Next Article in Journal
Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio
Previous Article in Journal
Academic Excellence, Website Quality, SEO Performance: Is there a Correlation?
Open AccessArticle

Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM

by Wenjie Zhang 1, Pin Wu 1,*, Yan Peng 2 and Dongke Liu 2
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Author to whom correspondence should be addressed.
Future Internet 2019, 11(11), 243;
Received: 14 October 2019 / Revised: 1 November 2019 / Accepted: 3 November 2019 / Published: 18 November 2019
(This article belongs to the Section Big Data and Augmented Intelligence)
The prediction of roll motion in unmanned surface vehicles (USVs) is vital for marine safety and the efficiency of USV operations. However, the USV roll motion at sea is a complex time-varying nonlinear and non-stationary dynamic system, which varies with time-varying environmental disturbances as well as various sailing conditions. The conventional methods have the disadvantages of low accuracy, poor robustness, and insufficient practical application ability. The rise of deep learning provides new opportunities for USV motion modeling and prediction. In this paper, a data-driven neural network model is constructed by combining a convolution neural network (CNN) with long short-term memory (LSTM) for USV roll motion prediction. The CNN is used to extract spatially relevant and local time series features of the USV sensor data. The LSTM layer is exploited to reflect the long-term movement process of the USV and predict roll motion for the next moment. The fully connected layer is utilized to decode the LSTM output and calculate the final prediction results. The effectiveness of the proposed model was proved using USV roll motion prediction experiments based on two case studies from “JingHai-VI” and “JingHai-III” USVS of Shanghai University. Experimental results on a real data set indicated that our proposed model obviously outperformed the state-of-the-art methods. View Full-Text
Keywords: CNN; data-driven; LSTM; roll motion prediction; unmanned surface vehicle CNN; data-driven; LSTM; roll motion prediction; unmanned surface vehicle
Show Figures

Figure 1

MDPI and ACS Style

Zhang, W.; Wu, P.; Peng, Y.; Liu, D. Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM. Future Internet 2019, 11, 243.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop