An exoskeleton robot impedance adjustment and trajectory planning method, medium and device

Abstract

The application provides an exoskeleton robot impedance adjustment and trajectory planning method, medium and equipment, relates to the exoskeleton robot control technical field, and the method obtains the multidimensional original signal related to exoskeleton movement, and carries out pretreatment to it;The complex motion trajectory of the exoskeleton is decomposed into basic motion component, working condition adaptive component;The Fourier coefficient of abnormal harmonic is adjusted to suppress abnormal harmonic, and the anti-interference basic motion component is obtained according to the adjusted Fourier coefficient;The terrain feature matching and gain control are carried out on the working condition adaptive component, and the reconstructed working condition adaptive component is obtained;The anti-interference basic motion component and the reconstructed working condition adaptive component are recombined to obtain the final motion trajectory;According to the error between the final motion trajectory of the exoskeleton and the expected trajectory, the impedance model parameters are adjusted in real time, so that the exoskeleton tracks the expected trajectory and is optimized in real time.The above-mentioned method can widen the application scene of the exoskeleton, and significantly improve its practicability in complex environment.

Description

Technical Field

[0001] This invention relates to the field of exoskeleton robot control technology, and in particular to a method, medium, and device for impedance adjustment and trajectory planning of an exoskeleton robot. Background Technology

[0002] As a cutting-edge product in the field of human-computer interaction, exoskeleton robots have shown broad application prospects in many fields. In the field of medical rehabilitation, exoskeletons can help people with limb disabilities and rehabilitation patients to carry out autonomous movement training and accelerate the rehabilitation process. In industrial scenarios, they can help workers carry heavy objects, reduce labor intensity, and improve work efficiency. However, the complex and ever-changing actual working conditions have placed stringent requirements on their trajectory planning and impedance adjustment technologies.

[0003] Currently, existing exoskeleton trajectory planning algorithms have significant shortcomings when dealing with complex terrain. Most algorithms use fixed trajectory models, which are difficult to adjust in real time according to changes in terrain. This results in poor motion stability of the exoskeleton when facing rugged terrain or obstacles, and may even lead to safety accidents such as falls. In terms of impedance adjustment, traditional methods cannot make accurate responses based on the real-time motion state of the exoskeleton and the external environment, resulting in a poor interactive experience between the exoskeleton and the user. This not only fails to provide effective assistance but may also place an additional burden on the user. In addition, existing algorithms often fail to fully consider the synergy between motion trajectory and impedance adjustment, making it difficult to improve the overall performance of the exoskeleton under complex working conditions. Summary of the Invention

[0004] The purpose of this invention is to provide a method, medium, and device for impedance adjustment and trajectory planning of exoskeleton robots. This aims to accurately plan trajectories to adapt to complex terrain, adjust impedance in real time to improve the human-machine interaction experience, and ensure the coordinated optimization of motion trajectory and impedance adjustment. This enables stable and efficient exoskeleton control of the robot under complex working conditions. The specific technical solution is as follows:

[0005] An impedance adjustment and trajectory planning method for an exoskeleton robot, the method comprising the following steps:

[0006] S100: Acquire multi-dimensional raw signals related to exoskeleton movement and preprocess them;

[0007] S200 performs Fourier series transformation and wavelet packet transformation on the preprocessed multi-dimensional original signals respectively, decomposing the complex motion trajectory of the exoskeleton into basic motion components that reflect periodic gait and working condition adaptation components that reflect terrain features.

[0008] S300. Determine whether there are abnormal harmonics in the basic motion component. If there are abnormal harmonics, adjust the Fourier coefficients of the abnormal harmonics to suppress them. Obtain the anti-disturbance basic motion component based on the adjusted Fourier coefficients. Perform terrain feature matching and gain control on the working condition adaptation component to obtain the reconstructed working condition adaptation component.

[0009] S400: The anti-disturbance basic motion components and the reconstructed working condition adaptation components are recombined to obtain the final motion trajectory;

[0010] S500 adjusts the impedance model parameters in real time based on the error between the exoskeleton's final motion trajectory and the desired trajectory, thereby optimizing the exoskeleton's tracking of the desired trajectory in real time.

[0011] Furthermore, in step S100, the multi-dimensional raw signals include joint angles, joint torques, trunk acceleration, plantar pressure change rate, gait cycle, and terrain elevation data.

[0012] Furthermore, in step S100, the preprocessing includes normalizing, low-pass filtering, and sliding window segmentation of the multi-dimensional original signal in sequence.

[0013] Furthermore, step S200 specifically includes the following steps:

[0014] S201, Basic Motion Component Extraction: Receive the preprocessed joint angle signal and gait cycle, fit the joint angle signal with Fourier series, and extract the basic motion components.

