Multi-dimensional dynamic stability collaborative control method and device for multi-degree-of-freedom humanoid robot

Abstract

The present application discloses a collaborative control method and device for the multi-dimensional dynamic stability of a multi-degree-of-freedom humanoid robot, which relates to the field of robot control technology. The method includes: establishing a dynamic model; the dynamic model includes a trunk pitch model, a trunk roll model, a hip joint swing model, a knee joint flexion and extension model, and an ankle joint rotation model; determining a comprehensive evaluation index of the stability of the humanoid robot in the longitudinal, lateral, and roll dimensions; the hip joint swing model and the knee joint flexion and extension model correspond to the lower limb swing dimension, the trunk pitch model and the trunk roll model correspond to the trunk balance dimension, and the ankle joint rotation model corresponds to the plantar contact dimension; obtaining the dynamic parameters of each joint of the humanoid robot, and determining the desired control amount in combination with the comprehensive stability evaluation index; and collaboratively controlling the actuator of the humanoid robot according to the desired control amount. The present application can achieve full-dimensional stability control in complex environments by establishing a multi-dimensional stability collaborative control scheme.

Description

Technical Field

[0001] The present application relates to the field of robot control technology, and in particular to a method and device for collaborative control of multi-dimensional dynamic stability of a multi-degree-of-freedom humanoid robot. Background Art

[0002] In existing technologies, stability control for humanoid robots primarily focuses on controlling a single dimension or independent joint. For example, ZMP (zero torque point) control is used to address pitch stability during walking, or joint torque feedback is used to control the position accuracy of a single joint. However, humanoid robots face the following issues during actual movement: 1. Strong multi-dimensional coupling: Pitch instability of the trunk during walking simultaneously triggers a chain reaction of hip roll and ankle rotation, making it difficult to separate instabilities in each dimension in time and space. 2. Rough stability domain calculation: Existing stability domain models fail to account for factors such as joint friction and ground contact nonlinearity and are only effective under ideal working conditions. 3. Actuator conflict: When multi-joint motors are independently controlled, torque conflicts such as hip swing and knee flexion can lead to a decrease in overall stability.

[0003] For example, when a robot walks on a slope, traditional ZMP control can only handle pitch dimension stability, but in reality it is also necessary to adjust the ankle joint inclination (roll dimension) and hip joint side swing (yaw dimension) at the same time. The existing technology lacks a multi-dimensional collaborative control mechanism, which makes humanoid robots prone to falling in complex terrain. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a method and device for collaborative control of multi-dimensional dynamic stability of a multi-degree-of-freedom humanoid robot, so as to comprehensively deal with the multi-dimensional stability coupling problem of the humanoid robot.

[0005] To achieve the above objectives, an embodiment of the present application provides a method for collaboratively controlling multi-dimensional dynamic stability of a multi-degree-of-freedom humanoid robot, the method comprising the following steps:

[0006] Establishing a dynamic model including multiple degrees of freedom; wherein the dynamic model includes a trunk pitch model, a trunk roll model, a hip joint swing model, a knee joint flexion and extension model, and an ankle joint rotation model of the humanoid robot;

[0007] Determining a comprehensive stability evaluation index of the humanoid robot in the longitudinal, lateral, and roll dimensions based on the multi-degree-of-freedom dynamic model; wherein the hip joint swing model and the knee joint flexion and extension model correspond to the lower limb swing dimension, the trunk pitch model and the trunk roll model correspond to the trunk balance dimension, and the ankle joint rotation model corresponds to the plantar contact dimension;

[0008] Obtaining dynamic parameters of each joint of the humanoid robot;

[0009] Determining the desired control amount for the dynamic control of the humanoid robot in different dimensions according to the dynamic kinetic parameters and the comprehensive stability evaluation index;

[0010] The actuators of the humanoid robot are cooperatively controlled according to the desired control quantities in different dimensions.

[0011] In some embodiments, determining the comprehensive stability evaluation index of the humanoid robot in the longitudinal sliding, side sliding, and roll dimensions based on the multi-degree-of-freedom dynamic model includes the following steps:

[0012] constructing a Jacobian matrix based on the dynamic model, and dividing the stability boundary according to the Jacobian matrix;

[0013] The stability boundary is fitted using a nonlinear numerical method and a linearized analytical method to determine the stability domain of the humanoid robot in different dimensions; wherein the stability domain includes an absolute stability domain and a relative stability domain;

[0014] The stability domains in different dimensions are coordinated and coupled to establish the comprehensive stability evaluation index.

[0015] In some embodiments, constructing a Jacobian matrix based on the dynamic model comprises the following steps:

[0016] The Jacobian matrix including the joint angle, angular velocity and plantar contact force is constructed; wherein the Jacobian matrix includes the dynamic parameters of the dynamic model.

[0017] In some embodiments, dividing the stability boundary according to the Jacobian matrix comprises the following steps:

[0018] Solving the eigenvalues ​​of the Jacobian matrix; wherein the Jacobian matrix includes the dynamic parameters of the dynamic model;

[0019] Determining bifurcation values ​​of various dynamic parameters of the dynamic model when different dimensions are unstable according to the characteristic values;

[0020] The stability boundary is divided according to the bifurcation value.

[0021] In some embodiments, determining the desired control amount for the dynamic control of the humanoid robot in different dimensions based on the dynamic kinetic parameters and the comprehensive stability evaluation index comprises the following steps:

[0022] determining a phase sequence of instability in different dimensions according to dynamic parameters of the humanoid robot when the robot is instability;

[0023] Simulating an unstable working condition in a single dimension according to the phase sequence, collecting dynamic parameters corresponding to other dimensions, and determining a weight ratio of the influence of different dynamic parameters on the stable state of the humanoid robot;

[0024] The expected control torque of each joint is calculated as the expected control amount by combining the comprehensive stability evaluation index and the weight ratio.

[0025] In some embodiments, the collaboratively controlling the actuators of the humanoid robot according to the desired control quantities in different dimensions comprises the following steps:

[0026] Determine the degree of influence of the actuator in each dimension on the stability of other dimensions, and obtain the influence degree result;

[0027] Optimizing and compensating the desired control amount according to the impact degree result;

[0028] The actuator is driven to perform coordinated control according to the desired control amount after optimization and compensation; wherein the actuator includes a joint motor and an active suspension.

[0029] In some embodiments, optimizing and compensating the desired control amount according to the impact degree result includes the following steps:

[0030] Using a Hildreth quadratic programming algorithm to solve an optimization problem for the desired control amount according to the influence degree result, so as to perform the optimization compensation;

[0031] The optimization problem includes the following constraints:

[0032] ;

[0033] in, To control the increment vector, is the weight matrix, is the joint control torque, is the joint torque limit, is the position of the zero moment point; is the constraint boundary of the ZMP position; is the objective function of multi-actuator coordinated control; is the gradient vector, Including the gradient information of the objective function.

[0034] To achieve the above objectives, another aspect of the present application provides a multi-degree-of-freedom humanoid robot multi-dimensional dynamic stability collaborative control device, the device comprising:

[0035] A model building unit, configured to build a dynamic model including multiple degrees of freedom; wherein the dynamic model includes a trunk pitch model, a trunk roll model, a hip joint swing model, a knee joint flexion and extension model, and an ankle joint rotation model of the humanoid robot;

[0036] an evaluation index determination unit, configured to determine, based on the multi-degree-of-freedom dynamic model, a comprehensive stability evaluation index of the humanoid robot in the dimensions of longitudinal sliding, lateral sliding, and roll; wherein the hip joint swing model and the knee joint flexion and extension model correspond to the lower limb swing dimension, the trunk pitch model and the trunk roll model correspond to the trunk balance dimension, and the ankle joint rotation model corresponds to the plantar contact dimension;

[0037] A parameter acquisition unit, configured to acquire dynamic parameters of each joint of the humanoid robot;

[0038] a control amount determination unit, configured to determine expected control amounts for dynamic control of the humanoid robot in different dimensions based on the dynamic kinetic parameters and the comprehensive stability evaluation index;

[0039] A collaborative control unit is used to collaboratively control the actuators of the humanoid robot according to the desired control quantities in different dimensions.

[0040] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method when executing the computer program.

[0041] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above-mentioned method when executed by a processor.

[0042] The embodiments of the present application include at least the following beneficial effects:

[0043] The present application provides a multi-dimensional dynamic stability collaborative control method and device for a multi-degree-of-freedom humanoid robot. The present application scheme establishes a dynamic model including multiple degrees of freedom; wherein the dynamic model includes the humanoid robot's trunk pitch model, trunk roll model, hip joint swing model, knee joint flexion and extension model, and ankle joint rotation model; determines the comprehensive stability evaluation index of the humanoid robot in the longitudinal, lateral, and roll dimensions based on the multi-degree-of-freedom dynamic model; wherein the hip joint swing model and the knee joint flexion and extension model correspond to the lower limb swing dimension, the trunk pitch model and the trunk roll model correspond to the trunk balance dimension, and the ankle joint rotation model corresponds to the plantar contact dimension; obtains the dynamic dynamic parameters of each joint of the humanoid robot; determines the expected control amount of the humanoid robot's dynamic control in different dimensions based on the dynamic dynamic parameters and the comprehensive stability evaluation index; and performs collaborative control of the humanoid robot's actuator based on the expected control amount in different dimensions. The present application can achieve full-dimensional stable control of the humanoid robot in complex environments by establishing a multi-dimensional stability collaborative control scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 A schematic flow chart of a method for collaboratively controlling multi-dimensional dynamic stability of a multi-degree-of-freedom humanoid robot according to an embodiment of the present application;

[0046] Figure 2 An example flow chart of a method for collaboratively controlling multi-dimensional dynamic stability of a multi-degree-of-freedom humanoid robot provided in an embodiment of the present application;

[0047] Figure 3A A diagram of the head / waist model structure provided in an embodiment of the present application;

[0048] Figure 3B A structural diagram of the upper limb model provided in an embodiment of the present application;

[0049] Figure 3C A structural diagram of the left leg model provided in an embodiment of the present application;

[0050] Figure 3D A structural diagram of the right leg model provided in an embodiment of the present application;

[0051] Figure 3E A structural diagram of the torso model provided in an embodiment of the present application;

[0052] Figure 4A schematic diagram of the stable domain division based on the radial basis function neural network provided in an embodiment of the present application;

[0053] Figure 5 A flowchart of the steps of collaborative control of multiple actuators provided in an embodiment of the present application;

[0054] Figure 6 A schematic diagram of the structure of a multi-dimensional dynamic stability collaborative control device for a multi-degree-of-freedom humanoid robot provided in an embodiment of the present application;

[0055] Figure 7 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0057] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0058] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" as used herein.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0060] The embodiments of the present application provide a method and device for collaborative control of multi-dimensional dynamic stability of a multi-degree-of-freedom humanoid robot, which relates to the field of robot control technology. The method and device for collaborative control of multi-dimensional dynamic stability of a multi-degree-of-freedom humanoid robot provided in the embodiments of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the method for collaborative control of multi-dimensional dynamic stability of a multi-degree-of-freedom humanoid robot, etc., but is not limited to the above forms.

[0061] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0062] Reference Figure 1 The embodiment of the present application provides a method for collaboratively controlling multi-dimensional dynamic stability of a multi-degree-of-freedom humanoid robot. The method may include but is not limited to S100 to S140, as follows:

[0063] S100: Establishing a dynamic model including multiple degrees of freedom; wherein the dynamic model includes a trunk pitch model, a trunk roll model, a hip joint swing model, a knee joint flexion and extension model, and an ankle joint rotation model of the humanoid robot;

[0064] S110: Determining, based on the multi-degree-of-freedom dynamic model, a comprehensive stability evaluation index of the humanoid robot in the longitudinal sliding, lateral sliding, and roll dimensions; wherein the hip joint swing model and the knee joint flexion and extension model correspond to the lower limb swing dimension, the trunk pitch model and the trunk roll model correspond to the trunk balance dimension, and the ankle joint rotation model corresponds to the plantar contact dimension;

[0065] S120: Obtaining dynamic parameters of each joint of the humanoid robot;

[0066] S130: Determining expected control quantities for dynamic control of the humanoid robot in different dimensions according to the dynamic kinetic parameters and the comprehensive stability evaluation index;

[0067] S140: Coordinately control the actuators of the humanoid robot according to the desired control quantities in different dimensions.

[0068] Optionally, determining the comprehensive stability evaluation index of the humanoid robot in longitudinal sliding, side sliding, and roll dimensions according to the multi-degree-of-freedom dynamic model comprises the following steps:

[0069] constructing a Jacobian matrix based on the dynamic model, and dividing the stability boundary according to the Jacobian matrix;

[0070] The stability boundary is fitted using a nonlinear numerical method and a linearized analytical method to determine the stability domain of the humanoid robot in different dimensions; wherein the stability domain includes an absolute stability domain and a relative stability domain;

[0071] The stability domains in different dimensions are coordinated and coupled to establish the comprehensive stability evaluation index.

[0072] Optionally, constructing a Jacobian matrix based on the dynamic model comprises the following steps:

[0073] The Jacobian matrix including the joint angle, angular velocity and plantar contact force is constructed; wherein the Jacobian matrix includes the dynamic parameters of the dynamic model.

[0074] Optionally, dividing the stability boundary according to the Jacobian matrix comprises the following steps:

[0075] Solving the eigenvalues ​​of the Jacobian matrix; wherein the Jacobian matrix includes the dynamic parameters of the dynamic model;

[0076] Determining bifurcation values ​​of various dynamic parameters of the dynamic model when different dimensions are unstable according to the characteristic values;

[0077] The stability boundary is divided according to the bifurcation value.

[0078] Optionally, determining the desired control amount for the dynamic control of the humanoid robot in different dimensions according to the dynamic kinetic parameters and the comprehensive stability evaluation index comprises the following steps:

[0079] determining a phase sequence of instability in different dimensions according to dynamic parameters of the humanoid robot when the robot is instability;

[0080] Simulating an unstable working condition in a single dimension according to the phase sequence, collecting dynamic parameters corresponding to other dimensions, and determining a weight ratio of the influence of different dynamic parameters on the stable state of the humanoid robot;

[0081] The expected control torque of each joint is calculated as the expected control amount by combining the comprehensive stability evaluation index and the weight ratio.

[0082] Optionally, the collaboratively controlling the actuators of the humanoid robot according to the desired control quantities in different dimensions comprises the following steps:

[0083] Determine the degree of influence of the actuator in each dimension on the stability of other dimensions, and obtain the influence degree result;

[0084] Optimizing and compensating the desired control amount according to the impact degree result;

[0085] The actuator is driven to perform coordinated control according to the desired control amount after optimization and compensation; wherein the actuator includes a joint motor and an active suspension.

