Application of improved dynamic proximity method in unmanned water quality monitoring ship

To obtain the quality of water more economically and flexibly, it improves the traditional node optimization layout method — dynamic proximity degree method from the perspective of unmanned ship. Based on considering the data correlation and time factors of the monitoring nodes, and considering the influence of the relative position relationship between the nodes on the length of the inspection path, a simple and efficient genetic algorithm is designed to further optimize the node selection with a faster convergence speed. The water quality monitoring experiment of lake in the university was carried out by an unmanned surface vehicle and MATLAB platform. The results show that the improved dynamic proximity algorithm retains the representativeness of the nodes to the water quality and greatly shorts the cruise path of the ship. In the optimization experiment of 14 nodes, compared with the traditional node selection method, the path length is shortened by 14.85% on average.