Abstract: Baggage is connected to an aerial vehicle in a state in which the baggage is suspended by a connector such as rope. A learning unit of a server device performs machine learning on the relationship between the piloting of an aerial vehicle and the behavior of baggage on the basis of an aerial vehicle behavior history and a piloting history acquired by a first acquisition unit and a baggage behavior history acquired by a second acquisition unit. With this arrangement, the automatic piloting of the aerial vehicle at the time of lowering baggage is achieved.

Abstract: A server device performs machine learning on the relationship between the content of piloting of an aerial vehicle and the behavior of the aerial vehicle in response to the content of the piloting, and generates a learning model for automatically piloting the aerial vehicle. However, the aerial vehicle is piloted in various environments and conditions, and there are environments and conditions that are unsuited for achieving highly accurate and stable automatic piloting. Therefore, the server device performs the machine learning only in an environment or a condition suited for realizing the automatic piloting.

Abstract: Baggage is connected to an aerial vehicle in a state in which the baggage is suspended by a connector such as rope. A learning unit of a server device performs machine learning on the relationship between the piloting of an aerial vehicle and the behavior of baggage on the basis of an aerial vehicle behavior history and a piloting history acquired by a first acquisition unit and a baggage behavior history acquired by a second acquisition unit. With this arrangement, the automatic piloting of the aerial vehicle at the time of lowering baggage is achieved.

Abstract: In a server device, an acquisition unit acquires information generated by a first detection unit and a second detection unit through a network. An identification unit identifies a wind condition and the state of flight of an aerial vehicle on the basis of the information acquired by the acquisition unit. More specifically, the identification unit identifies the wind direction and the wind speed, which indicate the wind condition, and the position, the flight direction, and the flight speed of the aerial vehicle, which indicate the state of flight of the aerial vehicle. The estimation unit estimates a landing area where the aerial vehicle is likely to land according to the wind condition and the state of flight of the aerial vehicle which have been identified.

Abstract: A method for a device to determine a group owner that functions as an access point in a wireless network is provided. In the method, a first device acquires first intention information indicating the first device's intention to become a group owner and receives, from a second device, second intention information indicating the second device's intention to become a group owner, and compares the first intention information and the second intention information to determine that one of the first and second devices is a group owner. This method enables smooth communication between devices in a wireless network since a device which has more available power or can operate for a longer time can become a group owner.

Abstract: A method for a device to determine a group owner that functions as an access point in a wireless network is provided. In the method, a first device acquires first intention information indicating the first device's intention to become a group owner and receives, from a second device, second intention information indicating the second device's intention to become a group owner, and compares the first intention information and the second intention information to determine that one of the first and second devices is a group owner. This method enables smooth communication between devices in a wireless network since a device which has more available power or can operate for a longer time can become a group owner.