Towards robust intelligent shipping via graph-informed representation, generative routing, and certified maneuvering
• Hierarchical Spatio-Temporal Representation. We propose a graph-based maritime representation that integrates AIS trajectories, bathymetry, and regulatory constraints into multi-scale embeddings, enabling preference-conditioned and structure-aware route generation. • Constraint-Embedded Generative Routing. We design a generative routing network with in-loop feasibility projection, ensuring that candidate routes are feasible by construction with respect to geometric, dynamic, and regulatory manifolds. • Certified Robust Maneuvering. Develops a maneuvering controller that combines Model Predictive Control (MPC), robust invariant tube sets, and control barrier functions, providing formal guarantees of safety and robustness under environmental disturbances and multi-ship interactions. Intelligent shipping requires route planning frameworks that jointly capture operator preferences, vessel maneuverability, and verifiable safety under environmental disturbances. For this, we propose a unified data-to-control framework for maritime routing in which representation, generation, and execution are tightly coupled rather than assembled in isolation. A preference-conditioned hierarchical spatio-temporal graph fuses AIS traffic, bathymetry, and regulatory layers to gate the generative search; a constraint-embedded generative router performs in-loop feasibility projection onto a joint geometric-dynamic-regulatory manifold, producing feasible-by-construction waypoint-speed sequences; and a certified maneuvering layer attaches tube model predictive control and control-barrier constraints, yielding verifiable artifacts (tube membership, barrier margins) that form a route-to-execution contract and close the planning-execution loop. Extensive experiments demonstrate that the framework generates preference-aware, maneuverability-consistent, and provably safe maritime routes, establishing a solid foundation for large-scale Intelligent Shipping applications.
Authors
- Xiangjun Yin (ORCID: https://orcid.org/0000-0002-4829-9019)
- Xiongfei Geng
- Qiaochan Yu
- Hui Feng
- Huihui Yue
Institutions
- Tianjin University (CN)
- National University of Singapore (SG)
- Nanyang Technological University (SG)
- Nankai University (CN)
- China Waterborne Transport Research Institute (CN)
Publication Details
- Journal
- Ocean Engineering
- Published
- 2025-12-26
- DOI
- https://doi.org/10.1016/j.oceaneng.2025.123957
- Primary Topic
- Maritime Navigation and Safety
- Type
- article
- Field-Weighted Citation Impact
- 0.00