A method for controlling an ankle-type walking assistance device may include measuring an angle of a joint of the walking assistance apparatus, calculating an angular velocity and a linear velocity of a frame of the walking assistance device using an inertial measurement unit (IMU) attached to the frame, generating a dynamics model for the walking assistance device based on the angle of the joint, the angular velocity and the linear velocity of the frame, calculating a disturbance applied to the walking assistance device based on the dynamics model, and controlling the walking assistance device based on the calculated force, equivalent, or wrench.

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.

This disclosure is related to a robot motion control method and apparatus. The method includes: obtaining a center-of-mass reference trajectory used for guiding the robot to execute a target motion; obtaining, based on optimization of an objective function, center-of-mass control information for controlling the robot to follow the center-of-mass reference trajectory to move; generating joint control information according to the center-of-mass control information and a structure matrix of the robot; and controlling the robot to execute the target motion based on the joint control information.

A motion control method, a robot controller, and a computer readable storage medium are provided. The method includes: calculating an inverse Jacobian matrix of a whole-body generalized coordinate vector at a current time relative to an actual task space vector of a humanoid robot; calculating a target generalized coordinate vector corresponding to a to-be-executed task space vector at a current moment by combining an actual task space vector and the to-be-executed task space vector into a null space of the inverse Jacobian matrix according to preset position constraint(s) corresponding to the whole-body generalized coordinate vector; and controlling a motion state of the humanoid robot according to the target generalized coordinate vector. In this manner, the motion of the humanoid robot is optimized as a whole to achieve the purpose of controlling the humanoid robot to avoid the limits of the motion of joints.

To calculate a holding position of a workpiece with high accuracy and place the workpiece in a placement position with high accuracy based on the holding position. A workpiece holding apparatus includes: holding means for holding a workpiece; first information acquisition means for acquiring three-dimensional information of the workpiece held by the holding means; position calculation means for calculating a lowest center point of the workpiece as position information of the workpiece based on the three-dimensional information of the workpiece acquired by the first information acquisition means; and control means for calculating a placement position where the workpiece is to be placed based on the position information of the workpiece calculated by the position calculation means and controlling, based on the placement position, the holding means so as to move the workpiece to the placement position and place the workpiece in the placement position.

A method of determining an installation site of a robot manipulator at a workstation, the method including: recording a respective image of the robot manipulator and of the workstation of the robot manipulator, and of a workpiece to be machined at the workstation via a camera unit, wherein the respective image contains spatial information; transmitting the respective image to a computing unit; and determining the installation site of the robot manipulator by applying a non-linear optimization of a predefined cost function and/or of a neural network via the computing unit based on a predefined task for machining the workpiece and based on the spatial information determined by the computing unit from the respective image.

A kinematics model-free trajectory tracking method for a robotic arm includes the following steps. Obtain an actual trajectory equation r.sub.a(t) of the robotic arm at time t according to a sensor, and combines the actual trajectory equation r.sub.a(t) with a predetermined target trajectory equation r.sub.d(t) to obtain a first error function e(t). Obtain a differential equation (I) of a state change rate of a driver of the robotic arm. Obtain a second error function ϵ(t). Pass the second error function c(t) through the applied gradient neural network to obtain equation (IV). Jointly solve equation (I) and equation (IV) to obtain an joint state vector θ(t) of the robotic arm. Drive a motion of the robotic arm by a controller according to the joint state vector θ(t) of the robotic arm to complete trajectory tracking.

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.

The present invention proposes an inverse kinematics modeling and solving principle for multi-axis systems based on axis invariant, including: the D-H and D-H parameter determination principle based on fixed axis invarian, “Ju-Gibbs” quaternion and class direction cosine matrix principle, the inverse solution principle of general 6R and 7R robotic arms based on axial invariant. These principles are versatile, convenient, and precise. They can be set up as circuits, code, directly or indirectly, partially or completely within a multi-axis robot system. In addition, the present invention also includes analysis verification system constructed on these principles for designing and verifying multi-axis robot systems.

A direct force feedback control method as well as a controller and a robot using the same are provided. The method includes: obtaining an actual position and an actual speed of an end of the robotic arm and an actual external force acting on the end in a Cartesian space; calculating an impedance control component of the end in the Cartesian space based on the obtained actual position, the obtained actual speed, the obtained actual external force, an expected position, an expected speed, and an expected acceleration of the end; calculating a force control component of the end in the Cartesian space based on an expected interaction force acting on the end, the actual external force, and the actual speed; determining whether the actual external force is larger than a preset threshold, and obtaining a total force control quantity of the end of the robotic arm in the Cartesian space.