A control method for exoskeleton to walk self-balancing under unknown terrain

Abstract

The application provides a control method for self-balancing walking of an exoskeleton in unknown terrain, comprising: simplifying the foot end of an exoskeleton robot into a virtual impedance compliant control model; performing stress analysis on the exoskeleton robot according to the impedance compliant control model to obtain expected force and actual force of the exoskeleton robot in a movement process; performing calculation and processing according to the expected force and the actual force to convert a walking problem into an optimization problem; solving the optimization problem to obtain new expected values of left and right feet; and performing walking and standing tasks by the exoskeleton robot based on the new expected values. The method optimizes the expected force and improves the stability of the exoskeleton robot in unknown terrain.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of robot technology, and in particular to a control method for self-balancing walking of an exoskeleton in unknown terrain. BACKGROUND

[0002] In recent years, with the aging of the population and the aggravation of related diseases, the number of patients with lower limb dysfunction or loss caused by factors such as stroke, accidents, and spinal cord injury, such as hemiplegia, quadriplegia, etc. rapidly increases. Globally, there are more than 13.7 million new cases of stroke each year, becoming the third largest cause of disability worldwide. Previous studies have shown that through early intervention and gradually adapting to the target-oriented training of the injury degree and recovery stage of patients, the functional prognosis of patients with lower limb weakness or loss of function can be improved. Therefore, people with loss of motor function are increasingly dependent on various medical devices in daily life. As an assistive device, lower limb exoskeleton robots can provide support and balance for patients with motor dysfunction, enabling them to restore normal walking ability.

[0003] Generally, the gait generation of lower limb exoskeleton robots is based on the assumption of a horizontal flat rigid ground, however, the real application scenario is often an unstructured environment and an uneven road, and the walking control algorithm of the exoskeleton robot is very sensitive to the different heights of the ground, and a small height difference may cause the exoskeleton to fall, thereby causing harm to the wearer. Therefore, solving the walking problem of the exoskeleton robot on uneven roads has great significance for the practical application of the exoskeleton robot. SUMMARY

[0004] Therefore, the present application provides a control method for self-balancing walking of an exoskeleton in unknown terrain to solve the above problems.

[0005] The present application provides a control method for self-balancing walking of an exoskeleton in unknown terrain, comprising: simplifying the foot end of the exoskeleton robot into a virtual impedance compliant control model; performing force analysis on the exoskeleton robot according to the impedance compliant control model to obtain the expected force and the actual force of the exoskeleton robot in the movement process; calculating and processing according to the expected force and the actual force, converting the walking problem into an optimization problem; solving the optimization problem to obtain new expected values of the left and right feet; and the exoskeleton robot performing walking and standing tasks based on the new expected values.

[0006] In another implementation manner of the present application, the state space equation of the expected force is expressed as:

[0007]

[0008] wherein, y z =F m, z represents the position of the foot tip, F m represents the force feedback measured by the sensor;

[0009]

[0010] where F represents the force received by the foot tip, M represents the mass coefficient, V represents the damping coefficient, and K represents the spring coefficient.

[0011] In another implementation of the present application, the state space equation of the actual force is represented as:

[0012]

[0013] where, y z = F m , z represents the position of the foot tip, F m represents the force feedback measured by the sensor;

[0014]

[0015] where F represents the force received by the foot tip, M represents the mass coefficient, V represents the damping coefficient, and K represents the spring coefficient.

[0016] In another implementation of the present application, the optimization problem is represented as:

[0017]

[0018] where Q and R are both real symmetric positive definite matrices, Δu z represents the state feedback.

[0019] In another implementation of the present application, it further includes: in order to enable the system to be stable, a state feedback is designed as follows:

[0020]

[0021] where ΔF = F d - F r , F d is the desired force, F r is the actual force.

[0022] In another implementation of the present application, the new desired values of the left and right feet are represented as:

[0023]

[0024] where, is the new desired position of the left and right feet after adjustment, is the desired position of the left and right feet, and ΔPL / R Adjustment amount of the left and right feet for the current period.

[0025] In another implementation of the present application, further comprising: according to the law of self-balancing exoskeleton robot movement, the walking process of the self-balancing exoskeleton robot can be simplified as a center of mass dynamics model; the expected force is calculated and optimized based on the center of mass dynamics model.

