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.
Highlights
In recent years, researches on Unmanned Surface Vehicles (USV) have obtained a series of important achievements in environmental sensing technologies, communication navigation technologies, intelligent sailing control technologies and lane planning technologies, etc
Current researches on USV dynamic obstacle avoidance planning can be divided into global path planning based on maritime environmental information and local path planning
The dynamic path planning issue under the rasterized electronic chart environment and USV visual field restraining can be converted to the issue that the included the angle α which exists between the USV position coordinates (xi, yi ) at kk the current moment and the position coordinates k−1 k−1 of the previous moment and the horizontal coordinates is taken as the sailing trajectory direction, while the local target can only be selected in a sector area within a certain negative or positive angle along the sailing trajectory, so the visual field restraining problem can be solved
Summary
Researches on Unmanned Surface Vehicles (USV) have obtained a series of important achievements in environmental sensing technologies, communication navigation technologies, intelligent sailing control technologies and lane planning technologies, etc. 3) In order to enhance reliability of obstacle avoidance planning and shorten optimization time of the algorithm, the complexity of obstacle distribution is recognized by the clustering algorithm and the self-adaptive adjustment of the search range according to the complexity degree is achieved In this way, the problem that the classic ant colony algorithm is only applicable to problems concerning to static path planning, large calculated amount and slow solution speed is solved effectively. Complex environments and limited sensing ability of USV, a self-adaptive search mechanism for obstacle distribution recognition in local complex environments is designed based on the clustering algorithm and combines it with an ant colony intelligent algorithm, so that the dynamic path planning under restraints of sensing range and computing ability can be realized.
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