[0086] Optionally, the optimizing and compensating the desired control amount according to the impact degree result comprises the following steps:

[0087] Using a Hildreth quadratic programming algorithm to solve an optimization problem for the desired control amount according to the influence degree result, so as to perform the optimization compensation;

[0088] The optimization problem includes the following constraints:

[0089] ;

[0090] in, To control the increment vector, is the weight matrix, is the joint control torque, is the joint torque limit, is the position of the zero moment point; is the constraint boundary of the ZMP position; is the objective function of multi-actuator coordinated control; is the gradient vector, Including the gradient information of the objective function.

[0091] Next, the solution of the embodiment of the present application will be introduced and explained in detail with reference to specific application examples.

[0092] This embodiment addresses the coupled control challenges of multi-dimensional instability in humanoid robots during dynamic motion. By establishing a multi-dimensional stability collaborative control system, it achieves full-dimensional stability control in complex environments. This embodiment addresses the coupled control challenges of multi-dimensional instability in high-degree-of-freedom robots during complex motion, and is applicable to dynamic stability regulation in all scenarios, including walking and grasping. This embodiment is suitable for complex tasks such as walking, jumping, and grasping by humanoid robots.

[0093] Specifically, this embodiment includes the following technical solutions:

[0094] Establish a dynamic model with multiple degrees of freedom; the dynamic model includes a trunk pitch model, a trunk roll model, a hip joint swing model, a knee joint flexion and extension model, and an ankle joint rotation model;

[0095] Determine comprehensive evaluation indicators for the robot's stability in the longitudinal, lateral, and roll dimensions; the hip swing model and knee flexion and extension model correspond to the lower limb swing dimension, the trunk pitch model and trunk roll model correspond to the trunk balance dimension, and the ankle rotation model corresponds to the plantar contact dimension;

[0096] Obtain the real-time dynamic parameters of each joint of the robot, and combine them with the comprehensive stability evaluation index to determine the expected control amount of the robot dynamic parameters in different dimensions;

[0097] The robot actuators are collaboratively controlled according to the desired control quantities in different dimensions.

[0098] Exemplarily, establishing a kinetic model and determining a comprehensive stability evaluation index include:

[0099] Construct the Jacobian matrix based on the dynamic model, solve the eigenvalue to determine the bifurcation value when each dimension is unstable, and divide the stability boundary;

[0100] The stability boundary is fitted using nonlinear numerical methods and linearized analytical methods to determine the stability domain of the robot in different dimensions. The stability domain includes absolute stability domain and relative stability domain.

[0101] The stability domains in different dimensions are coordinated and coupled to establish a comprehensive stability evaluation index.

[0102] Exemplarily, constructing the Jacobian matrix and solving the eigenvalues ​​include:

[0103] Construct a Jacobian matrix containing joint angles, angular velocities, and plantar contact forces. The Jacobian matrix contains the dynamic parameters of the dynamic model.

[0104] The eigenvalues ​​of the Jacobian matrix are solved, and the bifurcation values ​​of each dynamic parameter when different dimensions are unstable are determined according to the solution results. The stability boundary is divided based on the bifurcation values.

[0105] Exemplarily, determining the desired control amount includes:

[0106] According to the dynamic parameters of the robot when it becomes unstable, the phase sequence of instability in different dimensions is determined;

[0107] Simulate the instability condition in a single dimension in phase order, collect the dynamic parameters corresponding to other dimensions, and determine the weight ratio of the influence of different dynamic parameters on the stable state of the robot;

[0108] Combined with the comprehensive stability evaluation index and weight ratio, the expected control torque of each joint is calculated.

[0109] Exemplarily, performing collaborative control includes:

[0110] Determine the degree of influence of the actuator in each dimension on the stability of other dimensions and obtain the results of the degree of influence;

[0111] Optimize and compensate the expected control amount according to the impact degree results;

[0112] The actuator is driven according to the desired control amount after optimization compensation, and the actuator includes a joint motor and an active suspension equivalent mechanism.

[0113] For example, the optimization compensation adopts the Hildreth quadratic programming algorithm to solve the optimization problem with the following constraints:

[0114] ;

[0115] in:

[0116] is the objective function of multi-actuator coordinated control;

[0117] : Control increment vector, the dimension should be , m is the number of joints, corresponding to the increment of control amount of each joint;

[0118] : Weight matrix, representing the weight of the control amount increment;

[0119] : Gradient vector, containing the gradient information of the objective function;

[0120] : control torque of the j-th joint;

[0121] : The torque limit of the jth joint, determined by the motor performance;

[0122] : ZMP position constraint boundary, usually the plantar support area.

[0123] In one embodiment, the specific values ​​of the parameters are:

[0124] (weights of 3 joints);

[0125] Joint torque limit: ;

[0126] ZMP position constraints: (±5cm from the center of the plantar support surface).

[0127] Reference Figure 2 More specifically, this embodiment can be implemented in the following ways:

[0128] 1. Construction of multi-joint dynamics model.

[0129] For example, Figure 3A For the head / waist model structure diagram, Figure 3B The upper limb model structure diagram, Figure 3C This is the left leg model structure diagram, Figure 3D This is the structure diagram of the right leg model. Figure 3E This is a structural diagram of the torso model.

