A dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance

Abstract

The present disclosure relates to a dynamic balance recovery control method, device, electronic device and storage medium guided by a three-dimensional equilibrium state manifold. The method includes: collecting sensor signals based on the sensors of the exoskeleton to calculate multi-order derivatives and generate the system center of mass motion state; fitting the forward and backward instability boundary functions of the three-dimensional equilibrium state manifold based on the center of mass position, center of mass velocity and inverted pendulum length; calculating the target transient capture point corresponding to the optimal regression motion state point; calculating the horizontal dynamic balance correction force acting at the center of mass position based on the preset balance constraint relationship between the transient capture point, the center of mass state point and the pressure center point; and generating a balance recovery correction torque mapped to each joint according to the dynamic balance correction force distributed to each joint of each leg of the exoskeleton according to the gait phase and the balance adjustment effect. The present disclosure helps to improve the ability of the lower limb exoskeleton to detect the desired target balance state and anthropomorphic dynamic balance adjustment.

Description

Background Art

[0002] With the rapid development of exoskeleton robot technology, lower limb exoskeletons have been widely used in medical rehabilitation, weight-bearing, sports assistance and other fields. During the movement of the wearer-exoskeleton human-machine coupling system, when external forces act, pits are missed, obstacles are tripped and external interference acts, the system is prone to instability or even falls. At this time, the exoskeleton needs to promptly detect whether the dynamic balance state of the human-machine system is broken, and apply appropriate torque to the joints so that the wearer can quickly return to the original balance state. Different wearers have different heights, weights, and joint strengths. It is necessary to keep the movement trend of the joints consistent with the wearer's target movement intention under the applied torque. At present, the balance state assessment of human-machine coupling systems mostly adopts the center of mass inverted pendulum model. However, the balance judgment basis of this model is mostly that the equivalent center of mass or transient capture point in the horizontal direction in the sagittal plane does not exceed the plantar boundary or dynamic boundary. However, when the wearer's ankle, hip, and knee joints are bent, the center of mass will produce horizontal-vertical position changes in different postures, resulting in changes in the equivalent pendulum length of the inverted pendulum model. The use of the inverted pendulum model with a fixed pendulum length to calculate and evaluate the dynamic balance domain is likely to cause the intervention mechanism of the balance control strategy to be contrary to the wearer's actual intention of instability adjustment. In addition, the target balance regression point is mostly set as the absolute balance position or supporting ankle joint point of the inverted pendulum model. It is not appropriate to use the same target for balance adjustment for the center of mass motion state in different postures, and the target balance regression point should be set as a dynamic area associated with the center of mass posture, center of mass state, motion state, and support phase. When applying correction torque to different legs, the target motion state of the current leg and whether to fall back to the original support domain or step back to the next support domain should be considered, and then the torque adjustment of the swing leg and the supporting leg should be independently analyzed and parameterized to meet the dynamic balance recovery regulation under non-rhythmic motion laws.

[0003] Therefore, one or more methods are needed to solve the above problems.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the present disclosure is to provide a dynamic balance recovery control method, device, electronic device and computer-readable storage medium based on three-dimensional equilibrium state manifold guidance, thereby overcoming one or more problems caused by the limitations and defects of related technologies at least to a certain extent.

[0006] According to one aspect of the present disclosure, a dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance is provided, comprising:

[0007] Collect sensor signals based on the angle sensor, posture sensor, and pressure sensor of the exoskeleton, calculate multi-order derivatives of the sensor signals, and generate the system center of mass motion state in the sagittal plane under the current control cycle;

[0008] According to the preset instability state range of the human-machine system, the forward instability boundary function and the backward instability boundary function of the three-dimensional equilibrium state manifold are fitted based on the center of mass position, center of mass velocity, and inverted pendulum length;

[0009] Based on the dynamic instability region where the current instability state point is located, searching for the optimal regression motion state point corresponding to the dynamic instability region, and calculating the target transient capture point corresponding to the optimal regression motion state point;

[0010] Based on the preset equilibrium constraint relationship between the transient capture point, the center of mass state point, and the pressure center point, the horizontal dynamic equilibrium correction force acting at the center of mass position is calculated;

[0011] According to the preset gait phase of each leg of the exoskeleton and the balance regulation function played, the dynamic balance correction force distributed to each joint of each leg of the exoskeleton is calculated based on the horizontal dynamic balance correction force, and the balance recovery correction torque mapped to each joint is generated.

[0012] In an exemplary embodiment of the present disclosure, the method further includes:

[0013] The angle sensor based on the exoskeleton collects the joint angle sensor signal;

[0014] The exoskeleton-based attitude sensor collects linear acceleration sensor signals and attitude angle sensor signals;

[0015] The pressure sensor based on the exoskeleton collects sensor signals and collects contact force sensor signals.

[0016] In an exemplary embodiment of the present disclosure, the method includes the following constraints for fitting a three-dimensional equilibrium state manifold based on the center of mass position, center of mass velocity, and inverted pendulum length according to the instability state range preset by the human-machine system:

[0017] During the balance recovery process, the inverted pendulum is approximately subjected to variable acceleration rotation motion. The angular acceleration of the inverted pendulum at the next discretized time node is determined by the applied ankle joint torque, and the angular velocity and angle constraints.

[0018] The constraints on the torque applied during balance recovery and the range of ankle joint torque;

[0019] Inverted pendulum model and horizontal plane constraints;

[0020] Constraints of zero moment point and effective support domain;

[0021] Constraints on a tiny neighborhood of the target equilibrium state of an inverted pendulum.

[0022] In an exemplary embodiment of the present disclosure, the method further includes:

[0023] Based on the dynamic instability region where the current instability state point is located and the center of mass motion state point, the square of the Euclidean norm between the center of mass motion state point and the optimal regression motion state point is calculated;

[0024] Solving partial derivatives of the square of the Euclidean norm respectively;

[0025] Based on the minimum solution obtained by solving the partial derivatives, an optimal regression motion state point corresponding to the dynamic instability region is generated.

[0026] In an exemplary embodiment of the present disclosure, the method further includes:

[0027] Based on the generalized function and the preset equilibrium constraint relationship between the transient capture point, the center of mass state point, and the pressure center point, the simultaneous equations for the real-time adjustment speed of the transient capture point are generated;

[0028] Generate a target pressure center point function expression when the system is in equilibrium based on the simultaneous equations;

[0029] According to the target pressure center point function expression, the horizontal dynamic balance correction force acting on the center of mass position is calculated based on the deviation of the inertial force when the inertial force is balanced with the center of mass.

[0030] In an exemplary embodiment of the present disclosure, the method further includes:

[0031] According to the preset gait phase of each leg of the exoskeleton and the balance regulation function played, the dynamic balance correction force distributed to each joint of each leg of the exoskeleton is calculated based on the horizontal dynamic balance correction force;

[0032] According to the dynamic balance correction force of each joint, based on the preset distribution coefficient and direction coefficient, a balance recovery correction torque mapped to each joint is generated.

[0033] In one aspect of the present disclosure, a dynamic balance recovery control device based on three-dimensional equilibrium state manifold guidance is provided, comprising:

[0034] The system center of mass motion state calculation module is used to collect sensor signals based on the angle sensor, posture sensor, and pressure sensor of the exoskeleton, calculate the multi-order derivatives of the sensor signals, and generate the system center of mass motion state in the sagittal plane under the current control cycle;

[0035] An instability boundary function generation module is used to fit the forward instability boundary function and the backward instability boundary function of the three-dimensional equilibrium state manifold based on the center of mass position, center of mass velocity, and inverted pendulum length according to the instability state range preset by the human-machine system;

[0036] A target transient capture point calculation module is used to search for an optimal regression motion state point corresponding to the dynamic instability region based on the dynamic instability region where the current instability state point is located, and calculate a target transient capture point corresponding to the optimal regression motion state point;

[0037] A horizontal dynamic balance correction force calculation module is used to calculate the horizontal dynamic balance correction force acting at the center of mass position based on the preset balance constraint relationship between the transient capture point, the center of mass state point, and the pressure center point;

[0038] The balance recovery correction torque generation module is used to calculate the dynamic balance correction force allocated to each joint of each leg of the exoskeleton based on the horizontal dynamic balance correction force according to the preset gait phase of each leg of the exoskeleton and the balance regulation function played, and generate a balance recovery correction torque mapped to each joint.

[0039] In one aspect of the present disclosure, there is provided an electronic device, comprising:

[0040] Processor; and

[0041] A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method according to any one of the above items.

[0042] In one aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the above items is implemented.

[0043] A dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance in an exemplary embodiment of the present disclosure, wherein the method comprises: collecting sensor signals based on the angle sensor, posture sensor and pressure sensor of the exoskeleton, calculating the multi-order derivatives of the sensor signals, and generating the system center of mass motion state in the sagittal plane under the current control cycle; fitting the forward instability boundary function and the backward instability boundary function of the three-dimensional equilibrium state manifold based on the center of mass position, center of mass velocity and inverted pendulum length according to the preset instability state range of the human-machine system; searching for the optimal regression motion state point corresponding to the dynamic instability area based on the dynamic instability area where the current instability state point is located, and calculating the target transient capture point corresponding to the optimal regression motion state point; calculating the horizontal dynamic balance correction force acting on the center of mass position based on the preset balance constraint relationship between the transient capture point, the center of mass state point and the pressure center point; calculating the dynamic balance correction force allocated to each joint of each leg of the exoskeleton based on the horizontal dynamic balance correction force according to the preset gait phase of each leg of the exoskeleton and the balance regulation function played, and generating the balance recovery correction torque mapped to each joint. The present invention makes up for the deficiencies in the detection and evaluation of the dynamic balance state of the human-machine coupling system by the lower limb exoskeleton, and at the same time fills the gap in the balance adjustment of the lower limb exoskeleton when switching to the target balance state and when the swinging leg steps forward, which helps to improve the ability of the lower limb exoskeleton to detect the desired target balance state and the anthropomorphic dynamic balance adjustment.

[0044] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above and other features and advantages of the present disclosure will become more apparent by describing in detail example embodiments thereof with reference to the attached drawings.

[0046] Figure 1 A flow chart of a dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance according to an exemplary embodiment of the present disclosure is shown;

[0047] Figure 2 The overall structure of an exoskeleton and a distribution diagram of joint sensors according to a dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance according to an exemplary embodiment of the present disclosure are shown;

[0048] Figure 3 A schematic diagram of a three-dimensional equilibrium state manifold in a sagittal plane that varies with the pendulum length of an inverted pendulum model according to a dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance according to an exemplary embodiment of the present disclosure is shown;

[0049] Figure 4A schematic diagram of joint correction torque of an exoskeleton in a two-foot support state according to a dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance according to an exemplary embodiment of the present disclosure is shown;

[0050] Figure 5 A schematic diagram of joint correction torque of an exoskeleton in a single-leg support state according to a dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance according to an exemplary embodiment of the present disclosure is shown;

[0051] Figure 6 A flowchart showing a dynamic balance recovery correction torque of an exoskeleton system for calculating a dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance according to an exemplary embodiment of the present disclosure is shown;

[0052] Figure 7 A structural block diagram of a dynamic balance recovery control device based on three-dimensional equilibrium state manifold guidance according to an exemplary embodiment of the present disclosure is shown;

[0053] Figure 8 A block diagram schematically shows an electronic device according to an exemplary embodiment of the present disclosure; and

[0054] Fig. 9 A schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0055] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0056] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, materials, devices, steps, etc. may be adopted. In other cases, known structures, methods, devices, implementations, materials or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0057] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or these functional entities or parts of functional entities may be implemented in one or more software hardened modules, or these functional entities may be implemented in different networks and / or processor devices and / or microcontroller devices.

[0058] In this exemplary embodiment, a dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance is first provided; Figure 1 As shown in , the dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance may include the following steps:

[0059] Step S110, collecting sensor signals based on the angle sensor, posture sensor, and pressure sensor of the exoskeleton, calculating multi-order derivatives of the sensor signals, and generating a system center of mass motion state in the sagittal plane under the current control cycle;

[0060] Step S120, fitting a forward instability boundary function and a backward instability boundary function of a three-dimensional equilibrium state manifold based on the center of mass position, center of mass velocity, and inverted pendulum length according to the instability state range preset by the human-machine system;

[0061] Step S130, based on the dynamic instability region where the current instability state point is located, searching for an optimal regression motion state point corresponding to the dynamic instability region, and calculating a target transient capture point corresponding to the optimal regression motion state point;

[0062] Step S140, calculating the horizontal dynamic balance correction force acting at the center of mass position based on the preset balance constraint relationship between the transient capture point, the center of mass state point, and the pressure center point;

[0063] Step S150, according to the preset gait phase of each leg of the exoskeleton and the balance regulation function played, the dynamic balance correction force allocated to each joint of each leg of the exoskeleton is calculated based on the horizontal dynamic balance correction force, and the balance recovery correction torque mapped to each joint is generated.

[0064] A dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance in an exemplary embodiment of the present disclosure, wherein the method comprises: collecting sensor signals based on the angle sensor, posture sensor and pressure sensor of the exoskeleton, calculating the multi-order derivatives of the sensor signals, and generating the system center of mass motion state in the sagittal plane under the current control cycle; fitting the forward instability boundary function and the backward instability boundary function of the three-dimensional equilibrium state manifold based on the center of mass position, center of mass velocity and inverted pendulum length according to the preset instability state range of the human-machine system; searching for the optimal regression motion state point corresponding to the dynamic instability area based on the dynamic instability area where the current instability state point is located, and calculating the target transient capture point corresponding to the optimal regression motion state point; calculating the horizontal dynamic balance correction force acting on the center of mass position based on the preset balance constraint relationship between the transient capture point, the center of mass state point and the pressure center point; calculating the dynamic balance correction force allocated to each joint of each leg of the exoskeleton based on the horizontal dynamic balance correction force according to the preset gait phase of each leg of the exoskeleton and the balance regulation function played, and generating the balance recovery correction torque mapped to each joint. The present invention makes up for the deficiencies in the detection and evaluation of the dynamic balance state of the human-machine coupling system by the lower limb exoskeleton, and at the same time fills the gap in the balance adjustment of the lower limb exoskeleton when switching to the target balance state and when the swinging leg steps forward, which helps to improve the ability of the lower limb exoskeleton to detect the desired target balance state and the anthropomorphic dynamic balance adjustment.

[0065] Next, a dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance in this example embodiment will be further described.

[0066] Embodiment 1:

[0067] In step S110, sensor signals may be collected based on the angle sensor, posture sensor, and pressure sensor of the exoskeleton, and multi-order derivatives of the sensor signals may be calculated to generate the system center of mass motion state in the sagittal plane in the current control cycle.

[0068] In the embodiment of this example, the method further includes:

[0069] The angle sensor based on the exoskeleton collects the joint angle sensor signal;

[0070] The exoskeleton-based attitude sensor collects linear acceleration sensor signals and attitude angle sensor signals;

[0071] The pressure sensor based on the exoskeleton collects sensor signals and collects contact force sensor signals.

[0072] In step S120, the forward instability boundary function and the backward instability boundary function of the three-dimensional equilibrium state manifold can be fitted based on the center of mass position, center of mass velocity, and inverted pendulum length according to the instability state range preset by the human-machine system.

[0073] In the embodiment of this example, the method includes the following constraints for fitting the three-dimensional equilibrium state manifold based on the center of mass position, center of mass velocity, and inverted pendulum length according to the instability state range preset by the human-machine system:

[0074] During the balance recovery process, the inverted pendulum is approximately subjected to variable acceleration rotation motion. The angular acceleration of the inverted pendulum at the next discretized time node is determined by the applied ankle joint torque, and the angular velocity and angle constraints.

[0075] The constraints on the torque applied during balance recovery and the range of ankle joint torque;

[0076] Inverted pendulum model and horizontal plane constraints;

[0077] Constraints of zero moment point and effective support domain;

[0078] Constraints on a tiny neighborhood of the target equilibrium state of an inverted pendulum.

[0079] In step S130, based on the dynamic instability region where the current instability state point is located, an optimal regression motion state point corresponding to the dynamic instability region may be searched, and a target transient capture point corresponding to the optimal regression motion state point may be calculated.

[0080] In the embodiment of this example, the method further includes:

[0081] Based on the dynamic instability region where the current instability state point is located and the center of mass motion state point, the square of the Euclidean norm between the center of mass motion state point and the optimal regression motion state point is calculated;

[0082] Solving partial derivatives of the square of the Euclidean norm respectively;

[0083] Based on the minimum solution obtained by solving the partial derivatives, an optimal regression motion state point corresponding to the dynamic instability region is generated.

[0084] In step S140, the horizontal dynamic balance correction force acting at the center of mass position may be calculated based on the preset balance constraint relationship among the transient capture point, the center of mass state point, and the pressure center point.

[0085] In the embodiment of this example, the method further includes:

[0086] Based on the generalized function and the preset equilibrium constraint relationship between the transient capture point, the center of mass state point, and the pressure center point, the simultaneous equations for the real-time adjustment speed of the transient capture point are generated;

[0087] Generate a target pressure center point function expression when the system is in equilibrium based on the simultaneous equations;

[0088] According to the target pressure center point function expression, the horizontal dynamic balance correction force acting on the center of mass position is calculated based on the deviation of the inertial force when the inertial force is balanced with the center of mass.

[0089] In step S150, according to the preset gait phase of each leg of the exoskeleton and the balance regulation function played, the dynamic balance correction force allocated to each joint of each leg of the exoskeleton can be calculated based on the horizontal dynamic balance correction force to generate a balance recovery correction torque mapped to each joint.

[0090] In the embodiment of this example, the method further includes:

[0091] According to the preset gait phase of each leg of the exoskeleton and the balance regulation function played, the dynamic balance correction force distributed to each joint of each leg of the exoskeleton is calculated based on the horizontal dynamic balance correction force;

[0092] According to the dynamic balance correction force of each joint, based on the preset distribution coefficient and direction coefficient, a balance recovery correction torque mapped to each joint is generated.

[0093] Embodiment 2:

[0094] In the embodiment of this example, a dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance of this embodiment includes:

[0095] Step S110: Collect the joint angles of the exoskeleton encoders Ⅱ-1, Ⅱ-2, Ⅱ-3, Ⅲ-1, Ⅴ-1, Ⅴ-2, Ⅴ-3, Ⅵ-1, the linear acceleration and posture angle of the multi-axis IMU sensor Ⅰ, and the contact force signals of the pressure sensors Ⅳ-1, Ⅳ-2, Ⅳ-3, Ⅳ-4, Ⅶ-1, Ⅶ-2, Ⅶ-3, Ⅶ-4, and calculate their multi-order derivatives to obtain the motion state of the center of mass of the system in the sagittal plane under the current control cycle.

[0096]

[0097] Step S120: According to the conventional instability state range of the human-machine system, fit the center of mass position (z CoM Corresponding to the X axis), center of mass velocity ( corresponding to the Y axis), inverted pendulum length (R CoM The forward instability boundary function and the backward instability boundary function f(X,Y,Z) of the three-dimensional equilibrium state manifold related to the Z axis;

[0098] Step S130: Search for the corresponding optimal regression motion state point according to the dynamic instability region where the current instability state point is located Calculate the corresponding target transient snap point

[0099] Step S140: According to the transient capture point z ICP , center of mass state point z CoP 、pressure center point z CoP The equilibrium constraint relationship between them is used to calculate the horizontal dynamic equilibrium correction force F acting at the center of mass. z,correct ;

[0100] Step S150: Calculate the dynamic balance correction force assigned to each joint according to the gait phase of each leg and the balance adjustment function played (0 represents the front leg, 1 represents the back leg), and the balance recovery correction torque mapped to each joint is obtained;

[0101] like Figure 2 Figure 1 is the overall structure of the exoskeleton and the distribution of joint sensors. I is the back motion sensing module, II is the right leg hip joint motion sensing module, III is the right leg knee joint motion sensing module, IV is the right leg foot end motion sensing module, V is the left leg hip joint motion sensing module, VI is the left leg knee joint motion sensing module, and VII is the left leg foot end motion sensing module. Among them, Ⅰ is a multi-axis IMU sensor for measuring the trunk posture angle and linear acceleration. The motion sensing module of the leg is described in detail taking the left leg as an example. Ⅴ-1, Ⅴ-2, and Ⅴ-3 are encoders for measuring the joint angle of the left hip joint. Ⅴ-4 is a torque sensor for measuring the interactive force of the left hip joint. Ⅵ-1 is an encoder for measuring the joint angle of the left knee joint. Ⅵ-2 is a torque sensor for measuring the interactive force of the left knee joint. Ⅶ-1, Ⅶ-2, Ⅶ-3, and Ⅶ-4 are pressure sensors for measuring the contact force of the left leg foot. The right leg and the left leg are equipped with the same sensors.

[0102] like Figure 3 It is a three-dimensional equilibrium state manifold with the change of the pendulum length of the inverted pendulum model under single-leg support in the sagittal plane. In the conventional instability area (center of mass position -0.2m~0.2m, center of mass speed -1.0m / s~1.0m / s), for each given inverted pendulum model pendulum length R CoM , there is a pair of equilibrium manifold boundaries, namely the forward instability boundary and the backward instability boundary. When the position-velocity-pendulum length motion state point of the inverted pendulum model's center of mass exceeds the forward instability boundary, the system has a tendency to fall forward. Similarly, when the position-velocity-pendulum length motion state point of the inverted pendulum model's center of mass exceeds the forward instability boundary, the system has a tendency to fall forward. When the backward instability boundary is exceeded, the system tends to fall backward. And as the length of the inverted pendulum model increases, the slope of the forward instability boundary and the backward instability boundary of the equilibrium state manifold gradually increases, that is, the absolute value gradually decreases, but the sign remains negative. Therefore, for the inverted pendulum model with a changing pendulum length, for different position-velocity-pendulum length state points, the nearest optimal regression motion state point is It is changing and needs to be solved in real time.

[0103] In step S120, the forward instability boundary function and the backward instability boundary function of the three-dimensional equilibrium state manifold related to the center of mass position, center of mass velocity, and inverted pendulum length are fitted, which involves the numerical solution of the three-dimensional equilibrium state manifold. First, for a given pendulum length R CoM Inverted pendulum model, the finite time interval [0,T] is discretized into N equal parts to obtain the discretized time node vector [0,Δt,2Δt,...,iΔt,...,T], (i∈[1,N]), the initial inverted pendulum motion state After a series of adjustments, the final inverted pendulum motion state Eventually, we can return to the target equilibrium state The computational constraints of the three-dimensional equilibrium state manifold in this process include:

[0104] (1) During the balance recovery process, the inverted pendulum is approximately subjected to variable acceleration rotation motion. The angular acceleration of the inverted pendulum at the next discretized time node is determined by the applied ankle joint torque. The angular velocity and angle are approximated by integration, that is,

[0105]

[0106] (2) The torque applied during balance recovery is always kept within the ankle joint torque range within, that is

[0107] (3) The inverted pendulum model always remains above the horizontal plane, that is, θ(iΔt)∈(0,π);

[0108] (4) The zero moment point always remains within the effective support domain, that is,

[0109]

[0110] (5) The inverted pendulum can eventually move to a small neighborhood of the target equilibrium state, that is,

[0111]

[0112] Where Q υIt is a diagonal matrix that performs weighted modulation on the angular acceleration deviation, angular velocity deviation, and angle deviation. It is used to adjust the weights between the three deviations so that the Euclidean norm of the deviation vector can quickly converge to the target micro-neighborhood. For each initial angle θ(0), there is a maximum limit initial angular velocity and a minimum limiting initial angular velocity Therefore, by fitting a series of θ(0) and You can get R CoM The forward instability boundary of the equilibrium state manifold under θ(0) is obtained by fitting a series of θ(0) and You can get R CoM The backward instability boundary of the equilibrium state manifold under Figure 3 As shown in the middle curve, in the conventional instability region (center of mass position -0.2m~0.2m, center of mass velocity -1.0m / s~1.0m / s), the forward instability boundary and the backward instability boundary can be approximately fitted as straight lines and are parallel to each other. CoM , we can finally get CoM The equilibrium state manifold.

[0113] Then establish the center of mass position (z CoM Corresponding to the X axis), center of mass velocity ( corresponding to the Y axis), inverted pendulum length (R CoM Corresponding to the Z axis) of the three-dimensional space coordinate system. CoM By analyzing the equilibrium state manifold under the condition of , we can get the expressions of the forward instability boundary function and the backward instability boundary function of the three-dimensional equilibrium state manifold as follows:

[0114]

[0115] In step S130, according to the current center of mass motion state point Solve the optimal regressive motion state point on the instability boundary surface of the corresponding three-dimensional equilibrium state manifold First, the quantitative function representing the instability degree of the human-machine system is set as the square of the Euclidean norm between the current center of mass motion state point and the optimal regression motion state point Then the search problem for the optimal regression motion state point can be simplified to the following minimum value solution problem:

[0116]

[0117] Then, the partial derivative of L is solved. If there is a minimum distance between the point and the three-dimensional surface, the two partial derivatives of L that need to be guaranteed to be zero in the process of solving the corresponding point on the three-dimensional surface are analyzed and solved through the following three cases.

[0118] (1) If Then it corresponds to Y=Y 0 , there are two feasible solutions as shown below, that is, we only need to ensure that the two are not zero at the same time, through Y = Y 0 , X=X 0 Or Y=Y 0 , Z=Z 0 Substitute the instability boundary function of the three-dimensional equilibrium manifold to solve the third unknown variable Z or X.

[0119]

[0120] (2) If Then Y≠Y 0 , there is a feasible solution as shown below, that is, we only need to ensure that both are zero at the same time. It can be seen from the analytical expression that and is a function related to the coupling of X and Z. Therefore, this solution adopts a simpler solution method, that is, and Rewrite it as a polynomial function of X.

[0121]

[0122] By matching the highest-order coefficient of X to make the coefficients of the two equations consistent, the unknown variable Z is solved, and then Z is substituted into the above equation to further solve X, and finally substituted into the instability boundary function of the three-dimensional equilibrium state manifold to solve the third unknown variable Y.

[0123]

[0124] Finally, by comparing the L values ​​corresponding to the three solutions, the motion state point corresponding to the minimum L value is selected as the optimal regression motion state point corresponding to the center of mass motion state point at this time. In order to avoid the situation where the center of mass state has a weak recovery adjustment effect when it is close to the optimal regression motion state point, a smaller bias increment can be applied to obtain an updated optimal regression motion state point

[0125]

[0126] So as to find the target transient capture point

[0127]

[0128] like Figure 4 , Figure 5It is a schematic diagram of the joint correction torque of the exoskeleton when it is in the state of double-foot and single-foot support. When the exoskeleton determines that the current system is in an unstable state, it will calculate in real time a correction force to assist the human-machine system in restoring balance. At this time, it is necessary to solve the correction torque acting on the joint in real time according to the size of the correction force and the relative position of the action position and the stressed joint, and then send it to the motor driver for control.

[0129] Real-time adjustment speed of transient capture point in step S140 Real-time deviation from transient capture point is related, and the function relationship it sets has an impact on the speed of the adjustment process. Therefore, in this scheme, a generalized function Φ is used for presetting, and the transient capture point z ICP , center of mass state point z CoP 、pressure center point z CoP There is a balance constraint relationship between them, so we can get The simultaneous equations

[0130]

[0131] By eliminating The target pressure center point z when the system is balanced can be obtained CoP The function expression of Φ is a monotonic odd function to ensure that the real-time deviation of the transient capture point can continue to converge.

[0132]

[0133] Finally, according to the deviation between the inertial force currently exerted on the center of mass and the inertial force exerted on the center of mass when it is in equilibrium, the horizontal dynamic equilibrium correction force acting on the center of mass and opposite to the instability trend can be obtained as

[0134]

[0135] like Figure 6 This is a flow chart of the exoskeleton calculating the dynamic balance recovery correction torque of the human-machine system. In each new control cycle, the exoskeleton needs to collect sensor information for the current center of mass motion state point S CoM Calculate and solve the optimal regression motion state point in real time and balance recovery correction force F z,correct , thereby obtaining the real-time dynamic balance recovery correction torque.

[0136] In step S150, it is necessary to determine the phase state of each leg of the current exoskeleton based on the contact force of the sole. For the supporting leg, the torque of the horizontal correction force mapped to the joint is mainly used to adjust the center of mass of the trunk and the supporting leg for deceleration movement, and its direction generally remains unchanged. For the swinging leg, the torque of the horizontal correction force mapped to the joint is mainly used to adjust the landing position of the foot end of the swinging leg. For forward instability, when the foot end is lifted to a certain height to the front side, it can quickly land on the ground so that the system can quickly reach a stable state of support with both feet. Similarly, for backward instability, when the foot end is lifted to a certain height to the rear side, it can quickly land on the ground so that the system can quickly reach a stable state of support with both feet. Therefore, the direction of the correction torque acting on the hip and knee joints is variable, which is related to the lifting height position of the front or rear side. The associated, designed directional coefficient is as follows

