Chongqing University

Star Institute of Intelligent Systems

Dibi (chongqing) Intelligent Technology Research Institute Co., Ltd.

Db (chongqing) Intelligent Technology Research Institute Co., Ltd.

Jie Zhang

Yongduan Song

Junfeng Lai

Ziqiang Jiang

Li Huang

Huan Liu

Hong Long

Huan Chen

Shilei Tan

Fang Hu

Jiangyu Wu

Qin Hu

Jian Li

Yating Yang

Qiang Wang

Feipeng Wang

Weigen Chen

Congyi Zhang

Chunxu Ren

Kuilong Yang

Ge Song

Bowen Zhang

Wenqi Li

Yushuang He

Zhengyong Huang

Han Bao

Xiao Zhang

Yingrui Chen

A method and a system for initial self-healing type classification of metallized film capacitors, which relate to the technical field of high-voltage capacitors. The method comprises: applying voltages at different voltage ramp rates to metallized film capacitors to be tested, and determining a voltage bearing range of the metallized film capacitors; determining, according to a corresponding voltage of the metallized film capacitors when initial self-healing occurs and a residual voltage after the initial self-healing occurs, corresponding initial self-healing energy of metallized film capacitors when the initial self-healing occurs; establishing an initial self-healing voltage-energy database according to an initial self-healing voltage of the samples when the initial self-healing occurs and the corresponding initial self-healing energy when the initial self-healing occurs; and classifying the metallized film capacitors by combining an initial self-healing recovery condition and the subsequent service life and performance data of the metallized film capacitors.

Method and system for initial self-healing type classification of metallized film capacitors

The present disclosure provides a neural network-based method for calibration and localization of an indoor inspection robot. The method includes the following steps: presetting positions for N label signal sources capable of transmitting radio frequency (RF) signals; computing an actual path of the robot according to numbers of signal labels received at different moments; computing positional information moved by the robot at a tmoment, and computing a predicted path at the tmoment according to the positional information; establishing an odometry error model with the neural network and training the odometry error model; and inputting the predicted path at the tmoment to a well-trained odometry error model to obtain an optimized predicted path. The present disclosure maximizes the localization accuracy for the indoor robot by minimizing the error of the odometer with the odometry calibration method.

Neural network-based method for calibration and localization of indoor inspection robot

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.

Method for obstacle detection and recognition for intelligent snow sweeping robot

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.

Neural network adaptive tracking control method for joint robots

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.

Neural network-based visual detection and tracking method of inspection robot

Growing community of inventors

Chongqing, China

Jie Zhang