Humanoid robot motion control method based on motion data analysis

Abstract

The invention discloses a humanoid robot motion control method based on motion data analysis, and belongs to the technical field of robot motion control. According to the method, multi-mode motion data in the motion process of the humanoid robot are collected, key motion features are extracted, motion state recognition is carried out on robot motion, key parameter change rules of all the motion states are analyzed, joint motion models in different motion states are constructed, switching between the different states is controlled, and the motion state of the humanoid robot is recognized. In combination with a current motion state and a target state, a robot motion trajectory is generated and optimized, energy consumption conditions of different trajectory schemes are calculated based on an energy consumption estimated value, and the trajectory is optimized by adopting an energy optimal control method, so that the robot can give consideration to both motion stability and energy efficiency when executing a task. Feedback data in the motion execution process of the robot are collected in real time, the motion error deviation is calculated, control signals are dynamically adjusted, and it is ensured that the robot can still keep the accurate motion track in the complex environment.

Description

Technical Field

[0001] The present invention relates to the technical field of robot motion control, and in particular to a humanoid robot motion control method based on motion data analysis. Background Art

[0002] Humanoid robots are increasingly used in industrial manufacturing, medical rehabilitation, service industry, and special task execution. Humanoid robots are similar to humans and can adapt to human environments more naturally, achieving flexible movement and operation. However, since humanoid robots usually have multi-degree-of-freedom joint systems, their motion control process involves complex dynamic characteristics and high-dimensional state variables, making accurate and efficient motion control one of the technical difficulties. Traditional humanoid robot motion control methods mainly rely on predefined motion trajectories and control strategies. When faced with complex environments or dynamic tasks, they have the following shortcomings: Since the preset trajectory and control strategy are difficult to adjust according to real-time environmental changes, it is difficult for the robot to adapt to complex dynamic environments when performing tasks. Based on static optimization or simple feedback control, it is difficult to accurately capture the key characteristics of the robot in different motion states, resulting in large trajectory errors during the robot's execution, affecting the quality of task completion. At the same time, humanoid robots usually need to consider energy consumption during movement, but traditional methods pay less attention to energy-optimal trajectory planning and joint control optimization, resulting in high energy consumption of the robot when performing tasks, affecting endurance and long-term stable operation. Summary of the invention

[0003] The purpose of the present invention is to provide a humanoid robot motion control method based on motion data analysis to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a humanoid robot motion control method based on motion data analysis, comprising: Acquire multimodal motion data of the robot during movement, extract key motion features based on the multimodal motion data and identify the motion state of the robot, build a robot motion model, combine the robot's current motion state with the target state, generate and optimize the robot's motion trajectory, generate corresponding control instructions based on the motion trajectory, compare with the preset trajectory, calculate the error deviation and dynamically adjust the control instructions based on the error deviation.

[0005] Furthermore, when obtaining multimodal motion data of the robot during motion, the multimodal motion data is obtained by installing an inertial measurement unit, a force sensor and an angle encoder on each joint of the humanoid robot, and the multimodal motion data includes joint angle, angular velocity, acceleration, joint torque and end effector force; Perform time alignment and interpolation on multimodal motion data to generate a standardized time data series.

[0006] Further, when extracting key motion features based on the multimodal motion data and performing motion state recognition on the robot motion, feature extraction is performed on the multimodal motion data, wherein the key motion features include motion phase, joint angle change rate, and energy consumption estimation; Based on the angle, angular velocity and acceleration data of each joint of the robot, calculate the phase information of the robot during continuous motion and extract key motion phase parameters; Calculate the change rate of the joint angle of each joint of the robot; Based on the joint torque and angular velocity data, the estimated energy consumption per unit time of each joint is calculated; Construct a multi-dimensional feature vector based on key motion features and reduce the dimension of the multi-dimensional feature vector; Based on the key motion features after dimensionality reduction, the robot's motion state is classified and identified through hierarchical clustering, and divided into different motion states; The motion states include starting, accelerating, steady state, decelerating and stopping.

