A method for generating anthropomorphic walking gait of a self-balancing lower limb exoskeleton

Abstract

This invention discloses a method for generating anthropomorphic walking gait for a self-balancing lower limb exoskeleton. The method includes: initializing a reference zero-moment point trajectory, a centroid sagittal plane trajectory, and a foot trajectory based on the laws of human walking motion; generating lateral motion of the self-balancing lower limb rehabilitation exoskeleton using a predictive control method based on the initialized reference zero-moment point trajectory; generating sagittal motion using a straight-knee optimizer and a stabilizing filter based on the initialized centroid sagittal plane trajectory and foot trajectory, wherein the straight-knee optimizer is used to optimize hip joint trajectories at different heights, and the stabilizing filter is used to generate hip joint trajectories that satisfy dynamic constraints; and solving the inverse kinematics using the Levenburg-Marquardt algorithm to obtain the joint trajectories of the self-balancing lower limb rehabilitation exoskeleton. Using this invention, an anthropomorphic walking gait with characteristics of varying centroid height, knee extension, heel strike, and toe lift-off can be generated.

Description

Technical Field

[0001] This invention relates to the field of robotics, and more specifically, to a method for generating anthropomorphic walking gait of a self-balancing lower limb exoskeleton. Background Technology

[0002] Self-balancing lower-limb rehabilitation exoskeletons (SLLREs) are a type of exoskeleton robot with high degrees of freedom, full drive, and the ability to dynamically balance and walk without the need for crutches or strollers. They can assist hemiplegic, paraplegic, and quadriplegic patients in lower limb rehabilitation training, provide support as a wheelchair replacement, and enable dynamic balanced walking. In physical therapy, the goal of gait rehabilitation training is to help individuals with lower limb dysfunction regain the gait of healthy individuals. Therefore, generating a healthy gait for the SLLRE is a key factor in improving its wearing comfort and rehabilitation effectiveness.

[0003] Currently, gait planning methods can be divided into model-based methods and model-free methods. Model-based methods first require establishing a physical model of the robot. Since the SLLRE has high degrees of freedom and is a very complex dynamic system with multivariable nonlinear characteristics, it is difficult to establish an accurate dynamic model. Therefore, it is usually modeled as a common, simple physical model, such as an inverted pendulum model, a car-table model, or a linkage model, and then the gait is planned based on the model. Taking the inverted pendulum model as an example, the SLLRE can be approximated as a linear inverted pendulum consisting of a point mass containing all the mass and massless legs connecting this point mass to a support point. The torque at the support point is zero, hence it is also called the Zero Moment Point (ZMP). Then, the model is set with a walking cycle, walking parameters (such as stride length, stride width, single / double leg support time, etc.), the initial position of the center of mass, and the initial landing point (x0, y0). Then, the expected landing point (x1, y1) for the next cycle is calculated based on the corresponding dynamic model, and this process is repeated until the final landing point (x0, y0) is obtained. n y n After obtaining the landing point trajectory, it is necessary to plan the ZMP trajectory, then calculate the corresponding center of mass trajectory using the linear inverted pendulum equation, and finally obtain the trajectory of each joint through inverse kinematics calculation using the ZMP trajectory and the center of mass trajectory. This method can satisfy the stability of the walking process, but because the solution is not unique and there is an optimization problem, it cannot guarantee the anthropomorphism of the gait.

[0004] Model-free gait generation methods typically constrain and control bipedal walking motion from the perspectives of robot stability and energy. Planning the gait of a SLLRE (Side-Loop Robot) with stability as a constraint and minimizing energy consumption as the optimization objective can make its walking style more similar to that of a human. This method can fully utilize the robot's performance and reduce walking energy consumption; however, the optimal planning requires a large amount of computation and may result in no solution in complex situations.

[0005] In the field of self-balancing lower limb exoskeleton robots, there is currently no technical solution for generating humanoid gait. However, in the field of humanoid robots, a proposed method for generating humanoid robot gait has been developed. This method includes the following steps: establishing an initial walking pattern generator by analyzing human driving characteristics to generate joint driving torques suitable for basic walking movements; establishing mathematical models of stretch reflexes and vestibular reflexes based on the local characteristics of sensor reflections, and superimposing the joint driving torques generated by stretch reflexes and vestibular reflexes onto the total joint driving torque; adjusting the joint driving torques to adjust the robot's joint trajectories in the motion space, ensuring that the supporting leg does not leave the ground and the swinging leg reaches the correct position during walking; establishing a ZMP reflection model, superimposing the joint driving torques generated by ZMP reflections onto the total joint driving torque, and further adjusting the joint trajectories by adjusting the joint driving torques to achieve stable walking movements and improve the robot's environmental adaptability.

[0006] Analysis shows that while current model-based gait generation methods can generate stable walking gaits relatively well, they cannot guarantee the anthropomorphism of the gait. Model-free gait generation methods, on the other hand, can guarantee the anthropomorphism of the gait, but they involve large computational costs and cannot guarantee the feasibility of the solution. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for generating anthropomorphic walking gait using a self-balancing lower limb exoskeleton. This method includes the following steps:

[0008] Based on the laws of human walking, initialize the reference zero torque point trajectory, the centroid sagittal plane trajectory, and the foot trajectory;

[0009] Based on the initial reference zero-torque point trajectory, the lateral motion of the self-balancing lower limb rehabilitation exoskeleton is generated using the predictive control method.

[0010] Based on the initialized centroid sagittal plane trajectory and foot trajectory, a straight-knee optimizer and a stabilizing filter are used to generate sagittal motion, wherein the straight-knee optimizer is used to optimize the hip joint trajectory at different heights, and the stabilizing filter is used to generate the hip joint trajectory that satisfies the dynamic constraints.

[0011] The inverse kinematics were solved using the Levenberg-Marquardt algorithm to obtain the joint trajectories of the self-balancing lower limb rehabilitation exoskeleton.

[0012] Compared with the prior art, the advantages of this invention are that, for the complex multi-degree-of-freedom, multi-rigid-body serial-parallel hybrid mechanism of the self-balancing lower limb exoskeleton robot, in order to improve the wearing comfort and rehabilitation efficiency of the SLLRE, this invention decouples the three-dimensional motion of the SLLRE based on the inverted pendulum model, adopts the idea of ​​hierarchical optimization, optimizes the motion in each single-dimensional direction in sequence, and tries to meet the laws of human walking motion as much as possible during optimization. Finally, the motion in each direction is coupled into the three-dimensional anthropomorphic walking gait of the SLLRE, thus designing a more suitable anthropomorphic gait for the SLLRE.

[0013] Other features and advantages of the invention will become clear from the following detailed description of exemplary embodiments of the invention with reference to the accompanying drawings. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments of the invention and, together with their description, serve to explain the principles of the invention.

[0015] Figure 1 This is a flowchart of a method for generating anthropomorphic walking gait using a self-balancing lower limb exoskeleton according to an embodiment of the present invention;

[0016] Figure 2 This is a schematic diagram of an anthropomorphic walking gait generation framework according to an embodiment of the present invention;

[0017] Figure 3 This is a schematic diagram of foot trajectory initialization based on a finite state machine according to an embodiment of the present invention.

[0018] Figure 4 This is a schematic diagram of virtual leg length constraints in a knee optimizer according to an embodiment of the present invention;

[0019] Figure 5 This is a hip joint position curve and a schematic diagram of the virtual lengths of the left and right legs obtained by a straight-knee optimizer according to an embodiment of the present invention.

[0020] Figure 6 This is a schematic diagram of the hip joint position curve and velocity curve after stabilization filter processing according to an embodiment of the present invention;

[0021] Figure 7 This is a schematic diagram of a humanoid walking gait simulation experiment of a self-balancing lower limb exoskeleton according to an embodiment of the present invention. Detailed Implementation

[0022] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0023] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0024] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0025] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0026] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0027] In general, the anthropomorphic walking gait generation method for a self-balancing lower limb exoskeleton provided by this invention includes: analyzing the laws of human walking motion and extracting walking feature parameters, such as the height of the change in the center of mass, the toe lift-off angle, and the knee extension angle; initializing a reference ZMP trajectory and generating the lateral motion of the SLLRE using a predictive control algorithm; initializing the center of mass sagittal plane trajectory and foot trajectory, and using a straight-knee optimizer and a stabilizing filter to ensure that the knee joint is as straight as possible during the single-leg support phase; and using the Levenberg-Marquardt (LM) algorithm to solve the inverse kinematics and obtain the trajectory of each joint of the robot.

