Humanoid robot control method, device and system, electronic equipment and storage medium

Abstract

The invention discloses a humanoid robot control method, device and system, electronic equipment and a storage medium, and relates to the technical field of robotics.The method comprises the steps that the spatial position of a working object in a moving state is dynamically obtained; establishing a capture system model according to the spatial position; wherein the capturing system model is used for describing the numerical relationship between the tail end attitude angles of the two arms of the humanoid robot and walking parameters of the two feet and the numerical relationship between the tail end attitude angles and the middle joint angles of the corresponding arms; solving according to the motion decomposition controller and the capture system model to obtain a tail end attitude angle and a middle joint angle; determining control parameters of the humanoid robot according to the tail end attitude angle and the middle joint angle; and controlling the humanoid robot to catch the working object according to the control parameters. The motion decomposition controller is solved and the control parameters are determined on the capturing system model, so that the humanoid robot can be controlled to stably and accurately capture the moving working object.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to a humanoid robot control method, device, system, electronic device and storage medium. Background Art

[0002] In many scenarios, humanoid robots need to capture moving objects, such as in logistics handling, industrial production, etc. However, current capture control schemes have defects such as difficulty in accurately capturing moving objects and unstable control. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to provide a humanoid robot control method, device, system, electronic device and storage medium, so that the humanoid robot can accurately capture the working object in motion.

[0004] To achieve the above object, an embodiment of the present application provides a method for controlling a humanoid robot, the method comprising the following steps:

[0005] Dynamically obtain the spatial position of the working object in a moving state;

[0006] A capture system model is established according to the spatial position; wherein the capture system model is used to describe the numerical relationship between the end posture angles of the two arms of the humanoid robot and the bipedal walking parameters, and the numerical relationship between the end posture angles and the intermediate joint angles of the corresponding arms;

[0007] The end posture angle and the intermediate joint angle are obtained by solving the motion decomposition controller and the capture system model;

[0008] Determining control parameters of the humanoid robot according to the terminal posture angle and the intermediate joint angle;

[0009] The humanoid robot is controlled to capture the working object according to the control parameters.

[0010] In some embodiments, the dynamically acquiring the spatial position of the work object in a moving state comprises the following steps:

[0011] The spatial position of the working object in a moving state is dynamically acquired through one or at least two sensors on the humanoid robot; wherein the sensors include an RGBD sensor and a laser radar.

[0012] In some embodiments, establishing a capture system model according to the spatial position comprises the following steps:

[0013] Constructing a DH parameter table for each joint in the arms of the humanoid robot;

[0014] Obtaining a homogeneous transformation matrix of each of the joints according to the DH parameter table;

[0015] Determining a desired end position of an end effector of the humanoid robot according to the spatial position;

[0016] The numerical relationship between the terminal posture angle and the intermediate joint angle is solved according to the desired terminal posture and each homogeneous transformation matrix to obtain the capture system model.

[0017] In some embodiments, the step of obtaining the end attitude angle and the intermediate joint angle according to the motion decomposition controller and the capture system model comprises the following steps:

[0018] Determine a control relationship model according to the capture system model; wherein the control relationship model is used to describe the numerical relationship between the parameters of the motion decomposition controller and the terminal posture angle and the numerical relationship between the parameters of the motion decomposition controller and the intermediate joint angle;

[0019] Constructing a cost function based on the work object;

[0020] The cost function is used to constrain the control relationship model, and then the motion decomposition controller is used to solve the optimization problem of the control relationship model to obtain the end posture angle and the intermediate joint angle.

[0021] In some embodiments, the constructing a cost function based on the work object comprises the following steps:

[0022] The cost function based on the working object is constructed as:

[0023]

[0024] Among them, J c represents the cost function; θ a and θ b are the end attitude angles of the left arm and the right arm of the humanoid robot respectively, and are the current end posture angles of the left arm and the right arm respectively, θ=[θ 1, θ 2, θ 3, θ 4, θ5] T is the middle joint angle of the arm, and the middle joint angles of the left arm and the right arm are respectively represented by θ l and θ r , is the current arm middle joint angle; μ∈R 8x1 is the weight vector of each joint of the left arm and the right arm, and are the terminal angular velocities of the left and right arms, respectively. are the walking parameters of the humanoid robot, and λ1, λ2, λ3, λ4, and λ5 are the weights corresponding to the walking parameters.

[0025] In some embodiments, the step of determining the control parameters of the humanoid robot according to the end posture angle and the intermediate joint angle comprises the following steps:

[0026] The impedance characteristic function of the humanoid robot is determined according to the terminal posture angle and the intermediate joint angle; and the control parameter is determined according to the impedance characteristic function.

[0027] In some embodiments, controlling the humanoid robot to capture the work object according to the control parameters comprises the following steps:

[0028] Driving the bipedal movement of the humanoid robot according to the control parameters to drive the humanoid robot to reach a target position that matches the spatial position of the work object; and then controlling the end effector of the humanoid robot to capture the work object according to the control parameters;

[0029] Alternatively, the spatial position, motion state and motion state of the working object and the humanoid robot are continuously monitored, and the control parameters are dynamically adjusted; at least one of the end posture angle, the intermediate joint angle and the grasping mode of the end effector is adjusted according to the adjusted control parameters, so that the end effector grasps the working object.

[0030] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides a humanoid robot control device, the device comprising:

[0031] A position acquisition unit, used for dynamically acquiring the spatial position of a working object in a moving state;

[0032] A model building unit, used to build a capture system model according to the spatial position; wherein the capture system model is used to describe the numerical relationship between the end posture angles of the two arms of the humanoid robot and the bipedal walking parameters, and the numerical relationship between the end posture angles and the intermediate joint angles of the corresponding arms;

[0033] An angle solving unit, used for solving the end posture angle and the intermediate joint angle according to the motion decomposition controller and the capture system model;

[0034] a parameter determination unit, configured to determine a control parameter of the humanoid robot according to the terminal posture angle and the intermediate joint angle;

[0035] The object capturing unit is used to control the humanoid robot to capture the working object according to the control parameters.

