Robot control method and device, robot
By acquiring users' historical walking data to train an oscillator model and adjusting the gait trajectory to address the gait discomfort problem of the lower limb rehabilitation robot, the user experience was improved and the gait gradually approached the target gait.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HEBEI INST FOR DRUG & MEDICAL DEVICE CONTROL (HEBEI INST FOR COSMETICS CONTROL)
- Filing Date
- 2023-11-30
- Publication Date
- 2026-05-29
AI Technical Summary
Existing lower limb rehabilitation robots are prone to gait discomfort when walking with different users.
By acquiring the target user's historical walking data, an oscillator model is trained to obtain the first gait trajectory. Based on the preset target gait trajectory, the first gait trajectory is adjusted to generate the second gait trajectory. Robot control commands are then output to gradually adjust the user's gait until it approaches the target gait.
It enables gradual adjustment of gait based on the user's actual situation, avoiding discomfort, improving user experience, and gradually reducing gait differences through multiple training sessions to achieve the target gait.
Smart Images
Figure CN117357379B_ABST
Abstract
Description
Technical Field
[0001] This disclosure belongs to the field of intelligent control technology, and more specifically, relates to a robot control method and device, and a robot. Background Technology
[0002] Lower limb rehabilitation robots can assist users in lower limb movement and walking, and are currently widely used in rehabilitation care, prostheses, and rehabilitation therapy to help disabled users regain motor function and bring hope for reintegration into society. However, existing lower limb rehabilitation robots are controlled based on fixed gait trajectories, which can easily lead to gait discomfort when walking with different users, requiring further improvement. Summary of the Invention
[0003] The purpose of this disclosure is to provide a robot control method and device, and a robot, to solve the problem of gait discomfort that is common in existing lower limb rehabilitation robots.
[0004] A first aspect of this disclosure provides a robot control method, comprising:
[0005] Obtain the historical walking data of the first target user;
[0006] The oscillator model is trained based on the historical walking data of the first target user to obtain the first step state trajectory;
[0007] The first gait trajectory is adjusted based on the preset target gait trajectory to obtain the second gait trajectory;
[0008] Robot control commands are output based on the second gait trajectory.
[0009] A second aspect of this disclosure provides a robot control device, comprising:
[0010] The data acquisition unit is used to acquire the historical walking data of the first target user;
[0011] The data processing unit is used to train an oscillator model based on the historical walking data of the first target user to obtain the first step state trajectory;
[0012] The first calculation unit is used to adjust the first gait trajectory based on the preset target gait trajectory to obtain the second gait trajectory;
[0013] The instruction output unit is used to output robot control instructions based on the second gait trajectory.
[0014] A third aspect of this disclosure provides a robot, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the robot control method described above.
[0015] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the robot control method described above.
[0016] The beneficial effects of the robot control method and apparatus, and the robot provided in this disclosure are as follows:
[0017] This embodiment of the disclosure obtains a first gait trajectory by acquiring historical walking data of a first target user during multiple training sessions. This first gait trajectory characterizes the first target user's current walking ability. Based on the first gait trajectory, a second gait trajectory is obtained according to the difference between the first gait trajectory and the target gait trajectory, and the robot is controlled according to the second gait trajectory. This control method can gradually adjust the second gait trajectory based on the actual situation of the first target user, avoiding discomfort and improving the user experience. Moreover, during multiple training sessions, the difference between the first gait trajectory and the target gait trajectory gradually decreases, ultimately allowing the second gait trajectory to gradually approach the target gait trajectory. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 A flowchart illustrating a robot control method provided in an embodiment of this disclosure;
[0020] Figure 2 This is a structural block diagram of a robot control device provided in an embodiment of the present disclosure;
[0021] Figure 3 This is a schematic block diagram of a robot provided in one embodiment of the present disclosure. Detailed Implementation
[0022] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of the embodiments of this disclosure. However, those skilled in the art will understand that this disclosure may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this disclosure with unnecessary detail.
[0023] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.
[0024] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a robot control method provided in an embodiment of the present disclosure. The method includes:
[0025] S101: Obtain the historical walking data of the first target user.
[0026] In this embodiment, the first target user is a user who needs to be trained with the help of a robot. Historical walking data is obtained by monitoring the walking data of the first target user during multiple training sessions. The walking data includes hip joint angles, knee joint angles, etc. at multiple moments.
[0027] S102: Train the oscillator model based on the historical walking data of the first target user to obtain the first step state trajectory.
[0028] In this embodiment, an oscillator model is trained based on the historical walking data of the first target user to obtain the corresponding hip joint angle-time curve and knee joint angle-time curve, i.e., the first step state trajectory, which is a periodic oscillation curve. Specifically, a Hopf oscillator is selected as the oscillator model.