[0015] S202, Extraction of working condition adaptation components: Receive the preprocessed acceleration signal and terrain elevation data, perform wavelet packet decomposition on the acceleration signal, calculate the energy of each node, set the energy threshold, filter out the sub-band signal related to terrain disturbance, reconstruct the selected sub-band signal, and generate the working condition adaptation components.

[0016] S203. Decomposition effect verification: Calculate the residual energy ratio of the decomposed signal. If the residual energy ratio does not meet the requirements, increase the wavelet packet decomposition layer and the extended Fourier harmonic order, and return to step S201 to decompose and verify again until the residual energy ratio meets the requirements.

[0017] Further, in step S300, it is determined whether there are abnormal harmonics in the basic motion component. If abnormal harmonics exist, the Fourier coefficients of the abnormal harmonics are adjusted to suppress them. The anti-disturbance basic motion component is obtained based on the adjusted Fourier coefficients, specifically including:

[0018] S301, Abnormal Harmonic Detection: Calculate the joint torque fluctuation amplitude corresponding to each harmonic, set the joint torque threshold, and determine whether there are abnormal harmonics based on the magnitude of the joint torque fluctuation amplitude and the joint torque threshold.

[0019] S302, Abnormal Harmonic Suppression: Introduce a suppression coefficient to dynamically attenuate the Fourier coefficients of abnormal harmonics, and obtain the basic motion component for disturbance rejection.

[0020] S303. Characteristic Verification: Perform frequency domain energy comparison, calculate the energy ratio between the anti-disturbance basic motion component and the basic motion component, and calculate the residual between the joint angle signal and the anti-disturbance basic motion component. If the energy ratio and residual do not meet the requirements, adjust the parameters and re-optimize until the requirements are met.

[0021] Further, in step S300, terrain feature matching and gain control are performed on the working condition adaptation component to obtain the reconstructed working condition adaptation component, specifically including:

[0022] S304. Terrain Matching: Based on the extracted wavelet packet subband energy distribution and terrain elevation data, compare the node energy distribution of the current terrain signal after wavelet packet decomposition with the energy distribution of various terrains in the terrain database, find the terrain type with the highest similarity, and determine the optimal subband combination.

[0023] S305. Gain control: Based on the rate of change of plantar pressure, dynamically adjust the amplitude gain of the working condition adaptation component. After adjusting the amplitude of the working condition adaptation component, the reconstructed working condition adaptation component is obtained.

[0024] Further, step S400 specifically includes:

[0025] S401. Weight Determination: Determine the weight coefficients of the anti-disturbance basic motion component and the reconfiguration working condition adaptation component based on the terrain complexity.

[0026] S402, Weighted superposition: The reconstructed working condition adaptation components are weighted and superimposed according to the weight coefficients to obtain the final motion trajectory.

[0027] Further, step S500 specifically includes:

[0028] S501, Error Calculation: Calculate the tracking error between the expected trajectory and the actual joint angle, and divide the frequency band into low-frequency error and high-frequency error;

[0029] S502, Parameter Adjustment: Determine the stiffness coefficient based on the low-frequency error integral and the damping coefficient based on the high-frequency error peak value;

[0030] S503, Torque Calculation: The desired joint torque is calculated using a second-order impedance model based on the stiffness coefficient and damping coefficient.

[0031] S504, Current Conversion: Converts the desired joint torque into the motor's execution command current;

[0032] S505, Track Tracking Control: Employs a PID controller to output motor control current based on tracking error, enabling the motor to track the desired joint angle.

[0033] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the exoskeleton robot impedance adjustment and trajectory planning method as described above.

[0034] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the exoskeleton robot impedance adjustment and trajectory planning method as described above.

[0035] The present invention provides an impedance adjustment and trajectory planning method, medium, and device for an exoskeleton robot, which has the following beneficial effects:

[0036] The exoskeleton robot impedance adjustment and trajectory planning method provided by this invention decomposes complex motion trajectories into basic motion components and working condition adaptation components. Through Fourier series and wavelet packet transform, periodic gait features and terrain feature frequency components are extracted and recombined in the frequency domain. This enables the exoskeleton to accurately identify and adapt to different terrains. At the same time, the trajectory weights are dynamically adjusted according to the terrain complexity. In terms of impedance adjustment, the stiffness and damping parameters are adjusted in real time according to the trajectory tracking error to ensure that the exoskeleton can maintain stable motion under various complex working conditions. This greatly expands the application scenarios of exoskeletons and significantly improves their practicality in complex environments. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating an impedance adjustment and trajectory planning method for an exoskeleton robot provided by the present invention.