[0036] 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 humanoid robot control method when executing the computer program.

[0037] 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 when the computer program is executed by a processor, the above-mentioned humanoid robot control method is implemented.

[0038] The embodiments of the present application include at least the following beneficial effects:

[0039] The present application can dynamically obtain the spatial position of a working object in a moving state; establish a capture system model according to the spatial position; wherein the capture system model is used to describe the numerical relationship between the terminal attitude angle of the two arms of the humanoid robot and the bipedal walking parameters, and the numerical relationship between the terminal attitude angle and the intermediate joint angle of the corresponding arm; obtain the terminal attitude angle and the intermediate joint angle according to the motion decomposition controller and the capture system model; determine the control parameters of the humanoid robot according to the terminal attitude angle and the intermediate joint angle; and control the humanoid robot to capture the working object according to the control parameters. The capture system model is obtained by modeling the capture system and the capture contact process of the humanoid robot, and the motion decomposition controller is solved and the control parameters are determined based on the capture system model, so that the present application can control the humanoid robot to stably and accurately capture the moving working object. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. 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 creative work.

[0041] Figure 1 A schematic diagram of a flow chart of a humanoid robot control method provided in an embodiment of the present application;

[0042] Figure 2 An example flow chart of a humanoid robot control method provided in an embodiment of the present application;

[0043] Figure 3 A structural block diagram of a control system of a humanoid robot provided in an embodiment of the present application;

[0044] Figure 4A schematic diagram of an optional humanoid robot provided in an embodiment of the present application;

[0045] Figure 5 A schematic diagram of a humanoid robot and a working object provided in an embodiment of the present application;

[0046] Figure 6 An example block diagram of a control system of a humanoid robot provided in an embodiment of the present application;

[0047] Figure 7 A schematic diagram of the structure of a humanoid robot control device provided in an embodiment of the present application;

[0048] Figure 8 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with 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 only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0050] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment 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 can be interpreted as "at the time of" or "when" or "in response to determination".

[0051] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0053] Reference Figure 1 , the embodiment of the present application provides a humanoid robot control method, the method may include but is not limited to including S100 to S140, specifically as follows:

[0054] S100: Dynamically obtain the spatial position of the working object in a moving state.

[0055] It can be understood that the position of the working object in a moving state changes continuously, so this embodiment can obtain the spatial position of the working object in real time.

[0056] Further, S100 may include S101:

[0057] S101: Dynamically acquire the spatial position of the working object in a moving state through one or at least two sensors on the humanoid robot; wherein the sensors include an RGBD sensor and a laser radar.

[0058] Exemplarily, one or more sensors on the body of the humanoid robot, which are RGBD sensors and / or lidars, are used to perceive and determine the positional relationship between the working object and the humanoid robot through sensor data, thereby obtaining the spatial position.

[0059] S110: Establishing a capture system model according to the spatial position; wherein the capture system model is used to describe the numerical relationship between the end posture angles of the two arms of the humanoid robot and the bipedal walking parameters and the numerical relationship between the end posture angles and the intermediate joint angles of the corresponding arms.

[0060] Specifically, the arms of the humanoid robot of this embodiment include multiple joints, such as arm joints and end effector joints, and the intermediate joint angle is the angle between the arm joints and the end effector joints.

[0061] Furthermore, S110 may include S111 to S114:

[0062] S111: constructing a DH parameter table for each joint in the arms of the humanoid robot;

[0063] S112: Obtaining a homogeneous transformation matrix of each joint according to the DH parameter table;

[0064] S113: Determine a desired end position of the end effector of the humanoid robot according to the spatial position;

[0065] S114: Solve the numerical relationship between the terminal posture angle and the intermediate joint angle according to the desired terminal posture and each homogeneous transformation matrix to obtain the capture system model.

[0066] Specifically, the DH parameter table is a table used to describe the kinematic characteristics of a robot or a manipulator, including the following parameters: X-axis rotation (α), X-axis translation (a), Z-axis rotation (θ) and Z-axis translation (d). The above parameters are very important in forward kinematics and inverse kinematics, and can be used to describe the position and posture of a robot or a manipulator.

[0067] The specific meaning of DH parameters:

[0068] X-axis rotation (α): indicates the rotation angle of the robot on the X-axis. This parameter is used to describe the rotational movement of the robot in the X-axis direction. For example, if the robot rotates 90 degrees in the X-axis direction, the value of α is 90 degrees.

[0069] X-axis translation (a): Indicates the translation distance of the robot on the X-axis. This parameter is used to describe the linear motion of the robot in the X-axis direction. For example, if the robot moves 100 mm in the X-axis direction, the value of a is 100 mm.

[0070] Z-axis rotation (θ): indicates the rotation angle of the robot on the Z axis. This parameter is used to describe the rotational movement of the robot in the Z-axis direction. For example, if the robot rotates 45 degrees in the Z-axis direction, the value of θ is 45 degrees.

[0071] Z-axis translation (d): Indicates the translation distance of the robot on the Z axis. This parameter is used to describe the linear movement of the robot in the Z-axis direction. For example, if the robot moves 50 mm in the Z-axis direction, the value of d is 50 mm1.

[0072] Application of DH parameters in robot kinematics:

[0073] The DH parameter table is very important in robot kinematics because it can help describe the robot's motion trajectory and posture. When using the DH parameter table, you need to select appropriate parameter values ​​according to the actual situation to ensure that the robot's motion trajectory and posture are consistent with the actual needs.

[0074] S120: Obtaining the end posture angle and the intermediate joint angle by solving the motion decomposition controller and the capture system model.

[0075] Specifically, the terminal posture angle and the intermediate joint angle solved in this embodiment are adapted to the current spatial position of the working object, and based on the terminal posture angle and the intermediate joint angle, the end effector of the humanoid robot can be controlled to capture the working object in motion.

