Multi-joint cooperative motion control method and system for humanoid robot

Abstract

The invention provides a multi-joint cooperative motion control method and system of a humanoid robot, and relates to the technical field of robot control, the gait phase data of the humanoid robot in different motion states are obtained, and the motion states comprise the walking state, the running state and the sudden stop state; according to the gait phase data, generating a gait phase migration matrix so as to determine a motion mode switching point, further determining a hysteresis compensation opportunity of the pneumatic artificial muscle, and performing hysteresis compensation processing on the pneumatic artificial muscle of each lower limb joint of the humanoid robot so as to obtain a joint motion state; analyzing an angle change rate and a motion acceleration parameter in the joint motion state, and generating a biological heuristic electric signal; according to the joint cooperation angle threshold value and the motion triggering moment of the biological heuristic electric signal, all the lower limb joints of the humanoid robot are driven to conduct cooperative motion, so that seamless switching of multi-mode motion is achieved, and the motion flexibility and stability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot control, and in particular to a multi-joint cooperative motion control method and system of a humanoid robot. BACKGROUND

[0002] In service, industrial cooperation and emergency rescue scenarios, a humanoid robot needs to frequently switch between walking, running, emergency stopping and other motion states, which requires precise cooperative motion of its lower limb joints (such as hips, knees, ankles, etc.), both fast response to state conversion instructions and smoothness of the motion process and reasonable energy consumption, to meet the needs of complex tasks in dynamic environments for robot motion flexibility and stability.

[0003] At present, the mainstream solution to the above-mentioned needs is to use pre-programmed control based on kinematic model, combined with real-time feedback of joint position sensors, to adjust joint driving parameters through closed-loop control algorithm to track the preset motion trajectory; at the same time, by monitoring whether the physical quantities such as motion speed and acceleration reach the set threshold, the action sequence switching of different motion modes is triggered. The existing solution has obvious limitations, on the one hand, it relies on fixed threshold to determine the motion mode switching time, which is difficult to predict the switching demand in advance according to the subtle changes of the real-time motion state of the humanoid robot, resulting in obvious lag in mode conversion; on the other hand, it does not consider the characteristic delay of the driving component in the force transmission process, which causes deviation between the actual motion of the joint and the instruction, affecting the posture stability of the humanoid robot. SUMMARY

[0004] The purpose of the present application is to provide a multi-joint cooperative motion control method and system of a humanoid robot to solve the problems of lag in mode conversion, deviation between the actual motion of the joint and the instruction, and insufficient posture stability of the humanoid robot in the prior art.

[0005] To solve the above technical problems, in a first aspect, the present application provides a multi-joint cooperative motion control method of a humanoid robot, comprising:

[0006] Obtaining gait phase data of the humanoid robot in different motion states, the motion states including walking state, running state and emergency stopping state;

[0007] Generating a gait phase transition matrix according to the gait phase data to determine a motion mode switching point;

[0008] According to the motion mode switching point, determining a hysteresis compensation opportunity of the pneumatic artificial muscle to perform hysteresis compensation processing on the pneumatic artificial muscle of each lower limb joint of the humanoid robot, and obtaining a joint motion state;

[0009] Analyzing the angle change rate and motion acceleration parameters in the joint motion state to generate a bioinspired electrical signal;

[0010] According to the joint coordination angle threshold and motion triggering moment of the bioinspired electrical signal, the lower limb joints of the humanoid robot are driven to perform coordinated motion to achieve seamless switching of multimodal motion.

[0011] Optionally, generating a gait phase transition matrix based on the gait phase data to determine a motion mode switching point includes:

[0012] Extracting from the gait phase data a first phase characteristic value corresponding to a walking state, a second phase characteristic value corresponding to a running state, and a third phase characteristic value corresponding to an emergency stop state;

[0013] Counting the occurrence frequencies of the first phase eigenvalue, the second phase eigenvalue, and the third phase eigenvalue to calculate the transition probability between every two motion states;

[0014] Writing all transition probabilities into an initial matrix structure of preset dimensions to form a gait phase transition matrix, wherein the row dimension of the initial matrix structure corresponds to the starting motion state and the column dimension corresponds to the target motion state;

[0015] The gait phase transition matrix is ​​traversed, and the end moment of the initial motion state corresponding to the transition probability exceeding a preset probability threshold is determined as the motion mode switching point.

[0016] Optionally, extracting a first phase feature value corresponding to the walking state from the gait phase data includes:

[0017] Determining a data segment in the gait phase data whose movement speed is within a preset walking speed range as walking state data;

[0018] collecting the first rotation angle of each lower limb joint at each sampling moment in the walking state data, and arranging all the first rotation angles to form a first joint angle sequence;

[0019] Recording the time points of two adjacent foot landings in the walking state data, calculating a first foot landing interval duration, and determining the first foot landing interval duration as a first movement cycle duration;

[0020] In the walking state data, the movement starting time points of the hip joint and the knee joint are selected to calculate a first starting time difference, and the first starting time difference is used as a first adjacent joint movement time difference;

[0021] The first joint angle sequence, the first motion cycle duration, and the first adjacent joint motion time difference are combined into a first phase characteristic value corresponding to the walking state.

[0022] Optionally, determining a hysteresis compensation timing of the pneumatic artificial muscle according to the motion mode switching point, so as to perform hysteresis compensation processing on the pneumatic artificial muscle of each lower limb joint of the humanoid robot to obtain a joint motion state, includes:

[0023] Extracting the extension length value and internal pressure value of the pneumatic artificial muscle of the lower limb joint within a first preset time period before the motion mode switching point and a second preset time period after the motion mode switching point;

[0024] Under the same telescopic length value, the internal pressure value is calculated to be different from the corresponding standard pressure value in the standard pressure curve to obtain the hysteresis deviation value of the pneumatic artificial muscle;

[0025] Calculating a hysteresis compensation amount according to a time interval between the hysteresis deviation value and the motion mode switching point;

[0026] Taking the motion mode switching point as a time reference, setting the compensation start time to a preset advance time before the motion mode switching and setting the compensation end time to a preset duration after the motion mode switching point to generate a hysteresis compensation opportunity;

[0027] During the hysteresis compensation period, the internal pressure of the pneumatic artificial muscle is adjusted according to the hysteresis compensation amount, and the second rotation angle and rotation speed of the lower limb joint during the adjustment process are collected, and the second rotation angle and rotation speed are combined into a joint motion state.

[0028] Optionally, analyzing the angle change rate and motion acceleration parameters in the joint motion state to generate a bioinspired electrical signal includes:

[0029] Calculating the angle change rate and movement acceleration of each lower limb joint according to the joint movement state;

[0030] Compare the angle change rates of different lower limb joints at the same time to calculate the angle change differences between adjacent lower limb joints and determine the movement correlation between the lower limb joints;

[0031] Multiply all motion correlations by the preset angle ratio coefficient to obtain the corresponding joint coordination angle threshold;

[0032] The motion acceleration that appears most frequently under the same motion correlation is taken as the benchmark acceleration. All benchmark accelerations are multiplied by the preset time coefficient to obtain the time interval parameter of the corresponding motion trigger moment. Combined with the corresponding joint coordination angle threshold, a signal feature group is formed.

[0033] All signal feature groups are arranged according to the motion transmission order of each lower limb joint to form a bioinspired electrical signal, wherein the pulse amplitude of the bioinspired electrical signal is the corresponding joint coordination angle threshold, and the pulse interval is the corresponding motion trigger moment.

[0034] Optionally, according to the joint coordination angle threshold and motion triggering moment of the bioinspired electrical signal, driving the lower limb joints of the humanoid robot to perform coordinated motion to achieve seamless switching of multimodal motion, including:

[0035] According to the motion transmission order of each lower limb joint, with the motion triggering moment of the bio-inspired electrical signal as the benchmark, a corresponding action execution period is assigned to each lower limb joint, and the action execution period is a preset action window before and after the motion triggering moment;

[0036] Calculate the rotation angle amplitude based on the joint coordination angle threshold of each lower limb joint and the corresponding transmission ratio parameter;

[0037] Determining the rotation direction of each lower limb joint based on the degree of motion correlation between the joints, wherein the rotation direction maintains a coordinated relationship with the motion direction of the adjacent joints;

[0038] The rotation angle amplitude and rotation direction corresponding to each lower limb joint are combined into a target rotation angle instruction;

[0039] During the action execution period, driving the corresponding lower limb joints to move according to the target rotation angle instruction to collect a third rotation angle of each lower limb joint;

[0040] Calculate the angle deviation value between the third rotation angle and the target rotation angle instruction, compare the angle deviation value with the preset deviation threshold, and drive all lower limb joints to move in coordination according to the comparison result until the switching of different motion states is completed, so as to achieve seamless switching of multimodal motion.

[0041] Optionally, determining the rotation direction of each lower limb joint based on the degree of motion correlation between the lower limb joints, wherein the rotation direction maintains a coordinated relationship with the motion direction of the adjacent joints, includes:

[0042] Setting a synergistic relationship between adjacent lower limb joints, wherein the synergistic relationship includes a same-direction synergistic relationship and a reverse synergistic relationship, wherein the synergistic relationship between adjacent lower limb joints whose motion correlation is within a preset positive value range is determined as a same-direction synergistic relationship, and the synergistic relationship between adjacent lower limb joints whose motion correlation is within a preset negative value range is determined as a reverse synergistic relationship;

[0043] The rotation direction of each lower limb joint is determined based on the motion correlation and synergistic relationship between different adjacent lower limb joints, so that the rotation direction of each lower limb joint maintains a corresponding synergistic relationship with the motion direction of the adjacent lower limb joints.

