Exoskeleton robot gait planning method, device, system and storage medium

Abstract

The application relates to an exoskeleton robot gait planning method, device, system and storage medium. The method comprises the following steps: acquiring angle data of a human body wearing an exoskeleton robot in a current walking cycle; the angle data is output by a motor encoder at a joint position of the exoskeleton robot; performing time domain to frequency domain conversion on the angle data to obtain frequency domain data; determining a gait planning function based on a gait fitting function, gait fitting parameters and the frequency domain data, and outputting the gait planning function; and the gait planning function is used for gait planning of the exoskeleton robot. The method can effectively improve the matching degree of the motion trajectory of the exoskeleton robot and the motion trajectory of the human body.

Description

Technical Field

[0001] This application relates to the field of robotics technology, and in particular to a method, apparatus, system and storage medium for gait planning of an exoskeleton robot. Background Technology

[0002] Lower limb exoskeleton systems are wearable robots that provide external force support to reduce the load on the human body and improve endurance. They have wide applications in military, industrial, and medical fields. In military and industrial applications, lower limb weight-bearing exoskeletons help the human body bear weight and easily traverse various complex terrains. In rehabilitation medicine, combining exoskeleton technology with rehabilitation training can help patients with hemiplegia to walk upright. The motion planning of exoskeleton robots must satisfy both the robot's spatial joint constraints and the human body's kinematic constraints.

[0003] When using exoskeleton robots for medical rehabilitation or assistance, they need to be closely integrated with human gait movements, meaning the exoskeleton's movement trajectory must match the human's gait trajectory with a very high degree of accuracy. However, current exoskeleton robot gait planning methods rely on high-speed cameras to identify human walking, obtaining the person's coordinates in a world coordinate system, and finally converting these coordinates into angles for each joint. These joint angles are discrete coordinate points and cannot be directly sent to the motors to control the exoskeleton's movement; otherwise, it would cause problems such as machine vibration and inability to perform continuous gait movements.

[0004] During the implementation process, the inventors discovered at least the following problems in traditional technologies: the motion trajectory of existing exoskeleton robots has a low degree of matching with the gait trajectory of the human body. Summary of the Invention

[0005] Therefore, it is necessary to provide an exoskeleton robot gait planning method, device, system, and storage medium to address the aforementioned technical problems.

[0006] A gait planning method for an exoskeleton robot, the method comprising:

[0007] Acquire angle data of the wearable exoskeleton robot within the current walking cycle; the angle data is output by the motor encoders located at the joint positions of the exoskeleton robot;

[0008] The angle data is converted from the time domain to the frequency domain to obtain the frequency domain data;

[0009] Based on the gait fitting function, gait fitting parameters, and frequency domain data, the gait planning function is determined and output; the gait planning function is used for gait planning of exoskeleton robots.

[0010] In one embodiment, the step of determining and outputting a gait planning function based on a gait fitting function, gait fitting parameters, and frequency domain data includes:

[0011] Based on the gait fitting function, gait fitting parameters, and frequency domain data, the appropriate order is selected according to the order selection rules for iterative calculation to obtain the gait planning function and output it; the order selection rules are determined based on the fitting accuracy and iteration time.

[0012] In one embodiment, the step of determining and outputting the gait planning function based on the gait fitting function, gait fitting parameters, and frequency domain data further includes:

[0013] The least squares method was used to determine the gait fitting parameters.

[0014] In one embodiment, the angle data includes data on the change of joint angle over time, data on the change of the rate of change of joint angle over time, and data on the change of acceleration of the change of joint angle over time.

[0015] After determining and outputting the gait planning function based on the gait fitting function, gait fitting parameters, and frequency domain data, the following steps are also included:

[0016] Based on the gait planning function, the periodic gait trajectory curve is obtained;

[0017] The interpolation points on the periodic gait trajectory curve are output to the motors at the joint positions of the exoskeleton robot for position interpolation.

