Abstract: The present disclosure provides a traversal method and system, a robot and a readable storage medium. The method comprises: driving a robot to travel in a working region according to a predetermined mode and work synchronously; and adjusting the robot to continue working according to a predetermined first rule every time a turn sign is encountered, and adjusting the robot to return to the predetermined mode after the first rule is completed; wherein the first rule is: starting from the position where the turn sign is encountered, rotating clockwise or counterclockwise; after first rotation is carried out according to a first angle, continuing running for a first time according to the predetermined mode; and then, after second rotation is carried out according to a second angle in the same rotation direction as the previous rotation, continuing running for a second time according to the predetermined mode, the first angle being different from the second angle.

Abstract: Disclosed are a method and system for generating a virtual boundary of a working region of a self-moving robot, and a self-moving robot and a readable storage medium.

Abstract: The present disclosure provides a method for docking a self-moving device with a charging station, wherein the charging station is connected to a boundary line, and the method comprises the following steps: controlling the self-moving device to move from the current position close to a docking boundary; judging whether the self-moving device senses the boundary line signal during the moving process; if the self-moving device senses the boundary line signal before it reaches the docking boundary or when it reaches the docking boundary, controlling the self-moving device to move towards the charging station through the boundary line signal until the docking is successful; if the self-moving device has not sense the boundary line signal when it reaches the docking boundary, controlling the self-moving device to move from the docking boundary to the boundary line until the boundary line signal is sensed.

Abstract: A self-working system, a self-walking device, a control method therefor and a computer-readable storage medium are disclosed, wherein the control method may include the following steps: acquiring a captured image; processing the captured image to acquire a processed image; segmenting the processed image into at least one sub-region; respectively acquiring a representative pixel point PLn of each sub-region; calculating, in the processed image, the number of sub-regions of which the representative pixel point PLn is located below a comparison pixel point PC and marking same as the number Nb of special sub-regions, PC being a preset comparison pixel point; if Nb?1, judging that there is a boundary (2) or obstacle in the distance of the captured image; if Nb>1, judging that there is a boundary (2) or obstacle in the vicinity of the captured image.

Abstract: A self-working system, a self-walking device (1) and a method for controlling same, and a computer-readable storage medium. The control method comprises: acquiring a captured image; processing the captured image to acquire a processed image; segmenting the processed image into at least one sub-region; calculating the size An of each sub-region, respectively; counting the number of sub-regions with An>V in the processed image, and marking same as the number Nb of special sub-regions, wherein V is a preset quantity threshold; if Nb?1, judging that the captured image belongs to a lawn region; and if Nb> 1, judging that the captured image belongs to a non-complete lawn region. If it is judged that a captured image belongs to a non-complete lawn region, it can be determined that there is a large obstacle or a boundary (2), etc.

Abstract: An obstacle recognition method includes determining a candidate obstacle region in a candidate weeding region image according to color information of the candidate weeding region image; obtaining contour information of the candidate obstacle region, wherein the contour information includes chrominance information, range information, and roughness information; and determining, according to preset contour information determination conditions and the contour information, whether there is an obstacle in the candidate weeding region image. A related obstacle recognition apparatus, an electronic device, a computer-readable storage medium, and a weeding robot are also disclosed.

Abstract: An obstacle recognition method includes the steps of obtaining hue information of a candidate weeding region image; generating a target hue histogram of the candidate weeding region image according to the hue information, and obtaining peak information and hue range information of the target hue histogram; and determining whether there are obstacles in the candidate weeding region image according to the peak information and the hue range information. Related apparatus, electronic devices, computer readable storage media, and weeding robots are disclosed.

Abstract: An obstacle recognition method applied to an automatic traveling device may include the steps of: obtaining an image of an environment in a traveling direction of an automatic traveling device; separating out a chrominance channel image and a luminance channel image based on the image; performing pre-processing and edge processing on the luminance channel image to obtain an edge image; performing histogram statistics on the chrominance channel image to obtain a number of pixels with a maximum color proportion within a preset chrominance interval range, denoted as maxH; performing segmentation and contour processing on the chrominance channel image to obtain a chrominance segmentation threshold and a contour image; performing contour detection on the contour image to obtain a contour block; collecting statistics on feature values corresponding to the contour block in the edge image and the contour image; and comparing the maxH and the feature values with preset obstacle recognition and determination conditions

Abstract: An obstacle recognition method comprises the steps of: obtaining hue information, a number of exposed state pixels, and a number of non-exposed state white pixels of a candidate weeding region image; generating a hue histogram of the candidate weeding region image according to the hue information, and obtaining peak information of the hue histogram, wherein the peak information comprises a hue value of a sudden change peak point and a peak value of the sudden change peak point; determining target pixel position information and target pixel value information of the candidate weeding region image according to the hue value of the sudden change peak point; determining a quantity of hue valid pixels in a preset hue interval in the candidate weeding region image; and determining whether there is an exposed region in the candidate weeding region image according to the number of exposed state pixels, the number of non-exposed state white pixels, the peak value of the sudden change peak point, the target pixel positi

Abstract: An obstacle recognition method includes the steps of obtaining hue information and value information of a candidate weeding region image; generating a target hue histogram of the candidate weeding region image according to the hue information, and obtaining peak information of the target hue histogram; and determining whether there are obstacles in the candidate weeding region image according to the peak information and the value information. Related apparatus, electronic devices, computer readable storage media, and weeding robots are disclosed.