[0044] In a second aspect, the present application provides a multi-joint coordinated motion control system for a humanoid robot, comprising:

[0045] An acquisition module is used to acquire gait phase data of the humanoid robot in different motion states, wherein the motion states include walking state, running state and emergency stop state;

[0046] A generating module, configured to generate a gait phase transition matrix based on the gait phase data to determine a motion mode switching point;

[0047] a compensation module, configured to determine a hysteresis compensation timing of the pneumatic artificial muscles according to the motion mode switching point, so as to perform hysteresis compensation processing on the pneumatic artificial muscles of each lower limb joint of the humanoid robot to obtain a joint motion state;

[0048] An analysis module, configured to analyze the angle change rate and motion acceleration parameters in the joint motion state to generate a bioinspired electrical signal;

[0049] A driving module is used to drive the lower limb joints of the humanoid robot to perform coordinated motion according to the joint coordination angle threshold and motion triggering time of the bio-inspired electrical signal, so as to achieve seamless switching of multi-modal motion. In a third aspect, the present application provides an electronic device comprising:

[0050] Memory for storing computer programs;

[0051] The processor is configured to implement the steps of the multi-joint coordinated motion control method for a humanoid robot as described in the first aspect above when executing the computer program.

[0052] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the steps of a multi-joint collaborative motion control method of a humanoid robot as described in the first aspect above.

[0053] The present application provides a multi-joint collaborative motion control method for a humanoid robot, which obtains gait phase data of the humanoid robot in different motion states, wherein the motion states include walking state, running state and emergency stop state; generates a gait phase migration matrix based on the gait phase data to determine the motion mode switching point; determines the hysteresis compensation timing of the pneumatic artificial muscles based on the motion mode switching point, so as to perform hysteresis compensation processing on the pneumatic artificial muscles of each lower limb joint of the humanoid robot to obtain the joint motion state; analyzes the angle change rate and motion acceleration parameters in the joint motion state to generate a bio-inspired electrical signal; and drives the lower limb joints of the humanoid robot to perform collaborative motion based on the joint collaborative angle threshold and motion trigger moment of the bio-inspired electrical signal to achieve seamless switching of multi-modal motion. The technical solution provided by the present invention provides comprehensive and real-time basic information for subsequent analysis of motion pattern characteristics by collecting gait data in walking, running and sudden stop states, thereby solving the problem of one-sided motion state judgment caused by insufficient data coverage; by dynamically calculating the motion state conversion probability, it breaks through the limitation of relying on fixed thresholds to judge the switching timing, realizes early prediction of motion mode switching needs, and reduces mode conversion lag; for the characteristic delay of pneumatic artificial muscles, accurate compensation is implemented before and after switching, reducing the deviation between the actual movement of the lower limb joints and the instructions, and solving the problem of motion distortion caused by not considering the delay of the drive component; by extracting the angle change rate and acceleration parameters to generate an electrical signal containing collaborative information, it provides accurate timing and angle reference for multi-joint collaborative movement, and solves the problem of movement disconnection caused by the lower limb joint coordination relying only on the preset sequence; by coordinating the movements of each lower limb joint according to the signal instructions, it ensures the continuity and consistency of the lower limb joint movement during the motion mode switching, and solves the problems of the non-smooth switching process and poor posture stability in the existing scheme. Furthermore, the system allocates action execution periods based on the sequence of lower limb joint motion transmission and the motion triggering moment of the bioinspired electrodynamic signal. The rotation angle amplitude is calculated based on the joint coordination angle threshold and the transmission ratio parameter, and the rotation direction is determined by combining the inter-joint motion correlation to form a target rotation angle instruction. During the action execution period, the joints are driven to move, and the actual rotation angle is collected and dynamically adjusted by comparing the deviation value until the motion state switch is completed. This solves the problems of poor joint motion coordination and large deviations between instructions and actual motion, ensuring that multiple joints move synchronously according to the preset coordination relationship during the motion mode switch, ultimately achieving seamless switching of multimodal motion and improving the smoothness of the humanoid robot's motion and posture stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments or prior art of the present application, the accompanying drawings required by the embodiments or prior art description will be briefly introduced as follows. Obviously, the accompanying drawings described below are only some embodiments of the present application, and all other embodiments obtained by those of ordinary skill in the art without creative effort based on the embodiments of the present application shall fall within the scope of the present application.

[0055] Figure 1 A flow chart of a multi-joint cooperative motion control method of a humanoid robot provided by the present application;

[0056] Figure 2 A structural schematic diagram of a multi-joint cooperative motion control system of a humanoid robot provided by the present application;

[0057] Figure 3 A structural schematic diagram of a computing device provided by the present application. DETAILED DESCRIPTION

[0058] In view of the problems of response lag and poor stability of the current mainstream control strategy based on fixed threshold and idealized model in multi-joint cooperative control of a humanoid robot, the core defect is that the rigid threshold judgment of motion mode switching depends on macro parameters such as speed and acceleration, which is difficult to capture the continuous evolution of gait phase, leading to intention recognition lag, easy to cause switching delay or misjudgment, and causing the problem of stiff action or even instability. At the same time, the existing framework often ignores the strong nonlinearity, time-varying hysteresis and force transmission delay of actuators (such as pneumatic artificial muscles), which causes the output torque of the joint to deviate from the expectation, introduces cooperative control error, and affects the dynamic stability. In addition, lacking the reference of biological neural regulation mechanism, the control of each joint is isolated, and it is difficult to realize the bionic compliant coupling and energy efficient transmission. These problems jointly restrict the smooth switching of multi-modal motion and the overall motion performance of the humanoid robot in complex scenes, and it is difficult to meet the comprehensive needs of flexibility, stability and energy efficiency for high dynamic tasks.

[0059] In order to enable those skilled in the art to better understand the present application, the present application will be further described in detail below in combination with the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort fall within the scope of the present application.

[0060] The core of the present application is to provide a multi-joint cooperative motion control method of a humanoid robot, and a flowchart of one specific embodiment of the method is shown in Figure 1 The method comprises:

[0061] Step 101: Acquire gait phase data of a humanoid robot in different motion states, wherein the motion states include a walking state, a running state, and an emergency stop state;

[0062] In this step, gait phase data refers to a data set that reflects the motion characteristics of the lower limb joints of the humanoid robot in different motion states, including data such as joint rotation angle, motion speed, motion cycle, foot landing information, etc. collected based on sensors, which is used to analyze the robot's motion state and gait characteristics. The walking state refers to the motion state of the humanoid robot moving steadily at a relatively low speed, with its stride and motion cycle being relatively fixed, including characteristic data reflecting that the joint movement is smooth and the speed is moderate in this state. The running state refers to the motion state of the humanoid robot moving at a relatively high speed, with a large stride and a short motion cycle, including characteristic data reflecting that the joint movement amplitude is large and the speed is fast in this state. The emergency stop state refers to the motion state of the humanoid robot rapidly decelerating from a motion state to a stop, including characteristic data reflecting that the speed is sharply reduced and the joint force changes greatly in this state.

[0063] In an embodiment of the present application, angle sensors, speed sensors and pressure sensors are installed at the joints of the robot's lower limbs to collect data such as the joint rotation angle, movement speed, and foot landing pressure of the robot in real time when walking, running, and stopping suddenly. The data set formed by arranging these data in chronological order is gait phase data; among them, the movement speed of the robot in the walking state is in a lower range and the gait is stable, the movement speed in the running state is higher and the stride is larger, and the movement speed in the emergency stop state drops rapidly from a certain value to zero. The speed data collected by the sensor can distinguish these three movement states, and collect data in the corresponding states respectively to form complete gait phase data.

[0064] Step 102: Generate a gait phase transition matrix based on the gait phase data to determine a motion mode switching point;

[0065] In an embodiment of the present application, a first phase eigenvalue corresponding to the walking state, a second phase eigenvalue corresponding to the running state, and a third phase eigenvalue corresponding to the emergency stop state are extracted from the gait phase data; the frequency of occurrence of the three phase eigenvalues ​​is counted to calculate the transition probability between each two motion states; all transition probabilities are written into an initial matrix structure of a preset dimension to form a gait phase transition matrix; the gait phase transition matrix is ​​traversed, and the end moment of the starting motion state corresponding to the transition probability exceeding the preset probability threshold is determined as the motion mode switching point.

[0066] Step 103: determining a hysteresis compensation timing of the pneumatic artificial muscles according to the motion mode switching point, so as to perform hysteresis compensation processing on the pneumatic artificial muscles of each lower limb joint of the humanoid robot to obtain a joint motion state;

[0067] In an embodiment of the present application, the telescopic length value and the internal pressure value of the pneumatic artificial muscle of the lower limb joint within a first preset time length before the motion mode switching point and a second preset time length after the motion mode switching point are extracted; under the same telescopic length value, the difference between the internal pressure value and the corresponding standard pressure value in the standard pressure curve is calculated to obtain a hysteresis deviation value; the hysteresis compensation amount is calculated based on the time interval between it and the motion mode switching point; with the motion mode switching point as the time reference, the compensation start time is set to the preset advance time before the motion mode switching, and the compensation end time is set to the preset duration after the motion mode switching point to generate a hysteresis compensation opportunity; within the hysteresis compensation opportunity, the internal pressure of the pneumatic artificial muscle is adjusted according to the hysteresis compensation amount, and the second rotation angle and rotation speed of the lower limb joint during the adjustment process are collected, and the two are combined into a joint motion state.

