Abstract
This paper explores a fast and efficient method for identifying and modeling ship maneuvering motion, and conducts a comprehensive experiment. Through the ship maneuvering test, the dynamics interaction between ship and the environment is obtained. Then, the LWL (Locally Weighted Learning algorithm) underlying architecture is constructed by sparse Gaussian Process to reduce the data requirements of LWL-based ship maneuvering motion modeling and to improve the performance for LWL. On this basis, a non-parametric model of ship maneuvering motion is established based on the locally weighted sparse Gaussian Process, and the traditional mathematical model of ship maneuvering motion is replaced by the generative model. This generative model considers the hydrodynamic effects of ships, and reduces the sensitivity of local weighted learning to sample data. In addition, matrix operations are transferred to the auxiliary platform to optimize the calculation performance of the method. Finally, the simulation results of ship maneuvering motion indicate that this method has the characteristics of efficiency, rapidity and universality, and its accuracy conforms to engineering practice.
Highlights
The prediction of ship motion attitude is the prerequisite for ship maneuvering decision and motion planning
With the rapid development of machine learning and artificial intelligence, intelligent methods provide an effective way for non-parametric modeling, which has become popular in the ship motion modeling field from SVM (Support Vector Machines) [2,3] to ANN (Artificial neural networks) [4,5], from
In order to better explain the pros and cons of the LSGP algorithm, we demonstrate how the proposed algorithm can be executed on full-scale experimental data for different manipulation scenarios
Summary
The prediction of ship motion attitude is the prerequisite for ship maneuvering decision and motion planning. In ship motion identification modeling, LWL [9,10] is used as a non-parametric method [11] like the above methods, to construct a non-parametric model of ship maneuvering motion The difference of this method is that whenever it predicts a new sample value, it will solve the new parameter value according to the weight of the local model training set, so it can learn from a large amount of data and add data incrementally. We propose Sparse Gaussian process based locally weighted learning (LSGP) for ship maneuvering motion non-parametric modeling. In this way, we retain the advantages of the two methods.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