[0076] Furthermore, S120 may include S121 to S123:

[0077] S121: Determine a control relationship model based on the capture system model; wherein the control relationship model is used to describe the numerical relationship between the parameters of the motion decomposition controller and the end posture angle and the numerical relationship between the parameters of the motion decomposition controller and the intermediate joint angle.

[0078] Specifically, the meaning and function of the motion decomposition controller are as follows:

[0079] Meaning: A motion decomposition controller is a control mechanism used to decompose the overall motion goal or task of a humanoid robot into specific motion instructions for each joint and limb. Based on the robot's kinematic and dynamic models, the motion decomposition controller converts the desired end-effector motion (such as the motion of a dexterous hand) into control parameters such as the angle, velocity, and acceleration of each joint through specific algorithms and calculations, thereby achieving precise control of the overall motion of the humanoid robot.

[0080] effect:

[0081] 1. Achieve precise control of complex motion: Humanoid robots have multiple degrees of freedom, and their motion is the result of the coordinated action of multiple joints. The motion decomposition controller can decompose complex overall motion goals, such as capturing a moving work object, into specific actions of each joint, ensuring that each joint can move according to the predetermined trajectory and speed, thereby achieving precise position control and posture adjustment, and improving the accuracy of capture.

[0082] 2. Adapt to different work scenarios and task requirements: In different work scenarios, the motion state and position of the work object may be different, and the motion requirements for the humanoid robot may also be different. The motion decomposition controller can flexibly adjust the motion parameters of each joint according to the specific task requirements, so that the robot can adapt to various complex and changeable working environments and complete a variety of capture tasks.

[0083] 3. Optimize the robot's motion performance: By reasonably distributing the movement of the joints, the humanoid robot's motion performance can be optimized. For example, under the premise of ensuring that the capture task can be completed, the motion decomposition controller can minimize the movement amplitude and speed change of the joints, thereby reducing energy consumption, reducing joint wear, and extending the robot's service life. At the same time, it can also improve the stability and smoothness of the robot's movement.

[0084] 4. Handling kinematic and dynamic constraints: The joints of humanoid robots are subject to constraints such as range of motion and speed limits, and are also affected by dynamic factors such as inertia, friction, and gravity. The motion decomposition controller will fully consider these constraints and factors when calculating the joint motion parameters to ensure that the generated control instructions are physically feasible, avoid the robot from exceeding the joint limits or unstable motion, and ensure the robot's motion safety and reliability.

[0085] 5. Improve the response speed and stability of the system: When facing a work object that changes rapidly in motion, the motion decomposition controller can quickly calculate the response actions of each joint, so that the robot can adjust its posture and position in time, improving the response speed of the system. At the same time, through precise control and consideration of dynamic characteristics, it can also enhance the stability of the robot's movement, reduce the impact of external interference on the capture task, and ensure that the robot can stably approach and capture the work object.

[0086] Next, the parameters of the motion decomposition controller in this embodiment are described, and the parameters are as follows:

[0087] Joint angle parameters: including the joint angles θ of the humanoid robot’s arms l1 ,θ l2 ,θ l3 ,θ l4 ,θ l5 (left arm joint angle) and θ r1 ,θ r2 ,θ r3 ,θ r4 ,θ r5 (right arm joint angle), and the joint angles θ′ of both feet l1 ,θ′ l2 ,θ′ l3 ,θ′ l4 ,θ′ l5 (left leg joint angle) and θ′ r1 ,θ′ r2 ,θ′ r3 ,θ′ r4 ,θ′ r5 (right leg joint angle). The above parameters directly determine the position and posture of the humanoid robot's limbs.

[0088] Joint angular velocity parameters:

[0089] The angular velocity parameters of the joints are crucial to controlling the speed and direction of the humanoid robot's movement, which affects whether the humanoid robot can reach the target position and adjust its posture in a timely and accurate manner.

[0090] Joint angular acceleration parameters: Similarly, there are also joint angular acceleration parameters, such as The angular acceleration parameter is very important when it is necessary to quickly start, stop or change the direction of movement. It determines the dynamic response characteristics of the joint motion of the humanoid robot.

[0091] Weight parameters: The weight parameters λ1, λ2, λ3, λ4, λ5, etc. involved in the cost function are also important parameters of the motion decomposition controller. These weights are used to balance the importance of different control objectives. For example, λ1 is used to adjust the weight of the relationship between the end attitude angle and the intermediate joint angle, λ2 is used to control the weight of the joint movement speed, λ3 and λ4 are used for the weights of the left and right arm end attitude angular velocities, and λ5 is used for the weights of the parameters related to bipedal walking. Different weight settings will affect the robot's motion strategy and performance.

[0092] Other dynamic parameters: may also include parameters related to the dynamic characteristics of the robot, such as the inertia matrix of the joint, the friction coefficient, gravity compensation parameters, etc. These parameters are used to accurately describe the influence of various forces and torques on the robot during movement, so that the motion decomposition controller can more accurately calculate the required control instructions to overcome the interference of these dynamic factors and achieve stable motion control.

[0093] Next, the numerical relationship between the parameters of the motion decomposition controller and the end attitude angle is explained.

[0094] Forward kinematics relationship: With known joint angle parameters, the forward kinematics equation can be used to calculate the position and posture of the end effector (such as the hand), that is, the end posture angle. Taking the right arm as an example, the homogeneous transformation matrix from the base to the end of the right arm is: The matrix can be used to obtain the position and posture information of the right arm end in space, including the end posture angle. The situation of the left arm and the two feet is similar. The corresponding end posture angle is calculated through their respective homogeneous transformation matrices.