[0018] In one embodiment, the step of acquiring angle data of a wearable exoskeleton robot within the current walking cycle includes:

[0019] The joint angles are obtained using the following transformation model:

[0020]

[0021] Where θ represents the current joint angle, cureconder represents the current motor encoder value, zero represents the motor zero point value, encodercycle represents the motor encoder resolution, and reduce represents the reduction ratio.

[0022] In one embodiment, the frequency domain data includes frequency and amplitude;

[0023] In the step of determining and outputting the gait planning function based on the gait fitting function, gait fitting parameters, and frequency domain data, the gait planning function is determined based on the following gait fitting function:

[0024]

[0025] Where a0 represents the amplitude when the frequency is 0, a i Indicates a frequency of b i The amplitude at time, b i Let ψ represent the frequency of the i-th point, x represent time, and ψ i These are the gait fitting parameters.

[0026] A gait planning device for an exoskeleton robot, the device comprising:

[0027] The data acquisition module is used to acquire angle data of the wearable exoskeleton robot during the current walking cycle; the angle data is output by the motor encoder located at the joint position of the exoskeleton robot.

[0028] The data conversion module is used to convert angle data from the time domain to the frequency domain to obtain frequency domain data;

[0029] The data output module is used to determine and output the gait planning function based on the gait fitting function, gait fitting parameters, and frequency domain data; the gait planning function is used for gait planning of exoskeleton robots.

[0030] An exoskeleton robot gait planning system, the system including an exoskeleton robot and a gait planning processor connected to the exoskeleton robot;

[0031] The exoskeleton robot includes a motor encoder and a motor; a gait planning processor is connected to the motor encoder and the motor respectively; the motor encoder and the motor are both located at the joint positions of the exoskeleton robot;

[0032] The gait planning processor is used to execute the steps of the above method.

[0033] In one embodiment, the joint positions of the exoskeleton robot include any one or any combination of the hip joint, knee joint, and ankle joint.

[0034] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described above.

[0035] One of the above technical solutions has the following advantages and beneficial effects:

[0036] This application acquires angle data from the motor encoders of the joint positions of a wearable exoskeleton robot within the current walking cycle. This angle data is then converted from the time domain to the frequency domain to obtain frequency domain data. Based on this frequency domain data, gait fitting parameters, and the gait fitting function, a gait planning function is determined for gait planning of the exoskeleton robot. The exoskeleton robot gait planning method of this application employs a continuous gait fitting function for construction, ensuring that the fitted gait planning function trajectory is a continuous transition curve. This reduces joint angle following errors, effectively improves the matching degree between the exoskeleton robot's motion trajectory and the human gait motion trajectory, and thus improves the walking performance of the wearable exoskeleton robot. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating the gait planning method for an exoskeleton robot in one embodiment;

[0039] Figure 2 This is a flowchart illustrating the process of determining and outputting a gait planning function in one embodiment.

[0040] Figure 3 This is a flowchart illustrating the process after determining and outputting the gait planning function in one embodiment.

[0041] Figure 4 This is a structural block diagram of an exoskeleton robot gait planning device in one embodiment;

[0042] Figure 5 This is a block diagram of the gait planning system for an exoskeleton robot in one embodiment;

[0043] Figure 6 This is a coordinate graph of the hip joint's angle data during the current walking cycle in one embodiment;

[0044] Figure 7 This is a frequency domain data analysis diagram obtained after performing a fast Fourier transform on hip joint angle change data in one embodiment.

[0045] Figure 8 This is a gait fitting function curve of 5th-order hip joint angle change data in one embodiment;

[0046] Figure 9 This is a graph showing the periodic gait trajectory of the knee joint in one embodiment. Detailed Implementation

[0047] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0049] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to the other element or connected to the other element through an intermediary element. Furthermore, in the following embodiments, "connection" should be understood as "electrical connection," "communication connection," etc., if there is transmission of electrical signals or data between the connected objects.

[0050] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” or “having,” etc., specify the presence of the stated feature, whole, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.

