Research on Navigation and Obstacle Avoidance of Unmanned Ship Based on SLAM

Abstract As intelligent navigation technology advances, unmanned vessels are increasingly employed in tasks like water quality monitoring, ocean exploration, rescue missions, and port patrols. Navigating complex water environments autonomously requires accurate environmental mapping and real-time path planning. This study proposes a navigation framework leveraging Simultaneous Localization and Mapping (SLAM) technology to address these requirements. The framework includes a detailed modeling of vessel motion and sensor observation, along with global path planning using the A* algorithm and local path optimization via the Dynamic Window Approach (DWA). Results demonstrate that the integration of SLAM with A* and DWA algorithms enhances the vessel’s autonomous navigation and obstacle avoidance capabilities in complex scenarios, providing valuable insights for future developments in unmanned vessel navigation.