[0068] Step 104: analyzing the angle change rate and motion acceleration parameters in the joint motion state to generate a bio-inspired electrical signal;

[0069] In an embodiment of the present application, the angle change rate and motion acceleration of each lower limb joint are calculated according to the joint motion state; the angle change rates of different lower limb joints at the same time are compared to calculate the angle change difference between adjacent lower limb joints to determine the motion correlation between each lower limb joint; all motion correlations are multiplied by the preset angle proportional coefficient to obtain the corresponding joint coordination angle threshold; the motion acceleration that occurs the most times under the same motion correlation is used as the reference acceleration, and all reference accelerations are multiplied by the preset time coefficient to obtain the time interval parameter of the corresponding motion trigger moment, which is combined with the corresponding joint coordination angle threshold to form a signal feature group; all signal feature groups are arranged into bioinspired electrochemical signals according to the motion transmission order of each lower limb joint.

[0070] Step 105: driving the lower limb joints of the humanoid robot to perform coordinated motion according to the joint coordination angle threshold and motion triggering time of the bioinspired electrical signal to achieve seamless switching of multimodal motion;

[0071] In the embodiments of the present application, according to the motion transmission sequence of each lower limb joint, a motion trigger time of a bio-inspired electrical signal is taken as a reference to assign a corresponding motion execution period to each lower limb joint, the motion execution period being a preset motion window before and after the motion trigger time; based on a joint coordination angle threshold of each lower limb joint and a corresponding transmission ratio parameter, a rotation angle amplitude is calculated; according to a motion correlation between the lower limb joints, a rotation direction of each lower limb joint is determined; the rotation angle amplitude and the rotation direction of each lower limb joint are combined into a target rotation angle instruction; in the motion execution period, the corresponding lower limb joint is driven to move according to the target rotation angle instruction to collect a third rotation angle of each lower limb joint; an angle deviation value of the third rotation angle and the target rotation angle instruction is calculated, and the angle deviation value is compared with a preset deviation threshold to drive all the lower limb joints to move coordinately according to a comparison result until a switching of different motion states is completed, so as to realize seamless switching of multi-modal motion.

[0072] The embodiments of the present application realize accurate prediction of motion mode switching; by performing hysteresis compensation on the pneumatic artificial muscle before and after the switching point, the characteristic delay of the driving assembly is considered, and the deviation between joint motion and instruction is reduced; by analyzing the joint motion state to generate a bio-inspired electrical signal containing joint coordination information, the joint is driven to move according to the coordination relationship, the problem of poor joint coordination is solved, and finally seamless switching of the humanoid robot between walking, running, emergency stop and other multi-modal motions is realized, the stability, flexibility and posture stability of the motion are improved, and the needs of different scenes are met.

[0073] The present application provides a specific embodiment, step 102, generating a gait phase transition matrix according to the gait phase data to determine a motion mode switching point, specifically including the following steps:

[0074] Step 201: extracting a first phase feature value corresponding to a walking state, a second phase feature value corresponding to a running state, and a third phase feature value corresponding to an emergency stop state from the gait phase data;

[0075] In this step, the first phase feature value refers to a set of motion characteristics reflecting the walking state motion characteristics extracted based on the walking state data, including a joint angle sequence, a motion cycle length and a motion time difference between adjacent joints in the walking state, for representing the gait law of the walking state. The second phase feature value refers to a set of motion characteristics reflecting the running state motion characteristics extracted based on the running state data, including a second joint angle sequence, a second motion cycle length and a second motion time difference between adjacent joints in the running state, for representing the gait law of the running state. The third phase feature value refers to a set of motion characteristics reflecting the emergency stop state motion characteristics extracted based on the emergency stop state data, including a third joint angle sequence, a third motion cycle length and a third motion time difference between adjacent joints in the emergency stop state, for representing the gait law of the emergency stop state.

[0076] In the embodiment of the present application, the data segment with the motion speed in the gait phase data within the preset walking speed range is determined as the walking state data; the first rotation angle of each lower limb joint at each sampling time in the walking state data is collected, all the first rotation angles are arranged to form a first joint angle sequence; the time points of adjacent two foot landings in the walking state data are recorded, and the first landing interval duration is calculated, which is determined as the first motion cycle duration; in the walking state data, the motion starting time points of the hip joint and the knee joint are selected to calculate the first starting time difference, which is taken as the first adjacent joint motion time difference; the first joint angle sequence, the first motion cycle duration and the first adjacent joint motion time difference are combined as the first phase feature value corresponding to the walking state. Similarly, the corresponding second joint angle sequence, the second motion cycle duration and the second adjacent joint motion time difference are extracted from the running state data to form the second phase feature value; the corresponding third joint angle sequence, the third motion cycle duration and the third adjacent joint motion time difference are extracted from the emergency stop state data to form the third phase feature value.

[0077] Step 202: count the occurrence frequencies of the first phase feature value, the second phase feature value and the third phase feature value to calculate the conversion probability between each two motion states;

[0078] In this step, the conversion probability refers to the possibility of conversion between two different motion states, which is calculated based on the occurrence frequencies of the first, second and third phase feature values, and is used to reflect the trend of motion state conversion.

[0079] In the embodiment of the present application, the number of occurrences of the first phase feature value, the second phase feature value and the third phase feature value in the gait phase data is counted, the total number of occurrences of the corresponding feature values when the walking state is converted to the running state, the walking state is converted to the emergency stop state, the running state is converted to the walking state, the running state is converted to the emergency stop state, the emergency stop state is converted to the walking state, and the emergency stop state is converted to the running state is recorded, and then the number of occurrences of the phase feature value in each conversion direction is divided by the total number of occurrences of the phase feature value in all conversion directions to obtain the conversion probability between each two motion states. The determination boundary of state conversion is defined as: when the motion state feature values (such as joint angle sequence, motion cycle) of three continuous sampling time points in the gait phase data change from belonging to the starting motion state (such as walking) to belonging to the target motion state (such as running), it is determined as a complete state conversion, and the occurrence of the phase feature value corresponding to the conversion direction is recorded as 1. For example, the first phase feature value appears 20 times in the conversion from walking to running, and the total number of occurrences in all conversion directions is 100 times, so the conversion probability = 20 ÷ 100 = 0.2.

[0080] Step 203: writing all transition probabilities into an initial matrix structure of a preset dimension to form a gait phase transition matrix, wherein the row dimension of the initial matrix structure corresponds to the starting motion state and the column dimension corresponds to the target motion state;

[0081] In this step, the gait phase transition matrix refers to a set of transition probabilities between various motion states presented in matrix form, with its row dimension corresponding to the starting motion state before the transition and the column dimension corresponding to the target motion state after the transition, which is used to intuitively display the probability distribution of motion state transitions. The starting motion state refers to the initial motion state before the transition during the motion state transition, including the walking state, running state, and emergency stop state, which is the reference state for the conversion probability calculation. The target motion state refers to the final motion state after the transition during the motion state transition, including the walking state, running state, and emergency stop state, which is the result state of the conversion probability calculation.

[0082] In an embodiment of the present application, an initial matrix of 3 rows and 3 columns is preset, wherein the row dimensions correspond to the walking state, running state, and emergency stop state (i.e., the starting motion state) in sequence, and the column dimensions correspond to the walking state, running state, and emergency stop state (i.e., the target motion state) in sequence. The calculated transition probability from the walking state to the running state is written into the 1st row and 2nd column, the transition probability from the walking state to the emergency stop state is written into the 1st row and 3rd column, the transition probability from the running state to the walking state is written into the 2nd row and 1st column, the transition probability from the running state to the emergency stop state is written into the 2nd row and 3rd column, the transition probability from the emergency stop state to the walking state is written into the 3rd row and 1st column, and the transition probability from the emergency stop state to the running state is written into the 3rd row and 2nd column. The probabilities of the remaining diagonal positions (the starting and target states are the same) are set to 0 to form a gait phase transition matrix.

[0083] Step 204: traverse the gait phase transition matrix and determine the end time of the initial motion state corresponding to the transition probability exceeding a preset probability threshold as the motion mode switching point;

[0084] In this step, the preset probability threshold refers to a pre-set probability critical value used to determine whether the motion state needs to be switched. When the transition probability exceeds this threshold, it is determined that the corresponding motion state needs to be switched. Based on 100 successful switching experiments of humanoid robots in typical scenarios (such as switching from walking state on flat ground to running state), the minimum effective transition probability (i.e., the lowest probability to ensure a successful switch) is taken as the benchmark, combined with the safety redundancy factor (1.2), and the typical value is set to 0.6 (i.e., when the transition probability ≥ 0.6, it is determined that a state switch needs to be triggered). The motion mode switching point refers to the moment when the starting motion state ends and is about to switch to the target motion state, determined based on the transition probability exceeding the preset probability threshold in the gait phase transition matrix, and is used to trigger subsequent motion mode switching operations.

