A human-machine collaborative control method and related device based on human fatigue perception

Abstract

The present invention relates to a human-machine collaboration control method and related devices based on human fatigue perception, which construct a paired arm skeleton muscle model and a robotic arm model; obtain the expected joint angle and expected muscle force operation degree; obtain the terminal force and joint angle of the arm skeleton muscle model, and obtain the muscle force operation degree related to muscle fatigue according to the terminal force and joint angle; determine the target joint speed of the arm skeleton muscle model according to the muscle force operation degree, the expected joint angle and the expected muscle force operation degree; obtain the motion parameters of the robotic arm model according to the target joint speed; realize the combination of the human skeleton muscle model and the robotic arm model, reproduce the motion of the human object, obtain the muscle activation degree in real time to evaluate the human fatigue degree; optimize the muscle activation degree by optimizing the muscle force operation degree to determine the optimal motion process, and realize comfortable human-machine collaboration.

Claims

1. A human-machine collaborative control method based on human fatigue perception, characterized in that: The following steps are involved: Build a paired arm skeleton muscle model and a robotic arm model; Obtain the desired joint angle and desired muscle force operation degree; Acquiring the end force and joint angle of the arm skeleton muscle model, and obtaining the muscle force operation degree related to the muscle fatigue according to the end force and the joint angle; determining a target joint velocity of the arm skeleton muscle model according to the muscle force operation degree, the expected joint angle, and the expected muscle force operation degree; The motion parameters of the robotic arm model are obtained according to the target joint speed of the arm skeleton muscle model.

2. The human-machine collaborative control method based on human fatigue perception according to claim 1 is characterized in that: The muscle force operation degree is expressed as: M m =(F h -1 J m -T J T ) T (F h -1 J m -T J T ); where M m is the muscle force operation degree, J m is the matrix composed of each joint of the arm skeleton muscle model, F h is the matrix composed of the maximum axial force of the muscle and the function of muscle force and fiber length, J is the Jacobian matrix of the robotic arm model, and J m 、F h Both J and J are related to the joint angle.

3. The human-machine collaborative control method based on human fatigue perception according to claim 2 is characterized in that: The muscle activation degree is used as an indicator for evaluating muscle fatigue, and the muscle activation degree is expressed as: ||a||=F T (F h -1 J m -T J T ) T (F h -1 J m -T J T )F; ||a|| is the degree of muscle activation, and F is the end force.

4. The human-machine collaborative control method based on human fatigue perception according to claim 1, characterized in that: The determining of the target joint velocity of the arm skeleton muscle model according to the muscle force operation degree, the expected joint angle, and the expected muscle force operation degree includes: The ability to increase muscle force manipulation in the direction of gravity is optimized; The target joint velocity of the arm skeleton muscle model is determined according to the optimized muscle force operation degree, the expected joint angle and the expected muscle force operation degree.

5. The human-machine collaborative control method based on human fatigue perception according to claim 4 is characterized in that: The optimized muscle force operation degree is expressed as: Among them, Md is the optimized muscle force operation degree, diag is the function for constructing a diagonal matrix, a1, a2 and a3 are the parameters of the diagonal matrix, and C is the determinant of the muscle force operation degree.

6. The human-machine collaborative control method based on human fatigue perception according to claim 1 is characterized in that: The target joint velocity includes a primary joint velocity for tracking the end position and an auxiliary joint velocity for optimizing muscle force manipulation.

7. The human-machine collaborative control method based on human fatigue perception according to claim 6 is characterized in that: The main joint velocities are expressed as: The auxiliary joint velocity is expressed as: in, is the main joint velocity, J ι is the pseudo-inverse of the Jacobian matrix, K p is a constant diagonal matrix, x end is the end position vector of the arm skeleton muscle model, x t is the current end position vector of the arm skeleton muscle model, is the auxiliary joint velocity, ζ ι is the pseudo-inverse of the partial derivative of the muscle force operation with respect to the joint angle, K M is the operation coefficient matrix, M t is the current muscle force operation degree, The desired muscle force operation degree.

8. The human-machine collaborative control method based on human fatigue perception according to claim 1 is characterized in that: The obtaining of motion parameters of the robotic arm model according to the target joint velocity of the arm skeleton muscle model includes: Determining an interaction point between the arm skeleton muscle model and the robotic arm model according to a target joint velocity of the arm skeleton muscle model; The motion parameters of the robotic arm model are obtained according to the interaction points.

9. A computer-readable storage medium, characterized in that Program instructions are stored, and when the program instructions are executed by a processor, the human-machine collaborative control method based on human fatigue perception according to any one of claims 1 to 8 is implemented.

10. A human-machine collaborative control system, characterized in that: include: Computer device comprising the computer readable storage medium according to claim 9.

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