Zhang Huixia, Tao Yadong, Zhu Wenliang
School of Ocean Engineering, Jiangsu Ocean University, Lianyungang 222005, China.
School of Mechanical Engineering, Jiangsu Ocean University, Lianyungang 222005, China.
Sensors (Basel). 2023 Jul 24;23(14):6647. doi: 10.3390/s23146647.
To make unmanned surface vehicles that are better applied to the field of environmental monitoring in inland rivers, reservoirs, or coasts, we propose a global path-planning algorithm based on the improved A-star algorithm. The path search is carried out using the raster method for environment modeling and the 8-neighborhood search method: a bidirectional search strategy and an evaluation function improvement method are used to reduce the total number of traversing nodes; the planned path is smoothed to remove the inflection points and solve the path folding problem. The simulation results reveal that the improved A-star algorithm is more efficient in path planning, with fewer inflection points and traversing nodes, and the smoothed paths are more to meet the actual navigation demands of unmanned surface vehicles than the conventional A-star algorithm.
为了使无人水面航行器更好地应用于内河、水库或海岸的环境监测领域，我们提出了一种基于改进A算法的全局路径规划算法。采用栅格法进行环境建模，使用8邻域搜索法进行路径搜索：采用双向搜索策略和评估函数改进方法来减少遍历节点总数；对规划路径进行平滑处理以去除拐点并解决路径折叠问题。仿真结果表明，改进后的A算法在路径规划方面更高效，拐点和遍历节点更少，且平滑后的路径比传统A*算法更能满足无人水面航行器的实际航行需求。
