Abstract
To address the issues of poor environmental adaptability and high costs associated with traditional methods of measuring water body boundaries, this paper proposes an innovative path planning approach for water body boundary measurement based on Unmanned Surface Vehicles (USVs)—the Chemotactic Search Traversal (CST) algorithm. This method incorporates the chemotaxis operation mechanism of the Bacterial Foraging Optimization algorithm, integrating it with high-resolution grid maps to enable efficient traversal and accurate measurement of water body boundaries within large-scale grid environments. Simulation experiments demonstrate that the CST algorithm outperforms the Brute Force Algorithm (BFA), Roberts operator, Canny operator, Log operator, Prewitt operator, and Sobel operator in terms of optimal pathfinding, stability, and path smoothness. The feasibility and reliability of this algorithm in real water environments are validated through experiments conducted with actual USVs. These findings suggest that the CST algorithm not only enhances the accuracy and efficiency of water body boundary measurement but also offers a cost-effective and practical solution for measuring water body areas.