[0026] The control method of the exoskeleton for self-balancing walking in unknown terrain, based on the impedance control of the double-leg compliant control model, converts it into an optimization problem, thereby deducing the control law in the walking process of the human-machine hybrid system, the expected force calculation method based on the center of mass dynamics, and the expected force is optimized considering the zero moment point stability constraint, which improves the stability of the exoskeleton robot walking in unknown terrain. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Through reading the detailed description of the embodiments below, the advantages and benefits of the solutions will become clear to those skilled in the art. The drawings are only for the purpose of illustrating the preferred embodiments, and are not considered as limiting the present application.

[0028] In the drawings:

[0029] Figure 1 The control method flowchart of the exoskeleton for self-balancing walking in unknown terrain according to an embodiment of the present application.

[0030] Figure 2 The end of the foot impedance compliant control model according to an embodiment of the present application.

[0031] Figure 3 The simulation experiment diagram according to an embodiment of the present application.

[0032] Figure 4 The prototype experiment diagram according to an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the person in the art better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and in detail below, combined with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments. Based on the embodiments in the embodiments of the present application, all other embodiments obtained by those skilled in the art should belong to the scope of protection of the embodiments of the present application.

[0034] Figure 1A control method flowchart of a kind of exoskeleton for providing embodiment of the application self-balancing walking under unknown terrain, as shown in Figure 1 The embodiment mainly includes:

[0035] S101, the foot end of exoskeleton robot is simplified as a virtual impedance compliance control model.

[0036] Exemplarily, as shown in Figure 2 With the z direction of left foot as an example, the foot end of self-balancing exoskeleton robot is simplified as a virtual impedance compliance control model, and the force received by the foot end and the motion equation of the foot end are obtained as follows:

[0037]

[0038] Wherein, z is the position of the foot end, F is the force received by the foot end, M is the mass coefficient, V is the damping coefficient, and K is the spring coefficient.

[0039] In actual movement, since the value measured by the six-dimensional force / torque sensor is delayed by one detection period T relative to the theoretical value, it can be expressed as:

[0040]

[0041] Wherein, F m Is the force measured by the sensor, and in this paper, the detection period is the same as the control period.

[0042] After arranging the equation, the following can be obtained:

[0043]

[0044] The above equation is written in the form of state space as follows:

[0045]

[0046] Through the above equation, the state space equation of the foot of self-balancing exoskeleton robot can be written as:

[0047]

[0048] Wherein, y z =F m , and:

[0049]

[0050] S102, force analysis is carried out on the exoskeleton robot according to the impedance compliance control model, and the expected force and actual force of the exoskeleton robot in the movement process are obtained.

[0051] S103, performing calculation processing according to the expected force and the actual force, and converting the walking problem into an optimization problem.

[0052] S104, solving the optimization problem to obtain new expected values of the left and right feet.

[0053] S105, the exoskeleton robot performs walking and standing tasks based on the new expected values.

[0054] The control method for self-balancing walking of the exoskeleton under unknown terrain in the application is based on the impedance control of the double-leg compliant control model, which is converted into an optimization problem, so as to deduce the control law of the human-machine hybrid system in the walking process, the expected force calculation method based on the center of mass dynamics, and the expected force is optimized considering the zero moment point stability constraint, which improves the stability of the exoskeleton robot walking under unknown terrain.

[0055] In another implementation manner of the application, the state space equation of the expected force is represented as:

[0056]

[0057] Wherein, y z =F m , z represents the position of the foot end, F m represents the force feedback measured by the sensor;

[0058]

[0059] Wherein, F represents the force received by the foot end, M represents the mass coefficient, V represents the damping coefficient, and K represents the spring coefficient.

[0060] In another implementation manner of the application, the state space equation of the actual force is represented as:

[0061]

[0062] Wherein, y z =F m , z represents the position of the foot end, F m represents the force feedback measured by the sensor;

[0063]

[0064] Wherein, F represents the force received by the foot end, M represents the mass coefficient, V represents the damping coefficient, and K represents the spring coefficient.

[0065] Exemplarily, the formula of the expected force and the actual force is subtracted to obtain:

[0066]

[0067] In another implementation form of the present application, the optimization problem is represented as:

[0068]

[0069] where Q and R are both real symmetric positive definite matrices, Δu z represents a state feedback.

[0070] In another implementation form of the present application, further comprising: in order to make the system stable, a state feedback is designed as:

[0071]

[0072] where ΔF = F d - F r , F d is a desired force, F r is an actual force.

