Abstract: A robotic arm angle interval inverse solving method and a robotic arm using the same are provided. The method includes: obtaining a joint angle calculation model and a differential relationship model of a target joint of the robotic arm; obtaining extreme arm angles corresponding to a joint angle of the differential relationship model at extreme values based on the differential relationship model; obtaining a joint arm angle interval corresponding to the target joint based on the extreme arm angle and the joint angle calculation model; and obtaining a target arm angle interval corresponding to the robotic arm based on the joint arm angle interval corresponding to the target joint of the robotic arm. In comparison with the existing method to solve the arm angle interval of the robotic arm, a more accurate arm angle interval can be obtained.

Abstract: An impedance control method as well as a controller and a robot using the same are provided. The method includes: obtaining joint motion information and joint force information in the joint space of a robotic arm and an actual interaction force acting on an end-effector, and calculating actual motion information of the end-effector in the task space based on the joint motion information; calculating a corrected desired trajectory using environment information and a desired end-effector interaction force, and calculating the impedance control torque based on the joint force information, the actual interaction force, the actual motion information, and desired end-effector information including the corrected desired trajectory and determining a compensation torque based on a nonlinear term in a constructed dynamics equation so as to perform a joint torque control on the robotic arm based on the impedance control torque and the compensation torque.

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: The present disclosure provides an action imitation method as well as a robot and a computer readable storage medium using the same. The method includes: collecting a plurality of action images of a to-be-imitated object; processing the action images through a pre-trained convolutional neural network to obtain a position coordinate set of position coordinates of a plurality of key points of each of the action images; calculating a rotational angle of each of the linkages of the to-be-imitated object based on the position coordinate sets of the action images; and controlling a robot to move according to the rotational angle of each of the linkages of the to-be-imitated object. In the above-mentioned manner, the rotational angle of each linkage of the to-be-imitated object can be obtained by just analyzing and processing the images collected by an ordinary camera without the help of high-precision depth camera.

Abstract: An inverse kinematics solving method for redundant robot as well as a redundant robot using the same are provided. The method includes: obtaining an expression of a Jacobian matrix null space of a current configuration of each robotic arm of the redundant robot corresponding to a preset end pose of the robotic arm according to the preset end pose, and obtaining a relation between an angular velocity of the joints of the redundant robot in the Jacobian matrix null space of the current configuration based on the obtained expression; traversing the Jacobian matrix null space using the relation, and building an energy cost function of the redundant robot based on the relation; obtaining a target joint angle of each joint of the redundant robot based on the optimal inverse kinematics solution to transmit to the servo of the joint so as to control the joint.

Abstract: A method for detecting contact of a swinging leg of a robot with ground includes: obtaining a torque on each joint of the swinging leg when the robot is in a swing phase; estimating a force on a foot of the swinging leg by using a force Jacobian matrix based on the torque on each joint of the swinging leg, and calculating a rate of change of force of the foot in a vertical direction according to the force on the foot; and determining that the swinging leg has contacted the ground in response to a preset consecutive number of values of the rate of change of force being greater than a preset threshold.

Abstract: The present disclosure provides a method for controlling an arm of a robot, including obtaining obstacle information relating to the arm of the robot by at least one sensor, obtaining current posture information of the arm of the robot by a least one detector and obtaining an expected posture information of an end-portion of the arm of the robot, determining an expected trajectory of the end-portion of the arm of the robot, determining an expected speed of the end-portion of the arm of the robot in accordance with the expected trajectory of the end-portion, determining a virtual speed of a target point on the arm of the robot, and configuring a target join speed corresponding to a joint of the arm of the robot. Such that the redundant arm of the robot may be configured to prevent from contacting the obstacles in the complex environment while performing corresponding tasks.

Abstract: A robot control method, a computer-readable storage medium, and a robot are provided. The method includes: obtaining first motion data, where the first motion data is human arm end motion data collected by a virtual reality device; obtaining second motion data by mapping the first motion data to a working space of an end of a robotic arm of the robot; obtaining a state of each joint of the robotic arm of the robot, and obtaining control data of the joint by performing a quadratic programming solving on the second motion data and the state of the joint; and controlling, by a motion controller of the robot, the robotic arm of the robot to move according to the obtained control data of each joint of the robot by transmitting the control data of the joint to the motion controller, so that the control method is relatively more natural, intuitive, and flexible.

Abstract: A method for controlling a robot includes: obtaining current motion state information of the robot and desired motion trajectory information corresponding to a target task; determining task execution coefficient matrices corresponding to the robot performing the target task according to the desired motion trajectory information and the motion state information; constructing matching dynamic constraints for task-driven parameters of the robot according to the desired motion trajectory information and the motion state information; constructing matching parameter distribution constraints for the task-driven parameters according to the motion state information and body action safety constraints corresponding to the target task; solving a pre-stored task execution loss function by using the task execution coefficient matrices to obtain the target-driven parameters satisfying the dynamic constraints and the parameter distribution constraints; and controlling operation state of each joint end effector of the robot a

Abstract: A trajectory planning method, a computer-readable storage medium, and a robot are provided. The method includes: constructing a phase variable of a trajectory planning of a robot, where the phase variable is a function of two position components of a torso of the robot on a horizontal plane; and performing, using the phase variable replacing a time variable, the trajectory planning on a swinging leg of the robot in each preset coordinate axis direction. In this manner, the robot can no longer continue to follow the established trajectory after being disturbed by the environment, but make state adjustments according to the disturbance received to offset the impact of the disturbance, thereby maintaining walking stability and avoiding the problem of early or late landing of the swinging leg.

