Ship global path planning using jump point search and maritime traffic route extraction

Abstract

The rapid development of artificial intelligence and big data has elevated autonomous ships to a prominent position in maritime research, introducing significant challenges in automatic path planning. This study presents a systematic framework for global ship path planning by integrating heuristic search algorithms with maritime route extraction methods. The framework consists of three key components: trajectory compression, maritime traffic route extraction, and global path planning. An adaptive threshold model combined with a sliding window algorithm processes ship trajectories to capture motion state changes accurately. The subsequent turn-point identification algorithm maps turning points precisely, while waypoints are optimised through Cluster-based Kernel Density Estimation to enhance route topology. A path matching search algorithm incorporating Dynamic Time Warping, Traffic Separation Schemes, and historical ship data generates practical and smooth navigation paths. Experimental validation using Automatic Identification System (AIS) data from the Yangtze River Estuary and Ningbo-Zhoushan Port areas demonstrates the superior performance of the proposed framework. The generated paths show improved compliance with Traffic Separation Schemes requirements and enhanced smoothness compared to traditional path planning algorithms.

More information

Identifiers

Catalogue record

Export record

ASCII CitationAtomBibTeXData Cite XMLDublin CoreDublin CoreEP3 XMLEndNoteHTML CitationHTML CitationHTML ListJSONMETSMODSMPEG-21 DIDLOpenURL ContextObjectOpenURL ContextObject in SpanRDF+N-TriplesRDF+N3RDF+XMLRIOXX2 XMLReferReference ManagerSimple Metadata

Altmetrics