Shipboard Interaction

Motion Capture for Shipboard Launch and Recovery (Proprietary)

Principal Investigator: Dr. Cornel Sultan

When considering shipboard launch and recovery operations, be it for manned or unmanned aerial vehicles, including aircraft and rotorcraft, accurate motion prediction of the surface vessel can broaden safe operating envelopes enabling safer operations or increasing the number of sorties feasible. Figure 5 demonstrates an implementation of a neural network roll prediction tool to enable targeted recovery operations of an unmanned surface vehicle onto a larger host vessel by triggering a transition from station keeping to entry, when the predicted roll motion will be zero at the instant of ship interception. The PI proposes to seek to further understand and characterize how the use of ship motion prediction tools, coupled with appropriate air vehicle modelling and control algorithms, can influence successful launch and recovery operations for manned and unmanned vehicles, through broadening the operating envelope or increasing successful capture rates. Dynamic interface opportunities shall be defined broadly by coupling ship motion prediction with an understanding of the governing dynamics of the vehicle. Safe and effective recovery tools shall be developed.