[0028] Specifically, in combination Figure 1 and Figure 2 As shown, the method for generating anthropomorphic walking gait for a self-balancing lower limb exoskeleton includes the following steps:

[0029] Step S1: Based on the laws of human walking motion, initialize the reference zero torque point trajectory, the centroid sagittal plane trajectory, and the foot trajectory.

[0030] For example, the reference ZMP (zero moment point) and sagittal centroid trajectory are mainly determined based on the number of steps, step length, and period, while the foot trajectory is determined based on a finite state machine and fifth-order polynomial interpolation, where the states are similar. Figure 3 As shown, the phases include the toes off the ground, the single-foot support phase, and the heel-on-ground phase. In the sagittal plane, the position of the foot can be determined by (x, z, θ), and the specific calculation method for each parameter is given in formula (1).

[0031]

[0032] in, and The positions of the ankle joint of the swing leg along the x-axis, z-axis, and pitch angles are L. stride It's the step size, L forefoot and L heel θ represents the length of the forefoot and the length of the heel. toe and θ heel H represents the pitch angle limits for the toes and heels, respectively. ankle H represents ankle height. lift T represents the maximum height the foot can be raised. c T to T hs and T m These represent the single gait cycle, toe-off phase cycle, heel-on phase cycle, and single-foot support phase cycle, respectively. k represents a natural number (values ​​0, 1, 2, 3, ..., n), and t represents time. Figure 2 In the middle, P x It is the position of ZMP on the x-axis, P y It is the position of ZMP on the y-axis. This indicates the reference position of the centroid in the x-axis direction, and y... CoM Indicates the position of the centroid along the y-axis, z CoM Indicates the position of the centroid along the z-axis, x CoM It is the position of the ankle joint on the x-axis, z-axis ankle It is the height of the ankle joint on the z-axis, θ pitch Indicates the pitch angle.

[0033] The centroid sagittal plane trajectory is determined by the number of steps, step length, and period, which determine the start and end positions of the hip joint in the x-direction, and is obtained by piecewise interpolation using a cubic polynomial.

[0034] Step S2: Based on the centroid dynamics model, the lateral motion of the centroid is generated using a predictive control algorithm.

[0035] Specifically, based on the center-of-mass dynamics model, the dynamic equation of the self-balancing lower limb exoskeleton in the coronal plane can be expressed as:

[0036]

[0037] Where y and p represents the position and acceleration of the center of mass, respectively. y This indicates the position of ZMP on the y-axis, z cis the height of the center of mass in the world coordinate system, and g is the gravitational acceleration. Since the dynamic equation of the center of mass (2) is expressed in continuous space, in practical applications, it needs to be rewritten in discrete space. Assuming the sampling time is Δt, the jerk of the center of mass is... As the control input u(i) of the system, i.e. The discrete state equation is then:

[0038]

[0039] Where A is the system matrix, representing the dynamic relationships between the system's state variables; B is the input matrix, representing how input signals affect the system's state variables; and C is the output matrix, representing the relationship between the system's output and its state variables. Their values ​​are respectively... The current system input is The system output at the next moment is

[0040] To address the ZMP bias caused by long-distance walking patterns, the state-space equations are rewritten in the following extended form:

[0041]

[0042] The extended control input is Δu(i) = u(i) - u(i-1), the intermediate variable is ΔΦ(i) = Φ(i) - Φ(i-1), and the extended system input is... Extended system matrix Expanded input matrix Expanded output matrix

[0043] Where Φ(i+1) represents the output at the next time step, p y (i) represents the position of ZMP in the y-axis direction at time i, Φ * (i+1) represents the expanded output for the next time step.