[0038] Figure 2 This is a general flowchart of an embodiment of the present invention;

[0039] Figure 3 This is a structural block diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0040] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The advantages and features of the present invention will become clearer from the following description. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clearly illustrate the purpose of the embodiments of the present invention.

[0041] Example 1

[0042] This embodiment provides a method for impedance adjustment and trajectory planning of an exoskeleton robot. (See also...) Figure 1 , 2 As shown, the method includes the following steps:

[0043] S100: Acquire multi-dimensional raw signals related to exoskeleton movement and preprocess them.

[0044] In one embodiment, the multidimensional raw signals include joint angle θ(t), joint torque τ(t), trunk acceleration a(t), plantar pressure change rate ΔF, gait period T, and terrain elevation data z(x, y).

[0045] Specifically, multi-dimensional raw signals are obtained in real time from human kinematic signals using an exoskeleton joint encoder, an inertial measurement unit (IMU), a plantar pressure sensor, and a terrain detection radar. Specifically: the joint encoder acquires joint angle θ(t) and joint torque τ(t); the IMU collects trunk acceleration a(t) to calculate the position of the human center of mass; and the plantar pressure sensor acquires plantar pressure F. foot (t), and identify the gait phase, and calculate the rate of change of plantar pressure through plantar pressure. The gait period T is determined by the gait phase, where F foot (t) represents the plantar pressure at time t, F foot (t-Δt) represents the plantar pressure at time t-Δt, where Δt is the time interval; terrain elevation data z(x, y) is acquired in real time by terrain detection radar, where x and y are the horizontal and vertical coordinates.

[0046] In simulated complex outdoor scenarios, the exoskeleton is equipped with multiple sensors to comprehensively acquire motion data and operational information. During human walking, changes in joint angles and torques trigger relative displacements or electrical signal changes in the encoder's internal components. Data processing converts these physical changes into digital signals, accurately obtaining joint angles θ(t) and joint torques τ(t). These parameters reflect the real-time motion state of the joints and are crucial for analyzing human movement patterns and meeting exoskeleton assistance requirements. The IMU (Integrated Measurement Unit) incorporates high-precision accelerometers and gyroscopes, enabling real-time sensing of the exoskeleton's overall acceleration and rotation. The accelerometer measures acceleration by detecting the displacement of the mass block under inertial forces, while the gyroscope measures angular velocity using the principle of conservation of angular momentum. Through integration and fusion algorithms of these data, the position of the human body's center of mass is calculated. This information is critical for determining the exoskeleton's posture and stability, assisting in subsequent trajectory planning and impedance adjustment to ensure the coordination between the exoskeleton and human movement. Foot pressure sensors are evenly distributed on the sole of the shoe, monitoring pressure changes in different areas of the foot in real time and acquiring the foot pressure distribution F. footDuring walking, plantar pressure dynamically changes with each step's landing, support, and lift-off. Analyzing this pressure data allows for the identification of gait phases, such as heel strike, ball of the foot support, and toe lift-off. Simultaneously, the rate of change of plantar pressure can be calculated. Among them, F foot (t) represents the plantar pressure at time t, F foot (t-Δt) represents the plantar pressure at time t-Δt, where Δt is the time interval. This parameter reflects the rate of change in plantar pressure and can effectively capture sudden changes in ground conditions, providing crucial information for the adaptive adjustment of the exoskeleton. The terrain detection radar detects the elevation data z(x, y) of the surrounding terrain by emitting and receiving electromagnetic waves. The electromagnetic waves emitted by the radar are reflected back after encountering the terrain surface. Based on the time delay and signal strength of the echo, the distance and height information of each point on the terrain surface relative to the exoskeleton are calculated.

[0047] In one embodiment, preprocessing includes normalizing, low-pass filtering, and sliding window segmentation of the multi-dimensional raw signal in sequence. Normalization unifies signals from different sensors to the same scale, low-pass filtering removes high-frequency noise interference and retains low-frequency signals, and sliding window segmentation divides the continuous signal into segments of fixed duration to extract time-domain features of different time periods.

[0048] Because different sensors have different measurement principles and ranges, their output signal ranges and units differ. To ensure consistency and comparability in subsequent data analysis and processing, the original signals need to be normalized, mapping all sensor data to the same numerical range [0, 1]. This eliminates the influence of dimensional and amplitude differences on subsequent calculations. In actual measurements, sensor signals are inevitably affected by high-frequency noise, which may originate from electromagnetic interference from electronic equipment, environmental noise, etc. The purpose of low-pass filtering is to remove this high-frequency noise and retain the effective low-frequency signal. To better analyze the temporal characteristics of the signal, continuous sensor signals are segmented into a series of time-series data at fixed intervals. Preferably, continuous data is segmented into fixed intervals with a certain window overlap rate, and temporal features are extracted within each window.