[0007] Furthermore, based on the joint torque and angular velocity data, the energy consumption estimate of each joint per unit time is calculated, including: Real-time monitoring of joint torque and angular velocity data of joint points; Retrieve the theoretical value of the joint torque corresponding to the sampling moment of each joint point; According to the torque data deviation between the actual value of the joint torque at the sampling moment of each joint point and the theoretical value of the joint torque; Retrieve the theoretical value of the angular velocity data corresponding to the sampling moment of each joint point; According to the angular velocity data deviation between the actual value of the angular velocity data at the sampling moment of each joint point and the theoretical value of the angular velocity data; When there is no torque data deviation and angular velocity data deviation, the basic energy consumption estimation model is used to obtain the energy consumption estimation of each joint point; The energy consumption estimation of each joint point is obtained by the following formula: Where E represents the estimated energy consumption of each joint point; n represents the number of sampling times of joint torque and angular velocity data; T i represents the joint torque at the i-th sampling time; w i represents the angular velocity data at the i-th sampling time; Δt represents the sampling time interval; When there is a torque data deviation and an angular velocity data deviation, the torque data deviation and the angular velocity data deviation are retrieved; The torque data deviation and the angular velocity data deviation are combined with a basic energy consumption estimation model to obtain an energy consumption estimation.

[0008] Furthermore, the torque data deviation and the angular velocity data deviation are combined with a basic energy consumption estimation model to obtain an energy consumption estimation, including: Retrieve all torque data deviations and angular velocity data deviations obtained by monitoring the current joint point; Performing standardization processing on the torque data deviation and the angular velocity data deviation to obtain the torque data deviation and the angular velocity data deviation after the standardization processing; Integrate all torque data deviations after standardization to generate a torque data deviation data set; Obtaining a torque deviation ratio average value according to each standardized torque data deviation in the torque data deviation data set; Integrate all the angular velocity data deviations after the standardization process to generate an angular velocity data deviation data set; Obtaining an angular velocity deviation ratio average value according to each standardized angular velocity data deviation in the angular velocity data deviation data set; Obtaining an energy loss coefficient according to the average value of the torque deviation ratio and the average value of the angular velocity deviation ratio; The energy loss coefficient is obtained by the following formula: Where, ξ represents the energy loss coefficient; T p and w p They represent the average value of the torque deviation ratio and the average value of the angular velocity deviation ratio respectively; α represents the preset power law scaling coefficient; β represents the damping coefficient corresponding to the joint point; γ represents the suppression coefficient of the coupling term by adjusting the torque amplitude; λ represents the preset exponential term weight coefficient; Using the energy loss coefficient in combination with a basic energy consumption estimation model to obtain an energy consumption estimation; The energy consumption estimation of each joint point is obtained by the following formula: Where E represents the estimated energy consumption of each joint point; n represents the number of sampling times of joint torque and angular velocity data; T i represents the joint torque at the i-th sampling time; w i represents the angular velocity data at the i-th sampling time; Δt represents the sampling time interval; ξ represents the energy loss coefficient.

[0009] Furthermore, when constructing the robot motion model, the key parameter variation patterns under each motion state are analyzed based on the extracted key motion features; Constructing a model of the angle, angular velocity, acceleration and torque changes of each joint under different motion states, wherein the change model is used to describe the dynamic change trend of the joint over time; According to the complete motion process of the robot from starting to stopping, the transfer relationship between different motion states is analyzed based on the time data series, and a robot motion state transfer model is established through a state machine modeling method. The motion state transfer model is used to define the transfer conditions and transition modes of different motion states; The mutual influence relationship of joint motion under different motion states is analyzed, which includes the synergy between adjacent joints, inertial influence and interference factors of the external environment on the robot motion, and a state dependence model is established based on the multivariate regression method.

[0010] Furthermore, when constructing the robot motion model, the Lagrangian method is used to establish the robot motion equation, which is used to describe the dynamic characteristics of each joint of the robot in different states. By analyzing the relationship between joint torque, speed, acceleration and energy consumption, the robot motion model of the robot in different motion states is established; Combined with the actual operation data of the robot, the robot motion model parameters are optimized in a data-driven way, and the robot motion model is trained through machine learning; Combined with the robot's historical motion data, the constructed robot motion model is simulated and tested and error analyzed. The error between the model prediction value and the actual measurement value is calculated, and the model is optimized through parameter adjustment.

[0011] Furthermore, when generating and optimizing the robot motion trajectory, based on the robot motion model, combined with the current motion state and the target state, the key parameters of trajectory generation are determined, the key parameters include joint angle, angular velocity, acceleration and path point information, and a trajectory parameterization model is established; Generate discrete trajectory points between the robot's current state and the target state; Based on the robot motion state transfer model, the motion time allocation of different trajectory segments is calculated, and the motion time allocation includes the time proportion of acceleration, uniform speed and deceleration stages; For the initially generated motion trajectory, constraints are set in combination with the robot's structural characteristics and task requirements. The constraints include joint angle range constraints, maximum speed constraints, maximum acceleration constraints, and joint torque constraints. The motion trajectory is optimized and adjusted using constraint optimization. Based on the energy consumption estimation in the robot motion model, the energy consumption of different motion trajectories is calculated, the motion trajectory is optimized through the energy optimal control method, and the optimized motion trajectory is smoothed.

