Dead Reckoning of Dynamically Positioned Ships: Using an Efficient Recurrent Neural Network

Skulstad, Robert; Li, Guoyuan; Fossen, Thor I.; Vik, Bjørnar; Zhang, Houxiang

doi:10.1109/mra.2019.2918125

Cited by 37 publications

(12 citation statements)

References 28 publications

(32 reference statements)

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“…Skulstad et al applied a long-short term memory (LSTM) network, a version of a recursive network, to maintain estimates of position and heading of a ship during loss of position reference signals from the Global Navigation Satellite System [20]. They used a deep neural network similar to the one described in Section III-C.…”

Section: B Data-based Motion Predictionmentioning

confidence: 99%

A Hybrid Approach to Motion Prediction for Ship Docking—Integration of a Neural Network Model Into the Ship Dynamic Model

Skulstad

Fossen

et al. 2021

IEEE Trans. Instrum. Meas.

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While automatic controllers are frequently used during transit operations and low-speed maneuvering of ships, ship operators typically perform docking maneuvers. This task is more or less challenging depending on factors such as local environment disturbances, number of nearby vessels, and the speed of the ship as it docks. This paper proposes a tool for onboard support that offers position predictions based on an integration of a supervised machine learning (ML) model of the ship into the ship dynamic model. The ML model is applied as a compensator of the unmodelled behaviour or inaccuracies from the dynamic model. The dynamic model increases the amount of predetermined knowledge about how the vessel is likely to move and thus reduces the black-box factor typically experienced in purely data-driven predictors. A prediction horizon of 30 seconds ahead of real time during docking operations is examined. History data from the 29meter coastal displacement ship RV (Research Vessel) Gunnerus is applied to validate the approach. Results show that the inclusion of the data-based ML model significantly improves the prediction accuracy.

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Section: B Data-based Motion Predictionmentioning

confidence: 99%

A Hybrid Approach to Motion Prediction for Ship Docking—Integration of a Neural Network Model Into the Ship Dynamic Model

Skulstad

Fossen

et al. 2021

IEEE Trans. Instrum. Meas.

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“…They evaluated their method on a pig stomach dataset and their experimental results show that the framework can realize high translational and rotational accuracies for different types of endoscopic capsule robot trajectories. However, research on navigation with DL mainly focus on the visual navigation or visual-inertial navigation field for outdoor robots and aerial robots, and only very few of them were applied in the maritime engineering field [35], [36].…”

Section: Introductionmentioning

confidence: 99%

Navnet: AUV Navigation Through Deep Sequential Learning

Zhang

et al. 2020

IEEE Access

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Achieving accurate navigation and localization is crucial for Autonomous Underwater Vehicle (AUV). Traditional navigation algorithms, such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), require the system model and measurement model for state estimation to obtain the AUV position. However, this may introduce modeling errors and state estimation errors which will affect the final precision of AUV navigation system to a certain extent. To avoid these problems, in this paper, we proposed a deep framework-NavNet-by taking AUV navigation as a deep sequential learning problem. Firstly, the proposed NavNet can take raw sensor data at different frequencies as input, which benefits from the sequential learning capability of Recurrent Neural Network (RNN). Secondly, NavNet takes advantage of a simplified attention mechanism and Fully Connected (FC) layers to output AUV displacements per unit time, which accomplishes low-frequency AUV navigation by accumulation of it. More importantly, there is no need for the model building and state estimation with NavNet, which avoids the import of relevant errors. We compare the performance of NavNet to EKF and UKF using collected data by running Sailfish in the sea. Experimental results show that NavNet has an excellent performance in terms of both the navigation accuracy and fault tolerance. In addition, a reliable fusion strategy of NavNet and conventional method is applied to achieve high-frequency AUV navigation. The experimental results show that the proposed architecture can be a reliable supplement to limit the error growth of conventional algorithms. INDEX TERMS Autonomous underwater vehicle, navigation, extended Kalman filter, unscented Kalman filter, sequential learning.

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“…A wide range of works have been conducted including collision avoidance (Eriksen, Breivik, Wilthil, FlÅten, & Brekke, 2019), navigation (Elkins, Sellers, & Monach, 2010), station‐staying control (Sarda, Qu, Bertaska, & vonEllenrieder, 2016), and marine sciences research (Dunbabin & Grinham, 2017). Varieties of approaches have been implemented in autonomous USV control including proportional‐integral‐derivative (PID) controller in dynamic positioning systems with a model vessel (Nguyen, Sørensen, & Quek, 2007) and collision avoidance with a catamaran‐shaped research vessel (Naeem, Irwin, & Yang, 2012), linear quadratic controller in tracking system using a model vessel (Lefeber, Pettersen, & Nijmeijer, 2003), model predictive control (MPC) in collision avoidance using the Telemetron ASV (Eriksen et al, 2019; Hagen, Kufoalor, Brekke, & Johansen, 2018), an autopilot system using a twin hull vessel (Annamalai, Sutton, Yang, Culverhouse, & Sharma, 2015), and neural networks for formation control (Peng, Wang, Chen, Hu, & Lan, 2013) and position estimation (Skulstad, Li, Fossen, Vik, & Zhang, 2019) using USV simulations.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the parameters of the controller were heuristically selected for good performance. Another recent work (Skulstad et al, 2019) learned a controller for autonomous ship driving by neural networks which requires preprepared samples for supervised learning. Its implementation was therefore limited to simulation due to the expensive cost of collecting real training data.…”

Section: Introductionmentioning

confidence: 99%

Autonomous boat driving system using sample‐efficient model predictive control‐based reinforcement learning approach

Cui

Osaki

Matsubara

2020

Journal of Field Robotics

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In this article, we propose a novel reinforcement learning (RL) approach specialized for autonomous boats: sample-efficient probabilistic model predictive control (SPMPC), to iteratively learn control policies of boats in real ocean environments without human prior knowledge. SPMPC addresses difficulties arising from large uncertainties in this challenging application and the need for rapid adaptation to dynamic environmental conditions, and the extremely high cost of exploring and sampling with a real vessel. SPMPC combines a Gaussian process model and model predictive control under a model-based RL framework to iteratively model and quickly respond to uncertain ocean environments while maintaining sample efficiency. A SPMPC system is developed with features including quadrant-based action search rule, bias compensation, and parallel computing that contribute to better control capabilities. It successfully learns to control a full-sized singleengine boat equipped with sensors measuring GPS position, speed, direction, and wind, in a real-world position holding task without models from human demonstration.

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Dead Reckoning of Dynamically Positioned Ships: Using an Efficient Recurrent Neural Network

Cited by 37 publications

References 28 publications

A Hybrid Approach to Motion Prediction for Ship Docking—Integration of a Neural Network Model Into the Ship Dynamic Model

A Hybrid Approach to Motion Prediction for Ship Docking—Integration of a Neural Network Model Into the Ship Dynamic Model

Navnet: AUV Navigation Through Deep Sequential Learning

Autonomous boat driving system using sample‐efficient model predictive control‐based reinforcement learning approach

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