[0085] In the embodiment of the present application, each transition probability in the gait phase transition matrix is traversed by row, each transition probability is compared with a preset probability threshold, when a certain transition probability (such as the transition probability from the walking state to the running state) is greater than the preset probability threshold, the starting motion state (i.e. the walking state) corresponding to the transition probability is found, and the time when the first phase feature value of the starting motion state appears last in the gait phase data is determined as the motion mode switching point.

[0086] The embodiment of the present application breaks through the limitation of relying on fixed threshold to judge the switching time, can dynamically predict the motion mode switching demand according to the real-time gait characteristics, solves the problem of mode conversion lag; based on the high-probability transition event in the gait phase transition matrix to determine the motion mode switching point, the switching time is more matched with the actual motion state of the robot, lays a precise time reference for subsequent hysteresis compensation and joint coordinated motion, and improves the accuracy and timeliness of multi-modal motion switching.

[0087] The present application provides a specific embodiment, step 201, extracting the first phase feature value corresponding to the walking state from the gait phase data, specifically including the following steps:

[0088] Step 211: determining the data segment with motion speed in the preset walking speed range in the gait phase data as the walking state data;

[0089] In this step, the preset walking speed range refers to the speed interval preset for distinguishing the walking state from other motion states, which is determined based on the typical speed range when the humanoid robot walks, including the numerical range reflecting that the speed under the walking state is not lower than the minimum walking speed and not higher than the maximum walking speed, which is used to filter the walking state data in the gait phase data. The walking state data refers to the continuous data segment filtered from the gait phase data, whose motion speed is in the preset walking speed range, including joint rotation angle, motion time, foot landing information and other data reflecting the walking state, which is used to extract the feature parameters of the walking state.

[0090] In the embodiment of the present application, the speed sensor installed on the lower limbs of the robot collects the motion speed in real time, compares the collected motion speed with the preset walking speed range, and selects the continuous data segment with the motion speed in the range, which contains the joint angle, motion time, foot pressure and other information in this time period, and together constitutes the walking state data.

[0091] Step 212: collecting the first rotation angle of each lower limb joint at each sampling time in the walking state data, arranging all the first rotation angles to form a first joint angle sequence;

[0092] In this step, the first rotation angle refers to the real-time rotation angle of each joint (including the hip joint, the knee joint, and the ankle joint) of the lower limb collected by the joint angle sensor at each sampling time in the walking state data, which is obtained based on the real-time detection of the sensor on the joint rotation, and is used to construct a sequence reflecting the dynamic change of the joint angle. The first joint angle sequence refers to a sequence formed by arranging the first rotation angle at each sampling time in the walking state data in chronological order, including a hip joint angle subsequence, a knee joint angle subsequence, and an ankle joint angle subsequence, and is used to reflect the change rule of the joint angle of each joint over time in the walking state.

[0093] In the embodiments of the present application, the walking state data is sampled at a preset sampling interval, the real-time rotation angles (i.e., the first rotation angles) of the hip joint, the knee joint, and the ankle joint are collected by the joint angle sensor at each sampling time, and the first rotation angles of the same lower limb joint are arranged in turn in chronological order of the sampling time to form an angle subsequence of the lower limb joint. Then, the angle subsequences of the lower limb joints are combined to form a first joint angle sequence.

[0094] Step 213: recording the time points of the adjacent two foot landings in the walking state data, and calculating a first landing interval duration, the first landing interval duration is determined as a first movement cycle duration;

[0095] In this step, the first landing interval duration refers to the time difference between the time points of the adjacent two foot landings in the walking state data, which is calculated based on the landing time recorded by the foot pressure sensor, and is used to represent the duration of one complete gait in the walking state. The first movement cycle duration refers to the time length of one complete gait cycle in the walking state determined by the first landing interval duration, including the time interval from one foot landing to the next foot landing, and is used to represent the gait periodicity in the walking state.

[0096] In the embodiments of the present application, the pressure sensor mounted on the foot detects the trigger signal of the contact between the foot and the ground, records the time point as the foot landing time point when the pressure value exceeds the preset contact threshold, selects the adjacent two landing time points, calculates the difference (i.e., the first landing interval duration) between the latter time point and the former time point, and the difference reflects the time of completing one complete gait in the walking state, so the difference is determined as the first movement cycle duration.

[0097] Step 214: selecting the movement starting time points of the hip joint and the knee joint in the walking state data to calculate a first starting time difference, and taking the first starting time difference as a first adjacent joint movement time difference;

[0098] In this step, the movement starting time point refers to the moment when the joint starts to move from a stationary or initial position in the walking state data, which is determined based on the moment when the rate of change of the joint angle exceeds a preset starting threshold value, including the hip joint movement starting time point and the knee joint movement starting time point, and is used to calculate the time difference of adjacent joint movements. The first starting time difference refers to the difference between the knee joint movement starting time point and the hip joint movement starting time point in the walking state data, which is calculated based on the numerical values ​​of the two joint movement starting time points, and is used to reflect the time difference between the start of movement of the hip joint and the knee joint. The first adjacent joint movement time difference refers to the time parameter determined by the first starting time difference, which reflects the synchronization of the hip joint and knee joint movements in the walking state, including the difference in the start time of the two joints, and is used to characterize the degree of coordination of the adjacent joint movements.

[0099] In an embodiment of the present application, by analyzing the angle changes of the hip joint and the knee joint in the first joint angle sequence, when the joint angle begins to deviate from the initial position and the angle change rate exceeds the preset starting threshold, this moment is recorded as the starting time point of the movement of the corresponding joint, the starting time points of the movement of the hip joint and the knee joint are selected, and the difference between the starting time point of the knee joint movement and the starting time point of the hip joint movement (i.e., the first starting time difference) is calculated. This difference reflects the time difference between the two joints starting to move, and therefore it is used as the first adjacent joint movement time difference.

[0100] Step 215: combining the first joint angle sequence, the first motion cycle duration, and the first adjacent joint motion time difference into a first phase characteristic value corresponding to the walking state;

[0101] In an embodiment of the present application, the first joint angle sequence reflecting the dynamic changes of joint angles, the first motion cycle duration reflecting the gait cycle, and the first adjacent joint motion time difference reflecting the synchronization of joint motion are integrated into a feature set according to a preset format. This set fully characterizes the gait characteristics of the walking state, that is, the first phase feature value corresponding to the walking state.

[0102] The embodiments of the present application solve the problem of fuzzy motion state feature extraction and difficulty in accurately distinguishing different motion states; the first phase eigenvalue provides reliable basic data for the subsequent calculation of motion state conversion probability and generation of gait phase migration matrix, ensuring the accuracy of motion mode switching point judgment, and thus laying a precise state recognition foundation for seamless switching of multimodal motion.

[0103] For example, in a service scenario, a humanoid robot needs to complete item delivery while walking, and its preset walking speed range is set to 0.5-1.5m / s; the robot's movement speed is collected through a speed sensor, and continuous data segments with a speed of 0.5-1.5m / s are screened out and determined as walking state data (including joint angles, time, foot pressure and other information within 5 minutes); with a sampling interval of every 10 milliseconds, the first rotation angles of the hip joint (such as 85°, 87°...), knee joint (such as 60°, 62°...), and ankle joint (such as 30°, 32°...) at each moment in the walking state data are collected and arranged in chronological order. A first joint angle sequence is formed; the foot pressure sensor detects that the two adjacent foot landing times are 10 seconds and 12 seconds respectively, and the first landing interval is calculated to be 2 seconds, which is determined as the first motion cycle length; the first joint angle sequence is analyzed, and it is found that the hip joint starts to move at 10.2 seconds (the angle change rate exceeds the starting threshold), and the knee joint starts to move at 10.3 seconds. The first starting time difference is calculated to be 0.1 seconds, which is used as the first adjacent joint motion time difference; finally, the above-mentioned first joint angle sequence, the first motion cycle length of 2 seconds, and the first adjacent joint motion time difference of 0.1 seconds are combined to form the first phase characteristic value corresponding to the walking state.

[0104] This application provides a specific embodiment, step 103, determining the hysteresis compensation timing of the pneumatic artificial muscle according to the motion mode switching point, so as to perform hysteresis compensation processing on the pneumatic artificial muscle of each lower limb joint of the humanoid robot to obtain the joint motion state, specifically comprising the following steps:

[0105] Step 301: extracting the extension length value and internal pressure value of the pneumatic artificial muscle of the lower limb joint within a first preset time period before the motion mode switching point and a second preset time period after the motion mode switching point;

[0106] In this step, the first preset duration refers to a specific time period before the motion mode switching point, which is determined based on the historical data of the hysteresis characteristics of the pneumatic artificial muscle, including a time interval for collecting state data before compensation, and is used to analyze the relationship between pressure and length before switching. The second preset duration refers to a specific time period after the motion mode switching point, which is shorter than the first preset duration, and is determined based on the dynamic response requirements after the motion mode is switched, including a time interval for collecting state data in the initial switching stage, and is used to supplement the analysis of real-time changes in hysteresis characteristics. The telescopic length value refers to the length data of the pneumatic artificial muscle during the telescopic process, which is directly measured by a length sensor, including a specific value reflecting the degree of muscle contraction or elongation, and is used to associate the relationship between internal pressure and motion state. The internal pressure value refers to the air pressure data inside the pneumatic artificial muscle, which is collected in real time by a pressure sensor, including a value reflecting the size of the muscle driving force, and is used to analyze the hysteresis deviation of the muscle.

