Abstract: A center of mass (COM) planning method includes: obtaining a planning position of the COM and a planning speed of the COM of a robot, and calculating a planning capture point of the robot according to the planning position of the COM and the planning speed of the COM; obtaining a measured position of the COM and a measured speed of the COM, and calculating a measured capture point of the robot according to the measured position the measured speed; calculating a desired zero moment point (ZMP) of the robot based on the planning capture point and the measured capture point; obtaining a measured ZMP of the robot, and calculating an amount of change in a position of the COM according to the desired ZMP and the measured ZMP; and correcting the planning position of the COM according to the amount of change in the position of the COM.

Abstract: A filtering method for a two-dimensional laser point cloud includes: acquiring a pitch angle and a roll angle of a mobile robot when the mobile robot is collecting a two-dimensional laser point cloud; determining a target tilt direction corresponding to the mobile robot and a target tilt angle corresponding to the two-dimensional laser point cloud according to the pitch angle and the roll angle; determining an angular filtering interval according to the target tilt direction and the target tilt angle, wherein the angular filtering interval is an angle interval corresponding to the laser point cloud to be filtered out; and filtering, by the mobile robot, the two-dimensional laser point cloud based on the angular filtering interval.

Abstract: A robotic arm control method and an intelligent mobile device using the same are provided. The method includes: obtaining N end forces applied to end-effectors of N robotic arms of a robot, in response to detecting that the N robotic arms clamp the same object, where N is a natural number larger than or equal to 2; performing, according to the N end forces, an admittance control for eliminating an internal stress of the N end-effectors on the N robotic arms; and adjusting, according to results of the admittance control on the N robotic arms, joint angles of the N robotic arms. Through the above-mentioned method, the success rate of performing tasks can be improved.

Abstract: A method for binocular depth estimation is provided, including: obtaining binocular images and performing feature extraction on the binocular images to obtain left and right feature mappings; performing disparity construction by using the left and right feature mappings to obtain a disparity cost volume with a reduced dimension; performing attention feature learning on the disparity cost volume to obtain an attention feature vector and performing feature weighting on the disparity cost volume by using the attention feature vector to obtain a weighted cost volume; performing disparity regression on the weighted cost volume based on a two-dimensional convolution to obtain a prediction disparity map; and performing disparity depth conversion on the prediction disparity map to obtain a depth map of the binocular images.

Abstract: An automatic cat litter box includes a rotating drum mechanism. The rotating drum mechanism includes a rotating drum, a door assembly movably connected to the rotating drum, and a waste collection compartment. The door assembly includes a pressing portion. The waste collection compartment is disposed in the rotating drum. The waste collection compartment includes an opening portion. The opening portion is annular. When the door assembly is closed, the pressing portion is engaged with at least a portion of the opening portion. At this time, the pressing portion and the opening portion limit and support each other. The pressing portion limits a deformation of the opening portion, and the opening portion also limits a deformation of the pressing portion, thereby preventing cat litter leakage caused by deformations of the door assembly and the waste collection compartment.

Abstract: A method for trajectory planning of a turning motion of a spider-type quadruped robot includes: acquiring a desired turning angle of the spider-type quadruped robot in a floating base coordinate system during a current gait cycle; calculating a desired displacement for each support leg of the spider-type quadruped robot in the floating base coordinate system during the current gait cycle based on the desired turning angle; and performing discrete trajectory planning in the floating base coordinate system based on the desired displacements of the support legs, to obtain a desired turning motion trajectory for each of the support legs of the spider-type quadruped robot in the floating base coordinate system during the current gait cycle.

Abstract: A massage motion control method, a robot controller using the same, and a storage medium are provided. The method includes: calculating a robot end desired speed and a robot end desired angular speed corresponding to a desired massage trajectory of a massage robot to compensate using two obtained robot end compensation amounts in the case that the desired massage trajectory for a target massage area at a current control cycle, a robot end speed compensation amount meeting a desired massage intensity requirement, and a robot end angular speed compensation amount adapted to an environmental curvature of the target massage area are obtained, and controlling the massage robot by determining a corresponding to-be-outputted joint angle based on motion parameter(s) obtained by the compensation. In this manner, the adaptive change of the massage position and the massage intensity for the massage area of the patient can be realized.

Abstract: A target identification method includes: obtaining an image containing a target to be identified, performing feature extraction on the image to obtain image features in the image; and inputting the image features into a target identification network model to obtain an identification result that determines a class to which the target to be identified belongs. The target identification network model includes a loss function that is based on intra-class constraints and inter-class constraints. The intra-class constraints are to constrain an intra-class distance between sample image features of a sample target and a class center of a class to which the sample target belongs, and the inter-class constraints are to constrain inter-class distances between class centers of different classes, and/or inter-class angles between the class centers of different classes.

