Ship path planning methods: a state-of-the-art survey

Abstract

The rapid increase in the number and size of commercial ships has led to growing congestion in marine transportation, significantly heightening the risk of ship collisions that pose serious risks to crew lives, environmental and property damage. As a result, ship collision avoidance has been a critical research focus, leading to the development of diverse path planning algorithms. This study presents a systematic review of ship path planning research from 2015 to 2024, aiming to classify the state-of-the-art algorithms, explore their core methodologies, and evaluate their applicability across various maritime scenarios. This review covers five primary categories of ship path planning algorithms. These approaches encompass numerical, graph-based, sampling-based, AI-driven, and hybrid methods. The analysis reveals that AI-driven and hybrid approaches have gained significant momentum in recent years, reflecting a paradigm shift toward more intelligent and flexible path planning systems, with growing attention to real-world applicability and regulatory compliance. This review not only maps the evolution of ship path planning techniques but also identifies promising directions to guide future research and innovation of ship path planning, which makes new and significant contributions to maritime transport evolution from pure manned vessels to the mixed traffic of manned and autonomous ships.

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ORCID for Huanhuan Li: orcid.org/0000-0002-4293-4763

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