A method is provided for supporting flight operations of an aircraft in an airspace system. The method includes accessing a flight plan that indicates clearances that describe a planned route the aircraft is authorized to travel through the airspace system. The clearances are applied to an airborne navigation database to map the clearances to a sequence of procedural legs of procedures to be followed by the aircraft, the sequence of procedural legs including a sequence of position fixes and indicating leg types of the procedural legs. A ground track and vertical guidance for the aircraft are determined from the sequence of procedural legs, subject to rules and constraints of the clearances and the procedures. And a three-dimensional trajectory for the aircraft that follows the planned route, from the ground track and the vertical guidance is generated and output for use in guidance, navigation or control of the aircraft.

Embodiments of the present application relate to the technical field of robot control, and in particular, to an unmanned aerial vehicle (UAV) path planning method and apparatus and a UAV. The UAV path planning method includes: acquiring a depth map of an environment in front of a UAV; acquiring a grid map centered on a body of the UAV according to the depth map; determining candidate flight directions for the UAV according to the grid map; determining an optimal flight direction for the UAV from the candidate flight directions; and controlling the UAV to fly in the optimal flight direction to avoid an obstacle in the environment in front of the UAV. In this way, the embodiments of the present application can accurately determine an obstacle that suddenly appears in an unknown environment and a dynamic environment, so as to achieve real-time path planning.

Embodiments of the present application relate to the technical field of robot control, and in particular, to an unmanned aerial vehicle (UAV) path planning method and apparatus and a UAV. The UAV path planning method includes: acquiring a depth map of an environment in front of a UAV; acquiring a grid map centered on a body of the UAV according to the depth map; determining candidate flight directions for the UAV according to the grid map; determining an optimal flight direction for the UAV from the candidate flight directions; and controlling the UAV to fly in the optimal flight direction to avoid an obstacle in the environment in front of the UAV. In this way, the embodiments of the present application can accurately determine an obstacle that suddenly appears in an unknown environment and a dynamic environment, so as to achieve real-time path planning.

Methods and systems are provided for autonomous vehicle operation for collision avoidance. One method involves identifying an autonomous operating mode and one or more settings associated with the autonomous operating mode prior to entering an autonomous collision avoidance mode in response to an output from a collision avoidance system, and after entering the autonomous collision avoidance mode, automatically restoring autonomous operation upon deactivation of the autonomous collision avoidance mode. For example, the autonomous operating mode may be automatically reinitiated based on the current vehicle status using the one or more settings in response to deactivation of the autonomous collision avoidance mode when it is determined that the autonomous operating mode is viable based on a relationship between a current vehicle status and the one or more settings associated with the autonomous operating mode.

An intelligent rescue method applied to a vehicle-mounted device and an airborne rescue device to enable semi-automatic warning and rescue of a broken-down, crashed, drowned, or stranded vehicle, enables communication between the vehicle-mounted device and the rescue device. The vehicle-mounted device determines by sensors a type of emergency of a vehicle, and performs a first assistance action and sends the rescue device a distress signal corresponding to the type of the emergency of the vehicle. The rescue device receives the distress signal and takes off from an initial position of the vehicle to a target position in response to the distress signal. Once the rescue device reaches the target position, the rescue device performs a second action for assistance.

A drone and an operating method for assisting player training are disclosed. The method for operating the drone for training sports events according to the technical idea of the present disclosure may include receiving a speed profile, starting acceleration based on a start signal, and controlling speed based on the speed profile, wherein the speed profile includes information on speed by time as a feature.

A system may include a processor installed in an aircraft. The processor may be configured to: obtain runway friction coefficient data and runway surface condition data for a runway; obtain braking coefficient data and braking action index data; obtain equivalent runway condition data and runway length data for the runway; obtain aircraft speed data of the aircraft and aircraft configuration data; based at least on the runway friction coefficient data, the runway surface condition data, the braking coefficient data, the braking action index data, the equivalent runway condition data, the aircraft speed data, and the aircraft configuration data, determine a rejected takeoff (RTO) initiating point (RIP) and a start automated RTO sequence point; and cause an automated RTO sequence to be performed if the start automated RTO sequence point is reached without the automated RTO sequence being manually overridden.

Described is a method that involves operating an unmanned aerial vehicle (UAV) to begin a flight, where the UAV relies on a navigation system to navigate to a destination. During the flight, the method involves operating a camera to capture images of the UAV's environment, and analyzing the images to detect features in the environment. The method also involves establishing a correlation between features detected in different images, and using location information from the navigation system to localize a feature detected in different images. Further, the method involves generating a flight log that includes the localized feature. Also, the method involves detecting a failure involving the navigation system, and responsively operating the camera to capture a post-failure image. The method also involves identifying one or more features in the post-failure image, and determining a location of the UAV based on a relationship between an identified feature and a localized feature.

Disclosed is a configuration to control automatic return of an aerial vehicle. The configuration stores a return location in a storage device of the aerial vehicle. The return location may correspond to a location where the aerial vehicle is to return. One or more sensors of the aerial vehicle are monitored during flight for detection of a predefined condition. When a predetermined condition is met a return path program may be loaded for execution to provide a return flight path for the aerial vehicle to automatically navigate to the return location.

A method, preferably including: sampling inputs, determining aircraft conditions, and/or acting based on the aircraft conditions. A method, preferably including: sampling inputs, determining input reliability, determining guidance, and/or controlling aircraft operation. A method, preferably including: operating the vehicle, planning for contingencies, detecting undesired flight conditions, and/or reacting to undesired flight conditions. A system, preferably an aircraft such as a rotorcraft, configured implement the method.