Path Planning of Unmanned Surface Vessel Based on Improved RRT*

With the continuous development of artificial intelligence, the application of unmanned surface vessels (USVs) in the field of intelligent maritime vessels is becoming increasingly mature. Significant achievements have been made in several areas such as ocean environmental monitoring, maritime rescue, and maritime security. To further enhance the autonomous exploration capability, navigation safety, and operational efficiency of USV in route planning, this paper proposes an improved rapidly-exploring random tree* (RRT*) algorithm for USV path planning in known environments. Through simulation experiments, it has been verified that the proposed RRT* can achieve superior results in USV path planning. The proposed RRT* algorithm imposes restrictions on the sampling point range, building upon the original RRT* algorithm, resulting in more precise path planning. By comparing with the classical RRT algorithm and other traditional RRT algorithms, our approach significantly reduces the required distance for path planning while ensuring the safety of ship navigation. However, this improvement also comes with an increase in the time cost as it enhances the quality of the planned path.