[0044] To better track ZMP, the walking problem can be written as a linear quadratic optimal control problem, and the quadratic performance index can be expressed as:

[0045]

[0046] Where Q and R are positive weighting factors. Let J represent the position of the reference ZMP on the y-axis at time j, J represent the performance index, also called the cost function, and j be a time-time variable, taking the form of a natural number 1, 2, ..., n. According to predictive control theory, this function can be minimized by inputting the target reference values ​​for the next N steps.

[0047]

[0048] in, Indicates an increase in state. This represents the extended state at time k. p represents the expected ZMP gain. ref (j) represents the reference ZMP at time j.

[0049] in:

[0050]

[0051] Where R represents the penalty term coefficient matrix, and T(i-1) represents the (i-1)th period.

[0052] Matrix P is a solution to the following Riccati equation:

[0053]

[0054] The lateral trajectory of the center of mass can be obtained by combining formulas (4), (6), (7), and (8).

[0055] Step S3: A straight-knee optimizer and a stabilizing filter are used to generate sagittal anthropomorphic walking motion. The straight-knee optimizer is used to optimize the hip joint trajectory with varying heights, and the stabilizing filter is used to generate a hip joint trajectory that satisfies dynamic constraints.

[0056] The sagittal plane anthropomorphic walking motion generation mainly consists of two steps. First, a straight-knee optimizer is used to optimize the hip joint trajectory at different heights to achieve knee extension characteristics. Second, a stabilizing filter with dynamic constraints is used to generate a stable hip joint trajectory.

[0057] According to the pattern of human walking, during the single-leg support phase, the height of the center of gravity increases with the walking process, while during the double-leg support phase, the main change is the exchange of supporting legs, accompanied by a smaller change in height. Therefore, in one embodiment, the center point of the single-leg support phase and the starting point of the double-leg support phase are selected as variables to be optimized, and the hip joint height design problem is expressed as a multi-variable optimization problem of formula (9), and then the points are connected by a fifth-order polynomial.

[0058]

[0059] Among them, h j (j = 1, 2, ..., 2Nstep-1) are the points to be optimized on the hip height curve, where Nstep is the total number of steps taken, and z hip (i) represents the i-th point in the hip joint height curve. and L represents the virtual lengths of the left and right legs, i.e., the straight-line distance from the hip joint to the ankle joint.leg That's the actual length of the leg. and h These are the upper and lower threshold values ​​for height. H stand Indicates the height when standing.

[0060] In the optimization, to avoid an excessively high hip joint height leading to an unsolvable inverse kinematics problem, constraints were imposed on leg length and height. In the objective function, soft constraints were introduced to ensure the height is as close as possible to the height in the standing state, and the distance from the center of mass to the distal end is to be as close as possible to the length of the leg when fully extended. In the inequality constraints, threshold constraints were used to maximize the height while ensuring the distance from the center of mass to the distal end does not exceed the leg length. Figure 4 As shown, the orange dashed line represents the virtual leg length, and its calculation formula is as follows:

[0061]

[0062] The stabilizing filter is primarily used to ensure that the generated joint trajectories satisfy dynamic constraints, which requires optimization of the hip joint in the x-direction. First, a reference acceleration value is designed as follows:

[0063] a d =k p (x d -x)+k d (v d -v) (11)

[0064] Among them, y hip Indicates the position of the hip joint on the y-axis, x hip Indicates the position of the hip joint on the x-axis, x1 angle Indicates the position of the left ankle joint on the x-axis, z-axis hip This indicates the position of the hip joint on the z-axis. Indicates the position of the left ankle joint on the z-axis, x r angle This indicates the position of the right ankle joint on the x-axis. This indicates the position of the right ankle joint along the z-axis. p k d It is the control factor, x d v d a dLet x and v represent the desired position, desired velocity, and desired acceleration of the hip joint, respectively, and x and v represent the actual position and velocity. The optimization objective is to track the desired acceleration and ensure that the generated ZMP tracks the desired ZMP within the supporting polygon. Therefore, the objective function consists of several terms: acceleration tracking error, ZMP tracking error, quadratic term of acceleration, and acceleration variation. The quadratic term of acceleration ensures moderate acceleration and limits deviations in position and velocity, maintaining consistency with the target tracking acceleration. Furthermore, the introduction of the acceleration variation term avoids generating spike-like movements in the gait. Regarding inequality constraints, the same geometric virtual leg length constraint as in height optimization is first introduced. Secondly, stability constraints are added to ensure that the generated ZMP lies within the supporting polygon by calculating the ZMP through a centroid dynamics model. Therefore, the optimization problem can be written as:

[0065]

[0066] Where W1, W2, and W3 are different weighting coefficients. and p Let be the upper and lower thresholds of ZMP, respectively, and let a(i) be the acceleration at the current time. d (i) is the expected acceleration at the current moment, a(i-1) is the acceleration at the previous moment, and p x (i) is the value of ZMP on the X-axis at the current time, p d (i) is the expected value of ZMP on the X-axis at the current time.

[0067] Step S4: The inverse kinematics are solved using the Levenberg-Marquardt algorithm to obtain the trajectories of each joint of the robot.

[0068] To further verify the effectiveness of the present invention, experimental verification was conducted. The verification results of the straight-knee optimizer are as follows: Figure 5 As shown, the blue line represents hip height, while the orange and green lines represent the virtual leg length of the exoskeleton. Alternating gray and white backgrounds represent the single-leg stance phase (SSP) and double-leg stance phase (DSP), respectively. Analysis reveals that the optimized hip height exhibits periodic variations, reaching its maximum during SSP and its minimum during DSP. Simultaneously, the virtual leg length also shows periodic variations. For example, in the initial cycle, as hip height increases and the gait transitions to SSP, the right leg acts as the swing leg, while the left leg acts as the supporting leg. As hip height continues to rise, the virtual leg length of the supporting leg approaches its maximum, while the knee angle reaches its minimum. Subsequently, as hip height decreases and the gait transitions to DSP, the virtual leg lengths of both legs gradually converge, and the roles of the supporting and swing legs are reversed. These observations are consistent with characteristics obtained from human straight-knee walking motion analysis.

[0069] Figure 6The results of the stabilization filter are shown, where dashed lines represent trajectory reference values, gray lines represent position reference values, purple lines represent velocity reference values, solid lines represent the optimized result, blue lines represent position, and orange lines represent velocity. In the experimental verification, a six-step walking plan was performed, with each step having a size of 0.15m. Figure 6 As can be seen, the reference value generally exhibits uniform motion, but to ensure the overall stability of the robot, the stabilizing filter also undergoes acceleration and deceleration while tracking the reference value. For example, the speed is relatively fast during the bipedal support phase and relatively slow during the unipedal support phase. Considering the changes in the support polygon during the walking cycle, this acceleration and deceleration pattern is consistent with the human straight-knee walking motion pattern.

[0070] The results of the anthropomorphic walking gait simulation experiment of the self-balancing lower limb exoskeleton are as follows: Figure 7 As shown, this gait allows the self-balancing lower limb exoskeleton to have characteristics such as changes in center of mass height, knee extension, heel strike, and toe lift-off.

[0071] In summary, compared with existing technologies, this invention has the following main advantages: It decouples the motion trajectory into sagittal and coronal planes, generating a sagittal motion trajectory under the premise of determining the coronal (lateral) motion trajectory, thus ensuring trajectory stability; it uses an optimization method to generate the coronal plane trajectory of the exoskeleton's centroid, ensuring the stability of the exoskeleton's motion in the coronal direction; it uses state vector machines and polynomial interpolation to switch foot trajectories, ensuring smooth trajectories and giving the gait human-like characteristics; it uses an optimization method to optimize the sagittal plane trajectory of the exoskeleton's centroid, ensuring that the knee joint can be extended as much as possible while satisfying the virtual leg length constraint, resulting in a human-like gait that satisfies kinematic constraints; it introduces geometric constraints, stability constraints, and acceleration constraints into the coupled trajectory, making the trajectory smooth and stable; and it uses the Levenberg-Marquardt (LM) algorithm to solve the robot's inverse kinematics problem, handling joint singularity issues by adjusting the damping coefficient.