[0130] Establish a multi-DOF dynamic model that includes the trunk, two feet, and upper limbs (optional). Taking a bipedal single leg with n degrees of freedom as an example (n ≥ 5, with a typical configuration of 6 degrees of freedom), the core dynamic equations are as follows:

[0131] Hip forward swing ( ): .

[0132] in, is the moment of inertia of the hip joint, is the angular acceleration of the hip joint, Control torque for the hip joint, is the mass of the lower limbs, is the acceleration due to gravity, is the length of the lower limb, is the hip joint angle, is the hip joint damping coefficient, is the angular velocity of the hip joint, is the longitudinal friction of the ground, is the friction force arm.

[0133] In one embodiment, the specific values ​​of the parameters are:

[0134] Moment of inertia: (single-leg hip);

[0135] Lower limb mass: ;

[0136] Lower limb length: ;

[0137] Damping coefficient: ;

[0138] Friction force arm: ;

[0139] In one embodiment, the specific values ​​of the parameters are:

[0140] When the hip angle , angular velocity When the longitudinal friction of the ground , then the hip joint control torque is:

[0141] ;

[0142] Assume that the angular acceleration of the hip joint is When:

[0143] .

[0144] Ankle pitch ( ): .

[0145] in, is the ankle joint pitch angle, is the ankle joint moment of inertia, is the angular acceleration of the ankle joint, Control torque for the ankle joint, is the vertical reaction force of the plantar foot, is the longitudinal friction force, are the vertical reaction arm and the friction arm respectively. is the ankle joint damping coefficient, is the angular velocity of the ankle joint.

[0146] In one embodiment, the specific values ​​of the parameters are:

[0147] Ankle joint moment of inertia: ;

[0148] Vertical reaction arm: ;

[0149] Friction arm: ;

[0150] Ankle joint damping coefficient: ;

[0151] If the vertical reaction force of the sole of the foot Friction , the angular velocity of the ankle joint , then the ankle joint control torque for:

[0152] ;

[0153] Assumptions When:

[0154] .

[0155] It can be concluded that negative torque is direction-dependent.

[0156] 2.Reference Figure 4 , a multidimensional stability index is established.

[0157] (1) Stability boundary division:

[0158] Construct the Jacobian matrix containing m state variables (joint angles, angular velocities) and plantar contact forces:

[0159] ;

[0160] in:

[0161] : Jacobian matrix, representing the partial derivatives of the state variables with respect to the dynamic model;

[0162] : state variable vector;

[0163] : Dynamic model equations, input state variables and contact forces, output acceleration;

[0164] : No. j joint angles, ;

[0165] : No. j Joint angular velocity;

[0166] : Total number of joints.

[0167] Solve the eigenvalue to determine the instability bifurcation value of each dimension, such as the critical angular velocity of instability of joint i .

[0168] (2) Stable domain fitting:

[0169] The radial basis function neural network (RBFNN) is used to perform fuzzy partitioning of high-dimensional state space:

[0170] Input layer: Select key state quantities (such as torso pitch angle , ankle pitch angle , hip joint angular velocity , plantar vertical reaction force ;

[0171] Hidden layer: Gaussian basis function ;

[0172] in:

[0173] : The i-th Gaussian basis function, input state variable X, output activation value;

[0174] : the center vector of the i-th Gaussian basis function;

[0175] : The width parameter of the i-th Gaussian basis function, which controls the scope of the function;

[0176] : state vector X and center vector The Euclidean distance of

[0177] Output layer mapping formula:

[0178] ;

[0179] in, : Stability probability (range: [0,1]). The closer the output value is to 1, the closer it is to instability.

[0180] : The number of neurons in the hidden layer is adjusted according to the dimension of the state space;

[0181] : Output layer weight, representing the influence of each hidden layer neuron on the output.

[0182] : the i-th Gaussian basis function.

[0183] Divide the absolute stability region , relatively stable domain ,in is an adjustable threshold.

[0184] In one embodiment, the specific values ​​of the parameters are:

[0185] RBFNN stable probability threshold:

[0186] Absolute stability region: (When the stable probability <0.3, the state is absolutely stable);

[0187] Relatively stable domain: (when When the warning is activated);

[0188] Dangerous areas: >0.7 (triggering emergency control);

[0189] Ankle joint safety margin:

[0190] Critical Angle: (plantar flexion threshold);

[0191] Safety margin: ;

[0192] Safety perspective: (Adjust the torque when this value is exceeded).

[0193] (3) Comprehensive evaluation function:

[0194] ;

[0195] in:

[0196] : Comprehensive evaluation index of stability, the smaller the value, the better the stability;

[0197] n: number of stability dimensions, typically 3-5 (e.g., pitch, roll, joint yaw);

[0198] : stability index of the i-th dimension (such as trunk pitch, bipedal joint coupling, upper limb movement influence, etc.);

[0199] : Weight coefficient of the i-th dimension (Dynamically adjusted according to the motion scene).

[0200] 3. Calculation of expected control quantity.

