A situation awareness approach for USV based on Artificial Potential Fields

Abstract

The USV(Unmanned Surface Vessel) different from land robot is that the obstacles has uncertainty when the USV autonomous navigation. The core factors of autonomous navigation include risk measurement and route planning. Proposed a route planning method for the USV based on the probability of collision and A∗ algorithm. First, the core risk objects in the channel have been selected, such as channel, beacon and pier. The probability distribution of collision is described by Artificial Potential Field (APF) model. Then, the track distribution in the different key sections has been acquired by the history AIS data. The parameter values in the APF model are solved by non-linear optimization on the history AIS data. After the Potential Field distribution is obtained, the optimal route is solved by A∗ algorithm. To solve the problem that the route may be hard to handle, we proposed a smoothing algorithm based on regression analysis. That algorithm for the probability distribution is based on the actual vehicle routes, therefore it is more reliable. The planning routes take the manoeuvrability of the vehicle into consideration. In the simulation, the path of the USV is closed to the real vessel and it performs well.