[0049] S200 performs Fourier series transformation and wavelet packet transformation on the preprocessed multi-dimensional original signals respectively, decomposing the complex motion trajectory of the exoskeleton into basic motion components that reflect periodic gait and working condition adaptation components that reflect terrain features.

[0050] In one embodiment, the specific steps include:

[0051] S201, Basic Motion Component Extraction: Receive the preprocessed joint angle signal and gait cycle, fit the joint angle signal with Fourier series, and extract the basic motion components.

[0052] Specifically, Fourier fitting is performed on the preprocessed joint angle signal θ(t) to obtain the basic motion component x. base (t), the expression is as follows:

[0053]

[0054] Where ω0=2π / T is the fundamental frequency, a0 is the static offset component of the joint angle, representing the reference angle when standing and knee flexed, a n and b n Fourier coefficients determine the proportion of different frequency harmonic components in the motion signal and the contribution of each frequency component to the overall motion. N is the harmonic order, ranging from 3 to 5, used to describe the periodic characteristics of gait.

[0055] In this embodiment, for the basic motion component, the preprocessed joint angle signal θ(t) and gait period T are received and treated as a periodic signal composed of superimposed components of different frequencies. The periodic motion of the exoskeleton is described by superimposing harmonics of finite order, and the basic motion component x that can describe the periodic gait characteristics of the exoskeleton is extracted. base (t).

[0056] S202, Extraction of working condition adaptation components: Receive the preprocessed acceleration signal and terrain elevation data, perform wavelet packet decomposition on the acceleration signal, calculate the energy of each node, set the energy threshold, filter out the sub-band signal related to terrain disturbance, reconstruct the selected sub-band signal, and generate the working condition adaptation components.

[0057] Specifically, wavelet packet decomposition is performed on the preprocessed acceleration signal a(t), as shown in the following expression:

[0058] WPT j,k (t)=∑ m h j (m)ψ j,k (2 j tm)

[0059] Among them, WPT j，k (t) is the signal being analyzed, ψ j，kLet be the wavelet packet basis function, and j represent the decomposition level, for example, j=4. Wavelet packet decomposition can perform finer frequency band division of signals at different scales. As the decomposition level increases, the signal is decomposed into increasingly narrow frequency bands, thus allowing for more accurate analysis of the signal's frequency components. k is the node index, and k∈0~15. Each value corresponds to a frequency band. Different node indices represent sub-band signals in different frequency ranges. By analyzing these sub-band signals, the characteristics of the signal in different frequency bands can be obtained. h j (m) represents the filtering coefficients corresponding to the j-th layer decomposition. After decomposing the signals collected by the terrain sensor into different frequency bands through wavelet packet transform, the energy threshold method is used to filter the high-frequency sub-band signals related to terrain features, and the energy E of each node is calculated. j，k =Σ t WPT j，k (t)| 2 Set energy threshold E th =0.2·max(E j，k Subbands exceeding a threshold are considered terrain-related disturbances. The selected subband signal is reconstructed to generate the operating condition adaptation component x. adapt (t), enabling the exoskeleton to adapt to the complex terrain and working conditions.

[0060] S203. Decomposition effect verification: Calculate the residual energy ratio of the decomposed signal. If the residual energy ratio does not meet the requirements, increase the wavelet packet decomposition layer and the extended Fourier harmonic order, and return to step S201 to decompose and verify again until the residual energy ratio meets the requirements.

[0061] Specifically, calculate the residual energy ratio of the signal after decomposition. This metric measures the difference between the reconstructed signal after decomposition and the original joint angle signal. If η is less than the set value, for example, 5%, it means that the basic motion component and the working condition adaptation component after decomposition can reconstruct the original joint angle signal well, and the decomposition effect is good. If η is greater than the set value, it indicates that there is a certain error in the decomposition and further optimization is needed. At this time, the wavelet packet decomposition layer j is increased, for example, j=5, and the Fourier harmonic order N is expanded, for example, N=6. The decomposition and verification are repeated until the residual energy ratio meets the requirements. This verification and adjustment process ensures the accuracy of trajectory decomposition and provides a reliable data foundation for subsequent trajectory optimization and reconstruction.

[0062] S300. Determine whether there are abnormal harmonics in the basic motion component. If there are abnormal harmonics, adjust the Fourier coefficients of the abnormal harmonics to suppress them. Obtain the anti-disturbance basic motion component based on the adjusted Fourier coefficients. Perform terrain feature matching and gain control on the working condition adaptation component to obtain the reconstructed working condition adaptation component.