[0012] Furthermore, when generating corresponding control instructions according to the motion trajectory, based on the optimized motion trajectory, the target state of each joint of the robot is extracted, and the target state includes angle, angular velocity and acceleration parameters, and the expected motion state of each joint at different time steps is determined in combination with the trajectory time allocation information; Based on the robot motion model, calculate the joint torque and control torque required for each joint of the robot to perform the target action, and generate the target joint torque distribution; The calculated joint target state, joint torque and control torque are converted into standardized control signals, and the control signals are sent to the robot joint drive unit through a real-time control bus.

[0013] Furthermore, when calculating the error deviation and dynamically adjusting the control instruction based on the error deviation, the motion state data of each joint during the robot's motion is collected in real time, and the motion state data includes the joint angle, angular velocity, acceleration, joint torque and the force condition of the end effector; Based on the optimized motion trajectory, the robot's current joint angle data is compared with the target angle in the preset trajectory to calculate the angle error of each joint; Combined with the robot motion model, the actual trajectory of the robot end effector is calculated, and compared with the preset trajectory to calculate the trajectory deviation, which includes the spatial position deviation and the direction deviation; Based on the joint angle error and trajectory deviation, the error compensation factor is generated in combination with the robot motion model. The control signal of each joint is adjusted based on the error compensation factor to correct the target joint angle, angular velocity and control torque in real time.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention collects multimodal motion data of a humanoid robot during its motion process, generates a standardized time data sequence, extracts key motion features, identifies the motion state of the robot, and subdivides the motion into motion states such as start, acceleration, steady state, deceleration, and stop, so that the robot can accurately determine its own motion state. The motion recognition method based on multimodal data fusion improves the robot's ability to understand complex motion processes, enabling it to adapt to different task scenarios more intelligently.

[0015] 2. When constructing the robot motion model, the present invention constructs joint motion models under different motion states by analyzing the changing rules of key parameters of each motion state, and uses the state machine modeling method to establish the robot motion state transfer model, so as to accurately control the switching between different states, and generate and optimize the robot motion trajectory in combination with the current motion state and the target state. The trajectory parameterization modeling and constraint optimization technology are used to ensure that the trajectory meets the joint angle, speed, acceleration and torque constraints. The energy consumption of different trajectory schemes is calculated based on the energy consumption estimation, and the energy optimal control method is used to optimize the trajectory, so that the robot can take into account both motion stability and energy efficiency when performing tasks, thereby improving the robustness and adaptability of the overall motion control.

[0016] 3. The present invention collects feedback data of the robot during the motion process in real time, compares it with the preset trajectory, calculates the motion error deviation, calculates the error compensation factor based on the robot motion model, and dynamically adjusts the control signal in combination with the robot's current motion state. By continuously correcting the target joint angle, angular velocity and control torque, high-precision error compensation is achieved to ensure that the robot can still maintain an accurate motion trajectory in a complex environment. The control parameters are dynamically adjusted according to the real-time feedback data, so that the robot can maintain stable motion under different loads, different terrains and different task conditions, thereby improving the robot's adaptability in real environments and improving the reliability and execution efficiency of the overall motion control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flow chart of the motion control method of a humanoid robot according to the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 , the present invention provides the following technical solutions: The humanoid robot motion control method based on motion data analysis includes: Acquire multimodal motion data of the robot during movement, extract key motion features based on the multimodal motion data and identify the motion state of the robot, build a robot motion model, combine the robot's current motion state with the target state, generate and optimize the robot's motion trajectory, generate corresponding control instructions based on the motion trajectory, compare with the preset trajectory, calculate the error deviation and dynamically adjust the control instructions based on the error deviation.

[0020] When obtaining multimodal motion data of the robot during motion, the inertial measurement unit, force sensor and angle encoder installed on each joint of the humanoid robot are used to obtain the multimodal motion data, including joint angle, angular velocity, acceleration, joint torque and end effector force; Perform time alignment and interpolation processing on multimodal motion data to generate standardized time data series.

[0021] In the above embodiment, by acquiring the multimodal motion data of the robot during motion, and performing time alignment and interpolation processing on the data, high-precision data acquisition and standardized preprocessing are achieved. The method of acquiring multimodal motion data fully considers various key parameters that may be involved in the robot during motion, such as joint angle, angular velocity, acceleration, joint torque and end effector force, and truly reflects the current motion state of the robot. Time alignment and interpolation processing ensure the synchronization of different sensor data. Through the timestamp alignment method, all data can be adjusted to a unified time base, and interpolation processing can fill in missing data points, so that the final time data sequence has the characteristics of equal time intervals, which effectively improves the data quality and provides more accurate and stable input data for the robot motion control system, thereby improving the reliability and adaptability of the entire control method.

