Clustering Theta* based segmented path planning method for vessels in inland

To address the challenges of path planning posed by the dynamic water depth, water flow, navigation rules and the computational burden with large-scale grid maps specific to inland waterways, this study introduces a segmented navigational strategy incorporating with Theta -star (Theta*) and K -means algorithm, namely CTSPP method to generate safe and efficient inland vessel paths. A K -means clustering algorithm is employed to generate an appropriate grid map, and the overall waterway is divided into several continuous sub -segments. For each sub -segment, considering multiple factors such as water depth, current characteristics, navigation rules, obstacle risks, and the vessel's own maneuverability limitations, an optimized path is generated by the improved Theta* algorithm. Moreover, through a linking mechanism, the paths of adjacent sub -segments are connected to form a complete and optimal navigation path. During the experimental verification, based on actual geographical and hydrological data from the Wuhan to Nanjing section of the Yangtze River, 5 cases demonstrate that the proposed CTS -PP method outperforms rapidly -exploring random tree (RRT), artificial potential field (APF), A -star (A*) methods in terms of the computation time, path length, navigation rule compliance.