[0072] In summary, in existing technologies, most motion pattern generators decouple motion generation into sagittal and coronal planes, plan them separately, and then recouple the trajectories manually. This approach often leads to robots exceeding the final task space when pursuing small knee angles during knee extension planning. This invention introduces a virtual leg length geometric constraint to address this problem. This constraint also kinematically recouples the motion of the two planes to satisfy the geometric constraint, ensuring stability in each plane and meeting the knee extension requirements. In conclusion, the anthropomorphic walking gait generation method for a self-balancing lower limb exoskeleton provided by this invention, based on hierarchical optimization, predictive control, and state vector machines, can generate anthropomorphic walking gait characteristics with changes in centroid height, knee extension, heel strike, and toe lift-off.

[0073] This invention can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the invention.

[0074] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0075] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0076] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, Python, etc., and conventional procedural programming languages ​​such as "C" or similar languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0077] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0078] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0079] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0080] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. It will be known to those skilled in the art that implementation in hardware, implementation in software, and implementation using a combination of software and hardware are equivalent.

[0081] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A method for generating anthropomorphic walking gait of a self-balancing lower limb exoskeleton, comprising the following steps: Based on the laws of human walking, initialize the reference zero torque point trajectory, the centroid sagittal plane trajectory, and the foot trajectory; Based on the initial reference zero-torque point trajectory, the lateral motion of the self-balancing lower limb rehabilitation exoskeleton is generated using the predictive control method. Based on the initialized centroid sagittal plane trajectory and foot trajectory, a straight-knee optimizer and a stabilizing filter are used to generate sagittal motion, wherein the straight-knee optimizer is used to optimize the hip joint trajectory at different heights, and the stabilizing filter is used to generate the hip joint trajectory that satisfies the dynamic constraints. The inverse kinematics were solved using the Levenberg-Marquardt algorithm to obtain the joint trajectories of the self-balancing lower limb rehabilitation exoskeleton; The straight-knee optimizer optimizes the hip joint trajectory at different heights by solving the following objective function: in, Z represents the point to be optimized on the hip joint height curve, where N is the total number of steps taken and z is the number of steps taken. hip (i) represents the i-th point in the hip joint height curve. and These represent the virtual lengths of the left and right legs, respectively. That's the actual length of the leg. and It is the upper and lower threshold of the height, H stand Indicates the height when standing. , , For different weighting coefficients, "subject to" indicates that it is subject to.

2. The method according to claim 1, characterized in that, The virtual left leg length and right leg length Set them to: Among them, y hip Indicates the position of the hip joint on the y-axis, x hip This indicates the position of the hip joint on the x-axis, x l ankle Indicates the position of the left ankle joint on the x-axis, z-axis hip This indicates the position of the hip joint on the z-axis. Indicates the position of the left ankle joint on the z-axis, x r ankle This indicates the position of the right ankle joint on the x-axis. This indicates the position of the right ankle joint on the z-axis.

3. The method according to claim 2, characterized in that, The stabilizing filter generates a hip joint trajectory that satisfies dynamic constraints by solving the following objective function: in, , , and W 4 represents different weighting coefficients. and These are the upper and lower thresholds of ZMP, respectively. It is the acceleration at the current moment. It is the expected acceleration at the current moment. It is the acceleration of the previous moment. It is the value of the zero torque point on the x-axis at the current moment. It is the expected value of the zero torque point on the x-axis at the current moment.

4. The method according to claim 3, characterized in that, The desired acceleration is expressed as: where k p and k d are control coefficients, x d , v d , a d are the desired position, velocity and acceleration of the hip joint, respectively, and x and v represent the actual position and velocity.

5. The method according to claim 1, characterized in that, During initialization, the reference zero-moment point trajectory and the centroid sagittal plane trajectory are determined based on the number of steps, step size, and period.

6. A computer-readable storage medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

7. A computer device comprising a memory and a processor, wherein a computer program capable of running on the processor is stored in the memory, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

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