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Global path planning for autonomous ship

A hybrid approach of Fast Marching Square and velocity obstacles methods

Journal Article (2020)
Author(s)

Pengfei Chen (TU Delft - Safety and Security Science)

Yamin Huang (Wuhan University of Technology, TU Delft - Safety and Security Science)

Eleonora Papadimitriou (TU Delft - Safety and Security Science)

Junmin Mou (Wuhan University of Technology, Hubei Key Laboratory of Inland Shipping Technology)

Pieter van Gelder (TU Delft - Safety and Security Science)

DOI related publication
https://doi.org/10.1016/j.oceaneng.2020.107793 Final published version
More Info
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Publication Year
2020
Language
English
Journal title
Ocean Engineering
Volume number
214
Article number
107793
Downloads counter
435
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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.