References
- 1. I. Stenius et al., “A System for Autonomous Seaweed Farm Inspection with an Underwater Robot,” Sensors, vol. 22, no. 13, Jul 2022, doi: 10.3390/s22135064.926977835808560
- 2. L. Rowinski and M. Kaczmarczyk, “Evaluation of Effectiveness of Waterjet Propulsor for a Small Underwater Vehicle,” Polish Marit. Res., vol. 28, no. 4, 2022, doi: 10.2478/pomr-2021-0047.
- 3. H. Choukri and L. Z. Qidan, “Path Following Control of Fully Actuated Autonomous Underwater Vehicle Based on LADRC,” Polish Marit. Res., vol. 25, no. 4, 2018, doi: 10.2478/pomr-2018-0130.
- 4. L. Li, Z. Pei, J. Jin, and Y. Dai, “Control of Unmanned Surface Vehicle along the Desired Trajectory Using Improved Line of Sight and Estimated Sideslip Angle,” Polish Marit. Res., vol. 28, no. 2, 2021, doi: 10.2478/pomr-2021-0017.
- 5. E. Vidal, N. Palomeras, M. Carreras, “Online 3D Underwater Exploration and Coverage,” in IEEE/OES Autonomous Underwater Vehicle Workshop (AUV), Rectory Univ Porto, Porto, Portugal, 2018.10.1109/AUV.2018.8729736
- 6. J. H. Wan et al., “Motion Control of Autonomous Underwater Vehicle Based on Fractional Calculus Active Disturbance Rejection,” Journal of Marine Science and Engineering, vol. 9, no. 11, Nov 2021, doi: 10.3390/jmse9111306.
- 7. J. J. Zhou, X. Y. Zhao, T. Chen, Z. P. Yan, and Z. W. Yang, “Trajectory Tracking Control of an Underactuated AUV Based on Backstepping Sliding Mode With State Prediction,” IEEE Access, vol. 7, 2019, doi: 10.1109/access.2019.2958360.
- 8. M. P. R. Prasad and A. Swarup, “Model predictive control of an AUV using de-coupled approach,” International Journal of Maritime Engineering, vol. 160, Jan‒Mar 2018, doi: 10.3940/rina.ijme.2018.a1.459.
- 9. 9. J. H. Wan et al., “Multi-strategy fusion based on sea state codes for AUV motion control,” Ocean Engineering, vol. 248, Mar 2022, doi: 10.1016/j.oceaneng.2022.110600.
- 10. Y. K. Xia, K. Xu, Z. M. Huang, W. J. Wang, G. H. Xu, and Y. Li, “Adaptive energy-efficient tracking control of a X rudder AUV with actuator dynamics and rolling restriction,” Applied Ocean Research, vol. 118, Jan 2022, doi: 10.1016/j.apor.2021.102994.
- 11. K. Fang, H. L. Fang, J. W. Zhang, J. Q. Yao, and J. W. Li, “Neural adaptive output feedback tracking control of underactuated AUVs,” Ocean Engineering, vol. 234, Aug 2021, doi: 10.1016/j. oceaneng.2021.109211.
- 12. J. L. Zhang, X. B. Xiang, Q. Zhang, and W. J. Li, “Neural network-based adaptive trajectory tracking control of underactuated AUVs with unknown asymmetrical actuator saturation and unknown dynamics,” Ocean Engineering, vol. 218, Dec 2020, doi: 10.1016/j.oceaneng.2020.108193.
- 13. H. N. Esfahani and R. Szlapczynski, “Model Predictive Super-Twisting Sliding Mode Control for an Autonomous Surface Vehicle,” Polish Marit. Res., vol. 26, no. 3, 2019, doi: 10.2478/pomr-2019-0057.
- 14. C. X. Cheng, Q. X. Sha, B. He, and G. L. Li, “Path planning and obstacle avoidance for AUV: A review,” Ocean Engineering, vol. 235, Sep 2021, doi: 10.1016/j.oceaneng.2021.109355.
- 15. S. Brandi, M. Fiorentini, and A. Capozzoli, “Comparison of online and offline deep reinforcement learning with model predictive control for thermal energy management,” Automation in Construction, vol. 135, Mar 2022, doi: 10.1016/j.autcon.2022.104128.
- 16. Y. Fang, Z. W. Huang, J. Y. Pu, and J. S. Zhang, “AUV position tracking and trajectory control based on fast-deployed deep reinforcement learning method,” Ocean Engineering, vol. 245, Feb 2022, doi: 10.1016/j.oceaneng.2021.110452.
- 17. P. Zielinski and U. Markowska-Kaczmar, “3D robotic navigation using a vision-based deep reinforcement learning model,” Applied Soft Computing, vol. 110, Oct 2021, doi: 10.1016/j.asoc.2021.107602.
- 18. D. L. Song, W. H. Gan, P. Yao, W. C. Zang, Z. X. Zhang, and X. Q. Qu, “Guidance and control of autonomous surface underwater vehicles for target tracking in ocean environment by deep reinforcement learning,” Ocean Engineering, vol. 250, Apr 2022, doi: 10.1016/j.oceaneng.2022.110947.
- 19. E. Meyer, H. Robinson, A. Rasheed, and O. San, “Taming an Autonomous Surface Vehicle for Path Following and Collision Avoidance Using Deep Reinforcement Learning,” IEEE Access, vol. 8, 2020, doi: 10.1109/access.2020.2976586.
- 20. A. B. Martinsen and A. M. Lekkas, “Straight-Path Following for Underactuated Marine Vessels using Deep Reinforcement Learning,” in 11th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles (CAMS), Opatija, Croatia, 2018, vol. 51.10.1016/j.ifacol.2018.09.502
- 21. T. I. Fossen, Handbook of Marine Craft Hydrodynamics and Motion Control, John Wiley, Chichester, UK, doi: 10.1002/9781119994138.
- 22. M. Breivik, T. I. Fossen, “Principles of guidance-based path following in 2D and 3D,” in 44th IEEE Conference on Decision Control/European Control Conference (CCDECC), Seville, Spain, 2005.
- 23. T. Liu, Y. L. Hu, and H. Xu, “Deep Reinforcement Learning for Vectored Thruster Autonomous Underwater Vehicle Control,” Complexity, vol. 2021, Apr 2021, doi: 10.1155/2021/6649625.
- 24. S. Fujimoto, H. V. Hoof, D. Meger, Addressing Function Approximation Error in Actor-Critic Methods, ICML, 2018.