[0051] In one embodiment, such as Figure 1 As shown, a gait planning method for an exoskeleton robot is provided, which may include:

[0052] Step 202: Obtain the angle data of the human-wearable exoskeleton robot during the current walking cycle; the angle data is output by the motor encoder located at the joint position of the exoskeleton robot.

[0053] Step 204: Convert the angle data from the time domain to the frequency domain to obtain the frequency domain data;

[0054] Step 206: Based on the gait fitting function, gait fitting parameters, and frequency domain data, determine and output the gait planning function; the gait planning function is used for gait planning of the exoskeleton robot.

[0055] The current walking cycle can be the time it takes for a human-wearable exoskeleton robot to take one step on flat ground; a motor encoder is a device that encodes signals (such as bit streams) or data on a motor and converts them into a signal form that can be used for communication, transmission and storage. The motor encoder of this application is used to detect and output the angle data of the human-wearable exoskeleton robot in the current walking cycle.

[0056] In one example, the time-domain to frequency-domain conversion can include the Fast Fourier Transform.

[0057] Specifically, the angle data of the wearable exoskeleton robot during the current walking cycle is acquired. This angle data is detected and output by the motor encoders located at the joints of the exoskeleton robot. The angle data is then converted from the time domain to the frequency domain, that is, the signal of the angle data in the time domain is transformed into the frequency domain signal to obtain the frequency domain data, for example, by performing a fast Fourier transform on the angle data. Based on the gait fitting function, gait fitting parameters and frequency domain data, the gait planning function for gait planning of the exoskeleton robot is determined.

[0058] This application obtains accurate angle data by acquiring angle data of the wearable exoskeleton robot within the current walking cycle, detected by the motor encoders located at the joints of the exoskeleton robot. Frequency domain data is obtained by converting the angle data from the time domain to the frequency domain, and a gait planning function is determined based on the gait fitting function, gait fitting parameters, and frequency domain data. This gait planning function can be used for gait planning of the exoskeleton robot. The gait planning of the exoskeleton robot using the gait planning function of this application has a very high degree of matching with the human gait trajectory, improving the walking performance of the wearable exoskeleton robot.

[0059] In one embodiment, frequency domain data may include frequency and amplitude;

[0060] In the step of determining and outputting the gait planning function based on the gait fitting function, gait fitting parameters, and frequency domain data, the gait planning function is determined based on the following gait fitting function:

[0061]

[0062] Where a0 represents the amplitude when the frequency is 0, a i Indicates a frequency of b i The amplitude at time, b i Let ψ represent the frequency of the i-th point, x represent time, and ψ i represents the gait fitting parameters; f(x) represents the angle data corresponding to time x.

[0063] In one example, the steps of determining and outputting the gait planning function based on the gait fitting function, gait fitting parameters, and frequency domain data may also include:

[0064] The least squares method was used to determine the gait fitting parameters;

[0065] The gait planning function can also be determined based on the following gait fitting function:

[0066]

[0067] Specifically, since the human gait trajectory curve is a periodic Fourier series expansion, the Fourier series expansion is as follows: However, solving for the coefficients of this Fourier technique is quite complicated. Therefore, the gait fitting function used in this application is constructed as follows: By acquiring the angle data of a wearable exoskeleton robot during the current walking cycle and performing a Fast Fourier Transform (FFT) on the angle data (converting the time-domain signal to the frequency-domain signal), the relationship between frequency and amplitude is obtained. Frequency-domain analysis can also be performed on the frequency-amplitude curve to obtain an analysis graph of frequency and amplitude. Then, the frequency-amplitude relationship data is substituted into a gait fitting function for fitting, and the frequency 'a' is determined. i And the amplitude b corresponding to that frequency i Substitute these parameters into the gait planning function; where the gait fitting parameters are determined using the least squares method; thus, based on the gait fitting function, gait fitting parameters, and frequency domain data, determine and output the gait planning function.

[0068] This application constructs a gait fitting function, combines gait fitting parameters and frequency domain data, and thus determines a gait planning function with a higher degree of matching with human movement trajectory.

