Self-Adaptive Dynamic Obstacle Avoidance and Path Planning for USV under Complex Maritime Environment

Abstract

The unmanned surface vehicle (USV) is usually required to perform some tasks with the help of static and dynamic environmental information obtained from different detective systems such as shipborne radar, electronic chart, and AIS system. The essential requirement for USV is safe when suffered an emergency during the task. However, it has been proved to be difficult as maritime traffic is becoming more and more complex. Consequently, path planning and collision avoidance of USV has become a hot research topic in recent year. This paper focuses on dynamic obstacle avoidance and path planning problem of USV based on the Ant Colony Algorithm (ACA) and the Clustering Algorithm (CA) to construct an auto-obstacle avoidance method which is suitable for the complicated maritime environment. In the improved ant colony-clustering algorithm proposed here, a suitable searching range is chosen automatically by using the clustering algorithm matched to different environmental complexities, which can make full use of the limited computing resources of the USV and improve the path planning performances firstly. Second, the dynamic searching path is regulated and smoothed by the maneuvering rules of USV and the smoothing mechanism respectively, which can effectively reduce the path length and the cumulative turning angle. Finally, a simulation example is provided to show that our proposed algorithm can find suitable searching range according to different obstacle distributions, as well as accomplish path planning with good self-adaptability. Therefore, a safe dynamic global path with better optimize performances is achieved with the help of multi-source information.