A novel model predictive artificial potential field based ship motion planning method considering COLREGs for complex encounter scenarios

Abstract

Ship motion planning is a core issue of autonomous navigation for maritime autonomous surface ships (MASS). This paper proposes a novel model predictive artificial potential field (MPAPF) motion planning method for complex encounter scenarios considering collision avoidance rules. A new ship domain is established, in which a closed interval potential field function is designed to represent the inviolable properties of the ship domain. A Nomoto model with a predefined speed during motion planning is adopted to generate followable paths conforming to the ship kinematics. To solve the local optima problem of traditional artificial potential field (APF) method and guarantee the collision avoidance safety in complex encounter scenarios, a motion planning method based on model predictive strategy and artificial potential field, i.e., MPAPF, is proposed. In this method, the ship motion planning problem is transformed to a non-linear optimization problem with multiple constraints including maneuverability, navigation rules, navigable waterway, etc. Simulation results from 4 case studies show that the proposed MPAPF algorithm can solve the problems above and generate feasible motion paths to avoid ship collision in complex encounter scenarios compared to variants of APF, A-star and rapidly-exploring random trees (RRT).