[0022] When extracting key motion features based on multimodal motion data and identifying the motion state of the robot motion, feature extraction is performed on the multimodal motion data, and the key motion features include motion phase, joint angle change rate, and energy consumption estimation; Based on the angle, angular velocity and acceleration data of each joint of the robot, the phase information of the robot during continuous motion is calculated, and key motion phase parameters are extracted; Calculate the change rate of the joint angle of each joint of the robot; Based on the joint torque and angular velocity data, the estimated energy consumption per unit time of each joint is calculated; Construct a multi-dimensional feature vector based on key motion features and reduce the dimension of the multi-dimensional feature vector; Based on the key motion features after dimensionality reduction, the robot's motion state is classified and identified through hierarchical clustering, and divided into different motion states; The motion states include starting, accelerating, steady state, decelerating and stopping.

[0023] In the above embodiment, the most representative key motion features of the robot motion process, such as motion phase, joint angle change rate and energy consumption estimation, are extracted through feature extraction technology, so that the analysis of motion state is more accurate. Among them, the motion phase is used to describe the coordination of the robot's joints during continuous motion, the joint angle change rate reflects the changing trend of the robot's motion, and the energy consumption estimation helps to optimize the robot's energy utilization efficiency. Through multi-dimensional feature vector construction and dimensionality reduction processing, the data dimension is reduced, thereby reducing the computational complexity and improving processing efficiency. In the feature space after dimensionality reduction, the distribution of the robot's motion state is clearer, which is helpful for subsequent motion state classification. Using the hierarchical clustering method, the different motion states of the robot can be accurately classified, the accuracy and robustness of state recognition can be improved, the current motion state of the robot can be analyzed in real time, and trend prediction can be performed based on historical data, providing an important basis for subsequent trajectory optimization and control instruction adjustment.

[0024] Specifically, based on the joint torque and angular velocity data, the energy consumption estimate of each joint per unit time is calculated, including: Real-time monitoring of joint torque and angular velocity data of joint points; Retrieve the theoretical value of the joint torque corresponding to the sampling moment of each joint point; According to the torque data deviation between the actual value of the joint torque at the sampling moment of each joint point and the theoretical value of the joint torque; Retrieve the theoretical value of the angular velocity data corresponding to the sampling moment of each joint point; According to the angular velocity data deviation between the actual value of the angular velocity data at the sampling moment of each joint point and the theoretical value of the angular velocity data; When there is no torque data deviation and angular velocity data deviation, the basic energy consumption estimation model is used to obtain the energy consumption estimation of each joint point; The energy consumption estimation of each joint point is obtained by the following formula: Where E represents the estimated energy consumption of each joint point; n represents the number of sampling times of joint torque and angular velocity data; T i represents the joint torque at the i-th sampling time; w i represents the angular velocity data at the i-th sampling time; Δt represents the sampling time interval; When there is a torque data deviation and an angular velocity data deviation, the torque data deviation and the angular velocity data deviation are retrieved; The torque data deviation and the angular velocity data deviation are combined with a basic energy consumption estimation model to obtain an energy consumption estimation.

[0025] The technical effect of the above technical solution is: It represents the mechanical power at the current moment, the integral of all the absolute values ​​of instantaneous power per unit time, reflecting the total mechanical energy consumption; the power is accumulated within the unit time window Δt (such as 1 second) and multiplied by the time interval to obtain the work done by the joint within the i-th sampling time interval Δt, that is, the energy consumption of the joint within this time period.

[0026] By real-time monitoring of the joint torque and angular velocity data of the joint points and comparing them with the theoretical values, data deviations can be discovered and corrected in time. This real-time monitoring and correction mechanism ensures that the data used for energy consumption calculation is more accurate, thereby improving the accuracy of energy consumption estimation. When there is no data deviation, directly using the basic energy consumption estimation model for calculation can ensure that the most accurate energy consumption estimation is obtained under ideal conditions. The technical solution takes into account the existence of data deviations and provides a corresponding processing mechanism. When there are torque data deviations and angular velocity data deviations, these deviations are not simply ignored, but these deviation information is retrieved and used to correct the energy consumption estimation. This design enables the system to adapt to different working environments and conditions, improving the flexibility and adaptability of the system. Through real-time monitoring and data processing, abnormal data can be discovered and corrected in time, avoiding calculation errors and resource waste caused by inaccurate data. The technical solution ensures the reliability of energy consumption estimation through multiple links such as real-time monitoring, data correction and model application. Even in the presence of data deviations, the estimation can be corrected by retrieving and using these deviation information, thereby improving the reliability of the entire system.