Abstract: An image analysis method includes the steps of obtaining a saturation channel image according to an original image; extracting several contours from the saturation channel image; determining target parameter values corresponding to each contour; and determining according to analysis results of the target parameter values corresponding to each contour whether there is a non-working region in the original image. A related image analysis device, computer device, and computer-readable storage medium are also disclosed.

Abstract: An image analysis method includes the steps of obtaining a saturation channel image and a value channel image according to an original image; obtaining a relative deviation value image based on the saturation channel image and the value channel image, wherein the relative deviation value image represents relative deviations between saturation and value; extracting several contours from the relative deviation value image; determining target parameter values corresponding to each contour; and Determining according to analysis results of the target parameter values corresponding to each contour whether there is a non-working region in the original image. A related image analysis apparatus, computer device, and computer readable storage medium are also disclosed.

Abstract: This application discloses a virtual live streaming method and apparatus, a device, and a storage medium. The method includes: acquiring a live text content which is a text content broadcast by voice by a virtual character in a virtual live stream; segmenting the live text content to obtain text segments that are sequentially arranged; acquiring a live broadcast data packet of each of the text segments following a sequence of the text segments, the live broadcast data packet comprising mouth shape data corresponding to the each of the text segments and being used for determining a mouth shape of the virtual character corresponding to the text segment; and performing screen rendering based on the live broadcast data packet to obtain a live screen for the virtual live streaming, the live screen comprising the virtual character who expresses the each of the text segments with a corresponding mouth shape.

Abstract: A method for recognizing a working area based on an image includes the steps of: obtaining an original image; separating an H channel image from the original image; performing binarization processing on the original image to form a first binary image, wherein the first binary image comprises a working area and a non-working area that have different pixel values; collecting statistics on a histogram of the H channel image, and obtaining a first parameter representing a peak value of the histogram; collecting statistics on a second parameter representing a size of the non-working area in the first binary image based on the first binary image; and recognizing a working area according to magnitude relations of the first parameter and the second parameter with preset parameter thresholds. Other related methods, systems, and robots are disclosed.

Abstract: A robot automatic charging method includes the steps of: controlling the robot to start working within a working region; when the robot needs to be charged, controlling the robot to move towards a charging station and detecting a strength of a target signal in the working region during the movement of the robot; controlling the robot to adjust its moving direction based on detected changes in the strength of the target signal to control the robot to move towards a guiding device near the charging station; and controlling the robot to move towards the charging station according to the guiding device. A robot automatic charging system, a robot, and a computer-readable storage medium are also disclosed.

Abstract: Disclosed is an image shadow detection method, a system, an image segmentation device, and a readable storage medium. The method comprises the following steps: acquiring a chrominance component of an image; segmenting the image by a first segmentation rule based on the chrominance components of the image and the preset chrominance interval and acquiring a first segmentation result, the first segmentation result comparing a first region and a second region; calculating feature values of the first region or the second region in the image; carrying out the comparison processing to judge whether the image has shadow based on the feature value and the preset threshold value. According to the present disclosure, the image is segmented by the first segmentation rule and the first segmentation result is acquired. The first segmentation result is acquired based on the chrominance component and is not affected by shadows.

Abstract: An obstacle recognition method includes the steps of: determining a candidate obstacle region in a candidate weeding region image according to color information of the candidate weeding region image; obtaining contour information of the candidate obstacle region and value information of the candidate weeding region image; and determining, according to the contour information and the value information, whether there is an obstacle in the candidate weeding region image. A related obstacle recognition apparatus, an electronic device, a computer-readable storage medium, and a weeding robot are also disclosed.

Abstract: An obstacle recognition method includes determining a candidate obstacle region in a candidate weeding region image according to color information of the candidate weeding region image; obtaining contour information of the candidate obstacle region and chrominance information and value information of the candidate weeding region image; and determining, according to the contour information and the chrominance information, or the contour information, the chrominance information, and the value information, whether there is an obstacle in the candidate weeding region image. A related obstacle recognition apparatus, electronic device, computer-readable storage medium, and weeding robot are also disclosed.

Abstract: Disclosed are a roughness compensation method and system, an image processing device, and a readable storage medium. The roughness compensation method includes the following steps: acquiring a roughness value of a target region in an image and position information of pixels in the target region; averaging the position information of the pixels in the target region to obtain average position information; determining whether the average position information is located in a preset compensation region; and compensating and updating the roughness value when the average position information is located in the preset compensation region; or skipping compensating the roughness value when the average position information is not located in the preset compensation region.

Abstract: An image segmentation method includes the steps of: obtaining a hue histogram corresponding to a hue channel image based on an original image; determining a target parameter value based on the hue histogram; determining a saturation segmentation threshold as a first threshold range if the target parameter value satisfies a preset condition, otherwise determining the saturation segmentation threshold as a second threshold range, where a minimum value of the first threshold range is greater than a minimum value of the second threshold range; and performing image segmentation in combination with the first threshold range or the second threshold range as determined in the previous step. Related apparatus, computer devices, and computer readable storage media are also disclosed.