A Review of Ship Path Planning: Models, Algorithms, and Challenges

Abstract

Driven by advances in artificial intelligence (AI), ship path planning (SPP) has emerged as a core technique for autonomous ships to guarantee navigation safety and enhance energy efficiency. Nevertheless, complex marine environments impose multiple constraints and competing optimization objectives, which greatly limit the practical deployment of existing algorithms. This paper systematically reviews the modeling frameworks, algorithms, key technologies, and evaluation metrics for SPP, and establishes a comparative classification framework. Special attention is given to major challenges including integration with the International Regulations for Preventing Collisions at Sea (COLREGs), path smoothing, and uncertainty handling. Our review reveals that hybrid planning algorithms, which hierarchically combine global optimality with local real-time responsiveness, have become the dominant solution. Looking ahead, deep integration between planning, perception, prediction, and control modules will underpin reliable engineering implementations of autonomous maritime navigation.