Abstract: 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 tth moment, and computing a predicted path at the tth moment 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 tth moment 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.

Abstract: 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.

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.

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.

Abstract: A snow shovel structure of a snow plow robot. The snow shovel structure includes a housing where a snow shovel mechanism is. The snow shovel mechanism extends outside the housing and includes a first motor fixed on a top of the housing. The first motor is fixedly connected with a telescopic rod through an output shaft. A second motor is further provided on a top portion of an inner chamber of the housing, and a horizontal plate is fixedly arranged on a side wall of the inner chamber of the housing.

Abstract: 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 tth moment, and computing a predicted path at the tth moment 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 tth moment 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.

Abstract: A snow shovel structure of a snow plow robot is provided. The snow shovel structure includes a housing where a snow shovel mechanism is. The snow shovel mechanism extends outside the housing and includes a first motor fixed on a top of the housing. The first motor is fixedly connected with a telescopic rod through an output shaft. A second motor is further provided on a top portion of an inner chamber of the housing, and a horizontal plate is fixedly arranged on a side wall of the inner chamber of the housing.

Abstract: The present disclosure relates to a method for identification and recognition of an aircraft take-off and landing runway based on a PSPNet network, wherein the method: adopts a residual network ResNet and a lightweight deep neural network MobileNetV2 as the two backbone feature-extraction networks to enhance that feature extraction; at the same time adjusts an original four-layered pyramid pooling module into five layered, with each layer being respectively sized by 9×9, 6×6, 3×3, 2×2, 1×1; uses a finite self-made image about the aircraft take-off and landing terrain for training; and labels and extracts the aircraft take-off and landing runway in the aircraft take-off and landing terrain image. The method effectively combines ResNet and MobileNetV2, and improves the detection accuracy of the aircraft take-off and landing runway in comparison with the prior art.

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.

Abstract: 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.

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.

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.

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.

Abstract: The present disclosure relates to a method for preventing a maloperation of differential protection of an optical fiber caused by saturation of a single Current Transformer (CT) of 3/2 connection. By applying a combination of the differential judgment and the sub-CT current judgment, reliable identification of internal and external faults is ensured, and the problem of misjudging of the internal fault as the external fault can be prevented as well.

Abstract: The present disclosure relates to a method for preventing a maloperation of differential protection of an optical fiber caused by saturation of a single Current Transformer (CT) of 3/2 connection. By applying a combination of the differential judgment and the sub-CT current judgment, reliable identification of internal and external faults is ensured, and the problem of misjudging of the internal fault as the external fault can be prevented as well.