[0063] After completing the dynamic trajectory decomposition, based on the joint torque feedback during the actual movement of the exoskeleton, under complex working conditions, describing the periodic gait characteristics of the exoskeleton based on the extracted basic motion components will produce abnormal joint torque fluctuations, affecting the stability and comfort of the exoskeleton movement. Therefore, the basic motion components are optimized in the frequency domain, and the influence of abnormal harmonics is suppressed by adjusting the weight of the Fourier coefficients.

[0064] In one embodiment, it is determined whether there are anomalous harmonics in the basic motion component. If anomalous harmonics exist, the Fourier coefficients of the anomalous harmonics are adjusted to suppress them. The disturbance-resistant basic motion component is obtained based on the adjusted Fourier coefficients, specifically including:

[0065] S301, Abnormal Harmonic Detection: Calculate the joint torque fluctuation amplitude corresponding to each harmonic, set the joint torque threshold, and determine whether there are abnormal harmonics based on the magnitude of the joint torque fluctuation amplitude and the joint torque threshold.

[0066] Specifically, define τ n The joint torque fluctuation amplitude corresponding to the nth harmonic is calculated using the following formula:

[0067] By integrating the different frequency components of the joint torque within a gait cycle, the torque fluctuation amplitude corresponding to each harmonic is obtained, thereby quantifying the influence of each harmonic on the joint torque. A joint torque threshold τ is then set. th When τ n >τ th When the nth harmonic is determined to be an abnormal disturbance source.

[0068] S302, Abnormal Harmonic Suppression: Introduce a suppression coefficient to dynamically attenuate the Fourier coefficients of abnormal harmonics, and obtain the basic motion component for disturbance rejection.

[0069] Specifically, a suppression coefficient γ is introduced, and the Fourier coefficients a of the anomalous harmonics are... n and b n Dynamic attenuation is performed to obtain the optimized Fourier coefficients. The disturbance-resistant fundamental component X′ is generated based on the unoptimized Fourier coefficients and the optimized Fourier coefficients. base (t). In this way, the proportion of abnormal harmonics in the basic motion components is reduced, thereby improving the stability of exoskeleton movement.

[0070] S303, Characteristic Verification: Perform frequency domain energy comparison, calculate the energy ratio between the anti-disturbance basic motion component and the basic motion component, calculate the residual between the joint angle signal and the anti-disturbance basic motion component. If the energy ratio and residual do not meet the requirements, adjust the parameters and re-optimize until the requirements are met.

[0071] Specifically, calculate the energy ratio between the disturbance-resistant fundamental motion component and the fundamental motion component. η is required base The energy ratio should not be lower than a set value, such as 0.8. This is to ensure that while suppressing abnormal harmonics, the periodic characteristics of the fundamental motion components are not excessively damaged. If the energy ratio is lower than the set value, such as 0.8, it indicates that the optimization process may have significantly affected the normal motion characteristics, and relevant parameters such as the number of wavelet packet decomposition layers and harmonic order need to be adjusted and the optimization repeated. Calculate the residual e between the joint angle signal and the disturbed fundamental motion components. base (t)=θ(t)-X base (t), the optimization effect is evaluated by observing the magnitude of the residuals. When the maximum value of the absolute value of the residuals is greater than a set value, such as max(|e base (t)|>5° indicates that there is a large deviation between the optimized anti-disturbance basic motion component and the original signal, triggering parameter reset. The specific measures are to increase the harmonic order N, reduce the suppression coefficient γ, and then re-optimize and verify until the residual meets the requirements.

[0072] In one embodiment, terrain feature matching and gain control are performed on the working condition adaptation component to obtain the reconstructed working condition adaptation component, specifically including:

[0073] S304, Terrain Matching: Based on the extracted wavelet subband energy distribution (E... j，k By comparing the node energy distribution of the current terrain signal after wavelet packet decomposition with the energy distribution of various terrains in the terrain database, the terrain type with the highest similarity is identified, and the optimal sub-band combination is determined.

[0074] Specifically, suppose the current terrain signal is distributed via wavelet packet energy distribution (E... j，k The node energy distribution vector obtained after decomposition is E cu The energy distribution vector of the i-th terrain in the terrain database is E. i via Euclidean distance To measure similarity, j represents the decomposition level. The Euclidean distance calculation method here can intuitively reflect the degree of difference between two energy distribution vectors. We choose d... i The sub-zone combination corresponding to the smallest terrain type is taken as the optimal sub-zone combination to determine the current terrain type. For example, if the energy distribution vector calculated for the "slope" terrain yields d... i If the value is the smallest, then the current terrain is determined to be a slope, and the corresponding sub-band signal combination is determined to provide accurate terrain information for subsequent gain control.

[0075] S305. Gain control: Based on the rate of change of plantar pressure, dynamically adjust the amplitude gain of the working condition adaptation component, and obtain the reconstructed working condition adaptation component after amplitude adjustment of the working condition adaptation component.