[0107] In an embodiment of the present application, the motion mode switching point is used as the time origin, and the length sensor installed on the pneumatic artificial muscle collects the telescopic length data within the first preset time length (such as 1 second) before the switching point, and the pressure sensor collects the internal pressure data during the same period; at the same time, the telescopic length value and the internal pressure value within the second preset time length (such as 0.5 seconds) after the motion mode switching point are collected, and these data together serve as the analysis basis for hysteresis compensation.

[0108] Step 302: Under the same extension length value, the internal pressure value is calculated to be different from the corresponding standard pressure value in the standard pressure curve to obtain the hysteresis deviation value of the pneumatic artificial muscle;

[0109] In this step, the standard pressure curve refers to the curve of the correspondence between the telescopic length and the internal pressure of the pneumatic artificial muscle in an ideal state (i.e., without hysteresis influence), which is obtained based on factory calibration data or multiple experimental fittings, including standard pressure values ​​corresponding to different lengths, and is used as a basis for calculating the hysteresis deviation value. The standard pressure value refers to the pressure data corresponding to a certain telescopic length value in the standard pressure curve, which is obtained based on the standard pressure curve, including the pressure value reflecting the ideal state under the length, and is used to compare with the actual internal pressure value to calculate the hysteresis deviation value. The hysteresis deviation value refers to the difference between the actual internal pressure value and the standard pressure value of the pneumatic artificial muscle under the same telescopic length, which is calculated by subtracting the two, including positive deviation (i.e., the actual pressure is higher than the standard) and negative deviation (i.e., the actual pressure is lower than the standard), and is used to characterize the degree of influence of the hysteresis characteristic.

[0110] In the embodiment of the present application, a standard pressure curve is first obtained by the following method: in an ideal environment (temperature 25°C, humidity 50%), 50 repeated extension and contraction experiments are performed on the pneumatic artificial muscle. In each experiment, 20 sampling points are evenly selected from the minimum extension length (e.g., 2 cm) to the maximum extension length (e.g., 15 cm), and the stable internal pressure at each length is recorded (the value when the pressure fluctuation is ≤ 0.01 MPa). The pressure values ​​of the 50 experiments are averaged, and then a corresponding curve (e.g., a quadratic function curve) between the extension length and the standard pressure is obtained by least squares fitting. The same extension length value (e.g., 5 cm, 10 cm, etc.) is selected from the collected extension length values, and the standard pressure value corresponding to the extension length value is searched in the standard pressure curve (the standard pressure curve is a curve of the corresponding relationship between the extension length value and the pressure value of the pneumatic artificial muscle under ideal conditions). Then, the internal pressure value at the same extension length value is subtracted from the standard pressure value. The difference obtained is the hysteresis deviation value at that length. This process is repeated to obtain multiple hysteresis deviation values.

[0111] Step 303: Calculating a hysteresis compensation amount according to the time interval between the hysteresis deviation value and the motion mode switching point;

[0112] In this step, the hysteresis compensation value refers to the pressure value that needs to be adjusted to offset the hysteresis deviation value, which is obtained based on the weighted calculation of the hysteresis deviation value and the time interval, including the pressure value that needs to be increased or decreased, and is used to guide the pressure adjustment of the pneumatic artificial muscle.

[0113] In an embodiment of the present application, the time interval between the acquisition time and the motion mode switching point corresponding to each hysteresis deviation value is recorded and set to t, and the maximum range of the time interval is set to T (T is the sum of the first preset time length and the second preset time length); the weight coefficient corresponding to the time interval is determined based on the time attenuation characteristics of the hysteresis effect of the pneumatic artificial muscle, and the degree of influence of the hysteresis deviation on the joint movement at different time intervals (t) is measured experimentally (the greater the influence, the higher the weight), and a linear weight model is fitted to determine the weight coefficient k = 1-(t / T) to map the hysteresis deviation values ​​at different time points to the hysteresis compensation amount, thereby realizing the inverse correction of the hysteresis characteristics of the pneumatic artificial muscle. Among them, when t=0, k=1; when t=T, k=0, that is, the closer the time is to the motion mode switching point, the larger the weight coefficient; for example, when t=0.3 seconds (close to the motion mode switching point), k=1-(0.3 / 1.5)=0.8; when t=1.5 seconds (far away from the motion mode switching point), k=0, ensuring that the hysteresis deviation value closer to the motion mode switching point has a greater impact on the hysteresis compensation amount; multiply each hysteresis deviation value by its corresponding weight coefficient k to obtain multiple weighted hysteresis deviation values, and then sum all the weighted hysteresis deviation values ​​to obtain the hysteresis compensation amount to be applied. The calculation formula can be expressed as: hysteresis compensation amount = Σ(hysteresis deviation value×k).

[0114] Step 304: Using the motion mode switching point as a time reference, setting the compensation start time to a preset lead time before the motion mode switching, and setting the compensation end time to a preset duration after the motion mode switching point, to generate a hysteresis compensation opportunity;

[0115] In this step, the time reference refers to the time coordinate origin of the motion mode switching point, which is used to unify the calculation of the compensation start and end time reference, and ensures the accurate alignment of the compensation opportunity and the switching point. The compensation start time refers to the specific time when the hysteresis compensation starts, which is set as a preset advance time before the motion mode switching point, and is determined based on the response speed of the hysteresis characteristic, which is used to start the compensation in advance to offset the lag at the switching time. The preset advance time refers to the time difference between the compensation start time and the motion mode switching point, which is determined based on the experimentally measured hysteresis compensation response time, including the advance amount to ensure that the compensation effect takes effect at the switching time, and is used to realize the advance compensation. The compensation end time refers to the specific time when the hysteresis compensation ends, which is set as a preset duration after the motion mode switching point, and is determined based on the demand for dynamic stability after switching, which is used to continue the compensation until the motion state is stable. The preset duration refers to the time difference between the compensation end time and the motion mode switching point, which is shorter than the preset advance time, and is determined based on the stable time of the joint motion after switching, which is used to avoid overcompensation. The hysteresis compensation opportunity refers to the time period for hysteresis compensation, which is defined by the compensation start time and the compensation end time, and includes the time interval covering the key dynamic stages before and after the switching, which is used to implement pressure regulation at the best time.

[0116] In the embodiments of the present application, the compensation start time is determined as a preset advance time (such as 0.2 seconds) before the switching point, and the compensation end time is determined as a preset duration (such as 0.3 seconds) after the switching point, and the time period between the two (from 0.2 seconds before the switching point to 0.3 seconds after the switching point) is the hysteresis compensation opportunity.

[0117] Step 305: In the hysteresis compensation opportunity, the internal pressure of the pneumatic artificial muscle is adjusted according to the hysteresis compensation amount, and the second rotation angle and rotation speed of the lower limb joint in the adjustment process are collected, and the second rotation angle and rotation speed are combined into a joint motion state;

[0118] In the embodiments of the present application, in the hysteresis compensation opportunity, the internal pressure of the pneumatic artificial muscle is increased or decreased by the pressure regulation device according to the hysteresis compensation amount, and the rotation angle (i.e. the second rotation angle) and rotation speed of the hip joint, the knee joint and the ankle joint in the adjustment process are collected in real time by the joint angle sensor and the speed sensor, and the second rotation angle and rotation speed at the same time are classified and integrated according to the joint to form a joint motion state reflecting the real-time motion of the joint.

[0119] The embodiments of the present application solve the problem of deviation between joint movement and instructions caused by delays in driving component characteristics; by presetting the lead time and duration to determine the timing of hysteresis compensation, advance compensation and dynamic correction are achieved during the switching process, ensuring the accuracy of the joint movement state during mode switching, providing reliable motion data for the subsequent generation of bio-inspired electrical signals and driving joint coordinated movement, and improving the smoothness and posture stability of the humanoid robot's motion mode switching.

[0120] This application provides a specific embodiment, step 104, analyzing the angle change rate and motion acceleration parameters in the joint motion state to generate a bioinspired electrical signal, specifically including the following steps:

[0121] Step 401: Calculating the angle change rate and motion acceleration of each lower limb joint according to the joint motion state;

[0122] In an embodiment of the present application, the second rotation angle and rotation speed of each lower limb joint (including the hip joint, knee joint, and ankle joint) are extracted from the joint motion state, and the angle change rate of each joint is obtained by calculating the difference in the second rotation angle at adjacent moments and dividing it by the time interval; the motion acceleration of each joint is obtained by calculating the difference in the rotation speed at adjacent moments and dividing it by the time interval, thereby quantifying the dynamic characteristics of the joint motion.

[0123] Step 402: Compare the angle change rates of different lower limb joints at the same time to calculate the angle change differences between adjacent lower limb joints to determine the motion correlation between the lower limb joints;

[0124] In this step, the angle change difference refers to the difference in the angle change rates of adjacent lower limb joints at the same moment, obtained by subtracting the angle change rates of the hip joint from the knee joint, and from the knee joint from the ankle joint. It includes a value reflecting the difference in the movement speed of adjacent joints and is used to determine the movement correlation between joints. The movement correlation refers to a quantitative indicator reflecting the degree of joint movement coordination determined based on the angle change difference of adjacent lower limb joints. It includes positive values ​​(indicating similar movement trends) and negative values ​​(indicating opposite movement trends) and is used to characterize the movement coordination relationship between joints.