[0069] In one embodiment, such as Figure 2 As shown, the steps for determining and outputting the gait planning function based on the gait fitting function, gait fitting parameters, and frequency domain data may include:

[0070] Step 302: Based on the gait fitting function, gait fitting parameters, and frequency domain data, select the appropriate order according to the order selection rules and perform iterative calculations to obtain the gait planning function and output it; the order selection rules are determined based on the fitting accuracy and iteration time.

[0071] The rule for selecting the order is to determine the appropriate order for iterative calculation based on the fitting accuracy and iteration time. When the order is too low, the fitted trajectory may deviate too much from the actual trajectory, resulting in low fitting accuracy. When the order is too high, the solution iteration time is too long, and the time and computation costs are too high. Therefore, it is necessary to combine accuracy and iteration time to determine a suitable order.

[0072] In one example, the corresponding order could be 5.

[0073] Specifically, when the order is 1, the gait fitting function is f(x) = a0 + a1 cos(2πb1(x + ψ1)), with a variance of 496.5967. This is obtained by iteratively calculating 14 times using the MATLAB function lsqcurvefit. The error curve is obtained by subtracting the sampling points on the original curve from the sampling points on the fitted curve. When the order is 2, the gait fitting function is... The variance is 72.1424, and the iterations are performed 32 times. When the order is 3, the gait fitting function is... The variance is 3.3226, and the iterations are performed 35 times. When the order is 4, the gait fitting function is... The variance is 2.1413, and the iterations are performed 30 times. When the order is 5, the gait fitting function is... The variance is 0.4244, and the iterations are performed 30 times. When the order is 6, the gait fitting function is... The variance is 0.1049, and the iterations are performed 35 times. When the order is 7, the gait fitting function is... The variance is 0.0484, and the iterations are performed 40 times. When the order is 8, the gait fitting function is... The variance is 0.0255, and the calculation is performed 80 times.

[0074] According to the order selection rules, when the order is 5, its variance is less than 0.5, resulting in a high degree of fit with the original trajectory, meeting the requirements of human gait trajectory, and requiring fewer iterations, thus satisfying computational performance requirements. Therefore, order 5, with its moderate fitting accuracy and iteration time, can be selected. After obtaining the frequency-amplitude relationship data through Fast Fourier Transform of the angle data, based on the gait fitting function, gait fitting parameters, and frequency domain data, and following the order selection rules (i.e., based on fitting accuracy and iteration time), order 5 is selected for iterative calculation to obtain the corresponding gait planning function and output it.

[0075] In one embodiment, the angle data may include data on the change of joint angle over time, data on the change of the rate of change of joint angle over time, and data on the change of acceleration of the change of joint angle over time.

[0076] like Figure 3 As shown, after determining and outputting the gait planning function based on the gait fitting function, gait fitting parameters, and frequency domain data, the process may further include:

[0077] Step 402: Based on the gait planning function, obtain the periodic gait trajectory curve;

[0078] Step 404: Output the interpolation points on the periodic gait trajectory curve to the motors at the joint positions of the exoskeleton robot for position interpolation.

[0079] Interpolation is defined as a method of determining intermediate points by densifying data points between known points on an ideal trajectory based on a given straight line or arc (curve) function. It is also called "data point densification". The interpolation points on the periodic gait trajectory curve are the angle data on the trajectory curve corresponding to the corresponding time.

[0080] Specifically, fast Fourier transforms can be performed on the data of joint angle changes over time, the rate of change of joint angle changes over time, and the acceleration of joint angle changes over time to obtain the corresponding frequency domain data. Then, this frequency domain data is combined with a gait fitting function and gait fitting parameters to determine and output the corresponding gait planning function. This allows for gait planning of the exoskeleton robot from three dimensions: joint angle change, joint angle change rate, and joint angle change acceleration. This makes the gait planning of the exoskeleton robot more comprehensive and more closely resembles the gait trajectory of a human.