[0027] Specifically, the energy consumption estimation is obtained by using the torque data deviation and the angular velocity data deviation in combination with a basic energy consumption estimation model, including: Retrieve all torque data deviations and angular velocity data deviations obtained by monitoring the current joint point; Performing standardization processing on the torque data deviation and the angular velocity data deviation to obtain the torque data deviation and the angular velocity data deviation after the standardization processing; Integrate all torque data deviations after standardization to generate a torque data deviation data set; Obtaining a torque deviation ratio average value according to each standardized torque data deviation in the torque data deviation data set; Integrate all the angular velocity data deviations after the standardization process to generate an angular velocity data deviation data set; Obtaining an angular velocity deviation ratio average value according to each standardized angular velocity data deviation in the angular velocity data deviation data set; Obtaining an energy loss coefficient according to the average value of the torque deviation ratio and the average value of the angular velocity deviation ratio; The energy loss coefficient is obtained by the following formula: Where, ξ represents the energy loss coefficient; T p and w p They represent the average value of the torque deviation ratio and the average value of the angular velocity deviation ratio respectively; α represents the preset power law scaling coefficient; β represents the damping coefficient corresponding to the joint point; γ represents the suppression coefficient of the coupling term by adjusting the torque amplitude; λ represents the preset exponential term weight coefficient; Using the energy loss coefficient in combination with a basic energy consumption estimation model to obtain an energy consumption estimation; The energy consumption estimation of each joint point is obtained by the following formula: Where E represents the estimated energy consumption of each joint point; n represents the number of sampling times of joint torque and angular velocity data; T i represents the joint torque at the i-th sampling time; w i represents the angular velocity data at the i-th sampling time; Δt represents the sampling time interval; ξ represents the energy loss coefficient.

[0028] The technical effect of the above technical solution is: The influence of the torque-angular velocity product on energy consumption is dynamically adjusted by adjusting the power law scaling coefficient and damping coefficient and the suppression coefficient of the coupling term by adjusting the torque amplitude, thus avoiding the problem of excessive dependence on the torque amplitude in the direct form of the traditional linear product (P=τω). When the torque and angular velocity deviation are in the same direction Enhanced energy penalty when reverse bias occurs The penalty is reduced when the error is large, reflecting the energy feedback characteristics of the actual system. At the same time, the exponential function amplifies the contribution of large deviations, forcing the model to pay attention to abnormal conditions.

[0029] By introducing torque data deviation and angular velocity data deviation and standardizing them, various influencing factors in actual work can be considered more comprehensively, thereby improving the accuracy of energy consumption estimation. The energy loss coefficient is calculated using the average value of the torque deviation ratio and the average value of the angular velocity deviation ratio, further considering the impact of data deviation on energy consumption, making the estimation closer to the actual situation. This technical solution can dynamically adjust the energy loss coefficient according to different working conditions and characteristics of joint points (such as damping coefficient, suppression coefficient of coupling term by adjusting torque amplitude, etc.), thereby enhancing the adaptability of the system. By adjusting parameters such as power law scaling coefficient and exponential term weight coefficient, the calculation of energy loss coefficient can be further optimized to adapt to different application scenarios and needs. Through real-time monitoring and data processing, this technical solution can timely discover and correct data deviations, thereby avoiding valuation errors caused by inaccurate data. At the same time, by introducing the energy loss coefficient, it can more accurately reflect the energy consumption in actual work and improve the reliability of the valuation.

[0030] In summary, this technical solution has demonstrated remarkable technical effects in terms of valuation accuracy, adaptability, computational efficiency, reliability, and scalability, providing strong technical support for the accurate assessment of joint energy expenditure.

[0031] When constructing the robot motion model, the key parameters change rules under each motion state are analyzed based on the extracted key motion features; Construct the angle, angular velocity, acceleration and torque change model of each joint under different motion states. The change model is used to describe the dynamic change trend of the joint over time. According to the complete motion process of the robot from starting to stopping, the transfer relationship between different motion states is analyzed based on the time data series, and the robot motion state transfer model is established through the state machine modeling method. The motion state transfer model is used to define the transfer conditions and transition modes of different motion states; The mutual influence relationship of joint motion under different motion states is analyzed, which includes the synergy between adjacent joints, the influence of inertia and the interference factors of the external environment on the robot motion, and a state dependence model is established based on the multivariate regression method.

