Digital-intelligent navigation architecture and evolution paradigm for autonomous ships in key inland waterway areas

This paper provides a comprehensive review of the current research status of intelligent inland vessels, systematically analyzes the development of autonomous navigation technologies for inland ships, and identifies the key challenges and technical bottlenecks associated with autonomous navigation in critical inland waterways.

Based on the multi-space architecture of digital-intelligent mechanics, a novel paradigm for digital-intelligent navigation of autonomous inland vessels is proposed. This paradigm integrates theoretical modeling, physical experimentation, simulation-based computation, big data analytics, and artificial intelligence.

A digital-intelligent navigation theoretical framework for autonomous ships operating in critical inland waterways is established, revealing the underlying mechanisms of core concepts such as cross-space dynamic modeling, nonlinear intelligent computing, and cross-domain transfer learning. Furthermore, a "ship-shore-cloud" collaborative cloud-control testing platform architecture is developed, providing a theoretical approach to overcoming the limitations of traditional single-physical-space modeling in complex inland environments.

The development directions of navigation technologies and theoretical frameworks for autonomous inland vessels in the era of intelligent navigation are outlined, providing theoretical foundations and technical support for the continued advancement and engineering implementation of next-generation autonomous navigation systems.