[0125] In an embodiment of the present application, the same time point is selected to calculate the difference in the angle change rate of the hip joint and the knee joint (i.e., the hip joint angle change rate minus the knee joint angle change rate), and the difference in the angle change rate of the knee joint and the ankle joint (i.e., the knee joint angle change rate minus the ankle joint angle change rate), respectively, to obtain the angle change difference between adjacent joints. The positive and negative and size of the difference reflect the degree of coordination of the adjacent joint movements, that is, the movement correlation between the joints of the lower limbs. For example, if the angle change rate of the hip joint is 10° / s and that of the knee joint is 8° / s, then the movement correlation between the two = 2° / s (a positive value indicates that the movement trends are similar); if the angle change rate of the knee joint is 12° / s and that of the ankle joint is 15° / s, then the movement correlation = -3° / s (a negative value indicates that the movement trends are opposite).

[0126] Step 403: multiplying all motion correlations by the preset angle proportional coefficient to obtain the corresponding joint coordination angle threshold;

[0127] In this step, the preset angle proportional coefficient refers to a pre-set fixed coefficient used to convert the motion correlation into an angle threshold. It is determined based on the biomechanical constraints of the human lower limb joints, with specific reference to the natural synergistic angle proportional relationship of the hip-knee-ankle joints when the human body walks and runs (such as the angular linkage ratio of the hip joint and the knee joint in the gait cycle), and combined with the upper limit of the joint range of motion (such as the maximum flexion angle of the hip joint and the maximum extension angle of the knee joint) and the ratio of the muscle safety force threshold (the angle corresponding to the critical force value to avoid excessive muscle stretching or contraction). It is obtained by fitting multiple sets of human motion experimental data and is used to calculate the joint synergy angle threshold. The joint synergy angle threshold refers to the product of the motion correlation and the preset angle proportional coefficient, including the maximum angle deviation value allowed when each joint moves in coordination, which is used to limit the angle range of joint movement to ensure coordination.

[0128] In an embodiment of the present application, based on the biomechanical constraints of the human lower limb joints, a fixed angle conversion coefficient (i.e., a preset angle proportional coefficient) is preset. For example, referring to the human kinematics research, the hip-knee coordination angle ratio in the walking state is approximately 1:0.8, and it is set to 0.6 after correction of the robot joint structure parameters. This coefficient is used to convert the motion correlation into an angle threshold that can be executed by the joint. Each motion correlation (such as the motion correlation between the hip joint and the knee joint, and the motion correlation between the knee joint and the ankle joint) is multiplied by the preset angle proportional coefficient. The product result is the joint coordination angle threshold that must be followed during the coordinated movement of the corresponding joints, and this threshold does not exceed the joint biomechanical safety range.

[0129] Step 404: The motion acceleration that appears the most times under the same motion correlation is used as the reference acceleration. All reference accelerations are multiplied by the preset time coefficient to obtain the time interval parameter of the corresponding motion trigger moment. The time interval parameter is combined with the corresponding joint coordination angle threshold to form a signal feature group.

[0130] In this step, the baseline acceleration refers to the motion acceleration that appears the most times under the same motion correlation, which is determined by counting the frequency of acceleration occurrences, including the acceleration value reflecting the typical motion intensity under the motion correlation, and is used to calculate the time parameters of the motion trigger moment. The preset time coefficient refers to a pre-set fixed coefficient for converting the baseline acceleration into a time interval, which is determined based on the joint motion response speed, including the conversion parameter reflecting the correspondence between acceleration and time, and is used to calculate the interval duration of the motion trigger moment. The motion trigger moment refers to the time point when each lower limb joint is driven to start moving, which is determined based on the time interval parameter and the motion transfer sequence, including the specific moment when each lower limb joint action is started, and is used to control the sequence of joint movements. The signal feature group refers to a feature set composed of the joint coordination angle threshold and the corresponding time interval parameters of the motion trigger moment, which is calculated based on the motion correlation and the baseline acceleration, including the angle and time information reflecting the coordinated motion of a single lower limb joint, and is used to construct a bioinspired electrical signal.

[0131] In the embodiment of the present application, the statistics of the benchmark acceleration must meet the data sample size of ≥50 groups: for each motion correlation, at least 50 motion acceleration data with the same correlation are extracted from the joint motion state. If the number of occurrences of a certain acceleration value accounts for ≥30% (i.e. ≥15 times), it is determined as the benchmark acceleration. For example, among the 50 acceleration data corresponding to the motion correlation of 2° / s, 1.5° / s appears 18 times (accounting for 36%), then 1.5° / s is the benchmark acceleration to ensure the reliability of the statistical results under small samples; a time conversion coefficient (i.e. a preset time coefficient) is preset, and each benchmark acceleration is multiplied by the coefficient to obtain the time interval parameter of the motion trigger moment; and then the joint coordination angle threshold corresponding to each motion correlation is combined with the time interval parameter to form a signal feature group containing angle and time information.

[0132] Step 405: Arrange all signal feature groups according to the motion transmission order of each lower limb joint to form a bio-inspired electrical signal, wherein the pulse amplitude of the bio-inspired electrical signal is the corresponding joint coordination angle threshold, and the pulse interval is the corresponding motion triggering moment;

[0133] In this step, the motion transmission sequence refers to the order in which lower limb joint movements occur, starting from the hip joint and progressing to the knee and ankle joints. This motion transmission path is determined based on the physiological laws of human movement and is used to standardize the initiation sequence of lower limb joint movements. The bio-inspired electroelectric signal is a pulse sequence formed by arranging signal feature groups according to the motion transmission sequence. Its pulse amplitude corresponds to the joint coordination angle threshold, and the pulse interval corresponds to the motion triggering moment. It is used to drive the lower limb joints to move in a coordinated relationship, simulating the control characteristics of bioelectric signals.

[0134] In an embodiment of the present application, the signal feature groups corresponding to each lower limb joint are arranged in sequence according to the order of motion transmission from the hip joint to the knee joint and then to the ankle joint, the joint coordination angle threshold in each signal feature group is used as the pulse amplitude, and the time interval parameter is used as the interval length between adjacent pulses, thereby forming a continuous pulse sequence, which is the bioinspired electrical signal used to drive joint movement.

[0135] The embodiments of the present application solve the problems of poor joint coordination and disconnection between motion instructions and actual movements; the bioinspired electrochemical signal integrates the joint coordination angle and trigger time into a pulse sequence, so that each joint movement can not only follow the preset angle range, but also be started according to a reasonable timing, ensuring the accuracy and continuity of multi-joint coordinated movement, providing a reliable driving signal for achieving seamless switching of multi-modal movement, and improving the movement flexibility and stability of humanoid robots in dynamic environments.

[0136] This application provides a specific embodiment, step 105, driving the lower limb joints of the humanoid robot to perform coordinated motion according to the joint coordination angle threshold and motion triggering time of the bioinspired electrical signal to achieve seamless switching of multimodal motion, specifically comprising the following steps:

[0137] Step 501: According to the motion transmission order of each lower limb joint, with the motion triggering moment of the bio-inspired electrokinetic signal as a reference, a corresponding action execution period is assigned to each lower limb joint, and the action execution period is a preset action window before and after the motion triggering moment;

[0138] In this step, the motion transmission sequence refers to the sequential start-up sequence of the lower limb joint movements, that is, the motion conduction path starting from the hip joint, then to the knee joint and ankle joint. It is set based on the physiological laws of human movement and is used to standardize the timing of the lower limb joint movements to ensure coordinated movement. The action execution period refers to the movement time interval allocated to each lower limb joint, based on the movement trigger moment of the bio-inspired electrostatic signal, including the preset action windows before and after the trigger moment, which are used to limit the time range of the lower limb joint movement and avoid overlapping movements. The preset action window refers to the specific time range of the action execution period, which consists of the preset duration before the movement trigger moment and the preset duration after the trigger moment. It is set based on the response speed and duration of the joint movement to ensure that the lower limb joints have enough time to complete the movement.

[0139] In an embodiment of the present application, according to the order of motion transmission of the hip joint, knee joint, and ankle joint, and based on the motion triggering moment corresponding to each lower limb joint in the bioinspired electrical signal, an action execution period is defined for each lower limb joint. This period is a preset action window consisting of a preset time length before the triggering moment (such as 0.3 seconds) and a preset time length after the triggering moment (such as 0.2 seconds), to ensure that each lower limb joint completes the action within its respective action execution period to avoid motion conflicts.

[0140] Step 502: Calculate the rotation angle amplitude based on the joint coordination angle threshold of each lower limb joint and the corresponding transmission ratio parameter;

[0141] In this step, the transmission ratio parameter refers to the conversion coefficient between the joint coordination angle threshold and the actual rotation angle amplitude. It is determined based on the transmission characteristics of the drive mechanism (such as a pneumatic artificial muscle) and includes a numerical value reflecting the corresponding relationship between the input angle and the output angle, which is used to convert the coordination angle threshold into an actual executable angle. The rotation angle amplitude refers to the actual angle that the joint needs to rotate. It is calculated based on the product of the joint coordination angle threshold and the transmission ratio parameter. It includes a specific numerical value reflecting the range of motion of the lower limb joint and is used to clarify the range of joint motion.

[0142] In an embodiment of the present application, for the hip joint, knee joint, and ankle joint, their corresponding joint coordination angle thresholds (extracted from bioinspired electrical signals) and transmission ratio parameters (conversion coefficients between preset joint angles and output angles of the drive mechanism) are obtained respectively, and the joint coordination angle thresholds of each lower limb joint are multiplied by their corresponding transmission ratio parameters to obtain the angle that the joint actually needs to rotate, that is, the rotation angle amplitude.

