The Littoral Combat Ship (LCS) is a surface vessel commissioned to operate close to shore.
The development of a control and navigation platform for an autonomous surface vessel (ASV) being a scaled self-propelled model of a real ship is presented in this paper.
The concept behind the LCS calls for a small surface vessel, designed to operate close to the shore, which can be customized with different modules for three main missions: mine countermeasures, surface warfare, or anti-submarine warfare.
A targeted surface vessel with a diameter of 100 μm, for example, will produce a signal that is 100% representative of the vessel fluorescence at 415 nm, 80% representative of vessel fluorescence at 524 nm, and 50% representative of vessel fluorescence at 800 nm.
This paper investigates the problem of trajectory tracking control for an unmanned marine surface vessel (MSV) with external disturbances and asymmetric saturation actuators.
This paper investigates the path following control problem for an unmanned marine surface vessel (MSV) in the presence of uncertainties and input saturation.
A new riser system would be built that would allow for the surface vessel collecting the oil to disconnect and reconnect quickly in the event of a hurricane.
In this paper, our main goal is to solve optimal control problem by using reinforcement learning (RL) algorithm for marine surface vessel system with known dynamic.
Additionally, we found that using targeted illumination can provide for superior surface vessel sensitivity over wide-field illumination, with small area detection offering an even greater amount of sensitivity to surface vasculature.
