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

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, the number of nearby vessels, and the speed of the ship as it docks. This article 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 unmodeled behavior 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 s ahead of real time during docking operations is examined. History data from the 29-m coastal displacement ship Research Vessel (RV) Gunnerus are applied to validate the approach. Results show that the inclusion of the data-based ML model significantly improves the prediction accuracy.

[1]  Jiancheng Fang,et al.  A Hybrid Prediction Method for Bridging GPS Outages in High-Precision POS Application , 2014, IEEE Transactions on Instrumentation and Measurement.

[2]  Guoyuan Li,et al.  Dead Reckoning of Dynamically Positioned Ships: Using an Efficient Recurrent Neural Network , 2019, IEEE Robotics & Automation Magazine.

[3]  Michael S. Triantafyllou,et al.  Real time estimation of ship motions using Kalman filtering techniques , 1983 .

[4]  C. Guedes Soares,et al.  Mathematical models for ship path prediction in manoeuvring simulation systems , 2002 .

[5]  Lokukaluge P. Perera,et al.  Navigation vector based ship maneuvering prediction , 2017 .

[6]  M. Sidar,et al.  On the feasibility of real-time prediction of aircraft carrier motion at sea , 1983 .

[7]  Tomasz Praczyk.  Using evolutionary neural networks to predict spatial orientation of a ship , 2015, Neurocomputing.

[8]  Xu Li,et al.  Enhancing Localization Accuracy of MEMS-INS/GPS/In-Vehicle Sensors Integration During GPS Outages , 2018, IEEE Transactions on Instrumentation and Measurement.

[9]  Wang Rui,et al.  IRF - AR Model for Short-Term Prediction of Ship Motion , 2015 .

[10]  Ravi Sankar,et al.  Time Series Prediction Using Support Vector Machines: A Survey , 2009, IEEE Computational Intelligence Magazine.

[11]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[12]  Daniel F. Leite,et al.  Ensemble of evolving data clouds and fuzzy models for weather time series prediction , 2018, Appl. Soft Comput..

[13]  Rui Araújo,et al.  An adaptive ensemble of on-line Extreme Learning Machines with variable forgetting factor for dynamic system prediction , 2016, Neurocomputing.

[14]  Zhengjiang Liu,et al.  Real-Time Ship Motion Prediction Based on Time Delay Wavelet Neural Network , 2014, J. Appl. Math..

[15]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[16]  Øyvind Notland Smogeli,et al.  Control of Marine Propellers: from Normal to Extreme Conditions , 2006 .

[17]  Jose M. Giron-Sierra,et al.  The Problem of Quiescent Period Prediction for Ships: A Review , 2010 .

[18]  Tieshan Li,et al.  Modified minimal resource allocating network for ship motion predictive control , 2010, 2010 International Conference on Intelligent Control and Information Processing.

[19]  Zhouhua Peng,et al.  Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation , 2018, IEEE Transactions on Control Systems Technology.

[20]  Jun Wang,et al.  Distributed Containment Maneuvering of Multiple Marine Vessels via Neurodynamics-Based Output Feedback , 2017, IEEE Transactions on Industrial Electronics.

[21]  Lokukaluge P. Perera,et al.  Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction , 2012, IEEE Transactions on Intelligent Transportation Systems.

[22]  Zao-Jian Zou,et al.  On-line prediction of ship roll motion during maneuvering using sequential learning RBF neuralnetworks , 2013 .

[23]  Jie Ma,et al.  Comparison of Representative Method for Time Series Prediction , 2006, 2006 International Conference on Mechatronics and Automation.

[24]  Ulrik Dam Nielsen,et al.  Response predictions using the observed autocorrelation function , 2018 .

[25]  Leigh McCue,et al.  Handbook of Marine Craft Hydrodynamics and Motion Control [Bookshelf] , 2016, IEEE Control Systems.

[26]  Ming Hong,et al.  Prediction of the vertical vibration of ship hull based on grey relational analysis and SVM method , 2015 .

[27]  Robert Skulstad,et al.  An efficient neural-network based approach to automatic ship docking , 2019, Ocean Engineering.

[28]  Xilin Yang,et al.  Displacement motion prediction of a landing deck for recovery operations of rotary UAVs , 2013 .

[29]  J. G. Kusters,et al.  FutureWaves™: A real-time Ship Motion Forecasting system employing advanced wave-sensing radar , 2016, OCEANS 2016 MTS/IEEE Monterey.

[30]  Jan Tommy Gravdahl,et al.  On the influence of ship motion prediction accuracy on motion planning and control of robotic manipulators on seaborne platforms , 2010, 2010 IEEE International Conference on Robotics and Automation.

[31]  Duanfeng Han,et al.  Ship motion prediction using dynamic seasonal RvSVR with phase space reconstruction and the chaos adaptive efficient FOA , 2016, Neurocomputing.

[32]  Lokukaluge P. Perera,et al.  Ocean Vessel Trajectory Estimation and Prediction Based on Extended Kalman Filter , 2010 .

[33]  Jing Deng,et al.  Long-Term Ship Speed Prediction for Intelligent Traffic Signaling , 2017, IEEE Transactions on Intelligent Transportation Systems.

[34]  Dizan Vasquez,et al.  A survey on motion prediction and risk assessment for intelligent vehicles , 2014, ROBOMECH Journal.

[35]  O Sawodny,et al.  Active Control for an Offshore Crane Using Prediction of the Vessel’s Motion , 2011, IEEE/ASME Transactions on Mechatronics.

[36]  Zhe Xiao,et al.  Maritime Traffic Probabilistic Forecasting Based on Vessels’ Waterway Patterns and Motion Behaviors , 2017, IEEE Transactions on Intelligent Transportation Systems.