Methods and apparatus of notification of a flight asymmetry influencing an aircraft are disclosed herein. An example method includes monitoring a roll characteristic of an aircraft and determining an output of an autopilot system of the aircraft to control the roll characteristic. The example method further includes generating an alert based on the output and an authority of the autopilot system to control the roll characteristic.

Implementations of the present invention disclose an unmanned aerial vehicle ranging method, apparatus and an unmanned aerial vehicle. The method includes: controlling, by the unmanned aerial vehicle, to transmit, to a ground, a first modulated light whose frequency is a first modulation frequency, acquiring a first phase deviation between a received first modulated light and the transmitted first modulated light, and calculating a first to-ground distance of the unmanned aerial vehicle according to the first phase deviation and the first modulation frequency; controlling to transmit, to the ground, a second modulated light whose frequency is a second modulation frequency, acquiring a second phase deviation between a received second modulated light and the transmitted second modulated light and a second modulated frequency, and calculating a second to-ground distance of the unmanned aerial vehicle according to the second phase deviation; and calculating a final to-ground distance of the unmanned aerial vehicle according to the first to-ground distance and the second to-ground distance.

Embodiments of the present application relate to the technical field of aircrafts and disclose a method and apparatus for yaw fusion and an aircraft. The method for yaw fusion is applicable to an aircraft and includes: acquiring global positioning system (GPS) data, inertial measurement unit (IMU) data, and magnetometer data, wherein the GPS data includes GPS location, velocity, acceleration information, and GPS velocity signal quality, and the IMU data includes IMU acceleration information and IMU angular velocity information; determining a corrected yaw according to the IMU data, the GPS data, and the magnetometer data; determining a magnetometer alignment deviation angle according to the magnetometer data, the GPS data, and the corrected yaw; determining a GPS realignment deviation angle according to the GPS data and the IMU acceleration information; and generating a fused yaw according to the corrected yaw, the magnetometer alignment deviation angle, and the GPS realignment deviation angle.

Disclosed is a method for controlling stable flight of an unmanned aircraft, comprising the following steps: acquiring real-time flight operation data of the aircraft itself by means of an attitude sensor, a position sensor and an altitude sensor mounted to the unmanned aircraft, performing corresponding analysis on a kinematic problem of the aircraft by a processor mounted thereto, and establishing a dynamics model of the aircraft (S1); designing a controller of the unmanned aircraft according to a multi-layer zeroing neurodynamic method (S2); solving output control quantities of motors of the aircraft by the designed multi-layer zeroing neural network controller using the acquired real-time operation data of the aircraft and target attitude data (S3); and transferring solution results to a motor governor of the aircraft, and controlling powers of the motors according to a relationship between the control quantities solved by the controller and the powers of the motors of the multi-rotor unmanned aircraft, so as to control the motion of the unmanned aircraft (S4). Based on the multi-layer zeroing neurodynamic method, a correct solution to the problem can be approached rapidly, accurately and in real time, and a time-varying problem can be significantly solved.

The learning system SY1 acquires control information output from a control model M by inputting to the control model M environmental information including weather information in at least one of a surrounding environment of an unmanned aerial vehicle P and an environment of a planned flight area of an unmanned aerial vehicle P, and when the unmanned aerial vehicle P takes an action based on the control information, performs reinforcement learning of the control model M using a reward r representing an evaluation of a result of the action.

A method includes receiving first vehicle data from a sensor on a vehicle at one or more processors executing a first software module. The method includes determining a risk value, a confidence value associated with whether the first vehicle data matches a validated vehicle state, and a benefit value associated with providing the first vehicle data to a second software module. The method includes calculating a health score associated with the first vehicle data based on the risk value, the confidence value, and the benefit value. The method includes, in response to the health score meeting a threshold value, determining whether the first vehicle data is internally consistent, externally consistent, and stable over time. The method further includes, in response to the first vehicle data being internally consistent, externally consistent, and stable over time, providing the first vehicle data to a vehicle flight control system.

An aircraft defining an upright orientation and an inverted orientation, a ground station; and a control system for remotely controlling the flight of the aircraft. The ground station has an auto-land function that causes the aircraft to invert, stall, and controllably land in the inverted orientation to protect a payload and a rudder extending down from the aircraft. In the upright orientation, the ground station depicts the view from a first aircraft camera. When switching to the inverted orientation: (1) the ground station depicts the view from a second aircraft camera, (2) the aircraft switches the colors of red and green wing lights, extends the ailerons to act as inverted flaps, and (3) the control system adapts a ground station controller for the inverted orientation. The aircraft landing gear is an expanded polypropylene pad located above the wing when the aircraft is in the upright orientation.

A helicopter autopilot system includes an inner loop for attitude hold for the flight of the helicopter including a given level of redundancy applied to the inner loop. An outer loop is configured for providing a navigation function with respect to the flight of the helicopter including a different level of redundancy than the inner loop. An actuator provides a braking force on a linkage that serves to stabilize the flight of the helicopter during a power failure. The actuator is electromechanical and receives electrical drive signals to provide automatic flight control of the helicopter without requiring a hydraulic assistance system in the helicopter. The autopilot can operate the helicopter in a failed mode of the hydraulic assistance system. A number of flight modes are described with associated sensor inputs including rate based and true attitude modes.

The processor supplies flight commands to the flight control system by selectively blending pilot input with control signals from the autopilot. The processor generates a projected recovery trajectory through successive iterations, each beginning at the current aircraft location and using a recovery constraint selectable by the processor to influences a degree of flight aggressiveness. A detection system that identifies and invokes a state of threat existence if a threat exists along the projected recovery trajectory. The processor during threat existence in a first iteration commands an initial soft recovery, with permitted blended pilot input. If the threat exists on subsequent iteration, the processor commands a more aggressive recovery while attenuating blended pilot input.

There is provided a system for controlling at least first and second engines of an aircraft, comprising a common controlling unit configured to convert data representative of a thrust command transmitted by an actuating element controllable by a pilot or by an auto-throttle of the aircraft, into: (a) at least one first command usable by a controller of the first engine for controlling its operation based at least on said first command, and (b) at least one second command usable by a controller of the second engine for controlling its operation based at least on said second command, wherein said common controlling unit is operable to perform said conversion based at least on data representative of a level of operability of each engine, thereby making each engine to either comply with said thrust command or to operate differently from said thrust command, based at least on its level of operability.