Online Heuristically Planning for Relative Optimal Paths Using a Stochastic Algorithm for USVs

Abstract

This paper attempts to solve a challenge in online relative optimal path planning of unmanned surface vehicles (USVs) caused by current and wave disturbance in the practical marine environment. The asymptotically optimal rapidly extending random tree (RRT*) method for local path optimisation is improved. Based on that, an online path planning (OPP) scheme is proposed according to the USV's kinematic and dynamic model. The execution efficiency of RRT* is improved by reduction of the sampling space that is used for randomly learning environmental knowledge. A heuristic sampling scheme is proposed based on the proportional navigation guidance (PNG) method that is used to enable the OPP procedure to utilise the reference information of the global path. Meanwhile, PNG is used to guide RRT* in generating feasible paths with a small amount of gentle turns. The dynamic obstacle avoidance problem is also investigated based on the International Regulations for Preventing Collisions at Sea. Case studies demonstrate that the proposed method efficiently plans paths that are relatively easier to execute and lower in fuel expenditure than traditional schemes. The dynamic obstacle avoidance ability of the proposed scheme is also attested.