[0032] The Lagrangian method is used to establish the robot motion equation, which is used to describe the dynamic characteristics of each joint of the robot in different states. By analyzing the relationship between joint torque, speed, acceleration and energy consumption, the robot motion model of the robot in different motion states is established. Combined with the actual operation data of the robot, the robot motion model parameters are optimized in a data-driven way, and the robot motion model is trained through machine learning; Combined with the robot's historical motion data, the constructed robot motion model is simulated and tested and error analyzed. The error between the model prediction value and the actual measurement value is calculated, and the model is optimized through parameter adjustment.

[0033] In the above embodiment, a detailed joint motion change model is established for different motion states, including the changing trends of angles, angular velocities, accelerations and torques, so that the dynamic characteristics of the robot motion are accurately described, which can reflect the motion laws of the robot in different states, and help improve the predictability and stability of the motion trajectory. The state machine modeling method can effectively describe the transfer relationship between the robot's start-up, acceleration, steady state, deceleration and stop states, and define the transition mode between different states, so that the robot can automatically adjust to the optimal motion mode according to the current state during the actual execution process, avoid the impact of sudden changes, and improve the stability and coordination of the motion. The state-dependent modeling method takes into account the interaction between the joints of the robot, including synergy, inertial effects and external interference factors, making the motion model more adaptable and robust, and accurately predicting the dynamic response of the robot in different states.

[0034] When generating and optimizing the robot motion trajectory, based on the robot motion model, combined with the current motion state and the target state, the key parameters of trajectory generation are determined. The key parameters include joint angle, angular velocity, acceleration and path point information, and a trajectory parameterization model is established. Generate discrete trajectory points between the robot's current state and the target state; Based on the robot motion state transfer model, the motion time allocation of different trajectory segments is calculated. The motion time allocation includes the time proportion of acceleration, uniform speed and deceleration stages. For the initially generated motion trajectory, combined with the robot's structural characteristics and task requirements, set constraints, including joint angle range constraints, maximum speed constraints, maximum acceleration constraints and joint torque constraints, and use constraint optimization to optimize the motion trajectory; Based on the energy consumption estimation in the robot motion model, the energy consumption of different motion trajectories is calculated, the motion trajectory is optimized through the energy optimal control method, and the optimized motion trajectory is smoothed.

[0035] In the above embodiment, based on the robot motion model, combined with the current state and the target state, a trajectory that meets the robot motion characteristics is generated, and the smoothness and rationality of the trajectory are ensured. The trajectory parameterization model is adopted to make the trajectory optimization more free and can be flexibly adjusted according to different application scenarios. In the trajectory optimization process, the robot structural characteristics and task requirements are fully considered, and a variety of constraints are set, such as joint angle range constraints, maximum speed constraints, maximum acceleration constraints and joint torque constraints, so that the robot can meet physical limitations and safety requirements when performing motion, and avoid overload or instability. The application of the energy optimal control method enables the robot to minimize energy consumption and improve endurance when performing tasks. By optimizing the energy consumption of different motion trajectories, the optimal motion plan can be selected to further improve the economy and sustainability of the robot's motion.

[0036] When generating corresponding control instructions according to the motion trajectory, the target state of each joint of the robot is extracted based on the optimized motion trajectory. The target state includes angle, angular velocity and acceleration parameters. Combined with the trajectory time allocation information, the expected motion state of each joint at different time steps is determined; Based on the robot motion model, calculate the joint torque and control torque required for each joint of the robot to perform the target action, and generate the target joint torque distribution; The calculated joint target state, joint torque and control torque are converted into standardized control signals, and the control signals are sent to the robot joint drive unit through a real-time control bus.

[0037] In the above embodiment, based on the optimized motion trajectory, the target state of each joint of the robot is extracted, and combined with the trajectory time distribution information, it is ensured that the control instructions can be executed strictly according to the time step, so that the motion state of the robot at different stages can accurately match the trajectory planning result, and the joint torque and control torque required to execute the target action are calculated through the robot motion model, and the target joint torque distribution is generated, so that the robot can maintain a stable torque output in different motion states, avoiding motion deviation or oscillation due to unreasonable torque distribution, and converting the calculated joint target state, joint torque and control torque into standardized control signals, and sending them to the robot joint drive unit through the real-time control bus, so that the transmission of control signals is more stable and efficient, ensuring that the robot can accurately perform motion tasks according to the planned trajectory.

