AD-RRT*: An RRT*-based global path planning approach for underwater gliders with alpha shapes and DBSCAN

In the past decade, Rapidly-exploring Random Tree star (RRT*) and its extensions have been widely applied in robotic path planning due to their asymptotic optimality. This paper propose a novel global path planning method for underwater gliders, called the Alpha shapes and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based Rapidly-exploring Random Tree star (AD-RRT*). In this framework, on the basis of considering ocean currents conditions as well as the start and goal points, alpha shapes and DBSCAN are utilized to construct a preferred sampling strategy. In addition, we propose a feasibility assessment to ensure the validity of node connections. Building on this, a circular region sampling strategy inspired by the Monte Carlo method is proposed to enhance overall planning efficiency while maintaining feasibility. To further enhance the exploration process in ocean environments, we propose an ocean currents influence metric to guide parent node selection. Subsequently, edges are rewired based on the estimated travel time, and a time-based iterative optimization framework is employed to optimize the planned paths. Together, these three enhancements significantly improve the efficiency and adaptability of path planning. Finally, simulation experiments demonstrate the superiority of the proposed AD-RRT* method over related approaches, as well as the indispensable role of key components within the overall framework. Future work will focus on local path planning and combining both aspects to enhance the overall path planning of underwater gliders.