development of intelligent mining vehicle in the future. In addition, some key future research directions aimed at laying a solid foundation for the commercial development of deep-sea polymetallic nodules were identified.

Fields of Study & Subjects

Robotic Path Planning Algorithms Underwater Vehicles and Communication Systems Maritime Navigation and Safety

Publication details

Journal / source
Green and Smart Mining Engineering
Publisher
Elsevier BV
Volume
3
Issue
1
Pages
1–15
Publication date
13 February 2026
Language
EN