Abstract: A method includes: obtaining heat maps including a predetermined number of key points of a human body; performing depth separable convolution on a feature map corresponding to one of the heat maps corresponding to each of the key points and a convolution kernel of a corresponding channel of the human body posture recognition model to determine a key point feature map corresponding to each channel of the human body posture recognition model; performing local feature fusion processing and/or global feature fusion processing on the key point feature map corresponding to each channel to obtain fusion posture feature maps; determining a linear relationship between the channels of the human body posture recognition model based on the fusion posture feature maps; and updating weight coefficients of the corresponding channels of the human body posture recognition model by using the linear relationship between the channels of the human body posture recognition model.

Abstract: The present disclosure discloses an entity recognition model training method and an entity recognition method as well as an apparatus using them. The entity recognition model training method includes: obtaining a training text and matching the training text with a database to obtain a plurality of matching results; processing the matching results to obtain a plurality of feature vectors corresponding to the matching results; obtaining a word vector of each word in the training text by processing the training text; and training an initial entity recognition model based on the feature vector and the word vector to obtain an entity recognition model. By using this training manner, the entity recognition model obtained can have an improved accuracy of entity recognition.

Abstract: The present disclosure provides a robot posture control method as well as a robot and a computer readable storage medium using the same. The method includes: constructing a virtual model of the robot, wherein the virtual model comprises a momentum wheel inverted pendulum model of the robot and an angle between a sole surface of the robot and a horizontal plane; and performing a posture control based on outer-loop feedback control, inner loop compensation for the external disturbance rejection in position level, inner loop external disturbance rejection via null-space in velocity level, and inner loop external disturbance rejection in force/acceleration level on the robot. In this manner, a brand-new virtual model is provided, which can fully reflect the upper body posture, centroid, foot posture, and the like of the robot which are extremely critical elements for the balance and posture control of the robot.

Abstract: A target object detection model is provided. The target object detection model includes a YOLOv3-Tiny model. Through the target object detection model, low-level information in the YOLOv3-Tiny sub-model can be merged with high-level information therein, so as to fuse the low-level information and the high-level information. Since the low-level information can be further used, the comprehensiveness of target detection is effectively improved, and the detection effect of small targets is improved.

Abstract: A multi-legged robot load balancing method, a multi-legged robot, and a storage medium are provided. The method includes: calculating a current position and velocity of a load positioned on a torso according to a value measured by a force sensor; calculating, through a feedback control law, a desired posture of the torso required for keeping a balance of the load according to the current position and the current velocity of the load; determining an expected position of virtual joints according to the desired posture of the torso, and calculating, using a full dynamics control algorithm of the robot, a joint torque for each real joint of the robot according to the expected position; and transmitting the calculated joint torques to the corresponding real joints so that the torso is moved to reach the desired posture by moving the corresponding real joints.

Abstract: A computer-implemented method for speech synthesis, a computer device, and a non-transitory computer readable storage medium are provided. The method includes: obtaining a speech text to be synthesized; obtaining a Mel spectrum corresponding to the speech text to be synthesized according to the speech text to be synthesized; inputting the Mel spectrum into a complex neural network, and obtaining a complex spectrum corresponding to the speech text to be synthesized, wherein the complex spectrum comprises real component information and imaginary component information; and obtaining a synthetic speech corresponding to the speech text to be synthesized, according to the complex spectrum. The method can efficiently and simply complete speech synthesis.

Abstract: A navigation method for a disinfection robot includes: obtaining an initial map within a field of view of the robot; generating a disinfection grid having multiple disinfection squares on the initial map, and determining multiple disinfecting consideration points in the disinfection squares; determining a disinfection order of disinfecting targets corresponding to the disinfection squares according to distances between the robot after the robot completes disinfection of one of the targets corresponding to one of the disinfection squares and to-be-disinfected ones of the disinfection squares; according to distances between the position and the disinfecting consideration points in to-be-disinfected ones of the disinfection squares, combined with priority levels of the disinfecting consideration points in to-be-disinfected ones of the disinfection squares, determining disinfecting planning points in the disinfection squares; and performing disinfection to the targets corresponding to the disinfection squares acc

Abstract: A robot state estimation method, a computer-readable storage medium, and a legged robot are provided. The method includes: obtaining force information of a left leg of a robot and a right leg of the robot; calculating a ZMP of the robot in a world coordinate system based on the force information of the left leg and the force information of the right leg; and calculating a position of a center of mass (CoM) of the robot based on a preset linear inverted pendulum model. In this manner, a brand-new linear inverted pendulum model is constructed in advance, which uses the ZMP of the robot as a supporting point of the model, thereby fully considering the influence of the change of the position of the ZMP of the robot on the position of the CoM.

Abstract: A method for navigating a robot through a limited space includes: obtaining a planned first path for the robot to reach a target position; calculating curvature and/or normal vectors of points in the first path; according to the curvature and a preset curvature threshold, and/or according to the normal vectors and a preset normal vector change threshold, obtaining a first straight path for the robot outside the limited space by curve fitting; determining an intersection of a centerline of the limited space and the first straight path, and determining a second straight path for the robot to move through the limited space according to the centerline and the intersection; and generating a second path for the robot to move through the limited space based on the first straight path and the second straight path, and navigating the robot to move through the limited space according to the second path.

Abstract: A path generation method for a robot includes: based on a scene model of a current scene and a machine model corresponding to the robot, combined with multiple configurations of the robot, determining a plurality of initial points between a start point and an end point in the scene model; removing redundant points from the plurality of the initial points to obtain necessary points among the initial points; and connecting the necessary points to obtain a path between the start point and the end point.