COLREG-RRT: An RRT-Based COLREGS-Compliant Motion Planner for Surface Vehicle Navigation

Abstract

Motion planning for autonomous surface vehicles (ASVs) is challenging since surface vessels are nonlinear underactuated kinodynamic systems with often large inertia. Thus, ASV planners must identify long-term trajectories in order to avoid guiding the ASV into inevitable collision states. Furthermore, maritime vessels are required to follow COLlision REGulationS (COLREGS), which dictate collision avoidance patterns. Current state-of-the-art methods are based on model predictive control (MPC) and assume that other vessels move at constant velocities without the consideration of COLREGS. In this letter, we propose COLREG-RRT, an RRT-based planner capable of identifying long-term, COLREGS-compliant trajectories with a high navigation success rate. This is achieved by conducting joint forward simulations of both the ASV and the other vessels during RRT growth in order to anticipate future collisions. The COLREGS-compliance is enforced by constructing virtual obstacles that inhibit tree growth. We demonstrate COLREGS-compliance in single-ship encounters and compare against two state-of-the-art methods in multiship encounters with up to 20 other vessels. Experiments indicate that COLREG-RRT has a 32% higher success rate and is real-time capable in the most difficult environment tested. Additionally, COLREG-RRT identifies longer trajectories, as compared to MPC. This property aids with collision avoidance with other ships.