The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Collision avoidance for an unmanned surface vehicle using deep reinforcement learning
Abstract
Abstract In this paper, a deep reinforcement learning (DRL)-based collision avoidance method is proposed for an unmanned surface vehicle (USV). This approach is applicable to the decision-making stage of collision avoidance, which determines whether the avoidance is necessary, and if so, determines the direction of the avoidance maneuver. To utilize the visual recognition capability of deep neural networks as a tool for analyzing the complex and ambiguous situations that are typically encountered, a grid map representation of the ship encounter situation was suggested. For the composition of the DRL network, we proposed a neural network architecture and semi-Markov decision process model that was specially designed for the USV collision avoidance problem. The proposed DRL network was trained through repeated simulations of collision avoidance. After the training process, the DRL network was implemented in collision avoidance experiments and simulations to evaluate its situation recognition and collision avoidance capability.
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Citations
Cites
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00052-1 · Cited by 3,119
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Zhu, Y., Mottaghi, R., Kolve, E., Lim, J. J., Gupta, A., Fei-Fei, L., & Farhadi, A. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989381 · Cited by 1,533
Cited by
Deep reinforcement learning based mobile robot navigation: A review
Zhu, K., & Zhang, T. (2021) · Tsinghua Science and Technology · doi:10.26599/tst.2021.9010012 · Cited by 499
Deep reinforcement learning-based controller for path following of an unmanned surface vehicle
Woo, J., Yu, C., & Kim, N. (2019) · Ocean Engineering · doi:10.1016/j.oceaneng.2019.04.099 · Cited by 246
Self-citationSurvey of deep learning for autonomous surface vehicles in marine environments
Qiao, Y., Yin, J., Wang, W., Duarte, F., Yang, J., & Ratti, C. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3235911 · Cited by 192
Dynamic robotic tracking of underwater targets using reinforcement learning
Masmitja, I., Martin, M., O’Reilly, T., Kieft, B., Palomeras, N., Navarro, J., & Katija, K. (2023) · Science Robotics · doi:10.1126/scirobotics.ade7811 · Cited by 96
Metadata
- Authors
- Woo, J., & Kim, N.
- Venue
- Ocean Engineering
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 199
- Pages
- 107001
- ISSN
- 0029-8018 (ISSN-L)
- OpenAlex
- W3005337831
- Cited by
- 264
- References
- 3
- FWCI
- 19.86
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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