In recent years, as intelligent ship-navigation technology has advanced, the challenge of accurately modeling and predicting the dynamic environment and motion status of ships has emerged as a prominent area of research. In response to the diverse time scales required for the prediction of ship motion, various methods for modeling ship navigation environments, ship motion, and ship traffic flow have been explored and analyzed. Additionally, these motion-prediction methods are applied for motion control, collision-avoidance planning, and route optimization. Key issues are summarized regarding ship-motion prediction, including online modeling of motion models, real ship validation, and consistency in modeling, optimization, and control. Future technology trends are predicted in mechanism-data fusion modeling, large-scale model, multi-objective motion prediction, etc.

Sailing speed is a critical factor affecting the ship’s energy consumption and operating costs for a voyage. Inland waterways present a complex navigation environment due to their narrow channels, numerous curved segments, and significant variations in water depth and flow speed. This paper constructs a model of a ship’s energy consumption based on an analysis of ship resistance and the energy transfer relationship of ships. The K-means clustering algorithm is introduced to divide the Yangtze River waterway into multiple segments based on the similarity of navigation environments. Considering the constraints of the ship’s main engine and the desired arrival time, a multi-objective particle swarm optimization (MOPSO) algorithm, improved with cosine decreasing inertial weight and Gaussian random mutation, is employed to optimize segmented navigation speeds to achieve different goals. Finally, four cases are studied with a fully electric ship navigating the reaches of the Yangtze River. The results indicate that the optimized speed can reduce ship energy consumption by up to 6.18% and significantly reduce ship energy consumption and operational costs under different conditions.

Ensuring consistent and reliable collision avoidance under perception uncertainty remains a major challenge for maritime autonomous surface ships (MASS). This paper proposes a lightweight and consistency-aware local path planning framework, called consistent Kalman virtual potential field (CKVPF), to improve decision robustness in dynamic and uncertain maritime environments. The framework combines virtual potential field-based planning with Kalman-filtered state estimation and introduces a historical consistency constraint to reduce decision fluctuations over time. CKVPF is implemented on a 45-meter-long MASS platform and tested through full-scale sea trials, with onboard real-time execution achieved using a Raspberry Pi 5. The experiments cover representative encounter scenarios, including head-on, overtaking, crossing and mixed-traffic, where the target ships exhibit unstable motion patterns. Results show that CKVPF maintains consistent decision-making and safely avoids collisions, achieving a 48.9% improvement in navigation path stability compared to baseline methods, while maintaining an average planning time of 177.8 ms. These findings demonstrate the method’s practical applicability and real-time performance for autonomous navigation under real-world uncertainty.