Abstract: A method for controlling a robotic arm that includes an end effector and a sensor that are mounted at an end of the robotic arm includes: obtaining, by the sensor, n gravity matrix data, wherein the n gravity matrix data are gravity matrix data of the end effector in an end coordinate system when the robotic arm is in a different poses, n?3; determining n rotation transformation matrices from a base coordinate system of the robotic arm to the end coordinate system when the robotic arm is in n different poses; calculating coordinates of a center of mass and mass of the end effector based on the n gravity matrix data and the a rotation transformation matrices; and controlling the robotic arm based on the coordinates of the center of mass and the mass.

Abstract: A total centroid state estimation method as well as a humanoid robot and a computer readable storage medium using the same are provided. The method includes: obtaining a motion state of each real joint of the humanoid robot and a motion state of its floating base, where the floating base is equivalent to a plurality of sequent-connected virtual joints; calculating a joint position, a centroid position, and a rotation matrix of each link in the world coordinate system in sequence using the chain rule of homogeneous multiplication according to the position of the joint corresponding to the link to solve a Jacobian matrix of the centroid of the link; solving a total centroid Jacobian matrix based on the Jacobian matrix of the centroid of each link and the total mass; and calculating the total centroid velocity based on the total centroid Jacobian matrix and other parameters.

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: 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: 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 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 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 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 method for estimating a pose of a humanoid robot includes: processing obtained pose parameters of a waist of the humanoid robot and plantar motion parameters of the humanoid robot to obtain the measured pose parameters of a center point of the waist of the humanoid robot; calculating predicted pose parameters of the center point of the waist according to the obtained pose parameters of the waist; and fusing the measured pose parameters and the predicted pose parameters to obtain estimated pose parameters of the center point of the waist.

Abstract: A method for a multi-legged robot having a body and a number of legs, includes: obtaining a current pose of the body, forces applied to the body, and joint angles of each of supporting legs of the legs; creating a mapping matrix from the forces applied to the body to desired support forces applied to soles of the supporting legs; obtaining priority targets by prioritizing the forces acting in different directions, determining a weight matrix for each priority target, and creating an optimization model of the support forces for each priority target based on the mapping matrix and the weight matrices; solving the optimization model of each of the priority targets to obtain the desired support forces corresponding to each of the priority targets; and calculating joint torques of the supporting legs for joint control, based on the solved desired support forces and Jacobian matrices corresponding to the supporting legs.

Abstract: A humanoid robot and its balance control method and computer readable storage medium are provided. Expected accelerations of each of a sole and centroid of a humanoid robot corresponding to a current expected balance trajectory and an expected angular acceleration of the waist corresponding to the current expected balance trajectory are obtained based on current motion data of the sole, the centroid, and the waist, respectively first, then an expected angular acceleration of each joint meeting control requirements of the sole, the centroid, and the waist while the robot corresponds to the current expected balance trajectory is calculated based on an angular velocity of the joint, the expected accelerations of the waist, the sole, and the centroid, respectively, and then each joint of the robot is controlled to move at the obtained expected angular acceleration of the joint based on the angular displacement of the joint.

Abstract: A task hierarchical control method as well as a robot and a storage medium using the same are provided. The method includes: obtaining a task instruction for a robot, where the task instruction is for determining a target task card including an amount of selection matrices for dividing a target task into the amount of hierarchical subtasks and a controller name for executing each of the hierarchical subtasks; obtaining a null space projection matrix of each of the hierarchical subtasks based on the corresponding selection matrix; generating control finks of the amount according to the corresponding controller of each of the hierarchical subtasks and the corresponding null space projection matrix; calculating a control torque of each of the control links and obtaining a hierarchical control output quantity by adding ail the control torques; and controlling the robot to perform the target task using the hierarchical control output quantity.