[0143] Step 503: Determine the rotation direction of each lower limb joint based on the motion correlation between the lower limb joints, wherein the rotation direction maintains a coordinated relationship with the motion direction of the adjacent joints;

[0144] In this step, the synergistic relationship refers to the coordination rules of the rotation directions of adjacent lower limb joints, including same-direction synergy (same direction) and reverse synergy (opposite direction), which is determined based on the movement correlation between the lower limb joints and is used to ensure the coordination of the lower limb joint movements.

[0145] In an embodiment of the present application, the synergistic relationship is judged by the motion correlation degree (positive or negative) between each lower limb joint: if the motion correlation degree is positive, the adjacent lower limb joints maintain a synergistic relationship in the same direction (such as when the hip joint rotates forward, the knee joint also rotates forward); if the motion correlation degree is negative, the adjacent lower limb joints maintain a synergistic relationship in the opposite direction (such as when the hip joint rotates forward, the ankle joint rotates backward), thereby determining the rotation direction (forward or backward) of each lower limb joint.

[0146] Step 504: combining the rotation angle amplitude and rotation direction corresponding to each lower limb joint into a target rotation angle instruction;

[0147] In this step, the target rotation angle instruction refers to a complete motion instruction including the rotation angle amplitude and rotation direction, which is formed based on the angle and direction parameter combination of each lower limb joint and is used as an operation instruction to directly drive the joint movement.

[0148] In an embodiment of the present application, for each lower limb joint, its rotation angle amplitude (such as 30° for the hip joint and 20° for the knee joint) and rotation direction (forward or backward) are integrated into a complete target rotation angle instruction. This instruction clearly defines the angle and direction that the lower limb joint needs to rotate, which serves as the direct basis for driving the movement of the lower limb joint.

[0149] Step 505: During the action execution period, driving the corresponding lower limb joints to move according to the target rotation angle instruction to collect the third rotation angle of each lower limb joint;

[0150] In this step, the third rotation angle refers to the actual rotation angle of the lower limb joint collected by the angle sensor during movement, which is obtained based on real-time detection.

[0151] In an embodiment of the present application, during the action execution period assigned to each lower limb joint, the movement of the lower limb joint is controlled according to the target rotation angle instruction through a driving mechanism (such as a pneumatic artificial muscle), and at the same time, the actual rotation angle (i.e., the third rotation angle) during the movement of the lower limb joint is collected in real time through an angle sensor for subsequent deviation calculation.

[0152] Step 506: Calculating an angle deviation between the third rotation angle and the target rotation angle command, and comparing the angle deviation with a preset deviation threshold, so as to drive all lower limb joints to perform coordinated motion according to the comparison result until the switching between different motion states is completed, thereby achieving seamless switching of multimodal motion;

[0153] In this step, the angle deviation value refers to the difference between the third rotation angle and the target rotation angle command. It is calculated by subtracting the three angles and includes a numerical value reflecting the degree of deviation between the actual movement and the target rotation angle command, and is used to determine motion accuracy. The preset deviation threshold refers to the maximum threshold of allowable angle deviation, set based on the motion accuracy requirements, and includes a reference value for determining whether the motion command needs to be adjusted to ensure that the joint motion error is within an acceptable range.

[0154] In an embodiment of the present application, the difference between the third rotation angle of each lower limb joint and the target rotation angle instruction (i.e., the angle deviation value) is calculated. If the angle deviation value is less than a preset deviation threshold (such as 2°), the current instruction is maintained to continue moving; if the angle deviation value is greater than or equal to the preset deviation threshold, the rotation angle amplitude and rotation direction of the adjacent lower limb joints are adjusted, and the angle deviation value is reduced through collaborative compensation. This process is repeated until all lower limb joints complete the movement, thereby achieving seamless switching between different motion states.

[0155] The embodiments of the present application solve the problems of poor joint coordination and large deviation between motion instructions and actual movements; by combining target rotation angle instructions with dynamic deviation adjustment, it ensures that each lower limb joint moves according to a preset coordination relationship, reduces the jamming and instability when switching motion modes, realizes seamless switching of the humanoid robot between different states, and improves the smoothness, accuracy and environmental adaptability of the movement.

[0156] The present application provides a specific embodiment, step 503, determining the rotation direction of each lower limb joint based on the motion correlation between the lower limb joints, wherein the rotation direction maintains a coordinated relationship with the motion direction of the adjacent joints, specifically includes the following steps:

[0157] Step 511: Setting a synergistic relationship between adjacent lower limb joints, wherein the synergistic relationship includes a same-direction synergistic relationship and a reverse synergistic relationship. The synergistic relationship between adjacent lower limb joints whose motion correlation is within a preset positive range is determined as a same-direction synergistic relationship, and the synergistic relationship between adjacent lower limb joints whose motion correlation is within a preset negative range is determined as a reverse synergistic relationship.

[0158] In this step, the synergistic relationship refers to the coordination rules of the rotation directions of adjacent lower limb joints during movement, which is determined based on the numerical range of the movement correlation, including the same-direction synergistic relationship and the reverse synergistic relationship, and is used to standardize the directional coordination between the lower limb joints to ensure movement coordination. The same-direction synergistic relationship refers to the coordination relationship in which the rotation directions of adjacent lower limb joints are the same. This relationship is determined when the movement correlation of adjacent lower limb joints is within a preset positive value range, including situations where the hip joint and the knee joint rotate forward together, the knee joint and the ankle joint rotate forward together, etc., to ensure that the movement trends of the lower limb joints are consistent. The reverse synergistic relationship refers to the coordination relationship in which the rotation directions of adjacent lower limb joints are opposite. This relationship is determined when the movement correlation of adjacent lower limb joints is within a preset negative value range, including situations where the knee joint rotates backward when the hip joint rotates forward, the ankle joint rotates backward when the knee joint rotates forward, etc., to achieve complementary coordination of the lower limb joint movements.

[0159] In an embodiment of the present application, a numerical range of motion correlation is pre-defined: the preset positive range is an interval greater than 0 and less than or equal to a positive number (such as 5), and the preset negative range is an interval less than 0 and greater than or equal to a negative number (such as -5); when the motion correlation of adjacent lower limb joints (such as hip joints and knee joints) falls within the preset positive range, it is determined that the two need to maintain a synergistic relationship in the same direction (i.e., the same rotation direction); when the motion correlation falls within the preset negative range, it is determined that the two need to maintain a reverse synergistic relationship (i.e., opposite rotation directions), so as to clarify the coordination rules of adjacent joints.

[0160] Step 512: Determine the rotation direction of each lower limb joint based on the motion correlation and synergistic relationship between different adjacent lower limb joints, so that the rotation direction of each lower limb joint maintains a corresponding synergistic relationship with the motion direction of the adjacent lower limb joints;

[0161] In this step, the preset positive value range refers to a pre-set numerical interval of motion correlation used to determine whether adjacent lower limb joints are in a same-direction collaborative relationship. It is determined based on the historical data and motion characteristics of joint collaborative motion, and includes a numerical range greater than 0 and not exceeding a certain upper limit value, which is used to quantify the judgment standard of same-direction collaboration. The preset negative value range refers to a pre-set numerical interval of motion correlation used to determine whether adjacent lower limb joints are in a reverse collaborative relationship. It is determined based on the requirements of joint complementary motion, and includes a numerical range less than 0 and not less than a certain lower limit value, which is used to quantify the judgment standard of reverse collaboration.

[0162] In an embodiment of the present application, the initial rotation direction (forward or backward) of the starting joint (such as the hip joint) is first determined; then the motion correlation between the hip joint and the knee joint is obtained. If it is in a preset positive value range (corresponding to a same-direction collaborative relationship), the rotation direction of the knee joint is the same as that of the hip joint; if it is in a preset negative value range (corresponding to a reverse collaborative relationship), the rotation direction of the knee joint is opposite to that of the hip joint; similarly, based on the motion correlation between the knee joint and the ankle joint and the corresponding collaborative relationship, the rotation direction of the ankle joint is determined to ensure that the rotation directions of the ankle joint and the knee joint conform to the collaborative relationship, and ultimately the rotation directions of all lower limb joints are kept matched with the adjacent joints.

[0163] The embodiments of the present application solve the problems of ambiguous coordination and poor coordination of joint rotation directions; it provides a clear basis for the directional coordination of adjacent lower limb joints, avoids conflicts or asynchrony in movement directions, ensures the coordination of multi-joint collaborative movements, and provides precise control at the directional level for achieving seamless switching of multi-modal movements, thereby improving the smoothness of movement and posture stability of humanoid robots.

[0164] Figure 2 The present invention provides a schematic diagram of a multi-joint coordinated motion control system for a humanoid robot. Figure 2 As shown, the system includes:

[0165] An acquisition module 21 is used to acquire gait phase data of the humanoid robot in different motion states, wherein the motion states include a walking state, a running state, and an emergency stop state;

[0166] A generating module 22 is configured to generate a gait phase transition matrix based on the gait phase data to determine a motion mode switching point;

[0167] a compensation module 23 for determining a hysteresis compensation timing of the pneumatic artificial muscles according to the motion mode switching point, so as to perform hysteresis compensation processing on the pneumatic artificial muscles of each lower limb joint of the humanoid robot to obtain a joint motion state;

[0168] An analysis module 24 is used to analyze the angle change rate and motion acceleration parameters in the joint motion state to generate a bioinspired electrical signal;

[0169] The driving module 25 is used to drive the lower limb joints of the humanoid robot to perform coordinated movements according to the joint coordination angle threshold and movement triggering moment of the bio-inspired electrical signal, so as to achieve seamless switching of multi-modal movements.