[0095] Inverse kinematics relationship: Given the desired end-point posture angle, the motion decomposition controller uses the inverse kinematics algorithm to solve the angle parameters of each joint. This process is usually a complex numerical calculation process, which requires iteration or other numerical solution methods based on the specific structure and kinematic model of the robot to find the joint angle combination that meets the end-point posture angle requirements. For example, given a desired right arm end-point posture angle, the corresponding θ can be obtained by solving the corresponding inverse kinematics equation. r1 ,θ r2 ,θ r3 ,θ r4 ,θ r5 The value of .

[0096] Optimization relationship based on cost function: The motion decomposition controller optimizes the relationship between parameters such as the end posture angle and the joint angle by constructing a cost function, such as the cost function of this embodiment. Please refer to the description of the cost function below for details.

[0097] S122: Constructing a cost function based on the work object.

[0098] Specifically, the method for solving the cost function optimization problem includes the following steps:

[0099] Usually, numerical optimization algorithms can be used to solve the optimization problem of the cost function. The following is an optional solution step:

[0100] Step 1, initialization: First, the parameters such as joint angles and angular velocities need to be initialized. These initial values ​​can be determined based on the initial state of the robot or some empirical values. For example, all joint angles can be set to zero, or a reasonable initial guess can be made based on the robot's current posture and the general direction of the task.

[0101] Step 2: Calculate the cost function value: Substitute the current joint angle, angular velocity and other parameter values ​​into the cost function expression to calculate the current cost function value J c .

[0102] Step 3, calculate the gradient: for the cost function J c Regarding various parameters (such as joint angle θ, angular velocity etc.) to find the partial derivative and obtain the gradient vector of the cost function The gradient vector represents the rate of change of the cost function under the current parameter value, and its direction points to the direction where the cost function value increases fastest. Our goal is to find the parameter value that minimizes the cost function value, so we need to search in the opposite direction of the gradient.

[0103] Step 4: Determine the search direction: Based on the calculated gradient vector Determine the search direction. A common method is to use the negative gradient direction as the search direction, that is, to search in the direction that makes the cost function value decrease the fastest. However, in practical applications, in order to improve search efficiency and avoid falling into local minima, some improved search strategies may be adopted, such as conjugate gradient method, quasi-Newton method, etc. These methods will adjust the search direction according to previous search information.

[0104] Step 5, determine the step size: After determining the search direction, it is necessary to determine the step size α for each search. The choice of step size directly affects the convergence speed and stability of the optimization algorithm. If the step size is too large, the algorithm may diverge and fail to converge to the optimal solution; if the step size is too small, the algorithm will converge very slowly. Usually, the appropriate step size can be determined by some line search methods, such as the golden section method, quadratic interpolation method, etc. These methods will look for the step size value that reduces the cost function value the most in the search direction.

[0105] Step 6, update parameters: update the values ​​of parameters such as joint angle and angular velocity according to the determined search direction and step size. For example, for the joint angle parameter, the update formula can be expressed as:

[0106]

[0107] where θ new is the updated joint angle value, θ old is the current joint angle value, α is the step size, is the gradient vector of the cost function at the current joint angle value.

[0108] Convergence judgment: Repeat steps 2 to 6 above, continuously update parameters and calculate cost function values ​​until the convergence condition is met. The convergence condition can be set according to the specific situation. For example, when the change in the cost function value is less than a certain threshold, or the change in the parameter is less than a certain threshold, the algorithm is considered to have converged to the vicinity of the optimal solution.

[0109] As an optional implementation, the expression of the cost function constructed in S122 may be:

[0110]

[0111] Among them, J c represents the cost function; θ a and θ b are the end attitude angles of the left arm and the right arm of the humanoid robot respectively, and are the current end posture angles of the left arm and the right arm respectively, θ=[θ 1, θ 2, θ 3, θ 4, θ5] T is the middle joint angle of the arm, and the middle joint angles of the left arm and the right arm are respectively represented by θ l and θ r , is the current arm middle joint angle; μ∈R 8x1 is the weight vector of each joint of the left arm and the right arm, and are the terminal angular velocities of the left and right arms, respectively. are the walking parameters of the humanoid robot, and θ1, λ2, λ3, λ4, and λ5 are the weights corresponding to the walking parameters.

[0112] S123: Using the cost function to constrain the control relationship model, and then using the motion decomposition controller to solve the optimization problem of the control relationship model to obtain the end posture angle and the intermediate joint angle.

[0113] Specifically, by solving the optimization problem of the cost function, not only the end posture angle can be obtained, but also the intermediate joint angles and other related parameters can be obtained.

[0114] End attitude angle: This is one of the main goals of the optimization problem. By minimizing the cost function, we find the end attitude angle value that enables the robot's end effector (such as the hand) to approach the work object with the best attitude. These end attitude angles will be used to control the movement of the robot's arms in the future, so that it can accurately capture the moving work object.

[0115] Intermediate joint angle: During the solution process, the cost function contains items related to the intermediate joint angle, such as:

[0116] Therefore, during the optimization process, the values ​​of the intermediate joint angles will be adjusted at the same time to find an overall optimal combination of joint angles, so that the robot can meet the requirements of the terminal posture angle while ensuring the rationality of joint movement and minimization of energy consumption. Therefore, when solving the optimization problem, the corresponding values ​​of the intermediate joint angles will be obtained. These intermediate joint angles will be used together with the terminal posture angles to control the movement of the robot's arms to ensure that the robot can complete the capture task with appropriate postures and movements.

[0117] Other parameters: In addition to the end attitude angle and the intermediate joint angle, solving the optimization problem may also obtain the optimal values ​​of other related parameters, such as the angular velocity and angular acceleration of the joint. These parameters are very important for controlling the movement speed and dynamic response characteristics of the robot. Together with the joint angle, they will be converted into actual drive signals through the robot's control system to control the motors or drivers of each joint of the robot, so that the robot moves according to the optimal motion trajectory and speed, and realizes efficient and stable capture of the moving work object.

[0118] S130: Determine control parameters of the humanoid robot according to the end posture angle and the intermediate joint angle.