[0201] (1) Instability phase analysis:

[0202] Determine the typical instability sequence through dynamic simulation (e.g. ankle pitch → hip swing → trunk pitch) and establish the joint instability phase sequence .

[0203] (2) Weight matrix establishment:

[0204] By simulating the instability of a single joint, the changes in other joint parameters are collected and trained to obtain an m×m dimensional weight matrix , characterizes the joint coupling strength.

[0205] (3) Solution of expected control quantity:

[0206] Combined with real-time parameters (joint angle deviation , angular velocity , plantar strength ), the desired control quantity is calculated by weighted inverse dynamics:

[0207] ;

[0208] in, is the joint control torque vector (i.e. the desired control amount), is the inverse Jacobian matrix, is the proportional gain matrix, , is the joint angle deviation, is the joint angular velocity, is the joint coupling compensation torque (calculated by the weight matrix W).

[0209] 4.Reference Figure 5 , collaborative control of multiple actuators.

[0210] (1) Cross-influence coefficient matrix:

[0211] Establish n×n dimensional actuator influence matrix ,in Indicates joints Torque on joint Stability influence coefficient.

[0212] (2) Plantar contact force objective function:

[0213] ;

[0214] in:

[0215] : Weight distribution coefficient, which adjusts the weight of friction and yaw moment;

[0216] : longitudinal friction force vector of the plantar surface, friction force of the left and right feet;

[0217] : Friction control distribution matrix, adjusts the friction distribution of left and right feet;

[0218] : The conversion matrix from contact force to yaw moment, including the foot length and contact point position parameters;

[0219] : plantar contact force vector, vertical reaction force and friction force of left and right feet;

[0220] : Target yaw moment to maintain ZMP stability .

[0221] In one embodiment, the specific values ​​of the parameters are:

[0222] Friction Weight: (Friction accounts for 70% of the weight);

[0223] Yaw moment weight: ;

[0224] Target yaw moment: (Maintain ZMP stability).

[0225] (3) Hildreth quadratic programming optimization:

[0226] Solve constrained optimization problems:

[0227] ;

[0228] : Control increment vector (dimension should be , m is the number of joints);

[0229] Constraints include joint torque limits , ZMP position constraint wait.

[0230] As another optional implementation, this embodiment may include the following technical solutions:

[0231] 1. Example of a general model of a bipedal humanoid robot:

[0232] (1) Joint degree of freedom configuration:

[0233] Taking a typical 6-DOF single leg as an example (hip joint 3DOF + knee joint 1DOF + ankle joint 2DOF), the dynamic equations are as described above, and the model can be extended to different DOF configurations (such as 5DOF or 7DOF legs).

[0234] (2) Jacobian matrix construction:

[0235] Torso pitch angle For example, the Jacobian matrix element Indicates joints Torque on joint The influence of the state,matrix dimension is determined by the actual number of joints.

[0236] 2. Walking scene control embodiment:

[0237] (1) Real-time parameter collection:

[0238] Ankle pitch angle ( is a safety margin);

[0239] Hip joint forward swing angular velocity ( is the proportional coefficient);

[0240] in, is the critical angle of the ankle joint (theoretical instability threshold), is the nominal angular velocity of the hip joint forward swing, and the vertical reaction force of the plantar Obtained in real time through pressure sensor.

[0241] In one embodiment, the specific values ​​of the parameters are:

[0242] Ankle joint safety margin:

[0243] Critical Angle: (plantar flexion threshold);

[0244] Safety margin: ;

[0245] Safety perspective: (Adjust the torque when this value is exceeded).

[0246] In one embodiment, the specific values ​​of the parameters are:

[0247] Hip joint forward swing angular velocity adjustment:

[0248] Nominal angular velocity: ;

[0249] Acceleration coefficient: k=1.2 (when walking fast, );

[0250] Deceleration ratio coefficient: k=0.8 (when walking at low speed, ).

[0251] (2) Calculation of expected control quantity:

[0252] Key parameter selection: Based on the robot's degree of freedom configuration, select the main joints that affect stability (such as ankle joints and hip joints);

[0253] Weight distribution: the sum of the weights of the lower limb joints is ≥ 0.6, and the weight of the trunk is ≤ 0.3;

[0254] Expected control amount :

[0255] ;

[0256] in, is the proportional gain matrix, is the actual pitch angle of the ankle joint, is the ankle joint reference angle, is the differential gain matrix, is the ankle joint angular velocity, , Adjust the gain matrix dimensions according to the actual number of degrees of freedom.

[0257] In one embodiment, the specific values ​​of the parameters are:

[0258] Ankle joint proportional gain: ;

[0259] Ankle joint differential gain: ;

[0260] Lower limb joint weights: (65% of the total weight);

[0261] Torso joint weights: (25% of the total weight);

[0262] Application scenario: When the actual pitch angle of the ankle joint , ankle joint reference angle , ankle joint angular velocity When , the expected control quantity (expected control torque) for:

[0263] .

[0264] (3) Optimize execution:

[0265] Consider the cross-joint effects (such as the coupling coefficient of hip joint torque to ankle joint) ), the optimization problem involving n joint constraints is solved by the Hildreth algorithm, and the control variables adapted to different degrees of freedom configurations are output.

