US 12,154,009 B2
Information processing apparatus for controlling flight of an aerial vehicle with a generated learning model
Tadao Takami, Tokyo (JP); Koji Ishii, Tokyo (JP); Jooin Woo, Tokyo (JP); Hiroshi Kawakami, Tokyo (JP); Kaori Niihata, Tokyo (JP); Yuichiro Segawa, Tokyo (JP); and Yasuhiro Kitamura, Tokyo (JP)
Assigned to NTT DOCOMO, INC., Tokyo (JP)
Appl. No. 17/283,166
Filed by NTT DOCOMO, INC., Tokyo (JP)
PCT Filed Oct. 28, 2019, PCT No. PCT/JP2019/042194
§ 371(c)(1), (2) Date Apr. 6, 2021,
PCT Pub. No. WO2020/121665, PCT Pub. Date Jun. 18, 2020.
Claims priority of application No. 2018-234332 (JP), filed on Dec. 14, 2018.
Prior Publication US 2022/0004922 A1, Jan. 6, 2022
Int. Cl. G06N 20/00 (2019.01); G06V 20/59 (2022.01)
CPC G06N 20/00 (2019.01) [G06V 20/597 (2022.01)] 8 Claims
OG exemplary drawing
 
1. An information processing apparatus comprising:
a processor configured to:
learn a relationship between piloting of an aerial vehicle and a behavior of the aerial vehicle in response to the piloting;
determine whether a flight of the aerial vehicle is a flight that satisfies a condition determined as being not for the learning;
perform the learning to generate a learning model with less weight given to a relationship between the piloting and the behavior of the aerial vehicle in a period in which it is determined that the flight satisfies the condition, as compared with a period in which it is determined that the flight does not satisfy the condition;
wherein a flight that satisfies the condition is a low-visibility flight, in which the aerial vehicle flies in an environment in which the visibility of the aerial vehicle from an operator is lower than a predetermined level; and
wherein the processor is further configured to:
determine whether a flight of an aerial vehicle is the low-visibility flight;
perform the learning to generate the learning model with less weight given to a relationship between the piloting and the behavior of the aerial vehicle in a period in which a flight is determined as being the low-visibility flight, as compared with a period in which a flight is determined as being not the low-visibility flight; and
control the flight of the aerial vehicle with the generated learning model.