[0076] Specifically, the gain control dynamically adjusts the amplitude gain of the working condition adaptation component using the plantar pressure change rate ΔF, sets a reference gain K0, and sets a sensitivity coefficient β ∈ 0.05-0.2, according to formula K ad =K0·(1+tanh(β·△F)) calculates the amplitude gain K ad The working condition adaptation component x adapt (t) multiplied by the amplitude gain K ad Obtain the adaptive component X′ of the reconfiguration condition adapt The characteristics of the tanh function limit the gain range to [K0, 2K0], effectively preventing gain overshoot. When the rate of change of plantar pressure ΔF is large, it indicates a significant change in ground conditions, such as transitioning from flat ground to rough terrain. In this case, the value of tanh(β·ΔF) increases, and the amplitude gain K... ad This also increases the load, enhancing the role of the working condition adaptation component, enabling the exoskeleton to better adapt to changes in terrain.

[0077] In this embodiment, by matching terrain features and controlling gain, the trajectory's adaptability to rugged terrain is enhanced, accurately reflecting the impact of the current terrain on motion. The working condition adaptation component is further optimized, improving the exoskeleton's adaptability to complex terrain.

[0078] S400: The anti-disturbance basic motion components and the reconstructed working condition adaptation components are recombined to obtain the final motion trajectory.

[0079] In one embodiment, it specifically includes:

[0080] S401. Weight Determination: Determine the weight coefficients of the anti-disturbance basic motion component and the reconfiguration working condition adaptation component based on the terrain complexity.

[0081] Specifically, on flat terrain, such as stone slab roads, the movement of the exoskeleton mainly follows a periodic gait pattern. Therefore, the weight coefficient λ1 of the anti-disturbance basic motion component is set to a larger value to enhance the influence of the basic motion component and make the exoskeleton movement more stable and natural. On complex terrain such as slopes, the influence of terrain on movement is more significant. In this case, the weight coefficient λ2 of the reconstructed working condition adaptation component is set to a larger value to highlight the role of the working condition adaptation component.

[0082] S402, Weighted superposition: The reconstructed working condition adaptation components are weighted and superimposed according to the weight coefficients to obtain the final motion trajectory;

[0083] Specifically, the expression for weighted summation is as follows:

[0084] X final (t)=λ1X′ base (t)+λ2X′ adapt (t)

[0085] Among them, X final (t) represents the final motion trajectory, λ1 represents the weighting coefficient of the anti-disturbance basic motion component, and λ2 represents the weighting coefficient of the reconfiguration working condition adaptation component.

[0086] In this embodiment, the weights of the anti-disturbance basic motion component and the reconstructed working condition adaptation component are dynamically adjusted according to the terrain complexity and weighted superimposed to obtain the actual executable motion trajectory of the exoskeleton. This ensures that the exoskeleton can adjust its motion trajectory in a timely manner according to terrain changes, thus realizing adaptive trajectory planning for different terrains.

[0087] S500 adjusts the impedance model parameters in real time based on the error between the exoskeleton's final motion trajectory and the desired trajectory, thereby optimizing the exoskeleton's tracking of the desired trajectory in real time.

[0088] In one embodiment, it specifically includes:

[0089] S501, Error Calculation: Calculate the tracking error between the expected trajectory and the actual joint angle, and divide the frequency band into low-frequency error and high-frequency error;

[0090] Specifically, based on the final trajectory X final (t) and the actual joint angle x after execution actual (t), calculate the tracking error e(t)=X final (t)-x actual (t), this error accurately reflects the deviation between the current motion state and the desired state of the exoskeleton, and is a key basis for subsequent impedance adjustment. To more accurately analyze the impact of the error on the system, it is divided into frequency bands as low-frequency error e. low (t), high-frequency error e high (t), low-frequency error mainly reflects the overall trend deviation of exoskeleton movement, such as the situation of gradually deviating from the expected trajectory during long-term walking; high-frequency error focuses on capturing instantaneous changes and shaking during movement.

[0091] S502, Parameter Adjustment: Determine the stiffness coefficient based on the low-frequency error integral and the damping coefficient based on the high-frequency error peak value;

[0092] Specifically, the standard stiffness parameter of the model is set to K. nom The standard damping parameter is B. nom The adaptive coefficients are α and η (both ranging from 0.1 to 0.5), and the stiffness coefficient is determined based on the low-frequency error integral. Determine the damping coefficient based on the high-frequency error peak value. When the integral value of the low-frequency error is large, it indicates that the exoskeleton has accumulated a large positional deviation over a long period of time. At this time, the stiffness coefficient K is larger than the original K. nomIncreasing the damping coefficient B enhances the exoskeleton's ability to correct deviations, prompting it to return to the desired trajectory. A larger high-frequency error peak indicates severe shaking or momentary deviation of the exoskeleton during movement, and the damping coefficient B is higher than the original B. nom Enlarging the exoskeleton can effectively suppress this shaking and ensure the stability of its movement.

