Reinforcement Learning-Driven Autonomous Path Planning for Unmanned Surface Vehicles: Current Status, Challenges, and Future Prospects.
- DOI
- 10.3390/s26092852
- Published
- 2026 May 2
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s26092852,
title = {Reinforcement Learning-Driven Autonomous Path Planning for Unmanned Surface Vehicles: Current Status, Challenges, and Future Prospects.},
author = {Dong Z and Zheng J and Guo C and Zhao F and Chu Y and Chen X},
year = {2026},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s26092852},
url = {https://doi.org/10.3390/s26092852}
}RIS
TY - JOUR TI - Reinforcement Learning-Driven Autonomous Path Planning for Unmanned Surface Vehicles: Current Status, Challenges, and Future Prospects. AU - Dong Z AU - Zheng J AU - Guo C AU - Zhao F AU - Chu Y AU - Chen X PY - 2026 JO - Sensors (Basel, Switzerland) DO - 10.3390/s26092852 UR - https://doi.org/10.3390/s26092852 ER -
APA
Z, D., J, Z., C, G., F, Z., Y, C., & X, C. (2026). Reinforcement Learning-Driven Autonomous Path Planning for Unmanned Surface Vehicles: Current Status, Challenges, and Future Prospects.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26092852
Source records
- pubmed · retrieved 2026-11-10T07:32:06.018Z