accessibility__skip_menu__jump_to_main

Full text: Automatic, cooperative maneuvering of watercraft within ports

/.R. Marx et al. 
tonomous surface vessel for urban transportation. J. Field Rob. 40 (8), 1996-2009. 
https://doi.org/10.1002/rob.22237 
Wang, W., Hagemann, N., Ratti, C., Rus, D., 2021. Adaptive nonlinear model predictive 
zontrol for autonomous surface vessels with largely varying payload. In: 2021 IEEE 
{nternational Conference on Robotics and Automation (ICRA), pp. 7337-7343. https: 
//doi.org/10.1109/1CRA48506.2021.9561331 
Wirtensohn, S., Hamburger, O., Homburger, H., Kinjo, L.M., Reuter, J., 2021. Comparison 
of advanced control strategies for automated docking. In: IFAC-PapersOnLine. Vol. 54. 
pp- 295-300. https://doi.org/10.1016/j.ifacol.2021.10.107 
Wächter, A., Biegler, L.T., 2006. On the implementation of an interior-point filter line- 
search algorithm for large-scale nonlinear programming. Math. Program. (1), 25-57. 
nttps://doi.org/10.1007/s10107-004-0559-v 
Ocean Engineering 343 (2026) 123388 
Zanelli, A., Domahidi, A., Jerez, J., Morari, M., 2017. Forces nlp: an efficient implemen- 
tation of interior-point methods for multistage nonlinear nonconvex programs. Int. J. 
Control 93, 1-26. https://doi.org/10.1080/00207179.2017.1316017 
are, N., Brandoli, B., Sarvmaili, M., Soares, A., Matwin, S., 2021. Continuous control with 
deep reinforcement learning for autonomous vessels. In: ACM Proceedings, Dalhousie 
Jniversity. 
Zheng, Y., Cheng, L., Zhu, Q., Xue, X., 2014. Trajectory tracking control of autonomous 
underwater vehicle based on improved model predictive control. J. Mar. Sci. Appl. 13 
(4), 456-462. https: //doi.org/10.1007/s11804-014-1257-7
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.