[0081] Given a gait planning function, a periodic gait trajectory curve is obtained based on this function. The period time is calculated as follows: Where T is the periodic time, n is the order selected according to the order selection rules, and b i Let be the frequency of the i-th point; for example, if the selected order is 5, then the periodic time T = (b1 + b2 + b3 + b4 + b5) / (1 + 2 + 3 + 4 + 5). With a period of 10 and a periodic time of 0.98 seconds, a periodic gait trajectory curve is obtained; and the interpolation points on the periodic gait trajectory curve are output to the motors at the joint positions of the exoskeleton robot to perform position interpolation.

[0082] After confirming the gait planning function, this application obtains a periodic gait trajectory curve and outputs the interpolation points on the periodic gait trajectory curve to the motors at the joint positions of the exoskeleton robot for position interpolation. Thus, this application enables the exoskeleton robot to achieve a periodic continuous walking effect, which is more in line with the human gait trajectory. Furthermore, this application considers three dimensions—joint angle change, joint angle change rate, and joint angle change acceleration—to plan the gait of the exoskeleton robot, thereby further improving the gait trajectory of the exoskeleton robot, making the gait planning of the exoskeleton robot more closely match the human gait movement trajectory, and resulting in better walking performance.

[0083] In one embodiment, the step of acquiring angle data of a wearable exoskeleton robot within the current walking cycle may include:

[0084] The joint angles are obtained using the following transformation model:

[0085]

[0086] Where θ represents the current joint angle, cureconder represents the current motor encoder value, zero represents the motor zero point value, encodercycle represents the motor encoder resolution, and reduce represents the reduction ratio.

[0087] Specifically, by acquiring the current motor encoder value, motor zero point value, motor encoder resolution, and reduction ratio, the current joint angle can be obtained based on the conversion model. Based on the data of the joint angle changing over time within the current walking cycle, the data of the joint angle change rate changing over time, as well as the data of the joint angle change acceleration changing over time, can also be obtained.

[0088] In summary, this application obtains angle data of the human-worn exoskeleton robot within the current walking cycle from the motor encoder output at the joint positions of the exoskeleton robot. This angle data considers three dimensions: joint angle change, joint angle change rate, and joint angle change acceleration. The angle data is then converted from the time domain to the frequency domain to obtain frequency domain data. Based on the gait function, gait fitting parameters, and this frequency domain data, a gait planning function for gait planning of the exoskeleton robot is determined. Therefore, this application can improve the comprehensiveness of exoskeleton robot gait planning, solve the problems of joint movement continuity and periodic transition smoothness in exoskeleton robots, and make the matching between the exoskeleton robot's movement trajectory and the human gait trajectory more accurate, thereby improving the walking performance of the exoskeleton robot.

[0089] It should be understood that, although Figure 1-3 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1-3 At least some of the steps in the process may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the steps or stages in other steps.

[0090] In one embodiment, such as Figure 4 As shown, an exoskeleton robot gait planning device is provided, the device comprising:

[0091] The data acquisition module 510 is used to acquire angle data of the human-wearable exoskeleton robot during the current walking cycle; the angle data is output by the motor encoder located at the joint position of the exoskeleton robot.

[0092] Data conversion module 520 is used to convert angle data from the time domain to the frequency domain to obtain frequency domain data;

[0093] The data output module 530 is used to determine and output the gait planning function based on the gait fitting function, gait fitting parameters and frequency domain data; the gait planning function is used for gait planning of exoskeleton robots.

[0094] In one embodiment, the data output module is further configured to perform iterative calculations based on the gait fitting function, gait fitting parameters, and frequency domain data, according to the order selection rules, to obtain the gait planning function and output it; the order selection rules are determined based on the fitting accuracy and iteration time.

[0095] In one embodiment, the data output module is also used to determine gait fitting parameters using the least squares method.

[0096] In one embodiment, the angle data includes data on the change of joint angle over time, data on the change of the rate of change of joint angle over time, and data on the change of acceleration of the change of joint angle over time.

[0097] The device also includes a position interpolation module, which is used to obtain a periodic gait trajectory curve based on the gait planning function; and outputs the interpolation points on the periodic gait trajectory curve to the motors at the joint positions of the exoskeleton robot for position interpolation.