[0266] Beneficial effects:

[0267] 1. Multi-dimensional coupling control: Breaking through the limitations of single-dimensional control, achieving coordinated regulation of multi-dimensional stability, including pitch, roll, and joint swing;

[0268] 2. Dynamic adaptability: By updating the stability domain boundaries in real time, the robot can maintain stability in complex scenarios such as slopes and uneven surfaces;

[0269] 3. Actuator optimization: solve the problem of multi-motor control conflicts, improve overall control efficiency and reduce energy consumption.

[0270] Reference Figure 6 The embodiment of the present application further provides a multi-dimensional dynamic stability collaborative control device for a multi-degree-of-freedom humanoid robot, which can implement the multi-dimensional dynamic stability collaborative control method for a multi-degree-of-freedom humanoid robot. The device includes:

[0271] A model building unit, configured to build a dynamic model including multiple degrees of freedom; wherein the dynamic model includes a trunk pitch model, a trunk roll model, a hip joint swing model, a knee joint flexion and extension model, and an ankle joint rotation model of the humanoid robot;

[0272] an evaluation index determination unit, configured to determine, based on the multi-degree-of-freedom dynamic model, a comprehensive stability evaluation index of the humanoid robot in the dimensions of longitudinal sliding, lateral sliding, and roll; wherein the hip joint swing model and the knee joint flexion and extension model correspond to the lower limb swing dimension, the trunk pitch model and the trunk roll model correspond to the trunk balance dimension, and the ankle joint rotation model corresponds to the plantar contact dimension;

[0273] A parameter acquisition unit, configured to acquire dynamic parameters of each joint of the humanoid robot;

[0274] a control amount determination unit, configured to determine expected control amounts for dynamic control of the humanoid robot in different dimensions based on the dynamic kinetic parameters and the comprehensive stability evaluation index;

[0275] A collaborative control unit is used to collaboratively control the actuators of the humanoid robot according to the desired control quantities in different dimensions.

[0276] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0277] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method of the present application. The electronic device can be any smart terminal, such as a tablet computer or an in-vehicle computer.

[0278] It can be understood that the contents of the above method embodiments are all applicable to the embodiments of the present device, the functions specifically implemented by the embodiments of the present device are the same as those of the method of the present application, and the beneficial effects achieved are also the same as those achieved by the method of the present application.

[0279] See also Figure 7, Figure 7 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0280] The processor 701 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0281] The memory 702 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called by the processor 701 to execute the methods of the embodiments of this application.

[0282] Input / output interface 703, used to implement information input and output;

[0283] Communication interface 704, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0284] Bus 705 , which transmits information between various components of the device (e.g., processor 701 , memory 702 , input / output interface 703 , and communication interface 704 );

[0285] The processor 701 , the memory 702 , the input / output interface 703 and the communication interface 704 are connected to each other in communication within the device via a bus 705 .

[0286] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method of the present application is implemented.

[0287] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0288] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0289] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0290] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0291] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0292] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0293] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0294] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0295] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0296] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0297] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0298] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0299] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A collaborative control method for multi-dimensional dynamic stability of a multi-degree-of-freedom humanoid robot, characterized in that: The method comprises the following steps: Establishing a dynamic model including multiple degrees of freedom; wherein the dynamic model includes a trunk pitch model, a trunk roll model, a hip joint swing model, a knee joint flexion and extension model, and an ankle joint rotation model of the humanoid robot; Determining a comprehensive stability evaluation index of the humanoid robot in the longitudinal, lateral, and roll dimensions based on the multi-degree-of-freedom dynamic model; wherein the hip joint swing model and the knee joint flexion and extension model correspond to the lower limb swing dimension, the trunk pitch model and the trunk roll model correspond to the trunk balance dimension, and the ankle joint rotation model corresponds to the plantar contact dimension; Obtaining dynamic parameters of each joint of the humanoid robot; Determining the desired control amount for the dynamic control of the humanoid robot in different dimensions according to the dynamic kinetic parameters and the comprehensive stability evaluation index; performing collaborative control on the actuators of the humanoid robot according to the desired control quantities in different dimensions; Determining the comprehensive stability evaluation index of the humanoid robot in the longitudinal sliding, side sliding, and roll dimensions based on the multi-degree-of-freedom dynamic model includes the following steps: constructing a Jacobian matrix based on the dynamic model, and dividing the stability boundary according to the Jacobian matrix; The stability boundary is fitted using a nonlinear numerical method and a linearized analytical method to determine the stability domain of the humanoid robot in different dimensions; wherein the stability domain includes an absolute stability domain and a relative stability domain; Coordinate coupling of the stability domains in different dimensions to establish the comprehensive stability evaluation index; The method of constructing a Jacobian matrix based on the dynamic model comprises the following steps: Constructing the Jacobian matrix including the joint angle, angular velocity and plantar contact force; wherein the Jacobian matrix includes the dynamic parameters of the dynamic model; The step of dividing the stability boundary according to the Jacobian matrix comprises the following steps: Solving the eigenvalues ​​of the Jacobian matrix; wherein the Jacobian matrix includes the dynamic parameters of the dynamic model; Determining bifurcation values ​​of various dynamic parameters of the dynamic model when different dimensions are unstable according to the characteristic values; dividing the stability boundary according to the bifurcation value; Determining the desired control amount for the dynamic control of the humanoid robot in different dimensions according to the dynamic kinetic parameters and the comprehensive stability evaluation index comprises the following steps: determining a phase sequence of instability in different dimensions according to dynamic parameters of the humanoid robot when the robot is instability; Simulating an unstable working condition in a single dimension according to the phase sequence, collecting dynamic parameters corresponding to other dimensions, and determining a weight ratio of the influence of different dynamic parameters on the stable state of the humanoid robot; The expected control torque of each joint is calculated as the expected control amount by combining the comprehensive stability evaluation index and the weight ratio.