[0119] It can be understood that the control parameters of this embodiment can determine the motion state of the humanoid robot, that is, the humanoid robot is driven to perform corresponding actions based on the control parameters to capture the moving work object.

[0120] Further, S130 may include S131-S132:

[0121] S131: Determine the impedance characteristic function of the humanoid robot according to the end posture angle and the intermediate joint angle.

[0122] Exemplarily, this embodiment provides an optional impedance characteristic function as follows:

[0123]

[0124] Among them, Z SM (s,θ) represents the impedance characteristic function, s represents the Laplace variable, θ represents the intermediate joint angle vector, F(s,θ) represents the Laplace transform of the force and torque vector associated with the intermediate joint angle, and X(s,θ) represents the Laplace transform of the force and end posture vector associated with the intermediate joint angle.

[0125] S132: Determine the control parameter according to the impedance characteristic function.

[0126] S140: Controlling the humanoid robot to capture the work object according to the control parameters.

[0127] Specifically, in this embodiment, the controller in the humanoid robot can be used to control the humanoid robot to perform corresponding actions according to the control parameters, so as to capture the working object in motion.

[0128] Further, S140 may include S141 or S142:

[0129] S141: driving the bipedal movement of the humanoid robot according to the control parameters to drive the humanoid robot to reach a target position that matches the spatial position of the work object; and then controlling the end effector of the humanoid robot to capture the work object according to the control parameters;

[0130] S142: Continuously monitor the spatial position, motion state of the working object and the motion state of the humanoid robot, and then dynamically adjust the control parameters; adjust the end posture angle, the intermediate joint angle and at least one of the grasping methods of the end effector according to the adjusted control parameters, so that the end effector grasps the working object.

[0131] It is understandable that if the humanoid robot is far away from the work object, so that the end effector cannot capture the work object, this embodiment can drive the bipedal movement of the humanoid robot to move the humanoid robot near the work object and then capture it.

[0132] Alternatively, when the end effector of the humanoid robot is close to the work object, the humanoid robot is directly controlled to capture the work object. In the case of S141, when the humanoid robot approaches the work object, the capture can be implemented according to S142.

[0133] Next, the solution of the embodiment of the present application will be introduced and explained in detail with reference to specific application examples.

[0134] Reference Figure 2, this embodiment provides an example flow chart of a humanoid robot control method. Figure 3 The structure block diagram of a control system of a humanoid robot.

[0135] Specifically, this embodiment may include:

[0136] 1. Capture system module, used to establish capture system model.

[0137] 1.1 Real-time perception of working object movement:

[0138] Various sensors, such as visual sensors, distance sensors, etc., are used to continuously monitor the position, speed, and movement direction of the work object. The sensors of this embodiment can be installed on the head, hands, or other parts of the humanoid robot to fully sense the movement state of the work object.

[0139] The motion position information of the working object is updated in real time according to the sensor data and input into the capture system model.

[0140] 1.2 Dynamically establish capture system model:

[0141] When the work object is in motion, the capture system model will dynamically adjust the numerical relationship between the end attitude angle and the intermediate joint angle according to the real-time updated work object position information. For example, if the work object moves to the left, the model will adjust the joint angles of the humanoid robot arm and body accordingly to ensure that the end effector (such as a dexterous hand) can adjust its position and attitude toward the direction of the work object's movement.

[0142] Taking into account the speed and acceleration of the work object, the capture system model predicts the future position of the work object and adjusts the joint angles of the humanoid robot in advance so that it can be captured in time when the work object reaches the predicted position. This requires the establishment of a prediction model based on kinematics and dynamics, combining the current motion state of the work object and environmental factors to predict its future motion trajectory.

[0143] 2. Control strategy design module: used to design control strategies.

[0144] 2.1 Motion decomposition controller adapts to the moving object:

[0145] The motion decomposition controller adjusts the control strategy in real time based on the motion state of the work object. For example, if the work object moves quickly, the controller will increase the movement speed and acceleration of the humanoid robot's joints to ensure that the robot can respond quickly and catch up with the work object.

[0146] Taking into account the change in the direction of the work object, the controller adjusts the joint angles of the humanoid robot so that the robot can flexibly change the direction of movement to track and capture the moving work object. For example, if the work object suddenly turns, the controller will quickly calculate the appropriate joint angle adjustment plan so that the robot can quickly turn and continue to track the work object.

[0147] 2.2 Optimization of terminal attitude angle for moving objects:

[0148] In order to better capture the moving work object, the control strategy design module will optimize the end attitude angle according to the motion characteristics of the work object. For example, if the work object is a fast-moving ball, the end attitude angle can be adjusted to a more open state to more easily surround and capture the ball.

[0149] Considering the shape and size of the work object, the end attitude angle can be adjusted accordingly to ensure that the humanoid robot's hand (such as a dexterous hand) or other actuator can approach and grasp the work object in the best posture. For example, if the work object is a long object, the end attitude angle can be adjusted to a more parallel state to better clamp the object.

[0150] 3. A control parameter determination module is used to determine control parameters.

[0151] 3.1 Dynamically adjust control parameters:

[0152] According to the motion state of the work object and the output of the capture system model and control strategy design module, the control parameter determination module dynamically adjusts the control parameters of the humanoid robot. For example, if the work object accelerates, the control parameter determination module will increase the driving torque of the robot joint to increase the robot's motion speed and acceleration so that it can keep up with the motion of the work object.

[0153] Considering the motion uncertainty of the work object, the control parameter determination module can use an adaptive control algorithm to adjust the control parameters in real time to ensure that the humanoid robot can maintain stable capture performance in different motion situations. For example, if the motion trajectory of the work object suddenly changes, the adaptive control algorithm can quickly adjust the robot's control parameters so that the robot can quickly adapt to the new motion situation and continue to capture.

