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Języki publikacji
Abstrakty
This study proposes the use of generative adversarial networks (GANs) to solve two crucial problems in the unmanned ship navigation: insufficient training data for neural networks and convergence of optimal actions under discrete conditions. To achieve smart collision avoidance of unmanned ships in various sea environments, first, this study proposes a collision avoidance decision model based on a deep reinforcement learning method. Then, it utilizes GANs to generate enough realistic image training sets to train the decision model. According to generative network learning, the conditional probability distribution of ship maneuvers is learnt (action units). Subsequently, the decision system can select a reasonable action to avoid the obstacles due to the discrete responses of the generated model to different actions and achieve the effect of intelligent collision avoidance. The experimental results showed that the generated target ship image set can be used as the training set of decision neural networks. Further, a theoretical reference to optimize the optimal convergence of discrete actions is provided.
Rocznik
Tom
Strony
875--880
Opis fizyczny
Bibliogr. 10 poz., rys.
Twórcy
Bibliografia
- 1. Capraro, G. T., Farina, A., Griffiths, H. & Wicks, M. C. (2006). Knowledge‐based radar signal and data processing: a tutorial review. IEEE Signal Processing Magazine, 23(1), 18‐29.
- 2. Finn, A. & Scheding, S. (2010). Developments and challenges for autonomous unmanned vehicles. Intelligent Systems Reference Library, 3, 128‐154.
- 3. Goodfellow, I., Pouget‐Abadie, J., Mirza, M., Xu, B., WardeFarley, D., Ozair, S. ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672‐2680).
- 4. Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y., Baker, J. A. & Tourassi, G. D. (2008). Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural networks, 21(2‐3), 427‐436
- 5. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G. ... & Petersen, S. (2015). Human‐level control through deep reinforcement learning. Nature, 518(7540), 529
- 6. Sarukkai, R. R. (2000). Link prediction and path analysis using Markov chains1. Computer Networks, 33(1‐6), 377‐386
- 7. Scheffer, M., Carpenter, S. R., Lenton, T. M., Bascompte, J., Brock, W., Dakos, V., ... & Pascual, M. (2012). Anticipating critical transitions. science, 338(6105), 344348.
- 8. Trucco, P., Cagno, E., Ruggeri, F. & Grande, O. (2008). A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation. Reliability Engineering & System Safety, 93(6), 845‐856
- 9. Wang, X., Yadav, V. & Balakrishnan, S. N. (2007). Cooperative UAV formation flying with obstacle/collision avoidance.
- 10. Zhang, R. L. & Furusho, M. (2017). Conversion timing of seafarer’s decision‐making for unmanned ship navigation. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation,
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a4923893-b54f-4d1e-9ad0-8dc501ae1b09