[0038] When calculating the error deviation and dynamically adjusting the control command based on the error deviation, the motion state data of each joint during the robot's motion is collected in real time. The motion state data includes joint angle, angular velocity, acceleration, joint torque and the force of the end effector; Based on the optimized motion trajectory, the robot's current joint angle data is compared with the target angle in the preset trajectory to calculate the angle error of each joint; Combined with the robot motion model, the actual trajectory of the robot end effector is calculated, and compared with the preset trajectory to calculate the trajectory deviation, which includes spatial position deviation and direction deviation; Based on the joint angle error and trajectory deviation, the error compensation factor is generated in combination with the robot motion model. The control signal of each joint is adjusted based on the error compensation factor to correct the target joint angle, angular velocity and control torque in real time.

[0039] In the above embodiment, the joint motion state data of the robot during execution is collected in real time, and compared with the preset trajectory, the joint angle error and trajectory deviation are calculated, so that the robot control system can accurately identify the deviations in the motion process, calculate the error compensation factor based on the robot motion model, and dynamically adjust the control signal based on the error compensation factor to ensure that the robot can continuously correct the target joint angle, angular velocity and control torque during execution to minimize the trajectory error, and establish a complete error closed-loop compensation mechanism, so that the robot can continuously adjust its own state during the motion process, improve the adaptability and stability of the motion, and ensure that the robot can operate stably in complex environments.

[0040] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A humanoid robot motion control method based on motion data analysis, characterized in that: include: Acquire multimodal motion data of the robot during motion, extract key motion features based on the multimodal motion data and identify the motion state of the robot, build a robot motion model, combine the robot's current motion state with the target state, generate and optimize the robot's motion trajectory, generate corresponding control instructions based on the motion trajectory, compare with the preset trajectory, calculate the error deviation and dynamically adjust the control instructions based on the error deviation; When the robot motion is recognized, the joint torque and angular velocity data are obtained, the torque data deviation and the angular velocity data deviation are calculated, and the energy consumption estimation of each joint per unit time is calculated in combination with the basic energy consumption estimation model.

2. The humanoid robot motion control method based on motion data analysis according to claim 1, characterized in that: When obtaining multimodal motion data of the robot during motion, the multimodal motion data is obtained by installing an inertial measurement unit, a force sensor and an angle encoder on each joint of the humanoid robot, and the multimodal motion data includes joint angle, angular velocity, acceleration, joint torque and end effector force; Perform time alignment and interpolation on multimodal motion data to generate a standardized time data series.

3. The humanoid robot motion control method based on motion data analysis according to claim 1, characterized in that: When extracting key motion features based on the multimodal motion data and performing motion state recognition on the robot motion, feature extraction is performed on the multimodal motion data, wherein the key motion features include motion phase, joint angle change rate, and energy consumption estimation; Based on the angle, angular velocity and acceleration data of each joint of the robot, calculate the phase information of the robot during continuous motion and extract key motion phase parameters; Calculate the change rate of the joint angle of each joint of the robot; Based on the joint torque and angular velocity data, the estimated energy consumption per unit time of each joint is calculated; Construct a multi-dimensional feature vector based on key motion features and reduce the dimension of the multi-dimensional feature vector; Based on the key motion features after dimensionality reduction, the robot's motion state is classified and identified through hierarchical clustering, and divided into different motion states; The motion states include starting, accelerating, steady state, decelerating and stopping.

4. The humanoid robot motion control method based on motion data analysis as claimed in claim 3, characterized in that: Based on the joint torque and angular velocity data, the energy consumption of each joint per unit time is estimated, including: Real-time monitoring of joint torque and angular velocity data of joint points; Retrieve the theoretical value of the joint torque corresponding to the sampling moment of each joint point; According to the torque data deviation between the actual value of the joint torque at the sampling moment of each joint point and the theoretical value of the joint torque; Retrieve the theoretical value of the angular velocity data corresponding to the sampling moment of each joint point; According to the angular velocity data deviation between the actual value of the angular velocity data at the sampling moment of each joint point and the theoretical value of the angular velocity data; When there is no torque data deviation and angular velocity data deviation, the basic energy consumption estimation model is used to obtain the energy consumption estimation of each joint point; When there is a torque data deviation and an angular velocity data deviation, the torque data deviation and the angular velocity data deviation are retrieved; The torque data deviation and the angular velocity data deviation are combined with a basic energy consumption estimation model to obtain an energy consumption estimation.