[0154] 3.2 Determine parameters by considering the characteristics of the moving object:

[0155] The control parameter determination module determines the appropriate control parameters based on the characteristics of the work object, such as weight, shape, material, etc. For example, if the work object is heavy, the control parameter determination module will increase the driving torque of the robot joint to ensure that the robot can stably grasp the work object. If the work object is a soft object, the control parameter determination module will adjust the gripping force of the robot hand to avoid damaging the work object.

[0156] 4. Capture module, used to capture the working object.

[0157] 4.1 Real-time control of humanoid robot to capture moving objects:

[0158] The capture module determines the control parameters of the module output according to the control parameters, and controls the movement of the humanoid robot in real time, so that it can accurately approach and capture the moving work object. For example, the capture module can control the bipedal walking of the humanoid robot, so that it can quickly move to the vicinity of the work object; at the same time, it controls the robot's two arms to cooperate, adjust the end posture angle and the middle joint angle, so that the hand can approach and grasp the work object in the best posture.

[0159] During the capture process, the capture module continuously monitors the position and motion state of the work object, as well as the motion state of the humanoid robot, and adjusts the control parameters in real time according to the actual situation to ensure the accuracy and stability of the capture. For example, if the work object suddenly accelerates or changes direction, the capture module will quickly adjust the robot's motion so that it can continue to track and capture the work object.

[0160] 4.2 Dealing with the movement changes of the working object:

[0161] If the work object changes during the capture process, such as a sudden increase in speed or a change in shape, the capture module will respond quickly and adjust the control strategy. For example, if the work object speeds up, the capture module will increase the robot's movement speed and acceleration to ensure that it can keep up with the work object's movement; if the work object's shape changes, the capture module will adjust the robot's hand's capture method to adapt to the new shape.

[0162] The capture module can also use feedback control algorithms to adjust control parameters in real time according to the actual capture effect to improve the capture success rate. For example, if the first capture fails, the feedback control algorithm can analyze the cause of the failure and adjust the robot's movement and capture strategy for a second attempt.

[0163] In summary, this embodiment can perceive the motion state of the working object in real time, dynamically adjust the capture system model, control strategy and control parameters to adapt to the working object in motion, and achieve efficient and stable capture of the working object through precise control.

[0164] Next, a more specific embodiment will be described.

[0165] 1. Establish capture system model:

[0166] Reference Figure 4 and Figure 5 , this embodiment provides an optional humanoid robot. Among them, Figure 4 10 is the connecting rod 1, 11 is the connecting rod 2, 12 is the connecting rod 3, and 30 is the dexterous hand. Figure 5 20 is the rotary joint 1, 21 is the rotary joint 2, 22 is the rotary joint 3, 23 is the rotary joint 4, 24 is the rotary joint 5, and 100 is the working object. Figure 6 An example block diagram of a control system for a humanoid robot.

[0167] Construct parameter table and transformation matrix: Construct parameter table related to each joint of humanoid robot and obtain a series of homogeneous transformation matrix. For two arms, each arm has 5 degrees of freedom, and the homogeneous transformation matrix is ​​constructed as follows (taking the right arm as an example):

[0168]

[0169] Among them, θ r1 ,θ r2 ,θ r3 ,θ r4 ,θ r5 are the joint rotation angles of the right arm respectively. The homogeneous transformation matrix of the left arm is similar.

[0170] For both feet, each leg has 5 degrees of freedom, of which the ankle joint has one degree of freedom, and the other 2 degrees of freedom are at the hip joint to achieve lateral swing and rotation. The homogeneous transformation matrix is ​​constructed as follows (taking the right leg as an example):

[0171]

[0172]

[0173] Among them, θ′ r1 and θ′ r2 are the lateral swing and rotational degrees of freedom of the hip joint, θ′ r4 is the degree of freedom rotation of the knee joint, θ′ r5 is the degree of freedom rotation of the ankle joint, l′1 is the length of the thigh. The homogeneous transformation matrix of the left leg is similar.

[0174] According to the above homogeneous transformation matrix, the transformation relationship between the spatial coordinates of adjacent joints of the humanoid robot can be obtained.

[0175] Determine the desired end-position: Determine the desired end-position of the humanoid robot hand or other actuator based on the motion position of the work object.

[0176] Solve the numerical relationship: According to the desired end posture and a series of homogeneous transformation matrices, solve the numerical relationship between the end posture angle and the intermediate joint angle to obtain the capture system model.

[0177] 2. Use the motion decomposition controller to solve the end attitude angle:

[0178] Determine the control relationship model: Determine the numerical relationship between the motion decomposition controller parameters and the end attitude angle. Similar to the correspondence between the end posture in Cartesian space and the generalized coordinates in the joint space:

[0179] x(t)=f[θ(t)];

[0180]

[0181] Where J*(θ) is the generalized Jacobian matrix.

[0182] Construct a cost function based on the target: Construct a cost function, the expression is:

[0183]

[0184] Among them, θ a and θ b are the terminal attitude angles of the left and right arms, and are the current end attitude angles of the left and right arms respectively, θ=[θ1,θ2,θ3,θ4] T is the middle joint angle of the arm (the middle joint angles of the left and right arms are represented by θ l and θ r ), is the current arm middle joint angle, μ∈R 8x1 is the weight vector of each joint (including the middle joints and end joints of both arms), and are the angular velocities of the left and right arm ends, are the relevant parameters of bipedal walking (such as the speed and acceleration of the two feet), and λ1, λ2, λ3, λ4, and λ5 are the weights of the corresponding items respectively.

[0185] Use cost function to constrain the control relationship model: Use target-based cost function to constrain the control relationship model, solve the optimization problem of the control relationship model, and obtain the terminal attitude angle.

[0186] 3. Determine the control parameters of the humanoid robot based on the terminal posture angle and the intermediate joint angle:

[0187] Determine the impedance characteristic function: Determine the impedance characteristic function of the humanoid robot system based on the end posture angle and the intermediate joint angle. Similar to the system impedance characteristic function:

[0188]

[0189] Among them, Z SM (s,θ) represents the impedance characteristic function, s represents the Laplace variable, θ represents the intermediate joint angle vector, F(s,θ) represents the Laplace transform of the force and torque vector associated with the intermediate joint angle, and X(s,θ) represents the Laplace transform of the force and end posture vector associated with the intermediate joint angle.

