Autonomous collision avoidance control using deep reinforcement learning for maritime autonomous surface ships

Lee, P and Theotokatos, G and Boulougouris, E (2022) Autonomous collision avoidance control using deep reinforcement learning for maritime autonomous surface ships. In: The 4th International Conference on Maritime Autonomous Surface Ships (ICMASS) and The International Maritime and Port Technology and Development Conference, 2022-04-06 - 2022-04-07.

[thumbnail of Lee-etal-MTEC-ICMASS-2022-Autonomous-collision-avoidance-control-using-deep-reinforcement-learning-for-maritime-autonomous-surface-ships]
Preview
 Text. Filename: Lee_etal_MTEC_ICMASS_2022_Autonomous_collision_avoidance_control_using_deep_reinforcement_learning_for_maritime_autonomous_surface_ships.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (923kB)| Preview

Abstract

The maritime industry has been progressing towards autonomous shipping with the main barrier and scepticism eing on the safety assurance of the next-generation autonomous ships. This study aims to enhance the safety of the autonomous ships by developing an intelligent agent that makes evasive decisions considering the ship domain as a safety zone. The proposed approach is demonstrated by considering the case study of a short sea shipping cargo ship. An intelligent reinforcement learning agent is trained to maoneuvre the investigated ship in restricted sea area. The results of this study verify the agent's ability to make safe evasive decisions and control the autonomous collision avoidance for autonomous ships in known and unknown environments.

ORCID iDs

Lee, P, Theotokatos, G ORCID logoORCID: https://orcid.org/0000-0003-3547-8867 and Boulougouris, E ORCID logoORCID: https://orcid.org/0000-0001-5730-007X;
CORE (COnnecting REpositories)