[0093] S503, Torque Calculation: The desired joint torque is calculated using a second-order impedance model based on the stiffness coefficient and damping coefficient.

[0094] Specifically, a second-order impedance model is adopted. The desired joint moment τ′ for tracking the desired trajectory is calculated. Here, the stiffness coefficient K determines the exoskeleton's ability to resist positional deviations; a larger K value indicates a stronger response to positional errors and a greater force attempting to correct the deviations. B is the damping coefficient, used to suppress oscillations in exoskeleton movement. A larger B value effectively reduces excessive swaying of the exoskeleton during adjustment, resulting in smoother movement. The rate of change of error e(t) reflects how fast the error changes over time.

[0095] S504, Current Conversion: Converts the desired joint torque into the motor's execution command current;

[0096] Specifically, the conversion formula is as follows: Among them, I cmd To execute the command current, K t As the torque constant, it establishes the conversion relationship between torque and current.

[0097] S505, Trajectory Tracking Control: Employs a PID controller to output motor control current based on tracking error, enabling the motor to track the desired joint angle;

[0098] Specifically, a PID controller is used to achieve torque-to-current mapping and trajectory tracking control. Its input is the tracking error, and its output is the motor control current. Among them, K p K is a proportional coefficient that adjusts the control output proportionally to the magnitude of the error, enabling a rapid response to changes in error; d These are the differential coefficients, adjusted according to the rate of change of the error. They help predict the trend of error change, respond in advance, and suppress system oscillations. Initially, K... p =12, K d =0.6, finally obtaining the output current I = I cmd +I fb This allows the motor to track the desired joint angle, enabling the exoskeleton to move along the desired trajectory.

[0099] The exoskeleton robot impedance adjustment and trajectory planning method provided by this invention decomposes complex motion trajectories into basic motion components and working condition adaptation components. Through Fourier series and wavelet packet transform, periodic gait features and terrain feature frequency components are extracted and recombined in the frequency domain. This enables the exoskeleton to accurately identify and adapt to different terrains. At the same time, the trajectory weights are dynamically adjusted according to the terrain complexity. In terms of impedance adjustment, the stiffness and damping parameters are adjusted in real time according to the trajectory tracking error to ensure that the exoskeleton can maintain stable motion under various complex working conditions. This greatly expands the application scenarios of exoskeletons and significantly improves their practicality in complex environments.

[0100] Example 2

[0101] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned exoskeleton robot impedance adjustment and trajectory planning method.

[0102] The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0103] Example 3

[0104] This embodiment provides a computer device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the exoskeleton robot impedance adjustment and trajectory planning method described above.

[0105] like Figure 3As shown, the computer device 70 may include: at least one processor 71, such as a CPU (Central Processing Unit), at least one communication interface 73, a memory 74, and at least one communication bus 72. The communication bus 72 is used to enable communication between these components. The communication interface 73 may include a display screen and a keyboard; optionally, the communication interface 73 may also include a standard wired interface or a wireless interface. The memory 74 may be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. Optionally, the memory 74 may also be at least one storage device located remotely from the aforementioned processor 71. The memory 74 stores application programs, and the processor 71 calls the program code stored in the memory 74 to execute any of the above-described method steps.

[0106] The communication bus 72 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 72 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0107] The memory 74 may include volatile memory, such as random-access memory (RAM); the memory may also include non-volatile memory, such as flash memory, hard disk drive (HDD) or solid-state drive (SSD); the memory 74 may also include a combination of the above types of memory.

[0108] The processor 71 can be a central processing unit (CPU), a network processor (NP), or a combination of CPU and NP.

[0109] The processor 71 may further include a hardware chip. This hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0110] Optionally, the memory 74 is also used to store program instructions. The processor 71 can call the program instructions to implement the exoskeleton robot impedance adjustment and trajectory planning method of the present invention.

[0111] Those skilled in the art should understand that the present invention can be implemented in many other specific forms without departing from the spirit and scope of the invention. Any changes or modifications made by those skilled in the art based on the embodiments of the present invention and the above disclosure shall fall within the protection scope of the claims.