[0190] Next, the conversion procedure from the terminal pose angle to the terminal pose vector is described.

[0191] The end pose angle is the information describing the attitude angle of the robot's end effector in space, while the end pose vector is a more comprehensive way to describe the position and attitude of the end effector, usually in the form of a vector to represent the position and direction in three-dimensional space. The following is the specific process of converting the end pose angle to the end pose vector:

[0192] Position information determination: First, based on the robot's structural parameters and kinematic model, using known joint angles (including the end joint angles, i.e., the end pose angles), the position coordinates (x, y, z) of the end effector in three-dimensional space are calculated through forward kinematics. This is obtained by multiplying a series of homogeneous transformation matrices, gradually transforming from the robot's base coordinate system to the end effector coordinate system, thereby determining the position vector of the end effector:

[0193]

[0194] Posture information determination: The end pose angle can be further used to determine the posture information of the end effector, which is usually represented by a rotation matrix or quaternion. For example, a common method is to use Euler angles (rotation angles around three coordinate axes) to describe the posture of the end effector, and then convert it into a rotation matrix R. The rotation matrix can represent the direction of the end effector in three-dimensional space, and it together with the position vector constitutes the end pose vector.

[0195] Construct the end pose vector: transform the position vector and the rotation matrix Combined, we can get the terminal pose vector T. A common representation is in homogeneous coordinate form, namely:

[0196]

[0197] Where 0 is a 1x3 zero vector and 1 is a scalar. In this way, the end pose vector fully describes the position and posture information of the end effector in three-dimensional space, providing a unified representation for subsequent control and analysis.

[0198] Determine control parameters based on impedance characteristic function: Determine control parameters of the humanoid robot based on the impedance characteristic function.

[0199] 4. According to the control parameters, control the humanoid robot to capture the working object:

[0200] According to the obtained control parameters, the humanoid robot's two feet are controlled to walk to expand the activity space, and the two arms are controlled to cooperate to capture the working object in motion. During the control process, it is necessary to monitor the motion state of the humanoid robot and the position of the working object in real time, and adjust the control parameters according to the actual situation to ensure the accuracy and stability of the capture.

[0201] 5. According to the above homogeneous transformation matrix, the positive kinematics of the humanoid robot can be expressed as:

[0202] For the right arm, the homogeneous transformation matrix from the base to the tip is:

[0203]

[0204] For the left arm, the homogeneous transformation matrix from the base to the tip is:

[0205]

[0206] For the right leg, the homogeneous transformation matrix from the base to the tip is:

[0207]

[0208] For the left leg, the homogeneous transformation matrix from the base to the tip is:

[0209]

[0210] in, Represents the homogeneous transformation matrix from the i-th joint to the j-th joint.

[0211] The forward kinematics of the moving work object is related to the desired end position. Assume that the desired end position of the work object is T tar , the actual position T of the end effector (such as a dexterous hand) in space can be calculated through the homogeneous transformation matrix of the humanoid robot end .

[0212] The specific calculation method is:

[0213] Right Arm:

[0214] Left Arm:

[0215] in, and Represent the homogeneous transformation matrices from the 5th joint to the hand for the right arm and left arm respectively.

[0216] By T end Compare and T tar , it can be determined whether the humanoid robot can reach the desired position of the work object, thereby realizing the capture of the moving work object.

[0217] 6. The inverse kinematics of a humanoid robot is to know the desired end position T tar , solve the rotation angle of each joint:

[0218] θ=[θ r1, θ r2, θ r3, θ r4, θ r5, θ l1, θ l2, θ l3, θ l4, θ l5, θ′ r1, θ′ r2, θ′ r3, θ′ r4, θ′ r5, θ′ l1, θ′ l2, θ′ l3, θ ′l4, θ′ l5, ] T ;

[0219] where θ r1 ,θ r2 ,θ r3 ,θ r4 ,θ r5 is the right arm joint angle, θ l1 ,θ l2 ,θ l3 ,θ l4 ,θ l5 is the left arm joint rotation angle, θ′ r1 ,θ′ r2 ,θ′ r3 ,θ′ r4 ,θ′ r5 , is the right leg joint rotation angle, θ′ l1 ,θ′ l2 ,θ′ l3 ,θ′ l4 ,θ′ l5 is the left leg joint angle).

[0220] Since humanoid robots have many degrees of freedom, the solution to inverse kinematics may not be unique. In the solution process, the appropriate solution can be determined based on the specific constraints and optimization objectives.

[0221] In summary, this embodiment may include the entire process from establishing a capture system model, solving the end attitude angle, determining control parameters to controlling the humanoid robot for capture. By modeling and analyzing the kinematics and dynamics of the humanoid robot, accurate capture of the working object in motion can be achieved.

[0222] Reference Figure 7 The present application also provides a humanoid robot control device, which can implement the above-mentioned humanoid robot control method. The device includes:

[0223] A position acquisition unit, used for dynamically acquiring the spatial position of a working object in a moving state;

[0224] A model building unit, used to build a capture system model according to the spatial position; wherein the capture system model is used to describe the numerical relationship between the end posture angles of the two arms of the humanoid robot and the bipedal walking parameters, and the numerical relationship between the end posture angles and the intermediate joint angles of the corresponding arms;

[0225] An angle solving unit, used for solving the end posture angle and the intermediate joint angle according to the motion decomposition controller and the capture system model;

[0226] a parameter determination unit, configured to determine a control parameter of the humanoid robot according to the terminal posture angle and the intermediate joint angle;

[0227] The object capturing unit is used to control the humanoid robot to capture the working object according to the control parameters.

[0228] 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.

[0229] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above humanoid robot control method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0230] 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.

