An inverse kinematics solution system for use with a robot, which is used for obtaining a joint angle value corresponding to a target pose value on the basis of an inputted target pose value and degree of freedom of a robot and which comprises: a parameters initialization module, an inverse kinematics scheduler, a Jacobian calculating unit, a pose updating unit and a parameters selector. The system is implemented by means of hardware and may quickly obtain motion parameters, which are used for controlling a robot, while reducing power consumption.

Disclosed is a method for obtaining the inverse kinematics of a six-degree-of-freedom serial robot with an offset wrist. The method uses the analytical solution of the inverse kinematics of the six-degree-of-freedom serial robot with a non-offset wrist as an approximate solution of the inverse kinematics of the six-degree-of-freedom serial robot with an offset wrist and an initial point for iteration, and obtains a numerical solution of the inverse kinematics of the six-degree-of-freedom serial robot with an offset wrist meeting the accuracy through continuously iterative approaching. The present disclosure has faster convergence and less calculation amount relative to the traditional calculation method, reduces the computation burden for a robot controller, and improves the real-time performance.

A coordinated joint control system for controlling a coordinated joint motion system, e.g. an articulated arm of a hydraulic excavator blends automation of routine tasks with real-time human supervisory trajectory correction and selection. One embodiment employs a differential control architecture utilizing an inverse Jacobian. Modeling of the desired trajectory of the end effector in system space can be avoided. The disclosure includes image generation and matching systems.

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 according to the target-driven parameters.

An admittance control method, a robot, and a storage medium are provided. The method includes: obtaining, based on a first admittance controller transfer function between force and position, a desired position of a robot in a current control cycle; determining a corresponding Jacobian matrix according to a configuration of the robot in the current control cycle, and calculating an ill condition number of the Jacobian matrix; and controlling the robot to move by inputting the obtained desired position in the current control cycle to a corresponding joint, in response to the ill condition number being less than a preset maximum ill condition number. In this manner, the configuration of the robot can be maintained within a reasonable rang of the ill condition number, and singularities caused by the admittance controller exceeding the work space can be avoided while the velocity reachability and force reachability of the robot can be ensured.