Systems and methods are provided for landing site selection and flight path planning for an aircraft using soaring weather conditions. The methods may include, with one or more processors of a controller onboard the aircraft: receiving data indicative of terrain, airports, airspace, aerodynamics of the aircraft, real-time weather, and real-time status of the aircraft, determining a gliding range of the aircraft based at least in part on soaring weather conditions that include environmental regions of thermal draft capable of producing lift sufficient to extend the gliding range of the aircraft, determining a landing site for the aircraft based on the gliding range of the aircraft, and determining a flight path of the aircraft that uses the soaring weather conditions to extend the gliding range of the aircraft and land at the landing site.

A control method includes obtaining a current mission parameter of a current flight mission and historical flight data of a historical flight mission related to the current mission. The current mission parameter is related to an energy consumption of a current aerial vehicle performing the current flight mission, the historical flight data includes a historical mission parameter and a historical energy consumption in the historical flight mission, and the historical mission parameter is related to an energy consumption of a historical aerial vehicle performing the historical mission. The method further includes obtaining a current remaining power energy of the current aerial vehicle in real time, and determining whether the current aerial vehicle is able to reach a destination of the current flight mission based at least one on the current mission parameter, the historical flight data, and the current remaining power energy, to obtain a determination result.

An embodiment of the present invention provides an artificial intelligence cleaner comprising: a memory for storing a simultaneous localization and mapping (SLAM) map of a cleaning space; a travel driving part for driving the artificial intelligence cleaner; and a processor for collecting a plurality of cleaning records for the cleaning space, dividing the cleaning space into a plurality of cleaning areas by using the SLAM map and the plurality of collected cleaning records, determining a cleaning path of the artificial intelligence cleaner in consideration of the divided cleaning areas, and controlling the travel driving part according to the determined cleaning path, wherein when an abnormal situation occurs during cleaning on the basis of the determined cleaning path, the processor modifies the cleaning path by applying path simplification to a preconfigured area of the remaining cleaning area.

A technique controls a flight vehicle. In the technique, an operating mode for controlling the flight vehicle is set to one of normal modes when no abnormality has occurred in the flight vehicle. The operating mode is changed to one of fail-safe modes causing the flight vehicle to perform landing depending on current one of the normal modes when an abnormality has occurred in the flight vehicle.

A technique controls a flight vehicle. In the technique, an operating mode for controlling the flight vehicle is set to one of normal modes when no abnormality has occurred in the flight vehicle. The operating mode is changed to one of fail-safe modes causing the flight vehicle to perform landing depending on current one of the normal modes when an abnormality has occurred in the flight vehicle.

A mobile object includes: a remaining energy amount acquisition unit that acquires the amount of energy remaining in the mobile object; and a range specifying unit that specifics, on the basis of the amount of energy remaining in the mobile object, a movement range in which the mobile object can return from the current position to the return position.

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.

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.