5. The humanoid robot motion control method based on motion data analysis according to claim 4, characterized in that: The energy consumption estimation is obtained by using the torque data deviation and the angular velocity data deviation in combination with a basic energy consumption estimation model, including: Retrieve all torque data deviations and angular velocity data deviations obtained by monitoring the current joint point; Performing standardization processing on the torque data deviation and the angular velocity data deviation to obtain the torque data deviation and the angular velocity data deviation after the standardization processing; Integrate all torque data deviations after standardization to generate a torque data deviation data set; Obtaining a torque deviation ratio average value according to each standardized torque data deviation in the torque data deviation data set; Integrate all the angular velocity data deviations after the normalization process to generate an angular velocity data deviation data set; Obtaining an angular velocity deviation ratio average value according to each standardized angular velocity data deviation in the angular velocity data deviation data set; Obtaining an energy loss coefficient according to the torque deviation ratio average value and the angular velocity deviation ratio average value; The energy consumption loss coefficient is combined with a basic energy consumption estimation model to obtain an energy consumption estimation.

6. The humanoid robot motion control method based on motion data analysis according to claim 1, characterized in that: When constructing the robot motion model, the key parameters change rules under each motion state are analyzed based on the extracted key motion features; Constructing a model of the angle, angular velocity, acceleration and torque changes of each joint under different motion states, wherein the change model is used to describe the dynamic change trend of the joint over time; According to the complete motion process of the robot from starting to stopping, the transfer relationship between different motion states is analyzed based on the time data series, and a robot motion state transfer model is established through a state machine modeling method. The motion state transfer model is used to define the transfer conditions and transition modes of different motion states; The mutual influence relationship of joint motion under different motion states is analyzed, which includes the synergy between adjacent joints, inertial influence and interference factors of the external environment on the robot motion, and a state dependence model is established based on the multivariate regression method.

7. The humanoid robot motion control method based on motion data analysis according to claim 6, characterized in that: When constructing the robot motion model, the Lagrangian method is used to establish the robot motion equation, which is used to describe the dynamic characteristics of each joint of the robot in different states. By analyzing the relationship between joint torque, speed, acceleration and energy consumption, the robot motion model of the robot in different motion states is established; Combined with the actual operation data of the robot, the robot motion model parameters are optimized in a data-driven way, and the robot motion model is trained through machine learning; Combined with the robot's historical motion data, the constructed robot motion model is simulated and tested and error analyzed. The error between the model prediction value and the actual measurement value is calculated, and the model is optimized through parameter adjustment.

8. The humanoid robot motion control method based on motion data analysis according to claim 1, characterized in that: When generating and optimizing the robot motion trajectory, based on the robot motion model, combined with the current motion state and the target state, the key parameters of trajectory generation are determined, including joint angles, angular velocities, accelerations and path point information, and a trajectory parameterization model is established; Generate discrete trajectory points between the robot's current state and the target state; Based on the robot motion state transfer model, the motion time allocation of different trajectory segments is calculated, and the motion time allocation includes the time proportion of acceleration, uniform speed and deceleration stages; For the initially generated motion trajectory, constraints are set in combination with the robot's structural characteristics and task requirements. The constraints include joint angle range constraints, maximum speed constraints, maximum acceleration constraints, and joint torque constraints. The motion trajectory is optimized and adjusted using constraint optimization. Based on the energy consumption estimation in the robot motion model, the energy consumption of different motion trajectories is calculated, the motion trajectory is optimized through the energy optimal control method, and the optimized motion trajectory is smoothed.

9. The humanoid robot motion control method based on motion data analysis according to claim 1, characterized in that: When generating corresponding control instructions according to the motion trajectory, based on the optimized motion trajectory, the target state of each joint of the robot is extracted, and the target state includes angle, angular velocity and acceleration parameters. Combined with the trajectory time allocation information, the expected motion state of each joint at different time steps is determined; Based on the robot motion model, calculate the joint torque and control torque required for each joint of the robot to perform the target action, and generate the target joint torque distribution; The calculated joint target state, joint torque and control torque are converted into standardized control signals, and the control signals are sent to the robot joint drive unit through a real-time control bus.

10. The humanoid robot motion control method based on motion data analysis according to claim 1, characterized in that: When calculating the error deviation and dynamically adjusting the control instruction based on the error deviation, the motion state data of each joint during the robot's motion is collected in real time, and the motion state data includes the joint angle, angular velocity, acceleration, joint torque and the force condition of the end effector; Based on the optimized motion trajectory, the robot's current joint angle data is compared with the target angle in the preset trajectory to calculate the angle error of each joint; Combined with the robot motion model, the actual trajectory of the robot end effector is calculated, and compared with the preset trajectory to calculate the trajectory deviation, which includes the spatial position deviation and the direction deviation; Based on the joint angle error and trajectory deviation, the error compensation factor is generated in combination with the robot motion model. The control signal of each joint is adjusted based on the error compensation factor to correct the target joint angle, angular velocity and control torque in real time.

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