Dynamic Energy-Efficient Path Planning of Unmanned Surface Vehicle under Time-Varying Current and Wind

The unmanned surface vehicle (USV) is significantly affected by the ocean environment and weather conditions when navigating. The energy consumption is large, which is not conducive to completing water tasks. This study investigates the global energy-efficient path planning problem for the USV, wherein the goal is to obtain an optimal path under the interference of the ocean environment and control the USV to avoid static obstacles and arrive at its destination. Firstly, this paper extracts the coastline coordinates and water depth data from the S-57 electronic chart, applying the Voronoi diagram to describe spatial object information preliminarily. Secondly, the dynamic, safe water depth model is obtained using the improved Voronoi diagram algorithm after superimposing the interpolated tide with the water depth data. In order to construct the total energy consumption model, the mathematical model of wind and current is introduced into the linear dynamics model of a USV. Additionally, the timing breakpoints are planned. According to the energy consumption model, this paper improves the A* algorithm to replan the path to consider the distance costs and variation of ocean data in each timing breakpoint. Finally, this paper proposes a new path optimization algorithm to reduce the waypoints and smooth the path. Simulations verified the effectiveness of the method. The energy consumption in a favorable situation is less than in a counter situation. The higher the USV velocity, the higher the energy consumption. The proposed dynamic energy-efficient path considers the distance, ensures a shorter range, and improves the endurance of the USV, which is in line with the actual navigation requirement.

Automated summary

This study tackles the energy consumption problem of unmanned surface vehicles (USVs) by extracting ocean environment information from nautical charts and utilizing mathematical models for path planning and energy optimization. Simulation experiments verify the effectiveness of the proposed method.