[0029] S103: Adjust the first gait trajectory based on the preset target gait trajectory to obtain the second gait trajectory.
[0030] In this embodiment of the disclosure, the preset target gait trajectory is the gait trajectory that is expected to be achieved, and the first gait trajectory is the actual gait trajectory of the first user in the current training phase. The first gait trajectory is gradually adjusted based on the target gait trajectory to make the first gait trajectory gradually approach the target gait trajectory.
[0031] S104: Output robot control commands based on the second gait trajectory.
[0032] In this embodiment, the second gait trajectory is the expected trajectory that the first target user can easily achieve in the current training phase. The robot is controlled to move according to the second gait trajectory to avoid discomfort for the first user.
[0033] The process of steps S101 to S104 above is repeated in a loop. Based on the first gait trajectory of each training stage and the preset target gait trajectory, a second gait trajectory suitable for the corresponding stage is determined until the first gait trajectory is consistent with the target gait trajectory.
[0034] As can be seen from the above, this embodiment of the present disclosure obtains the historical walking data of the first target user during multiple training processes, and obtains the first gait trajectory based on the historical walking data. The first gait trajectory can represent the current walking ability of the first target user. Based on the first gait trajectory, a second gait trajectory is obtained according to the difference between the first gait trajectory and the target gait trajectory, and the robot is controlled according to the second gait trajectory. This control method can gradually adjust the second gait trajectory according to the actual situation of the first target user, avoid discomfort for the first target user, and improve the user experience; moreover, during multiple training processes, the difference between the first gait trajectory and the target gait trajectory gradually decreases, and finally the second gait trajectory gradually approaches the target gait trajectory.
[0035] In one embodiment of this disclosure, adjusting the first gait trajectory based on the preset target gait trajectory to obtain the second gait trajectory includes:
[0036] The first gait trajectory and the target gait trajectory are divided according to the gait cycle to obtain multiple first gait sub-trajectories and multiple target gait sub-trajectories;
[0037] Calculate the difference between the first step gait sub-trajectory and the target gait sub-trajectory corresponding to the first step gait sub-trajectory to obtain the first difference corresponding to the first step gait sub-trajectory;
[0038] The preset positive correlation coefficient corresponding to each first-step sub-trajectory is determined based on the first difference corresponding to each first-step sub-trajectory;
[0039] Each first-step sub-trajectory is adjusted based on a preset positive correlation coefficient corresponding to each first-step sub-trajectory to obtain multiple second-step sub-trajectories;
[0040] Multiple second gait sub-trajectories are arranged along the time axis and connected sequentially to obtain the second gait trajectory.
[0041] In this embodiment of the disclosure, both the first gait trajectory and the target gait trajectory are periodic oscillation curves. The first difference between the first gait sub-trajectory and the target gait sub-trajectory in each gait cycle is calculated, and the corresponding preset positive correlation coefficient is determined based on the first difference. Then, each first gait sub-trajectory is adjusted in a targeted manner based on the preset positive correlation coefficient. Finally, the second gait trajectory is obtained based on each adjusted second gait sub-trajectory.
[0042] Specifically, the first gait sub-trajectory includes a hip joint angle-time curve and a knee joint angle-time curve. Taking the hip joint angle-time curve as an example, the first difference is the difference between the hip joint angle at each moment in the first gait sub-trajectory and the hip joint angle at the same moment in the target gait sub-trajectory. After obtaining the first difference, a corresponding preset positive correlation coefficient is determined based on the magnitude of the first difference. Then, the first difference is multiplied by the preset positive correlation coefficient as the change in the first gait sub-trajectory. This change is then superimposed on the first gait sub-trajectory to obtain the second gait sub-trajectory.
[0043] In one embodiment of this disclosure, determining the preset positive correlation coefficient corresponding to each first-step sub-trajectory based on the first difference corresponding to each first-step sub-trajectory includes:
[0044] In response to the first difference being less than a first preset threshold, the first coefficient is determined as a preset positive correlation coefficient;
[0045] In response to the first difference being between a first set threshold and a second set threshold, the second coefficient is determined as a preset positive correlation coefficient;
[0046] In response to the first difference being greater than a second preset threshold, the third coefficient is determined as a preset positive correlation coefficient;
[0047] Among them, the first coefficient, the second coefficient, and the third coefficient decrease in that order.
