A method and system for controlling the flight of a plurality of quadcopters includes communicating a flight instruction from a user to a leader quadcopter. The method includes calculating a leader formation maneuver and a follower formation maneuver with a leader-follower formation controller configured to use affine transformations and stress matrices to convert a flight instruction into a leader formation maneuver and a follower formation maneuver. The method may include communicating the follower formation maneuver from the leader quadcopter to a follower quadcopter. The method may include executing the leader formation maneuver on the leader quadcopter. The method may include executing the follower formation maneuver on the follower quadcopter.

A method includes determining a portion of a flight path of an aerial vehicle. The method also includes determining an attribute value representing an operating condition expected to be experienced by the aerial vehicle at the portion of the flight path. The method additionally includes determining, based on the attribute value and using a non-linear model, a power value representing an amount of power expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method further includes determining, based on the power value, an energy value representing an amount of energy expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method yet further includes determining the flight path based on the energy value.

A vehicle control framework enables improved attitude tracking and mode switching of a vehicle by modeling the vehicle as a switched system, where the vehicle is operable for changing a geometric configuration during flight. The vehicle control framework implements a control law that accommodates modeling uncertainties and unknown external disturbances. The vehicle also enforces a switching time constrained by a minimum dwell time which can be adaptively updated based on attitude errors.

The present disclosure discloses an intelligent management method and management system of a natural pasture using multi-machine collaboration of UAVs, which belongs to the technical field of UAVs. The method includes: by a UAV group, obtaining pasture environment information, automatically planning a global grazing path, and performing automatic driving and grazing according to a user-defined grazing time, a starting point and an end point; during the process, collecting and analyzing herd activity images, and planning local grazing paths to assist in the automatic driving; and meanwhile, collecting vegetation coverage image data of a grazing area to further assist in the grazing.

Systems and methods for controlling an aircraft to hold altitude using GPS altitude readings. One example system includes a GPS inertial navigation system, an altimeter, a human machine interface, and one or more electronic processors coupled to the GPS inertial navigation system, the altimeter, and the human machine interface. The one or more electronic processors configured to receive, from the human machine interface, a user input selecting a reference altitude. The one or more electronic processors configured to receive, from the altimeter, a current altitude for the aircraft. The one or more electronic processors configured to: when the current altitude does not exceed an altitude threshold, operate the aircraft in an altimeter altitude hold mode based on the reference altitude; and when the current altitude exceeds the altitude threshold, operate the aircraft in a GPS altitude hold mode based on the reference altitude using the GPS inertial navigation system.

Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including: receiving a path of a threat vehicle; calculating a closest approach distance based on a current position of a drone and the path of the threat vehicle; determining that the closest approach distance is within a threshold; and sending a command to the drone to descend to a safe altitude. Other embodiments are disclosed.

A method and system for controlling position and attitude separation of a tiltable rotorcraft is provided, including a capability prediction module, a position control subsystem, a velocity control subsystem, an attitude angle control subsystem, an angular rate control subsystem, a control allocation module, and a tiltable rotorcraft. After expected position and attitude commands are corrected by the capability prediction module, expected force and torque commands are output by various control subsystems, and after receiving the force and torque commands, the control allocation module is further configured to calculate actual control commands of the aircraft, such as a tilt angle of a rotor assembly and a rotor speed, thus controlling the tiltable rotorcraft to perform the tracking of the expected position and attitude commands.

A vertical-takeoff-and-landing (VTOL) aircraft includes a plurality of navigation sensors and processing circuitry configured to implement a navigation system. The plurality of navigation sensors is configured to output navigation sensor data. The navigation system is configured to receive road map data, aviation map data, and navigation sensor data and execute an automated emergency landing control module. The automated emergency landing control module is configured to identify a plurality of candidate landing sites using the road map data, the aviation map data, and the navigation sensor data, and select a target landing site from among the plurality of candidate landing sites. Upon detection of an emergency condition, the automated emergency landing control module outputs the target landing site.

A system and method for tilt dead reckoning is provided. The system and method allows an autopilot of an unmanned aerial vehicle (UAV) to perform dead reckoning with a hovering vehicle during GNSS signal loss by estimating the position and velocity of the vehicle based on its pitch and roll angles and known vehicle dynamics. The position and velocity are estimated using tables set up by a UAV integration engineer that provide the expected airspeed at given pitch and roll angles in steady state. This allows the UAV to attempt to follow waypoints when GNSS signal is lost without using any additional sensors.