Claims

1. An exoskeleton robot impedance adjustment and trajectory planning method, characterized by, The method comprises the following steps: S100, acquiring a multi-dimensional original signal related to exoskeleton movement and pre-processing the same; S200, respectively performing Fourier series transformation and wavelet packet transformation on the pre-processed multi-dimensional original signal to decompose the exoskeleton complex movement trajectory into a basic movement component embodying periodic gait and a working condition adaptive component reflecting terrain characteristics, and specifically comprising the following steps: S201, basic movement component extraction: receiving the pre-processed joint angle signal and gait cycle, fitting the joint angle signal with Fourier series, and extracting the basic movement component; S202, working condition adaptive component extraction: receiving the pre-processed acceleration signal and terrain elevation data, performing wavelet packet decomposition on the acceleration signal, calculating the energy of each node, setting an energy threshold, screening out the sub-band signal related to terrain disturbance, reconstructing the selected sub-band signal, and generating the working condition adaptive component; S203, decomposition effect verification: calculating the residual energy ratio of the decomposed signal, if the residual energy ratio does not meet the requirements, increasing the wavelet packet decomposition level and expanding the Fourier harmonic number, returning to step S201 to re-decompose and verify until the residual energy ratio meets the requirements; S300, judging whether there is an abnormal harmonic in the basic movement component, if there is an abnormal harmonic, adjusting the Fourier coefficient of the abnormal harmonic to suppress the abnormal harmonic, and obtaining an anti-interference basic movement component according to the adjusted Fourier coefficient; performing terrain feature matching and gain control on the working condition adaptive component to obtain a reconstructed working condition adaptive component, specifically comprising: S301, abnormal harmonic detection: calculating the joint torque fluctuation amplitude corresponding to each harmonic, setting a joint torque threshold, and judging whether there is an abnormal harmonic according to the size of the joint torque fluctuation amplitude and the joint torque threshold; S302, abnormal harmonic suppression: introducing a suppression coefficient to dynamically attenuate the Fourier coefficient of the abnormal harmonic to obtain an anti-interference basic movement component; S303, characteristic verification: performing frequency energy comparison, calculating the energy ratio of the anti-interference basic movement component and the basic movement component, calculating the residual error of the joint angle signal and the anti-interference basic movement component, if the energy ratio and the residual error do not meet the requirements, adjusting the parameters to re-optimize until the requirements are met; S304, terrain matching: comparing the node energy distribution of the current terrain signal after wavelet packet decomposition with the energy distribution of various terrains in the terrain library according to the extracted wavelet packet sub-band energy distribution and the terrain elevation data, finding the terrain type with the highest similarity, and determining the optimal sub-band combination; S305, gain control: dynamically adjusting the amplitude gain of the working condition adaptive component according to the foot pressure change rate, and obtaining the reconstructed working condition adaptive component after amplitude adjustment of the working condition adaptive component; S400, recombining the anti-interference basic movement component and the reconstructed working condition adaptive component to obtain the final movement trajectory; S500, real-time adjusting the impedance model parameters according to the error between the exoskeleton final movement trajectory and the expected trajectory to optimize the exoskeleton tracking of the expected trajectory in real time.

2. The exoskeleton robot impedance adjustment and trajectory planning method of claim 1, wherein, In the step S100, the multi-dimensional original signal comprises joint angle, joint torque, trunk acceleration, foot pressure change rate, gait cycle, and terrain elevation data. 3.The exoskeleton robot impedance adjustment and trajectory planning method according to claim 1, wherein, The preprocessing in the step S100 includes normalization, low-pass filtering and sliding window segmentation processing on the multi-dimensional original signal in sequence.

4. The exoskeleton robot impedance adjustment and trajectory planning method of claim 3, wherein, The step S400 specifically includes: S401, weight determination: determining the weight coefficients of the anti-interference basic motion component and the reconstructed working condition adaptive component according to the terrain complexity; S402, weighted superposition: weighting and superimposing the reconstructed working condition adaptive component according to the weight coefficients to obtain the final motion trajectory. 5.The exoskeleton robot impedance adjustment and trajectory planning method according to claim 4, wherein, The step S500 specifically includes: S501, error calculation: calculating the tracking error of the expected trajectory and the actual joint angle, and dividing the frequency band into low-frequency error and high-frequency error; S502, parameter adjustment: determining the stiffness coefficient according to the low-frequency error integral, and determining the damping coefficient according to the high-frequency error peak value; S503, torque calculation: using a second-order impedance model to calculate the expected joint torque according to the stiffness coefficient and the damping coefficient; S504, current conversion: converting the expected joint torque into the execution instruction current of the motor; S505, trajectory tracking control: using a PID controller to output the control current of the motor according to the tracking error, so that the motor tracks the expected joint angle.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the exoskeleton robot impedance adjustment and trajectory planning method according to any one of claims 1-5.

7. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the steps of the exoskeleton robot impedance adjustment and trajectory planning method according to any one of claims 1-5.

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