[0231] See also Figure 8 , Figure 8 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0232] The processor 801 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0233] The memory 802 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 802 can store an operating system and other application programs. When the technical solution provided in the embodiments of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 calls and executes the humanoid robot control method of the embodiment of this application;

[0234] Input / output interface 803, used to implement information input and output;

[0235] The communication interface 804 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0236] A bus 805 that transmits information between the various components of the device (e.g., the processor 801, the memory 802, the input / output interface 803, and the communication interface 804);

[0237] The processor 801 , the memory 802 , the input / output interface 803 and the communication interface 804 are connected to each other in communication within the device via a bus 805 .

[0238] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned humanoid robot control method is implemented.

[0239] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium 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.

[0240] 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 disposed 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.

[0241] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present 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 the present application are also applicable to similar technical problems.

[0242] Those skilled in the art will appreciate 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.

[0243] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0244] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0245] 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 sequence. 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 of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising 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.

[0246] It should be understood that in the present 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 objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single 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.

[0247] In the several embodiments provided in the present 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 only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, 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.

[0248] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0249] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0250] 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, and the computer software product is stored in a storage medium, including multiple instructions to enable 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: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0251] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A humanoid robot control method, characterized in that: The method comprises the following steps: Dynamically obtain the spatial position of the working object in a moving state; A capture system model is established according to the spatial position; wherein the capture system model is used to describe the numerical relationship between the end posture angles of the two arms of the humanoid robot and the bipedal walking parameters, and the numerical relationship between the end posture angles and the intermediate joint angles of the corresponding arms; The end posture angle and the intermediate joint angle are obtained by solving the motion decomposition controller and the capture system model; Determining control parameters of the humanoid robot according to the terminal posture angle and the intermediate joint angle; The humanoid robot is controlled to capture the working object according to the control parameters.

2. The humanoid robot control method according to claim 1, characterized in that: The method of dynamically acquiring the spatial position of the working object in a moving state comprises the following steps: The spatial position of the working object in a moving state is dynamically acquired through one or at least two sensors on the humanoid robot; wherein the sensors include an RGBD sensor and a laser radar.

3. The humanoid robot control method according to claim 1, characterized in that: The step of establishing a capture system model according to the spatial position comprises the following steps: Constructing a DH parameter table for each joint in the arms of the humanoid robot; Obtaining a homogeneous transformation matrix of each of the joints according to the DH parameter table; Determining a desired end position of an end effector of the humanoid robot according to the spatial position; The numerical relationship between the terminal posture angle and the intermediate joint angle is solved according to the desired terminal posture and each homogeneous transformation matrix to obtain the capture system model.

4. The humanoid robot control method according to claim 1, characterized in that: The method of obtaining the terminal posture angle and the intermediate joint angle by solving the motion decomposition controller and the capture system model comprises the following steps: Determine a control relationship model according to the capture system model; wherein the control relationship model is used to describe the numerical relationship between the parameters of the motion decomposition controller and the terminal posture angle and the numerical relationship between the parameters of the motion decomposition controller and the intermediate joint angle; Constructing a cost function based on the work object; The cost function is used to constrain the control relationship model, and then the motion decomposition controller is used to solve the optimization problem of the control relationship model to obtain the end posture angle and the intermediate joint angle.

5. The humanoid robot control method according to claim 4, characterized in that: The constructing of the cost function based on the working object comprises the following steps: The cost function based on the working object is constructed as: Among them, J c represents the cost function; θ a and θ b are the end attitude angles of the left arm and the right arm of the humanoid robot respectively, and are the current end posture angles of the left arm and the right arm respectively, θ=[θ 1, θ 2, θ 3, θ 4, θ5] T is the middle joint angle of the arm, and the middle joint angles of the left arm and the right arm are respectively represented by θ l and θ r , is the current arm middle joint angle; μ∈R 8x1 is the weight vector of each joint of the left arm and the right arm, and are the terminal angular velocities of the left and right arms, respectively. are the walking parameters of the humanoid robot, and λ1, λ2, λ3, λ4, and λ5 are the weights corresponding to the walking parameters.

6. The humanoid robot control method according to claim 1, characterized in that: Determining the control parameters of the humanoid robot according to the terminal posture angle and the intermediate joint angle comprises the following steps: The impedance characteristic function of the humanoid robot is determined according to the terminal posture angle and the intermediate joint angle; and the control parameter is determined according to the impedance characteristic function.

7. The humanoid robot control method according to any one of claims 1 to 6, characterized in that: The step of controlling the humanoid robot to capture the working object according to the control parameters comprises the following steps: Driving the bipedal movement of the humanoid robot according to the control parameters to drive the humanoid robot to reach a target position that matches the spatial position of the work object; and then controlling the end effector of the humanoid robot to capture the work object according to the control parameters; Alternatively, the spatial position, motion state and motion state of the working object and the humanoid robot are continuously monitored, and the control parameters are dynamically adjusted; at least one of the end posture angle, the intermediate joint angle and the grasping mode of the end effector is adjusted according to the adjusted control parameters, so that the end effector grasps the working object.

8. A humanoid robot control device, characterized in that: The device comprises: A position acquisition unit, used for dynamically acquiring the spatial position of a working object in a moving state; A model building unit, used to build a capture system model according to the spatial position; wherein the capture system model is used to describe the numerical relationship between the end posture angles of the two arms of the humanoid robot and the bipedal walking parameters, and the numerical relationship between the end posture angles and the intermediate joint angles of the corresponding arms; An angle solving unit, used for solving the end posture angle and the intermediate joint angle according to the motion decomposition controller and the capture system model; a parameter determination unit, configured to determine a control parameter of the humanoid robot according to the terminal posture angle and the intermediate joint angle; The object capturing unit is used to control the humanoid robot to capture the working object according to the control parameters.

9. 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 humanoid robot control method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the humanoid robot control method according to any one of claims 1 to 7 is implemented.

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