Autoland systems and processes for landing an aircraft without pilot intervention are described. In implementations, the autoland system includes a memory operable to store one or more modules and at least one processor coupled to the memory. The processor is operable to execute the one or more modules to identify a plurality of potential destinations for an aircraft. The processor can also calculate a merit for each potential destination identified, select a destination based upon the merit; receive terrain data and/or obstacle data, the including terrain characteristic(s) and/or obstacle characteristic(s); and create a route from a current position of the aircraft to an approach fix associated with the destination, the route accounting for the terrain characteristic(s) and/or obstacle characteristic(s). The processor can also cause the aircraft to traverse the route, and cause the aircraft to land at the destination without requiring pilot intervention.

An aircraft control system (100) including an input interface (102), an output interface (114) and a processing engine (108) having a classifier (110) that applies input data (104) generated by the input interface (102) to generate output control data (112). The classifier (110) has a plurality of parameters which represent a control policy for operating the aircraft (800). The output interface (114) generates control outputs to control the aircraft (800) based on the output control data (112). A machine learning system (900) for training the classifier (110) including an environment (902), a pathway evaluation engine (904), storage (906), and a training engine (908). The machine learning system (900) generates training data (912) by selecting a pathway representing an operating procedure using the pathway evaluation engine (904). The training engine (908) trains the classifier (110) using the training data (912).

A computer-implemented method of enabling optimisation of trajectory for a vehicle, the method comprising: determining a trajectory for the vehicle using: an algorithm; a vehicle model defining path constraints for the vehicle through space; a propulsion system model defining parameters of a propulsion system of the vehicle; an objective function defining one or more objectives; and controlling output of the determined trajectory.

A computer-implemented method of enabling optimisation of trajectory for a vehicle, the method comprising: determining a trajectory for the vehicle using: an algorithm; a vehicle model defining path constraints for the vehicle through space; a propulsion system model defining parameters of a propulsion system of the vehicle; an objective function defining one or more objectives; and controlling output of the determined trajectory.

A method for hovering an aircraft having at least one wing and at least one rotary wing and at least one propeller, the aircraft comprising an autopilot system. The method comprises keeping the aircraft hovering, with the autopilot system, in the setpoint position, keeping the aircraft hovering in this way comprising controlling, with the autopilot system, a pitch of blades of the at least one propeller irrespective of the setpoint pitch angle and controlling, with the autopilot system, a pitch of blades of the at least one rotary wing as a function at least of the setpoint pitch angle.

A first sensitivity level is used to interpret an input signal received from an input device in a vehicle while the vehicle is in a first region. A second sensitivity level is used to interpret the input signal received from the input device in the vehicle while the vehicle is in a second region, wherein the second sensitivity level is greater than the first sensitivity level.

A first sensitivity level is used to interpret an input signal received from an input device in a vehicle while the vehicle is in a first region. A second sensitivity level is used to interpret the input signal received from the input device in the vehicle while the vehicle is in a second region, wherein the second sensitivity level is greater than the first sensitivity level.

An aircrew automation system that provides a pilot with high-fidelity knowledge of the aircraft's physical state, and notifies that pilot of any deviations in expected state based on predictive models. The aircrew automation may be provided as a non-invasive ride-along aircrew automation system that perceives the state of the aircraft through visual techniques, derives the aircraft state vector and other aircraft information, and communicates any deviations from expected aircraft state to the pilot.

To ensure stability of flying by an unmanned aerial vehicle, first acquisition means of an unmanned aerial vehicle control system acquires first information, which is at least one piece of information for operating an unmanned aerial vehicle that is flying or information on a result of detecting an operation of the unmanned aerial vehicle. Second acquisition means acquires second information for operating the unmanned aerial vehicle after switching of control of the unmanned aerial vehicle. Flight control means restricts, in accordance with the first information and the second information, switching to control of the unmanned aerial vehicle based on the second information.

Systems and methods for controlling aerial vehicles are provided. An aerial vehicle includes a single circuit board with a number of processor devices and a memory including instructions to perform autonomy operations. The autonomy operations include obtaining GNSS data from GNSS assemblies electrically connected to the processor devices, APNT data from APNT assemblies electrically connected to the processor devices, and radar data from the radar assemblies electrically connected to the processor devices. Each of the assemblies are disposed on the same circuit board that includes the number of processor devices. The processor devices determine a vehicle location based on the GNSS data, the APNT data, and the radar data, identify airborne objects based on the radar data, generate a motion plan based on the vehicle location and the identified objects, and initiate a motion of the aerial vehicle based on the vehicle location.