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Deep Convolutional Neural Network-Based Autonomous Marine Vehicle Maneuver

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Abstract

The automation level of autonomous marine vehicle is limited which is always semi-autonomy and reliant on operator interactions. In order to improve it, an autonomous collision avoidance method is proposed based on the visual technique as human’s visual system. A deep convolutional neural network (Alexnet), with strong visual processing capability, is adopted for encounter pattern recognition. European Ship Simulator is used to generate some encounter scenes and record the corresponding maneuver operation conforming to the COLREGs (International Regulations for Preventing Collisions at Sea) rules as samples. After the training phase, of Alexnet, it can successfully predict the collision avoidance operation according to the input scene image like crewman; moreover, this can provide operation guidance for the automatic navigation, guidance and control system. Some different encounter situations are simulated, and used to testify the validity of the proposed approach.

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Acknowledgements

The author acknowledges the support of National Natural Science Foundation of China (61603214, 61573213) and the Shandong Provincial Natural Science Foundation of China (ZR2015PF009, 2016ZRE2703).

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Authors and Affiliations

  1. School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, China

    Qingyang Xu, Yiqin Yang, Chengjin Zhang & Li Zhang

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  1. Qingyang Xu
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Correspondence to Qingyang Xu.

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Xu, Q., Yang, Y., Zhang, C. et al. Deep Convolutional Neural Network-Based Autonomous Marine Vehicle Maneuver. Int. J. Fuzzy Syst. 20, 687–699 (2018). https://doi.org/10.1007/s40815-017-0393-z

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  • DOI: https://doi.org/10.1007/s40815-017-0393-z

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