Robust decision-making for the reactive collision avoidance of autonomous ships against various perception sensor noise levels

Lee, Paul and Theotokatos, Gerasimos and Boulougouris, Evangelos (2024) Robust decision-making for the reactive collision avoidance of autonomous ships against various perception sensor noise levels. Journal of Marine Science and Engineering, 12 (4). 557. ISSN 2077-1312 (https://doi.org/10.3390/jmse12040557)

[thumbnail of Lee-etal-JMSE-2024-Robust-decision-making-for-the-reactive-collision-avoidance-of-autonomous-ships]
Preview
 Text. Filename: Lee-etal-JMSE-2024-Robust-decision-making-for-the-reactive-collision-avoidance-of-autonomous-ships.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (15MB)| Preview

Abstract

Autonomous ships are expected to extensively rely on perception sensors for situation awareness and safety during challenging operations, such as reactive collision avoidance. However, sensor noise is inevitable and its impact on end-to-end decision-making has not been addressed yet. This study aims to develop a methodology to enhance the robustness of decision-making for the reactive collision avoidance of autonomous ships against various perception sensor noise levels. A Gaussian-based noisy perception sensor is employed, where its noisy measurements and noise variance are incorporated into the decision-making as observations. A deep reinforcement learning agent is employed, which is trained in different noise variances. Robustness metrics that quantify the robustness of the agent’s decision-making are defined. A case study of a container ship using a LIDAR in a single static obstacle environment is investigated. Simulation results indicate sophisticated decision-making of the trained agent prioritising safety over efficiency when the noise variance is higher by conducting larger evasive manoeuvres. Sensitivity analysis indicates the criticality of the noise variance observation on the agent’s decision-making. Robustness is verified against noise variance up to 132% from its maximum trained value. Robustness is verified only up to 76% when the agent is trained without the noise variance observation with lack of its prior sophisticated decision-making. This study contributes towards the development of autonomous systems that can make safe and robust decisions under uncertainty.

ORCID iDs

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