[0073] Exemplarily, a new desired position is obtained through an iterative equation:

[0074]

[0075]

[0076] z * (k+1) = z d (k+1) + Δz(k+1)

[0077] In another implementation form of the present application, the new desired values of the left and right feet are represented as:

[0078]

[0079] where, is a new desired position of the left and right feet after adjustment, is a desired position of the left and right feet, ΔP L / R is an adjustment amount of the left and right feet in the current period.

[0080] In another implementation form of the present application, further comprising: according to the law of motion of the self-balancing exoskeleton robot, a walking process of the self-balancing exoskeleton robot can be simplified as a center of mass dynamics model; and the desired force is calculated and optimized based on the center of mass dynamics model.

[0081] Exemplarily, according to the law of motion of the self-balancing exoskeleton robot, a walking process of the self-balancing exoskeleton robot can be simplified as a center of mass dynamics model.

[0082] The forces and torques of the left and right feet are converted to a world coordinate system through a rotation matrix: ​

[0083]

[0084] The following equation is obtained by the center of mass dynamics:

[0085]

[0086] In addition, according to the relationship between the force and the position of the two feet, the positions of the zero moment points (ZMP) of the left and right feet are:

[0087]

[0088] The cost function is set as:

[0089]

[0090] The optimization objective is expressed as:

[0091] min{J L +J R}

[0092] And the equality constraints are satisfied as:

[0093]

[0094] The inequality constraints are satisfied as:

[0095]

[0096] Through the above formula, the biped expected force F of the self-balancing exoskeleton robot in the walking process is calculated d .

[0097] In another implementation manner of the present application, simulation experiments and prototype experiments are performed. As shown in Figure 3 , in the simulation environment, the lower limb self-balancing exoskeleton robot realizes stable walking of carrying a person on uneven road surfaces with different height differences; as shown in Figure 4 , in the prototype experiment, the lower limb self-balancing exoskeleton robot realizes stable walking of carrying a person on an uneven road surface with a height difference of 4 cm.

[0098] So far, specific embodiments of the present application have been described. Other embodiments are within the scope of the appended claims. In some cases, the acts recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0099] It should be noted that all directionality indications (such as up, down, left, right, back, etc.) in the embodiments of the present application are only used to explain the relative positional relationship, movement condition, etc. between components in a certain specific posture (as shown in the drawings), and if the specific posture changes, the directionality indications will also change accordingly.

[0100] In the description of the present application, the terms "first", "second" are only used for the convenience of describing different components or names, and cannot be understood as indicating or implying the order relationship, relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can be explicitly or implicitly included at least one of the features.

[0101] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments and are not intended to limit the present application.

[0102] It should be noted that, although the specific embodiments of the present application are described in detail with reference to the accompanying drawings, it should not be understood as limiting the scope of protection of the present application. Various modifications and variations made by those skilled in the art within the scope described in the claims are still within the scope of protection of the present application.

[0103] The examples of the embodiments of the present application are intended to simply illustrate the technical features of the embodiments of the present application, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present application, and are not improper limitations of the embodiments of the present application.

[0104] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A control method for an exoskeleton to self-balance walking on unknown terrain, characterized by, The method comprises the steps of: simplifying the foot end of the exoskeleton robot into a virtual impedance compliant control model; performing force analysis on the exoskeleton robot according to the impedance compliant control model to obtain expected force and actual force of the exoskeleton robot during movement; a state space equation of the expected force is expressed as: a state space equation of the actual force is expressed as: wherein, z represents the position of the end of the foot, F represents the force measured by the sensor wherein, M denotes a mass coefficient, V denotes a damping coefficient, K denotes a spring coefficient, T denotes a detection period; performing calculation and processing according to the expected force and the actual force to convert a walking problem into an optimization problem; solving the optimization problem to obtain new expected values of left and right feet; the exoskeleton robot performs walking and standing tasks based on the new expected values.

2. The method of claim 1, wherein, the optimization problem is expressed as: wherein Q and R are real symmetric positive definite matrices, denotes a state feedback.

3. The method of claim 2, wherein, The method further comprises the steps of: in order to enable the system to be stable, a state feedback device is designed as follows: wherein, , is the desired force, is the actual force.

4. The method of claim 3, wherein, the new expected values of the left and right feet are expressed as: wherein, is the new desired position for the left and right feet after adjustment, is the desired position for the left and right feet, is the adjustment amount for the left and right feet for the current cycle.

5. The method of claim 1, wherein, The method further comprises the steps of: simplifying a walking process of the self-balancing exoskeleton robot into a center of mass dynamics model according to a movement rule of the self-balancing exoskeleton robot; performing calculation and optimization on the expected force based on the center of mass dynamics model.

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