Abstract: A feedforward control method comprising steps of: acquiring kinematic parameters of each joint of a robot based on inverse kinematics according to a pre-planned robot motion trajectory, and setting a center of a body of the robot as a floating base; determining a six-dimensional acceleration of a center of mass of each joint of the robot in a base coordinate system using a forward kinematics algorithm, based on the kinematic parameters of each joint of the robot, and converting the six-dimensional acceleration of the center of mass of each joint of the robot in the base coordinate system to a six-dimensional acceleration in a world coordinate system; and calculating a torque required by a motor of each joint of the robot using an inverse dynamic algorithm, and controlling the motors of corresponding joints of the robot.

Abstract: A decoupling control method for a humanoid robot includes: decomposing tasks of the humanoid robot to obtain kinematic tasks and dynamic tasks, and classifying corresponding joints of the humanoid robot into kinematic task joints or dynamic task joints; solving desired positions and desired speeds of the kinematic task joints for performing the kinematic tasks according to desired positions and desired speeds of ends in the kinematic tasks using inverse kinematics; calculating torques of the kinematic task joints based on the desired positions and desired speeds of the kinematic task joints; and solving a pre-built optimization model of torques required for the dynamic task joints based on the calculated torques of the kinematic task joints, to obtain torques required by the dynamic task joints for performing the dynamic tasks.

Abstract: A linear joint includes a motor assembly includes a rotating shaft for outputting motion; a transmission mechanism including a screw and a nut threadedly connected to the screw, the nut being coaxial with respect to and securely connected to the rotating shaft so as to be rotatable together with the rotating shaft; and a rod connected to a first end of the screw so as to move together with the screw along a lengthwise direction of the screw.

Abstract: The present disclosure provides a humanoid robot and its control method and computer readable storage medium. The method includes: obtaining a current torque of a sole of the humanoid robot, an inclination angle of the sole, an inclination angle of a first joint of the humanoid robot, and an inclination angle of a second joint of the humanoid robot; calculating current feedforward angular velocities of motors of the first and second joints through the obtained information; calculating feedback angular velocities of the motors of the first and second joints; and obtaining inclination angles of the joints based on the feedforward angular velocities of the motors and the feedback angular velocities of the motors, and performing, through the motor of the second joint, a deviation control on the joints according to the inclination angles of the joints.

Abstract: A scene data obtaining method as well as a model training method and a computer readable storage medium using the same are provided. The method includes: building a virtual simulation scene corresponding to an actual scene, where the virtual simulation scene is three-dimensional; determining a view frustum corresponding to preset view angles in the virtual simulation scene; collecting one or more two-dimensional images in the virtual simulation scene and ground truth object data associated with the one or more two-dimensional images using the view frustum corresponding to the preset view angles; and using all the two-dimensional images and the ground truth object data associated with the one or more two-dimensional images as scene data corresponding to the actual scene. In this manner, the data collection does not require manual annotation, and the obtained data can be used for training deep learning-based perceptual models.

Abstract: A total centroid state estimation method as well as a humanoid robot and a computer readable storage medium using the same are provided. The method includes: obtaining a motion state of each real joint of the humanoid robot and a motion state of its floating base, where the floating base is equivalent to a plurality of sequent-connected virtual joints; calculating a joint position, a centroid position, and a rotation matrix of each link in the world coordinate system in sequence using the chain rule of homogeneous multiplication according to the position of the joint corresponding to the link to solve a Jacobian matrix of the centroid of the link; solving a total centroid Jacobian matrix based on the Jacobian matrix of the centroid of each link and the total mass; and calculating the total centroid velocity based on the total centroid Jacobian matrix and other parameters.

Abstract: The present disclosure provides a robot centroid position adjustment method as well as an apparatus and a robot using the same. The method includes: obtaining initial values; obtaining a waist velocity adjustment value; calculating a current value of the centroid position; and determining whether a current value of the centroid position is equal to the planning value of the centroid position; if the current value of the centroid position is not equal to the planning value of the centroid position, obtaining the current value of the centroid position to take as the initial value of the centroid position and returning to the step of obtaining the waist velocity adjustment value until the current value of the centroid position is equal to the planning value of the centroid position. In such a manner, the balance ability of the robot can be improved.