Chongqing University

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

Star Institute of Intelligent Systems

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

Li Huang

Yongduan Song

Ziqiang Jiang

Junfeng Lai

Huan Liu

Jie Zhang

Hong Long

Shilei Tan

Huan Chen

Fang Hu

Jiangyu Wu

Qin Hu

Lihui Tan

Chunxu Ren

Yating Yang

Jian Liu

Congyi Zhang

Kuilong Yang

Ge Song

Bowen Zhang

Wenqi Li

Han Bao

Yingrui Chen

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

The present disclosure provides a method for object detection and recognition based on a neural network. The method includes: adding a detection layer following three detection layers of an existing YOLOv5 network model, to construct a new YOLOv5 network model; then, training the new YOLOv5 network model by considering an overlapping area between a predicted box and a ground truth box, a center-to-center distance between the two boxes, and an aspect ratio of the two boxes; and finally, inputting a to-be-detected image into the trained new YOLOv5 network model, outputting a predicted box of an object and probability values corresponding to a class to which the object belongs, and setting a class corresponding to a maximum probability value as a predicted class of the object in the to-be-detected image. This method can quickly and effectively detect multiple classes of objects. Especially, a detection effect for small objects is more ideal.

Method for object detection and recognition based on neural network

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

Scratch resistant protective case for watch

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

Li Huang