2. The multi-dimensional dynamic stability collaborative control method of a multi-degree-of-freedom humanoid robot according to claim 1, characterized in that: The collaborative control of the actuator of the humanoid robot according to the desired control amounts in different dimensions includes the following steps: Determine the degree of influence of the actuator in each dimension on the stability of other dimensions, and obtain the influence degree result; Optimizing and compensating the desired control amount according to the impact degree result; The actuator is driven to perform coordinated control according to the desired control amount after optimization and compensation; wherein the actuator includes a joint motor and an active suspension.

3. The multi-dimensional dynamic stability collaborative control method of a multi-degree-of-freedom humanoid robot according to claim 2, characterized in that: The optimizing and compensating the desired control amount according to the impact degree result comprises the following steps: Using a Hildreth quadratic programming algorithm to solve an optimization problem for the desired control amount according to the influence degree result, so as to perform the optimization compensation; The optimization problem includes the following constraints: ； in, To control the increment vector, is the weight matrix, is the joint control torque, is the joint torque limit, is the position of the zero moment point; is the constraint boundary of the ZMP position; is the objective function of multi-actuator coordinated control; is the gradient vector, Including the gradient information of the objective function.

4. A multi-dimensional dynamic stability collaborative control device for a multi-degree-of-freedom humanoid robot, characterized in that: The device comprises: A model building unit, configured to build a dynamic model including multiple degrees of freedom; wherein the dynamic model includes a trunk pitch model, a trunk roll model, a hip joint swing model, a knee joint flexion and extension model, and an ankle joint rotation model of the humanoid robot; an evaluation index determination unit, configured to determine, based on the multi-degree-of-freedom dynamic model, a comprehensive stability evaluation index of the humanoid robot in the dimensions of longitudinal sliding, lateral sliding, and roll; wherein the hip joint swing model and the knee joint flexion and extension model correspond to the lower limb swing dimension, the trunk pitch model and the trunk roll model correspond to the trunk balance dimension, and the ankle joint rotation model corresponds to the plantar contact dimension; A parameter acquisition unit, configured to acquire dynamic parameters of each joint of the humanoid robot; a control amount determination unit, configured to determine expected control amounts for dynamic control of the humanoid robot in different dimensions based on the dynamic kinetic parameters and the comprehensive stability evaluation index; a collaborative control unit, configured to collaboratively control the actuators of the humanoid robot according to the desired control quantities in different dimensions; Determining the comprehensive stability evaluation index of the humanoid robot in the longitudinal sliding, side sliding, and roll dimensions based on the multi-degree-of-freedom dynamic model includes the following steps: constructing a Jacobian matrix based on the dynamic model, and dividing the stability boundary according to the Jacobian matrix; The stability boundary is fitted using a nonlinear numerical method and a linearized analytical method to determine the stability domain of the humanoid robot in different dimensions; wherein the stability domain includes an absolute stability domain and a relative stability domain; Coordinate coupling of the stability domains in different dimensions to establish the comprehensive stability evaluation index; The method of constructing a Jacobian matrix based on the dynamic model comprises the following steps: Constructing the Jacobian matrix including the joint angle, angular velocity and plantar contact force; wherein the Jacobian matrix includes the dynamic parameters of the dynamic model; The step of dividing the stability boundary according to the Jacobian matrix comprises the following steps: Solving the eigenvalues ​​of the Jacobian matrix; wherein the Jacobian matrix includes the dynamic parameters of the dynamic model; Determining bifurcation values ​​of various dynamic parameters of the dynamic model when different dimensions are unstable according to the characteristic values; dividing the stability boundary according to the bifurcation value; Determining the desired control amount for the dynamic control of the humanoid robot in different dimensions according to the dynamic kinetic parameters and the comprehensive stability evaluation index comprises the following steps: determining a phase sequence of instability in different dimensions according to dynamic parameters of the humanoid robot when the robot is instability; Simulating an unstable working condition in a single dimension according to the phase sequence, collecting dynamic parameters corresponding to other dimensions, and determining a weight ratio of the influence of different dynamic parameters on the stable state of the humanoid robot; The expected control torque of each joint is calculated as the expected control amount by combining the comprehensive stability evaluation index and the weight ratio.

5. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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