Global path planning for autonomous ship: A hybrid approach of Fast Marching Square and velocity obstacles methods. (15th October 2020)

• Record Type: Journal Article
• Authors: Chen, Pengfei; Huang, Yamin; Papadimitriou, Eleonora; Mou, Junmin; van Gelder, Pieter
• Is Part Of: Ocean engineering. Volume 214(2020)
• Publication Date: 2020-10-15
• Subjects: Global path planning -- Autonomous ship -- Fast marching method -- Fast marching square method -- Velocity obstacle; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2020.107793
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: In this research, a hybrid approach for global path planning for Maritime Autonomous Surface Ship (MASS) is proposed, which generates the shortest path considering the collision risk and the proximity between path and obstacles. The collision risk concerning obstacles is obtained using Time-Varying Collision Risk (TCR) concept, taking into account the velocity constraint of the ship that can achieve during operation. The influence of proximity from obstacles is measured with the Fast Marching (FM) algorithm. A new cost function is proposed allowing to combine the influence of obstacle proximity and collision risk in the region. Finally, the Fast Marching Square algorithm is applied to generate the globally optimal path that can reach the pre-set destination. The contribution of this work is two-fold: 1) considering the velocity constraint of the own ship, together with its influences of collision risk into the global path planning stage of autonomous navigation. 2) measuring the collision risk induced by the obstacles from their comprehensive influences on the achievable velocity range using TCR concept, instead of numerical integration of risk measurement. The results of the case study indicate that the proposed approach can find an optimal path considering the collision risk and proximity from the obstacles. Highlights: New approach for global path planning of autonomous ship considering both collision risk and path length. Collision risk is measured with TCR using velocity obstacle algorithm. Fast marching algorithm is applied to integrate the influence of collision risk and generate the optimal path.