The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques
Abstract
This study provides both a spherical understanding about autonomous ship navigation for collision avoidance (CA) and a theoretical background of the reviewed work. Additionally, the human cognitive abilities and the collision avoidance regulations (COLREGs) for ship navigation are examined together with water based collision avoidance algorithms. The requirements for autonomous ship navigation are addressed in conjunction with the factors influencing ship collision avoidance. Humans are able to appreciate these factors and also perform ship navigation at a satisfactory level, but their critical decisions are highly subjective and can lead to error and potentially, to ship collision. The research for autonomous ship navigation may be grouped into the classical and soft computing based categories. Classical techniques are based on mathematical models and algorithms while soft-computing techniques are based on Artificial Intelligence (AI). The areas of AI for autonomous ship collision avoidance are examined in this paper are evolutionary algorithms, fuzzy logic, expert systems, and neural networks (NN), as well as a combination of them (hybrid system).
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Zadeh, L. A. (1965) · Information and Control · doi:10.1016/s0019-9958(65)90241-x · Cited by 67,855
Zadeh, L. A. (1996) · Cited by 46,844
Real-Time Obstacle Avoidance for Manipulators and Mobile Robots
Khatib, O. (1986) · The International Journal of Robotics Research · doi:10.1177/027836498600500106 · Cited by 7,894
Bäck, T. (1996) · Oxford University Press eBooks · doi:10.1093/oso/9780195099713.001.0001 · Cited by 5,113
Rasmussen, J. (1983) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1983.6313160 · Cited by 3,434
Real-time obstacle avoidance for fast mobile robots
Borenstein, J., & Koren, Y. (1989) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/21.44033 · Cited by 1,258
An Introduction to Neural Networks
Anderson, J. A. (1995) · The MIT Press eBooks · doi:10.7551/mitpress/3905.001.0001 · Cited by 1,103
Artificial Neural Networks: Theory and Applications
Patterson, D. W. (1998) · Prentice Hall eBooks · Cited by 712
Foundations of neural networks
(1991) · Choice Reviews Online · doi:10.5860/choice.28-3925 · Cited by 546
Adaptive evolutionary planner/navigator for mobile robots
Xiao, J., Michalewicz, Z., Zhang, L., & Trojanowski, K. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585889 · Cited by 408
Cited by
Unmanned surface vehicles: An overview of developments and challenges
Liu, Z., Zhang, Y., Yu, X., & Yuan, C. (2016) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2016.04.018 · Cited by 1,235
Johansen, T. A., Perez, T., & Cristofaro, A. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2551780 · Cited by 363
Metadata
- Authors
- Statheros, T., Howells, G., & Maier, K. M.
- Venue
- Journal of Navigation
- Publisher
- Cambridge University Press (CUP)
- Published
- 2007-12-10
- Volume
- 61
- Issue
- 1
- Pages
- 129-142
- ISSN
- 0373-4633 (ISSN-L)
- 1469-7785
- OpenAlex
- W2107776472
- Cited by
- 366
- References
- 14
- FWCI
- 18.56
- Type
- journal article
- Language
- en
- Open access
- closed
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