[0098] In one embodiment, the data acquisition module is further configured to obtain the joint angle using the following transformation model:

[0099]

[0100] Where θ represents the current joint angle, cureconder represents the current motor encoder value, zero represents the motor zero point value, encodercycle represents the motor encoder resolution, and reduce represents the reduction ratio.

[0101] In one embodiment, the frequency domain data includes frequency and amplitude;

[0102] The data output module is also used to determine the gait planning function based on the following gait fitting function:

[0103]

[0104] Where a0 represents the amplitude when the frequency is 0, ai Indicates a frequency of b i The amplitude at time, b i Let ψ represent the frequency of the i-th point, x represent time, and ψ i The gait fitting parameters are given.

[0105] Specific limitations regarding the exoskeleton robot gait planning device can be found in the limitations of the exoskeleton robot gait planning method described above, and will not be repeated here. Each module in the aforementioned exoskeleton robot gait planning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module. It should be noted that the module division in this embodiment is illustrative and only represents a logical functional division; other division methods may be used in actual implementation.

[0106] In one embodiment, such as Figure 5 As shown, an exoskeleton robot gait planning system is provided, which may include an exoskeleton robot and a gait planning processor connected to the exoskeleton robot;

[0107] The exoskeleton robot may include a motor encoder and a motor; the gait planning processor is connected to the motor encoder and the motor respectively; the motor encoder and the motor are both located at the joint positions of the exoskeleton robot;

[0108] The gait planning processor is used to execute the steps of the above method.

[0109] The motor encoder is used to collect angle data of the wearable exoskeleton robot during the current walking cycle and transmit the angle data to the gait planning processor. The gait planning processor converts the angle data from the time domain to the frequency domain to obtain frequency domain data. Based on the gait fitting function, gait fitting parameters and frequency domain data, it determines the gait planning function. According to the gait planning function, a periodic gait trajectory curve can be obtained. The gait planning processor outputs the interpolation points on the periodic gait trajectory curve to the motor for position interpolation, so that the motor can control the exoskeleton robot to move.

[0110] In one embodiment, the joint positions of the exoskeleton robot may include any one or any combination of the hip joint, knee joint, and ankle joint.

[0111] Specifically, the motor encoder and motor can be set at any one or any combination of the human hip joint, knee joint and ankle joint. By performing gait planning on the motion trajectory of each joint position, the gait planning of the exoskeleton robot is further improved, making the motion trajectory of the exoskeleton robot closer to the human gait motion trajectory.

[0112] In a specific example, such as Figure 6 The image shows the coordinate data of the hip joint angles during the current walking cycle. Figure 7 This is a frequency domain data analysis diagram obtained after performing a fast Fourier transform on the hip joint angle variation data. Figure 8 A graph of the gait fitting function for 5th-order hip joint angle variation data; Figure 9 This is a graph showing the periodic gait trajectory of the knee joint.

[0113] This application can accurately collect angle data through a motor encoder. After processing the angle data, the gait planning processor can output a gait planning function for gait planning of the exoskeleton robot to the motor. Thus, the motor can control the exoskeleton robot to achieve a movement that is more in line with the human gait trajectory. This application solves the problem of smooth transition of continuous and periodic angle changes in exoskeleton robots, making the gait trajectory of the exoskeleton robot more in line with the gait trajectory of the human body. By gait planning for each joint position, the walking effect of the exoskeleton robot is further improved.

[0114] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0115] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0116] In the description of this specification, references to terms such as "some embodiments," "other embodiments," and "ideal embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative descriptions of the above terms do not necessarily refer to the same embodiments or examples.