Abstract: A person re-identification method, a storage medium, and a terminal device are provided. In the method, a preset ratio-based triplet loss function is used as a loss function during training. The ratio-based triplet loss function limits a ratio of a positive sample feature distance to a negative sample feature distance to be less than a preset ratio threshold. The positive sample feature distance is a distance between a reference image feature and a positive sample image feature, and the negative sample feature distance is a distance between the reference image feature and a negative sample image feature. Compared with the existing absolute distance-based triplet loss function, in the case of small inter-class differences and large intra-class differences, the ratio-based triplet loss function can effectively improve the stability of model training, the features extracted by the trained model are more discriminative and robust, thereby improving the accuracy of person re-identification results.

Abstract: A method for pedestrian body part feature extraction is provided, including: performing global feature extraction on a target pedestrian image to obtain a global feature map; learning each of body parts in the global feature map using a self-produced supervision signals-based self-regulated channel attention model to output first channel attention vectors each describing a respective one of the body parts; weighting the first channel attention vectors with the global feature map to obtain a weighted feature map describing the body parts; and extracting body part features of the target pedestrian image from the weighted feature map.

Abstract: A point cloud data processing method, and a robot and a robot control method using the same are provided. The method includes: obtaining image data including an RGB image and a depth image that is collected through an RGBD camera; obtaining an original mask image by segmenting out targets from the RGB image using a target segmentation mode; obtaining an optimized mask image by performing a pixel-level processing on the mask image; obtaining, based on the optimized mask image and the depth image, a plane equation of each of the targets in the optimized mask image; performing, using the plane equation of each of the targets, a depth value assignment on a plane position of the target in the optimized mask image that is not assigned with the depth value; and obtaining target point cloud data by performing a point cloud conversion on the depth image.

Abstract: A method for area dividing in a map for a mobile robot includes: obtaining a binary map corresponding to a target area; determining one or more first areas in the binary map according to partition information, wherein the one or more first areas are undivided areas in the binary map, the partition information corresponds to second areas in the binary map, and the second areas are divided areas in the binary map; for each first area of the one or more first areas, determining, according to size of the first area, the first area as a newly added second area, or merging the first area into a third area that is one of the second areas, thereby obtaining a target area-dividing map; and controlling the mobile robot according to the target area-dividing map.

Abstract: A method for extracting video features may include: obtaining a target video sequence that comprises a number of video frames; performing video frame feature extraction on the target video sequence to obtain video frame features of each of the video frames; performing feature weight calculation on each of the video frame features to obtain the feature weight of each of the video frame features; wherein the feature weight of each of the video frame features is determined by the video frame features of all of the video frames in the target video sequence; and performing feature weighting on each of the video frame features according to the feature weight of each of the video frame features to obtain video features of the target video sequence.

Abstract: An image quality assessing method, an electronic device, and a computer-readable storage medium are provided. The method includes: obtaining a to-be-assessed original image; obtaining a grayscale image and a histogram equalization image corresponding to the obtained original image by performing an image conversion on the original image; calculating an image similarity between the obtained grayscale image and the obtained histogram equalization image; and determining an image quality assessment result of the original image according to the calculated image similarity.

Abstract: A linkage mechanism includes: a base member; a first link rotatably connected to the base member, the first link defining a first arc-shaped guide groove centered on a pivot axis about which the first link rotates relative to the base member; a second link rotatably connected to the first link; a connecting member rotatably connected to the base member and the second link; an actuating mechanism including a linear actuator and a transmission member that is driven by the linear actuator, the transmission member having a first end rotatably connected to the output shaft, and a second end slidably received in the first arc-shaped guide groove. When the linear actuator drives the connecting member to extend and move, the second end of the transmission member abuts against one end of the first arc-shaped guide groove, which drives the first link to rotate relative to the base member.

Abstract: A method for synthesizing a talking head video includes: obtaining speech data to be synthesized and observation data, wherein the observation data is data obtained through observation other than the speech data; performing feature extraction on the speech data to obtain speech features corresponding to the speech data, and performing feature extraction on the observation data to obtain non-speech features corresponding to the observation data; performing temporal modeling on the speech features and first non-speech features to obtain low-dimensional representations, wherein the first non-speech features are non-speech features that are sensitive to temporal changes; and performing video synthesis based on the low-dimensional representations and second non-speech features, wherein the second non-speech features are non-speech features insensitive to temporal changes.

Abstract: A method for determining positions of navigation target points includes: acquiring an initial map, wherein the initial map represents a plan view corresponding to an explored area; performing contour extraction processing on the explored area to obtain a first contour map corresponding to the explored area, wherein the first contour map includes one or more connecting sections, and the one or more connecting sections are between adjacent ones of a number of sub-areas in the explored area, respectively; performing dilation processing on the first contour map to obtain a second contour map to remove the connecting sections; based on the initial map and the second contour map, extracting at least one navigation target point area from the explored area; and determining the positions of the navigation target points based on connected components corresponding to the at least one navigation target point area.