公开(公告)号：US20230267842A1

公开(公告)日：2023-08-24

申请号：US17869024

申请日：2022-07-20

Applicant: Electronics and Telecommunications Research Institute

Inventor： Ui Hwan CHOI

IPC: G08G5/00 ,  H04B7/185 ,  B64C39/02

CPC classification number: G08G5/0034 ,  B64C39/024 ,  H04B7/18504 ,  B64C2201/122 ,  B64C2201/123

Abstract: Disclosed are a method and a system for optimization of mission planning of a multi-UAV network for data collection and communication relay, which can perform optimization of an operation concept for an ad-hoc network based collaboration data collection mission, optimization of flight paths and data collection order of data collection UAVs, flight paths of communication relay UAVs, dynamic communication network topology, and optimization of high-capacity data transmission/reception bitrate scheduling.

公开(公告)号：US20230297859A1

公开(公告)日：2023-09-21

申请号：US17964536

申请日：2022-10-12

Applicant: Electronics and Telecommunications Research Institute

Inventor： Ui Hwan CHOI

IPC: G06N5/04 ,  G06N3/08 ,  G06N7/00 ,  G06K9/62 ,  B64C39/02

CPC classification number: G06N5/043 ,  G06N5/042 ,  G06N3/08 ,  G06N7/005 ,  G06K9/6262 ,  B64C39/024 ,  B64C2201/141

Abstract: The present disclosure relates to a method and apparatus for generating a multi-drone network operation plan based on reinforcement learning. The method of generating a multi-drone network operation plan based on reinforcement learning includes defining a reinforcement learning hyperparameter and training an actor neural network for each drone agent by using a multi-agent deep deterministic policy gradient (MADDPG) algorithm based on the defined hyperparameter, generating Markov game formalization information based on multi-drone network task information and generating state-action history information by using the trained actor neural network based on the formalization information, and generating a multi-drone network operation plan based on the state-action history information.