[0170] The multi-joint collaborative motion control system of a humanoid robot in an embodiment of the present application is used to implement the aforementioned multi-joint collaborative motion control method of a humanoid robot. Therefore, the specific implementation method of the multi-joint collaborative motion control system of a humanoid robot can be seen in the embodiment part of the multi-joint collaborative motion control method of a humanoid robot in the previous text. Its specific implementation method can refer to the description of the corresponding embodiments of each part, and will not be repeated here.

[0171] The present application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for implementing any of the steps of the multi-joint coordinated motion control method of a humanoid robot described above when executing the computer program.

[0172] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of any one of the above-mentioned methods for controlling multi-joint coordinated motion of a humanoid robot are implemented.

[0173] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, a read-only memory, a random access memory, a mobile hard disk, a magnetic disk, or an optical disk.

[0174] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any of the above-mentioned embodiments of the multi-joint coordinated motion control method for a humanoid robot are implemented.

[0175] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0176] The above is a detailed introduction to the multi-joint coordinated motion control method and system for a humanoid robot provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core ideas of the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the present application.

Claims

1. A multi-joint coordinated motion control method for a humanoid robot, characterized in that: include: Acquiring gait phase data of the humanoid robot in different motion states, wherein the motion states include a walking state, a running state, and an emergency stop state; generating a gait phase transition matrix based on the gait phase data to determine a motion mode switching point; Determining a hysteresis compensation timing for the pneumatic artificial muscles according to the motion mode switching point, so as to perform hysteresis compensation processing on the pneumatic artificial muscles of each lower limb joint of the humanoid robot to obtain a joint motion state; Analyzing the angle change rate and motion acceleration parameters in the joint motion state to generate a bioinspired electrical signal; According to the joint coordination angle threshold and motion triggering moment of the bioinspired electrical signal, the lower limb joints of the humanoid robot are driven to perform coordinated motion to achieve seamless switching of multimodal motion.

2. The method according to claim 1, characterized in that Generating a gait phase transition matrix according to the gait phase data to determine a motion mode switching point includes: Extracting from the gait phase data a first phase characteristic value corresponding to a walking state, a second phase characteristic value corresponding to a running state, and a third phase characteristic value corresponding to an emergency stop state; Counting the occurrence frequencies of the first phase eigenvalue, the second phase eigenvalue, and the third phase eigenvalue to calculate the transition probability between every two motion states; Writing all transition probabilities into an initial matrix structure of preset dimensions to form a gait phase transition matrix, wherein the row dimension of the initial matrix structure corresponds to the starting motion state and the column dimension corresponds to the target motion state; The gait phase transition matrix is ​​traversed, and the end moment of the initial motion state corresponding to the transition probability exceeding a preset probability threshold is determined as the motion mode switching point.

3. The method according to claim 2, characterized in that Extracting a first phase feature value corresponding to a walking state from the gait phase data includes: Determining a data segment in the gait phase data whose movement speed is within a preset walking speed range as walking state data; collecting the first rotation angle of each lower limb joint at each sampling moment in the walking state data, and arranging all the first rotation angles to form a first joint angle sequence; Recording the time points of two adjacent foot landings in the walking state data, calculating a first foot landing interval duration, and determining the first foot landing interval duration as a first movement cycle duration; In the walking state data, the movement starting time points of the hip joint and the knee joint are selected to calculate a first starting time difference, and the first starting time difference is used as a first adjacent joint movement time difference; The first joint angle sequence, the first motion cycle duration, and the first adjacent joint motion time difference are combined into a first phase characteristic value corresponding to the walking state.

4. The method according to claim 1, wherein Determining the hysteresis compensation timing of the pneumatic artificial muscles according to the motion mode switching point, so as to perform hysteresis compensation processing on the pneumatic artificial muscles of each lower limb joint of the humanoid robot to obtain the joint motion state, including: Extracting the extension length value and internal pressure value of the pneumatic artificial muscle of the lower limb joint within a first preset time period before the motion mode switching point and a second preset time period after the motion mode switching point; Under the same telescopic length value, the internal pressure value is calculated to be different from the corresponding standard pressure value in the standard pressure curve to obtain the hysteresis deviation value of the pneumatic artificial muscle; Calculating a hysteresis compensation amount according to a time interval between the hysteresis deviation value and the motion mode switching point; Taking the motion mode switching point as a time reference, setting the compensation start time to a preset advance time before the motion mode switching and setting the compensation end time to a preset duration after the motion mode switching point to generate a hysteresis compensation opportunity; During the hysteresis compensation period, the internal pressure of the pneumatic artificial muscle is adjusted according to the hysteresis compensation amount, and the second rotation angle and rotation speed of the lower limb joint during the adjustment process are collected, and the second rotation angle and rotation speed are combined into a joint motion state.

5. The method according to claim 1, wherein Analyzing the angle change rate and motion acceleration parameters in the joint motion state to generate a bioinspired electrical signal includes: Calculating the angle change rate and movement acceleration of each lower limb joint according to the joint movement state; Compare the angle change rates of different lower limb joints at the same time to calculate the angle change differences between adjacent lower limb joints and determine the movement correlation between the lower limb joints; Multiply all motion correlations by the preset angle ratio coefficient to obtain the corresponding joint coordination angle threshold; The motion acceleration that appears most frequently under the same motion correlation is taken as the benchmark acceleration. All benchmark accelerations are multiplied by the preset time coefficient to obtain the time interval parameter of the corresponding motion trigger moment. Combined with the corresponding joint coordination angle threshold, a signal feature group is formed. All signal feature groups are arranged according to the motion transmission order of each lower limb joint to form a bioinspired electrical signal, wherein the pulse amplitude of the bioinspired electrical signal is the corresponding joint coordination angle threshold, and the pulse interval is the corresponding motion trigger moment.

6. The method according to claim 1, characterized in that According to the joint coordination angle threshold and motion triggering moment of the bioinspired electrical signal, each lower limb joint of the humanoid robot is driven to perform coordinated motion to achieve seamless switching of multimodal motion, including: According to the motion transmission order of each lower limb joint, with the motion triggering moment of the bio-inspired electrical signal as the benchmark, a corresponding action execution period is assigned to each lower limb joint, and the action execution period is a preset action window before and after the motion triggering moment; Calculate the rotation angle amplitude based on the joint coordination angle threshold of each lower limb joint and the corresponding transmission ratio parameter; Determining the rotation direction of each lower limb joint based on the degree of motion correlation between the joints, wherein the rotation direction maintains a coordinated relationship with the motion direction of the adjacent joints; The rotation angle amplitude and rotation direction corresponding to each lower limb joint are combined into a target rotation angle instruction; During the action execution period, driving the corresponding lower limb joints to move according to the target rotation angle instruction to collect a third rotation angle of each lower limb joint; Calculate the angle deviation value between the third rotation angle and the target rotation angle instruction, compare the angle deviation value with the preset deviation threshold, and drive all lower limb joints to move in coordination according to the comparison result until the switching of different motion states is completed, so as to achieve seamless switching of multimodal motion.

7. The method according to claim 6, characterized in that Determine the rotation direction of each lower limb joint based on the degree of motion correlation between the joints, wherein the rotation direction maintains a coordinated relationship with the motion direction of the adjacent joints, including: Setting a synergistic relationship between adjacent lower limb joints, wherein the synergistic relationship includes a same-direction synergistic relationship and a reverse synergistic relationship, wherein the synergistic relationship between adjacent lower limb joints whose motion correlation is within a preset positive value range is determined as a same-direction synergistic relationship, and the synergistic relationship between adjacent lower limb joints whose motion correlation is within a preset negative value range is determined as a reverse synergistic relationship; The rotation direction of each lower limb joint is determined based on the motion correlation and synergistic relationship between different adjacent lower limb joints, so that the rotation direction of each lower limb joint maintains a corresponding synergistic relationship with the motion direction of the adjacent lower limb joints.

8. A multi-joint coordinated motion control system for a humanoid robot, characterized in that: include: An acquisition module is used to acquire gait phase data of the humanoid robot in different motion states, wherein the motion states include walking state, running state and emergency stop state; A generating module, configured to generate a gait phase transition matrix based on the gait phase data to determine a motion mode switching point; a compensation module, configured to determine a hysteresis compensation timing of the pneumatic artificial muscles according to the motion mode switching point, so as to perform hysteresis compensation processing on the pneumatic artificial muscles of each lower limb joint of the humanoid robot to obtain a joint motion state; An analysis module, configured to analyze the angle change rate and motion acceleration parameters in the joint motion state to generate a bioinspired electrical signal; The driving module is used to drive the lower limb joints of the humanoid robot to perform coordinated movement according to the joint coordination angle threshold and movement triggering moment of the bioinspired electrical signal, so as to achieve seamless switching of multimodal movement.

9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the multi-joint coordinated motion control method of a humanoid robot as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it can implement the multi-joint coordinated motion control method of a humanoid robot according to any one of claims 1 to 7.

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