Ship Navigation Control Strategy in Curved River Sections Based on Deep Learning

Abstract

Curved river sections have complex water flow characteristics and difficulties in maneuvering ships through bends, which pose significant challenges to path planning and ship navigation control. The current path research algorithms still have limitations in dealing with curved and complex waterways. In view of this, this study proposes a convolutional neural network control model based on hybrid controllers and proximal strategy optimization for path planning of ships in curved river sections. The results showed that when the research model reached 200 iterations in the simulated curved river section, the average reward value was 0.0323, which was 19.36% higher than the average reward value of other algorithms. The average instantaneous reward of the research model in path planning was 7.95, which was 3.69 and 1.58 higher than the proximal policy optimization model and the convolutional neural network model based on proximal policy optimization, respectively. The success rate of path planning in complex curved river sections was 82%, significantly higher than the other two algorithms, verifying its effectiveness and superiority in complex path planning tasks. Therefore, this study contributes to improving the safety, efficiency, and economic benefits of ship navigation, and promoting the intelligent and automated growth of the shipping industry.