[0137]

[0138] in Indicates a forward instability trend, Indicates the backward instability trend, and the phase judgment sign function of each leg is defined as

[0139]

[0140] When the joints of the swinging leg rotate at the same small fixed angle, due to the different rotation radius, the influence of the hip and knee joints on the position of the terminal ankle is different. The correction force acting on the hip and knee joints should be distributed according to the actual effect of the rotational movement. The distribution coefficient is designed as follows:

[0141]

[0142] Finally, according to the distribution coefficient and direction coefficient, the correction force acting on each joint can be obtained as

[0143]

[0144] In the embodiment of this example, the three-dimensional dynamic balance domain that changes with the pendulum length of the inverted pendulum model is used to evaluate the human-in-the-loop balance state of the human-machine coupling system, and the influence of the wearer's ankle joint torque and the change of the inverted pendulum center of mass point position on the center of mass-velocity balance boundary is considered, so that the prediction of the balance state is more in line with the wearer's intention;

[0145] In the embodiment of this example, the target regression point of the balance state is set as a point on the nearest boundary, and the wearer's demand for balance intervention, as well as the intervention effects of different legs and the degree of contribution to balance regulation are taken into consideration. The directionality of balance regulation is clearer, and excessive balance intervention is avoided.

[0146] In the embodiment of this example, regional analysis and coefficient adjustment are performed on the subsequent landing points of the swinging leg in the single-leg support state, so that the balance control strategy during the leg step is more in line with the wearer's movement habits, meets the needs of rapid switching of the actual target balance state, and realizes effective safety assistance during non-rhythmic movement.

[0147] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0148] In addition, in this exemplary embodiment, a dynamic balance recovery control device based on three-dimensional equilibrium state manifold guidance is also provided. Figure 7 As shown, the dynamic balance recovery control device 200 based on three-dimensional equilibrium state manifold guidance may include: a system center of mass motion state calculation module 210, an instability boundary function generation module 220, a target transient capture point calculation module 230, a horizontal dynamic balance correction force calculation module 240 and a balance recovery correction torque generation module 250.

[0149] in:

[0150] The system center of mass motion state calculation module 210 is used to collect sensor signals based on the angle sensor, posture sensor, and pressure sensor of the exoskeleton, calculate the multi-order derivatives of the sensor signals, and generate the system center of mass motion state in the sagittal plane under the current control cycle;

[0151] An instability boundary function generating module 220 is used to fit a forward instability boundary function and a backward instability boundary function of a three-dimensional equilibrium state manifold based on the center of mass position, center of mass velocity, and inverted pendulum length according to an instability state range preset by the human-machine system;

[0152] The target transient capture point calculation module 230 is used to search for the optimal regression motion state point corresponding to the dynamic instability region based on the dynamic instability region where the current instability state point is located, and calculate the target transient capture point corresponding to the optimal regression motion state point;

[0153] A horizontal dynamic balance correction force calculation module 240 is used to calculate the horizontal dynamic balance correction force acting on the center of mass position based on a preset balance constraint relationship between the transient capture point, the center of mass state point, and the pressure center point;

[0154] The balance recovery correction torque generation module 250 is used to calculate the dynamic balance correction force allocated to each joint of each leg of the exoskeleton based on the horizontal dynamic balance correction force according to the preset gait phase of each leg of the exoskeleton and the balance regulation function played, and generate a balance recovery correction torque mapped to each joint.

[0155] The specific details of each of the above-mentioned dynamic balance recovery control device modules based on three-dimensional equilibrium state manifold guidance have been described in detail in the corresponding dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance, so they will not be repeated here.

[0156] It should be noted that, although several modules or units of the dynamic balance recovery control device 200 guided by a three-dimensional equilibrium state manifold are mentioned in the above detailed description, such division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0157] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0158] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0159] Refer to the following Figure 8 The electronic device 300 according to such an embodiment of the present invention is described. Figure 8 The electronic device 300 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0160] like Figure 8 As shown, the electronic device 300 is in the form of a general computing device. The components of the electronic device 300 may include but are not limited to: at least one processing unit 310, at least one storage unit 320, a bus 330 connecting different system components (including the storage unit 320 and the processing unit 310), and a display unit 340.

[0161] The storage unit stores program codes, which can be executed by the processing unit 310, so that the processing unit 310 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 310 can perform the following steps: Figure 1 Steps S110 to S150 shown in FIG.

[0162] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 3201 and / or a cache memory unit 3202 , and may further include a read-only memory unit (ROM) 3203 .

