Patents by Inventor Junfeng Lai

Junfeng Lai has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11462053
    Abstract: The present disclosure provides a neural network-based visual detection and tracking method of an inspection robot, which includes the following steps of: 1) acquiring environmental images of a dynamic background a movement process of the robot; 2) preprocessing the acquired images; 3) detecting human targets and specific behaviors in the images in the robot body, and saving the sizes, position information and features of the human targets with the specific behaviors; 4) controlling the orientation of a robot gimbal by using a target tracking algorithm to make sure that a specific target is always located at the central positions of the images; and 5) controlling the robot to move along with a tracked object. The neural network-based visual detection and tracking method of an inspection robot in the present disclosure has a quite high adaptive ability, achieves better detection and tracking effects on targets in a dynamic background scene.
    Type: Grant
    Filed: June 16, 2021
    Date of Patent: October 4, 2022
    Assignee: Chongqing University
    Inventors: Yongduan Song, Li Huang, Shilei Tan, Junfeng Lai, Huan Liu, Ziqiang Jiang, Jie Zhang, Huan Chen, Jiangyu Wu, Hong Long, Fang Hu, Qin Hu
  • Publication number: 20220180090
    Abstract: The present disclosure provides a neural network-based visual detection and tracking method of an inspection robot, which includes the following steps of: 1) acquiring environmental images of a dynamic background a movement process of the robot; 2) preprocessing the acquired images; 3) detecting human targets and specific behaviors in the images in the robot body, and saving the sizes, position information and features of the human targets with the specific behaviors; 4) controlling the orientation of a robot gimbal by using a target tracking algorithm to make sure that a specific target is always located at the central positions of the images; and 5) controlling the robot to move along with a tracked object. The neural network-based visual detection and tracking method of an inspection robot in the present disclosure has a quite high adaptive ability, achieves better detection and tracking effects on targets in a dynamic background scene.
    Type: Application
    Filed: June 16, 2021
    Publication date: June 9, 2022
    Inventors: Yongduan Song, Li Huang, Shilei Tan, Junfeng Lai, Huan Liu, Ziqiang Jiang, Jie Zhang, Huan Chen, Jiangyu Wu, Hong Long, Fang Hu, Qin Hu
  • Publication number: 20220171395
    Abstract: A method for obstacle detection and recognition for an intelligent snow sweeping robot is disclosed, comprising: 1) disposing ultrasonic sensors at a front end of the snow sweeping robot to detect distance information from an obstacle ahead; and disposing radar sensors at the front and rear of the snow sweeping robot to detect whether a creature suddenly approaches; 2) processing signals detected by each of the ultrasonic sensors and radar sensors, and calculating a forward distance of the snow sweeping robot; and 3) determining a snow cover extent of a working road, detecting a change of the distance from the obstacles, and recognizing the obstacles for conditions of an ultrasonic ranging variation ratio and a variation of the forward distance of the snow sweeping robot, a change of the signal detected by radar sensors, and a descriptive statistic of the snow cover extent within a specific time period.
    Type: Application
    Filed: July 22, 2021
    Publication date: June 2, 2022
    Applicant: Chongqing University
    Inventors: Yongduan Song, Ziqiang Jiang, Shilei Tan, Junfeng Lai, Huan Liu, Li Huang, Jie Zhang, Huan Chen, Hong Long, Fang Hu, Jiangyu Wu, Qin Hu, Wenqi Li
  • Publication number: 20220152817
    Abstract: The present disclosure discloses a neural network adaptive tracking control method for joint robots, which proposes two schemes: robust adaptive control and neural adaptive control, comprising the following steps: 1) establishing a joint robot system model; 2) establishing a state space expression and an error definition when taking into consideration both the drive failure and actuator saturation of the joint robot system; 3) designing a PID controller and updating algorithms of the joint robot system; and 4) using the designed PID controller and updating algorithms to realize the control of the trajectory motion of the joint robot. The present disclosure may solve the following technical problems at the same time: the drive saturation and coupling effect in the joint system, processing parameter uncertainty and non-parametric uncertainty, execution failure handling during the system operation, compensation for non-vanishing interference, and the like.
    Type: Application
    Filed: March 24, 2021
    Publication date: May 19, 2022
    Inventors: Yongduan SONG, Huan LIU, Junfeng LAI, Ziqiang JIANG, Jie ZHANG, Huan CHEN, Li HUANG, Congyi ZHANG, Yingrui CHEN, Yating YANG, Chunxu REN, Han BAO, Kuilong YANG, Ge SONG, Bowen ZHANG, Hong LONG
  • Publication number: 20210350800
    Abstract: In some embodiments, methods and systems for pushing audiovisual playlists based on a text-attentional convolutional neural network include a local voice interactive terminal, a dialog system server and a playlist recommendation engine, where the dialog system server and the playlist recommendation engine are respectively connected to the local voice interactive terminal. In some embodiments, the local voice interactive terminal includes a microphone array, a host computer connected to the microphone array, and a voice synthesis chip board connected to the microphone array. In some embodiments, the playlist recommendation engine obtains rating data based on a rating predictor constructed by the neural network; the host computer parses the data into recommended playlist information; and the voice terminal synthesizes the results and pushes them to a user in the form of voice.
    Type: Application
    Filed: January 5, 2021
    Publication date: November 11, 2021
    Inventors: Yongduan Song, Junfeng Lai, Xin Zhou