公开(公告)号：US20210041896A1

公开(公告)日：2021-02-11

申请号：US16633315

申请日：2019-08-14

Applicant: GOERTEK INC.

Inventor： Xueqiang WANG ,  Mengmeng WANG ,  Lu BAI ,  Xiangdong ZHANG

IPC: G05D1/10 ,  B64C39/02

Abstract: The present disclosure provides a method for controlling a drone, a drone, and a system. The method for controlling a drone comprises: determining operating parameters of a moving platform according to field-of-view images containing the moving platform collected at any two different moments and flight parameters of the drone; calculating a time-varying tracking position of the moving platform based on the operating parameters; controlling the drone to track the moving platform according to the time-varying tracking position of the moving platform; and controlling the drone to perform a landing operation according to a relative position of the moving platform and the drone during tracking. The technical solutions according to the present disclosure have high landing accuracy, rely less on device performance and have high versatility.

公开(公告)号：US20220171512A1

公开(公告)日：2022-06-02

申请号：US17594145

申请日：2020-10-30

Applicant: GOERTEK INC.

Inventor： Libing ZOU ,  Yifan ZHANG ,  Fuqiang ZHANG ,  Xueqiang WANG

IPC: G06F3/0487 ,  G06F3/14 ,  G06V40/16 ,  G06V40/18 ,  G06V10/82 ,  G06F3/0354 ,  G06F3/038 ,  G06F3/01

Abstract: A multi-screen display system and a mouse switching control method are disclosed. The mouse switching control method is applied to a multi-screen display system comprising a main display screen and at least one extended display screen, and comprises: obtaining user images collected by cameras installed on the main display screen and the extended display screen respectively; inputting the user images into a neural network model, and predicting a screen that a user is currently paying attention to using the neural network model to obtain a prediction result; and controlling to switch a mouse to the screen that a user is currently paying attention to according to the prediction result. The system and mouse switching control method are based on self-learning of visual attention, predict the current screen operated by the user, automatically switch the mouse to the corresponding screen position, and improve the user experience.

公开(公告)号：US20220317695A1

公开(公告)日：2022-10-06

申请号：US17309922

申请日：2020-09-10

Applicant: GOERTEK INC.

Inventor： Xueqiang WANG ,  Yifan ZHANG ,  Libing ZOU ,  Fuqiang ZHANG

IPC: G05D1/02 ,  G05D1/00 ,  G06N3/04 ,  G06N3/08 ,  G06N7/00

Abstract: A multi-AGV motion planning method, device and system are disclosed. The method of the present disclosure comprises: establishing an object model through reinforcement learning; building a neural network model based on the object model, performing environment settings including AGV group deployment, and using the object model of the AGV in a set environment to train the neural network model until a stable neural network model is obtained; setting an action constraint rule; and after the motion planning is started, inputting the state of current AGV, states of other AGVs and permitted actions in a current environment into the neural network model after trained, obtaining the evaluation indexes of a motion planning result output by the neural network model, obtaining an action to be executed of the current AGV according to the evaluation indexes, and performing validity judgment on the action to be executed using the action constraint rule.

公开(公告)号：US20220196414A1

公开(公告)日：2022-06-23

申请号：US17593618

申请日：2019-10-24

Applicant: GOERTEK INC.

Inventor： Xueqiang WANG ,  Yifan ZHANG ,  Libing ZOU ,  Baoming LI

IPC: G01C21/34 ,  G05B13/02

Abstract: A global path planning method and device for an unmanned vehicle are disclosed. The method comprises: establishing an object model through a reinforcement learning method, wherein the object model includes: a state of the unmanned vehicle, an environmental state described by a map picture, and an evaluation index of a path planning result; building a deep reinforcement learning neural network based on the object model established, to obtain a stable neural network model; inputting the map picture of the environment state and the state of the unmanned vehicle into the deep reinforcement learning neural network after trained, and generating a motion path of the unmanned vehicle. According to the present disclosure, the environment information in the scene is marked through the map picture, and the map features are extracted through the deep neural network, thereby simplifying the modeling process of the map scene.