4 research outputs found
Data Augmentation Methods of Parameter Identification of a Dynamic Model for Harbor Maneuvers
A dynamic model for an automatic berthing and unberthing controller has to
estimate harbor maneuvers, which include berthing, unberthing, approach
maneuvers to berths, and entering and leaving the port. When the dynamic model
is estimated by the system identification, a large number of tests or trials
are required to measure the various motions of harbor maneuvers. However, the
amount of data that can be obtained is limited due to the high costs and
time-consuming nature of full-scale ship trials. In this paper, we improve the
generalization performance of the dynamic model for the automatic berthing and
unberthing controller by introducing data augmentation. This study used slicing
and jittering as data augmentation methods and confirmed their effectiveness by
numerical experiments using the free-running model tests. The dynamic model is
represented by a neural network-based model in numerical experiments. Results
of numerical experiments demonstrated that slicing and jittering are effective
data augmentation methods but could not improve generalization performance for
extrapolation states of the original dataset.Comment: 12 pages, 11 figures, Submitted to Journal of Marine Science and
Technolog
Collision probability reduction method for tracking control in automatic docking/berthing using reinforcement learning
Automation of berthing maneuvers in shipping is a pressing issue as the berthing maneuver is one of the most stressful tasks seafarers undertake. Berthing control problems are often tackled by tracking a predefined trajectory or path. Maintaining a tracking error of zero under an uncertain environment is impossible; the tracking controller is nonetheless required to bring vessels close to desired berths. The tracking controller must prioritize the avoidance of tracking errors that may cause collisions with obstacles. This paper proposes a training method based on reinforcement learning for a trajectory tracking controller that reduces the probability of collisions with static obstacles. Via numerical simulations, we show that the proposed method reduces the probability of collisions during berthing maneuvers. Furthermore, this paper shows the tracking performance in a model experiment.The version of record of this article, first published in Journal of Marine Science and Technology (Japan), is available online at Publisher’s website: https://doi.org/10.1007/s00773-023-00962-
Collision probability reduction method for tracking control in automatic docking / berthing using reinforcement learning
Automation of berthing maneuvers in shipping is a pressing issue as the
berthing maneuver is one of the most stressful tasks seafarers undertake.
Berthing control problems are often tackled via tracking a predefined
trajectory or path. Maintaining a tracking error of zero under an uncertain
environment is impossible; the tracking controller is nonetheless required to
bring vessels close to desired berths. The tracking controller must prioritize
the avoidance of tracking errors that may cause collisions with obstacles. This
paper proposes a training method based on reinforcement learning for a
trajectory tracking controller that reduces the probability of collisions with
static obstacles. Via numerical simulations, we show that the proposed method
reduces the probability of collisions during berthing maneuvers. Furthermore,
this paper shows the tracking performance in a model experiment.Comment: 14 pages, 15 figures, Submitted to Journal of Marine Science and
Technolog
On Neural Network Identification for Low-Speed Ship Maneuvering Model
Several studies on ship maneuvering models have been conducted using captive
model tests or computational fluid dynamics (CFD) and physical models, such as
the maneuvering modeling group (MMG) model. A new system identification method
for generating a low-speed maneuvering model using recurrent neural networks
(RNNs) and free running model tests is proposed in this study. We especially
focus on a low-speed maneuver such as the final phase in berthing to achieve
automatic berthing control. Accurate dynamic modeling with minimum modeling
error is highly desired to establish a model-based control system. We propose a
new loss function that reduces the effect of the noise included in the training
data. Besides, we revealed the following facts - an RNN that ignores the memory
before a certain time improved the prediction accuracy compared with the
"standard" RNN, and the random maneuver test was effective in obtaining an
accurate berthing maneuver model. In addition, several low-speed free running
model tests were performed for the scale model of the M.V. Esso Osaka. As a
result, this paper showed that the proposed method using a neural network model
could accurately represent low-speed maneuvering motions.Comment: 13 pages, 7 figures, submitted to Journal of Marine Science and
Technology for peer-revie