Predictive navigation of unmanned surface vehicles in a dynamic maritime environment when using the fast marching method

Effective and intelligent path planning algorithms designed for operation in a dynamic marine environment are essential for the safe operation of unmanned surface vehicles (USVs). Most of the current research deals with the ‘dynamic problem’ by basing solutions on the nonpractical assumption that each USV has a robust communication channel to obtain essential information such as position and velocity of marine vehicles. In this paper, a Kalman filter-based predictive path planning algorithm is proposed. The algorithm has been designed to predict the trajectories of moving ships, and the USV's own position in real time and accordingly assesses collision risk. For path planning, a weighted fast marching square method is proposed and developed to search for the optimal path. The path can be optimised for mission requirements such as minimum distance to travel and the most safety path by adjusting weighting parameters. The proposed algorithm has been validated using a number of simulations that include practical environmental aspects. The results show that the algorithms can sufficiently deal with complex traffic environments and that the generated practical path is suited for both unmanned and manned vessels.