An apparatus for artificial intelligence (AI) bathymetry is disclosed. The apparatus includes a sonic unit attached to a boat, the sonic unit configured to generate a plurality of metric data as a function of a plurality of ultrasonic pulses and a plurality of return pulses. An image processing module is configured to generate a bathymetric image as a function of the plurality of metric data, identify, as a function of the bathymetric image, an underwater landmark, and register the bathymetric image to a map location as a function of the underwater landmark. A communication module is configured to transmit the registered bathymetric image to at least a computing device. An autonomous navigation module is configured to determine a heading for the boat as a function of a path datum and command boat control to navigate the boat as a function of the heading.

A system for aiding formation flying of a follower aircraft with respect to a wake vortex from a leader aircraft comprises a controller and at least one accelerometer installed on the follower aircraft. The controller receives acceleration measurements performed by the at least one accelerometer and processes the measurements to obtain a value representative of vibrations generated by the wake vortex from the leader aircraft. The controller compares the value with at least one predetermined threshold representative of excessive vibrations with regard to location of the at least one accelerometer on the follower aircraft. One or more notifications, such as alerts, are generated based on the result of the comparison. It is easier to position the follower aircraft in formation flying to benefit from a rising airflow phenomenon brought about by the wake vortex from the leader aircraft.

A method and system for validating performance of an Auto Flight Control System (AFCS) of an vehicle has been developed. A target for the vehicle is entered into the AFCS by a pilot of the vehicle. A mode of operation for the vehicle is entered into the AFCS by the pilot of the vehicle. The pilot's intention is determined based on the entered target and mode of operation. A maneuver plan of the vehicle is predicted based on the entered target and mode of operation. The predicted maneuver plan is validated based on environmental conditions affecting the target for the vehicle. The predicted maneuver plan is validated based on operational performance characteristics of the vehicle. The pilot is alerted if predicted maneuver plan fails validation. A suggested corrective action is provided to the pilot if predicted maneuver plan fails to be validated.

A method and system for validating performance of an Auto Flight Control System (AFCS) of an vehicle has been developed. A target for the vehicle is entered into the AFCS by a pilot of the vehicle. A mode of operation for the vehicle is entered into the AFCS by the pilot of the vehicle. The pilot's intention is determined based on the entered target and mode of operation. A maneuver plan of the vehicle is predicted based on the entered target and mode of operation. The predicted maneuver plan is validated based on environmental conditions affecting the target for the vehicle. The predicted maneuver plan is validated based on operational performance characteristics of the vehicle. The pilot is alerted if predicted maneuver plan fails validation. A suggested corrective action is provided to the pilot if predicted maneuver plan fails to be validated.

A lighter than air platform an unfinned envelope having two or more propulsion elements coupled with the unfinned envelope proximate to the center of gravity. At least one navigation sensor is configured to monitor an actual flight path of the unfinned envelope, and at least one perturbation sensor is configured to monitor one or more perturbations of the unfinned envelope. A navigation controller is configured to guide the unfinned envelope with coordinated propulsion of the two or more propulsion elements. The navigation controller includes a navigation comparator that compares the actual flight path with a specified flight path of the unfinned envelope and determine a navigation instruction. A perturbation comparator compares the navigation instruction with the monitored one or more perturbations to determine a perturbation compensation. A propulsion coordinator controls propulsion values of each of the propulsion elements based on the navigation instruction and the perturbation compensation.

A lighter than air platform an unfinned envelope having two or more propulsion elements coupled with the unfinned envelope proximate to the center of gravity. At least one navigation sensor is configured to monitor an actual flight path of the unfinned envelope, and at least one perturbation sensor is configured to monitor one or more perturbations of the unfinned envelope. A navigation controller is configured to guide the unfinned envelope with coordinated propulsion of the two or more propulsion elements. The navigation controller includes a navigation comparator that compares the actual flight path with a specified flight path of the unfinned envelope and determine a navigation instruction. A perturbation comparator compares the navigation instruction with the monitored one or more perturbations to determine a perturbation compensation. A propulsion coordinator controls propulsion values of each of the propulsion elements based on the navigation instruction and the perturbation compensation.

Disclosed is a multi-modal communication system for unmanned aerial vehicles (UAVs) that integrates multiple wireless interfaces, such as point-to-point (P2P) wireless links and cellular links, to ensure seamless connectivity during flight. The system dynamically selects between wireless interfaces based on known, predicted or real-time link quality, plans a flight path based on a connectivity map and adapts flight paths in real-time. The system adapts to changing link quality. Adaptive responses include modifying a flight path, backtracking to a last known location with satisfactory signal coverage, RF channel switching in P2P link, and suspending video transmission while maintaining control links. A machine learning model predicts link conditions based on environmental conditions and historical data. The system may also leverage remote access points with Ethernet or satellite backhaul to extend coverage. These features provide resilient, autonomous communication for UAV operations in variable RF environments, improving reliability over traditional fixed-path, single-link systems.

Disclosed is a multi-modal communication system for unmanned aerial vehicles (UAVs) that integrates multiple wireless interfaces, such as point-to-point (P2P) wireless links and cellular links, to ensure seamless connectivity during flight. The system dynamically selects between wireless interfaces based on known, predicted or real-time link quality, plans a flight path based on a connectivity map and adapts flight paths in real-time. The system adapts to changing link quality. Adaptive responses include modifying a flight path, backtracking to a last known location with satisfactory signal coverage, RF channel switching in P2P link, and suspending video transmission while maintaining control links. A machine learning model predicts link conditions based on environmental conditions and historical data. The system may also leverage remote access points with Ethernet or satellite backhaul to extend coverage. These features provide resilient, autonomous communication for UAV operations in variable RF environments, improving reliability over traditional fixed-path, single-link systems.

Methods, systems and apparatus, including computer programs encoded on computer storage media for unmanned aerial vehicle beyond visual line of sight (BVLOS) flight operations. In an embodiment, a flight planning system of an unmanned aerial vehicle (UAV) can identify handoff zones along a UAV flight corridor for transferring control of the UAV between ground control stations. The start of the handoff zones can be determined prior to a flight or while the UAV is in flight. For handoff zones determined prior to flight, the flight planning system can identify suitable locations to place a ground control station (GCS). The handoff zone can be based on a threshold visual line of sight range between a controlling GCS and the UAV. For determining handoff zones while in flight, the UAV can monitor RF signals from each GCS participating in the handoff to determine the start of a handoff period.

Methods, systems and apparatus, including computer programs encoded on computer storage media for unmanned aerial vehicle beyond visual line of sight (BVLOS) flight operations. In an embodiment, a flight planning system of an unmanned aerial vehicle (UAV) can identify handoff zones along a UAV flight corridor for transferring control of the UAV between ground control stations. The start of the handoff zones can be determined prior to a flight or while the UAV is in flight. For handoff zones determined prior to flight, the flight planning system can identify suitable locations to place a ground control station (GCS). The handoff zone can be based on a threshold visual line of sight range between a controlling GCS and the UAV. For determining handoff zones while in flight, the UAV can monitor RF signals from each GCS participating in the handoff to determine the start of a handoff period.