[0117] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0118] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A gait planning method for an exoskeleton robot, characterized in that, The method includes: The angle data of the wearable exoskeleton robot within the current walking cycle is acquired; the angle data is output by the motor encoder located at the joint position of the exoskeleton robot. The angle data is converted from the time domain to the frequency domain to obtain frequency domain data; wherein, the frequency domain data includes frequency and amplitude; Based on the gait fitting function, gait fitting parameters, and the frequency domain data, a gait planning function is determined and output; the gait planning function is used to perform gait planning for the exoskeleton robot. The step of determining and outputting the gait planning function based on the gait fitting function, gait fitting parameters, and the frequency domain data includes: Frequency domain analysis is performed on the curve of frequency versus amplitude to obtain an analysis graph of frequency versus amplitude; then the relationship data between frequency and amplitude is substituted into the gait fitting function for fitting, and the frequency and the amplitude corresponding to the frequency are substituted into the gait planning function.

2. The exoskeleton robot gait planning method according to claim 1, characterized in that, The step of determining and outputting the gait planning function based on the gait fitting function, gait fitting parameters, and the frequency domain data includes: Based on the gait fitting function, gait fitting parameters, and the frequency domain data, the appropriate order is selected according to the order selection rule for iterative calculation to obtain the gait planning function and output it; the order selection rule is determined based on the fitting accuracy and iteration time.

3. The exoskeleton robot gait planning method according to claim 1, characterized in that, The step of determining and outputting the gait planning function based on the gait fitting function, gait fitting parameters, and the frequency domain data further includes: The gait fitting parameters are determined using the least squares method.

4. The exoskeleton robot gait planning method according to claim 1, characterized in that, The angle data includes data on the change of joint angle over time, data on the change of joint angle rate over time, and data on the change of joint angle acceleration over time. After the step of determining and outputting the gait planning function based on the gait fitting function, gait fitting parameters, and the frequency domain data, the method further includes: Based on the gait planning function, a periodic gait trajectory curve is obtained; The interpolation points on the periodic gait trajectory curve are output to the motors at the joint positions of the exoskeleton robot for position interpolation.

5. The exoskeleton robot gait planning method according to claim 4, characterized in that, The step of acquiring angle data of the wearable exoskeleton robot within the current walking cycle includes: The joint angles are obtained using the following transformation model: Where θ represents the current joint angle, cureconder represents the current motor encoder value, zero represents the motor zero point value, encodercycle represents the motor encoder resolution, and reduce represents the reduction ratio.

6. The exoskeleton robot gait planning method according to claim 1, characterized in that, In the step of determining and outputting the gait planning function based on the gait fitting function, gait fitting parameters, and the frequency domain data, the gait planning function is determined based on the following gait fitting function: where a0represents the amplitude at a frequency of 0, a i b represents the amplitude at a frequency of b i , b i represents the frequency of the i-th point, x represents time, and ψ i is the gait fitting parameter.

7. A gait planning device for an exoskeleton robot, characterized in that, The device includes: The data acquisition module is used to acquire angle data of the wearable exoskeleton robot within the current walking cycle; the angle data is output by the motor encoder located at the joint position of the exoskeleton robot. The data conversion module is used to convert the angle data from the time domain to the frequency domain to obtain frequency domain data; wherein, the frequency domain data includes frequency and amplitude; The data output module is used to determine and output a gait planning function based on the gait fitting function, gait fitting parameters, and the frequency domain data; the gait planning function is used to perform gait planning for the exoskeleton robot. The data output module is further configured to perform frequency domain analysis on the curve of frequency versus amplitude to obtain an analysis graph of frequency versus amplitude; then substitute the relationship data of frequency versus amplitude into the gait fitting function for fitting, and substitute the frequency and the amplitude corresponding to the frequency into the gait planning function.

8. A gait planning system for an exoskeleton robot, characterized in that, The system includes an exoskeleton robot and a gait planning processor connected to the exoskeleton robot; The exoskeleton robot includes a motor encoder and a motor; the gait planning processor is connected to the motor encoder and the motor respectively; the motor encoder and the motor are both located at the joint positions of the exoskeleton robot; The gait planning processor is used to perform the steps of the method according to any one of claims 1 to 6.

9. The exoskeleton robot gait planning system according to claim 8, characterized in that, The joint positions of the exoskeleton robot include any one or any combination of the hip joint, knee joint, and ankle joint.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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