[0163] The storage unit 320 may also include a program / utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0164] Bus 330 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0165] The electronic device 300 may also communicate with one or more external devices 370 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 300, and / or communicate with any device that enables the electronic device 300 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 350. Furthermore, the electronic device 300 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 360. As shown, the network adapter 360 communicates with other modules of the electronic device 300 via a bus 330. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0166] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0167] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to perform the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0168] refer to Fig. 9 As shown, a program product 400 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0169] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0170] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0171] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0172] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0173] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0174] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0175] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A dynamic balance recovery control method based on three-dimensional equilibrium state manifold guidance, It is characterized in that The method comprises: Collect sensor signals based on the angle sensor, posture sensor, and pressure sensor of the exoskeleton, calculate multi-order derivatives of the sensor signals, and generate the system center of mass motion state in the sagittal plane under the current control cycle; According to the preset instability state range of the human-machine system, the forward instability boundary function and the backward instability boundary function of the three-dimensional equilibrium state manifold are fitted based on the center of mass position, center of mass velocity, and inverted pendulum length; Based on the dynamic instability region where the current instability state point is located, searching for the optimal regression motion state point corresponding to the dynamic instability region, and calculating the target transient capture point corresponding to the optimal regression motion state point; Based on the preset equilibrium constraint relationship between the transient capture point, the center of mass state point, and the pressure center point, the horizontal dynamic equilibrium correction force acting at the center of mass position is calculated; According to the preset gait phase of each leg of the exoskeleton and the balance regulation function played, the dynamic balance correction force distributed to each joint of each leg of the exoskeleton is calculated based on the horizontal dynamic balance correction force, and the balance recovery correction torque mapped to each joint is generated.

2. The method according to claim 1, It is characterized in that The method further comprises: The angle sensor based on the exoskeleton collects the joint angle sensor signal; The exoskeleton-based attitude sensor collects linear acceleration sensor signals and attitude angle sensor signals; The pressure sensor based on the exoskeleton collects sensor signals and collects contact force sensor signals.

3. The method according to claim 1, It is characterized in that In the method, according to the instability state range preset by the human-machine system, the constraint conditions for fitting the three-dimensional equilibrium state manifold based on the center of mass position, center of mass velocity, and inverted pendulum length include: During the balance recovery process, the inverted pendulum is approximately subjected to variable acceleration rotation motion. The angular acceleration of the inverted pendulum at the next discretized time node is determined by the applied ankle joint torque, and the angular velocity and angle constraints. The constraints on the torque applied during balance recovery and the range of ankle joint torque; Inverted pendulum model and horizontal plane constraints; Constraints of zero moment point and effective support domain; Constraints on a tiny neighborhood of the target equilibrium state of an inverted pendulum.

4. The method according to claim 1, It is characterized in that The method further comprises: Based on the dynamic instability region where the current instability state point is located and the center of mass motion state point, the square of the Euclidean norm between the center of mass motion state point and the optimal regression motion state point is calculated; Solving partial derivatives of the square of the Euclidean norm respectively; Based on the minimum solution obtained by solving the partial derivatives, an optimal regression motion state point corresponding to the dynamic instability region is generated.

5. The method according to claim 1, It is characterized in that The method further comprises: Based on the generalized function and the preset equilibrium constraint relationship between the transient capture point, the center of mass state point, and the pressure center point, the simultaneous equations for the real-time adjustment speed of the transient capture point are generated; Generate a target pressure center point function expression when the system is in equilibrium based on the simultaneous equations; According to the target pressure center point function expression, the horizontal dynamic balance correction force acting on the center of mass position is calculated based on the deviation of the inertial force when the inertial force is balanced with the center of mass.

6. The method according to claim 1, It is characterized in that The method further comprises: According to the preset gait phase of each leg of the exoskeleton and the balance regulation function played, the dynamic balance correction force distributed to each joint of each leg of the exoskeleton is calculated based on the horizontal dynamic balance correction force; According to the dynamic balance correction force of each joint, based on the preset distribution coefficient and direction coefficient, a balance recovery correction torque mapped to each joint is generated.

7. A dynamic balance recovery control device based on three-dimensional equilibrium state manifold guidance, It is characterized in that The device comprises: The system center of mass motion state calculation module is used to collect sensor signals based on the angle sensor, posture sensor, and pressure sensor of the exoskeleton, calculate the multi-order derivatives of the sensor signals, and generate the system center of mass motion state in the sagittal plane under the current control cycle; An instability boundary function generation module is used to fit the forward instability boundary function and the backward instability boundary function of the three-dimensional equilibrium state manifold based on the center of mass position, center of mass velocity, and inverted pendulum length according to the instability state range preset by the human-machine system; A target transient capture point calculation module is used to search for an optimal regression motion state point corresponding to the dynamic instability region based on the dynamic instability region where the current instability state point is located, and calculate a target transient capture point corresponding to the optimal regression motion state point; A horizontal dynamic balance correction force calculation module is used to calculate the horizontal dynamic balance correction force acting at the center of mass position based on the preset balance constraint relationship between the transient capture point, the center of mass state point, and the pressure center point; The balance recovery correction torque generation module is used to calculate the dynamic balance correction force allocated to each joint of each leg of the exoskeleton based on the horizontal dynamic balance correction force according to the preset gait phase of each leg of the exoskeleton and the balance regulation function played, and generate a balance recovery correction torque mapped to each joint.

8. An electronic device, It is characterized in that include Processor; and A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, It is characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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