Research on 3D Obstacle Avoidance of Autonomous Underwater Vehicle Based on Deep Reinforcement Learning
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1095-9_36
Reference11 articles.
1. Cong, Z., et al.: A storage-saving quadtree-based multibeam bathymetry map representation method. J. Marine Sci. Eng. 11(4), 709 (2023)
2. Hong, L., Fang, R., Cai, X., Wang, X.: Numerical investigation on hydrodynamic performance of a portable AUV. J. Marine Sci. Eng. 9(8), 812 (2021)
3. Thomas, C., Simetti, E., Casalino, G.: A unifying task priority approach for autonomous underwater vehicles integrating homing and docking maneuvers. J. Marine Sci. Eng. 9(2), 162 (2021)
4. Uchihori, H., et al.: Linear parameter-varying model predictive control of AUV for docking scenarios. Appl. Sci. 11(10), 4368 (2021)
5. Wang, L., Zhu, D., Pang, W., Zhang, Y.: A survey of underwater search for multi-target using Multi-AUV: task allocation, path planning, and formation control. Ocean Eng. 278, 114393 (2023)
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