Abstract
With the growth in total trade volume and the scale of shipping fleets, maritime accidents have occurred frequently, most of which are attributed to human errors. Consequently, Maritime Autonomous Surface Ships (MASS) have attracted widespread attention. The core technology enabling autonomous decision-making in autonomous ships is the route planning algorithm; however, there is currently a lack of a unified classification framework to elucidate the evolution of these path planning algorithms. This paper presents a systematic review focusing on the evolutionary history and the latest advancements in autonomous ship route planning algorithms. First, the basic concepts and developmental background of autonomous ship route planning are expounded. Second, the existing mainstream planning algorithms are categorized into three groups: baseline trajectory methods, environment-adaptive methods, and multi-objective intelligent decision-making methods. The applications and limitations of these algorithms in autonomous ship path planning are analyzed, and a systematic comparison is conducted regarding their encoding methods for the International Regulations for Preventing Collisions at Sea (COLREGs) as well as their comprehensive performance. Concurrently, the characteristics, advantages, disadvantages, and potential future trends of each category are elaborated. The study reveals that current route planning algorithms still possess deficiencies; the efficient integration of multiple algorithms and deep interdisciplinary cross-fertilization will be the future trend, while the incorporation of uncertainty considerations will become a major research hotspot. Furthermore, the refinement and embedding of COLREGs will benefit both the development of autonomous ships and global trade. This review aims to provide a valuable reference for future scholarly research.