Effects of adverse sea conditions on propulsion and manoeuvring performance of low-powered ocean-going cargo ship

Sui, Congbiao; Vos, P. de; Hopman, Hans; Visser, Klaas; Stapersma, Douwe; Ding, Yu

doi:10.1016/j.oceaneng.2022.111348

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…The two most widely used system-based models are the Abkowitz model [56], also known as the whole ship model, which expresses the hydrodynamic forces and moments acting on a ship as Taylor series, and the manoeuvring modelling group (MMG) model [57], also known as the modular model, which decomposes the forces and moments acting on different components, typically the ship hull, propeller, and rudder [58]. Compared to the Abkowitz model that considers the ship as a whole, the MMG model is based on first principles that carry explicit and clear physical meaning of each component including their interactions [59]. In addition, it has the capability to accommodate different actuators configurations, such as single-screw [60], twin-screw [61], and twin azimuth thrusters [62], integrate external disturbances, such as currents, waves, and wind, and be combined with other numerical, experimental, or empirical methods [59].…”

Section: Ship Manoeuvrabilitymentioning

confidence: 99%

“…Compared to the Abkowitz model that considers the ship as a whole, the MMG model is based on first principles that carry explicit and clear physical meaning of each component including their interactions [59]. In addition, it has the capability to accommodate different actuators configurations, such as single-screw [60], twin-screw [61], and twin azimuth thrusters [62], integrate external disturbances, such as currents, waves, and wind, and be combined with other numerical, experimental, or empirical methods [59].…”

Section: Ship Manoeuvrabilitymentioning

confidence: 99%

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Robust Decision-Making for the Reactive Collision Avoidance of Autonomous Ships against Various Perception Sensor Noise Levels

Lee,

Theotokatos,

Boulougouris

2024

JMSE

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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.

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