[0048] In this embodiment, a preset positive correlation coefficient for the corresponding first gait sub-trajectory is determined based on the magnitude of each first difference. Specifically, when the difference between the first gait sub-trajectory and the target gait sub-trajectory is large (greater than a second preset threshold), the first difference is large, and the preset positive correlation coefficient can be set smaller to avoid excessive changes in the first gait sub-trajectory, which could cause discomfort to the first target user. When the difference between the first gait sub-trajectory and the target gait sub-trajectory is small (less than a first preset threshold), the first difference is small, and the preset positive correlation coefficient can be set larger, which is beneficial for the first gait sub-trajectory to quickly approach the target gait sub-trajectory.
[0049] In one embodiment of this disclosure, the second gait trajectory includes a hip joint angle-time curve and a knee joint angle-time curve. Before outputting robot control commands based on the second gait trajectory, the method further includes:
[0050] The angular velocity curve is obtained by differentiating the second gait trajectory.
[0051] Filter out abrupt change points on the angular velocity curve; wherein, the abrupt change point is a point whose difference from the previous angular velocity data is greater than an angular velocity threshold;
[0052] Based on the mutation point, anomalies in the second gait trajectory are determined; wherein, the anomalies are data points on the second gait trajectory that occur at the same time as the mutation point.
[0053] Remove the outlier from the second gait trajectory and refit the second gait trajectory based on the other data points besides the outlier.
[0054] In this embodiment, if the angular velocity of the robot changes abruptly during walking, it will affect its smooth operation. To avoid this problem, the second gait trajectory needs to be corrected. Taking the hip joint angle-time curve as an example, the corresponding angular velocity-time curve is obtained by differentiating the hip joint angle-time curve. Abrupt points in the angular velocity-time curve are filtered out and mapped onto the hip joint angle-time curve to obtain abnormal points on the hip joint angle-time curve. These abnormal points are then deleted, and the second gait trajectory is refitted based on the data before and after the abnormal point.
[0055] In one embodiment of this disclosure, the step of outputting robot control commands based on the second gait trajectory includes:
[0056] Obtain the current walking data of the first target user;
[0057] Calculate the second difference between the current walking data and the target walking data; the target walking data is the data corresponding to the current moment in the second gait trajectory.
[0058] If the second difference is greater than the deviation threshold, then the robot control command is output based on the second difference.
[0059] In this embodiment, considering the magnitude of a second difference between the current walking data and the target walking data, the robot provides assistance only when the second difference is large, correcting the deviation between the current walking data and the target walking data. This helps to maximize the initiative of the first target user and improve the training effect.
[0060] In one embodiment of this disclosure, the robot control method further includes:
[0061] Acquire walking data from multiple secondary target users;
[0062] Draw the corresponding third gait trajectory based on the walking data of each second target user;
[0063] Multiple third-gait trajectories are fused to obtain the preset target gait trajectory.
[0064] In this embodiment of the disclosure, the second target user is a subject who can walk normally. By acquiring the walking data of multiple second target users, multiple third gait trajectories are obtained. Then, the multiple third gait trajectories are fused together to obtain a target gait trajectory that can be suitable for most users.
[0065] Those skilled in the art can achieve the fusion of multiple third-gait trajectories using existing methods. In this embodiment, the fusion is specifically achieved by calculating the average value of multiple third-gait trajectories.
[0066] Corresponding to the robot control method in the above embodiments, Figure 2 This is a structural block diagram of a robot control device provided according to an embodiment of the present disclosure. For ease of explanation, only the parts relevant to the embodiment of the present disclosure are shown. References Figure 2 The robot control device 20 includes: a data acquisition unit 21, a data processing unit 22, a first calculation unit 23, and an instruction output unit 24.
[0067] Among them, the data acquisition unit 21 is used to acquire the historical walking data of the first target user;
[0068] Data processing unit 22 is used to train an oscillator model based on the historical walking data of the first target user to obtain the first step state trajectory;
[0069] The first calculation unit 23 is used to adjust the first gait trajectory based on the preset target gait trajectory to obtain the second gait trajectory;
[0070] The instruction output unit 24 is used to output robot control instructions based on the second gait trajectory.
[0071] In one embodiment of this disclosure, the first computing unit 23 is specifically used for:
[0072] The first gait trajectory and the target gait trajectory are divided according to the gait cycle to obtain multiple first gait sub-trajectories and multiple target gait sub-trajectories;
[0073] Calculate the difference between the first step gait sub-trajectory and the target gait sub-trajectory corresponding to the first step gait sub-trajectory to obtain the first difference corresponding to the first step gait sub-trajectory;
[0074] The preset positive correlation coefficient corresponding to each first-step sub-trajectory is determined based on the first difference corresponding to each first-step sub-trajectory;
[0075] Each first-step sub-trajectory is adjusted based on a preset positive correlation coefficient corresponding to each first-step sub-trajectory to obtain multiple second-step sub-trajectories;
[0076] Multiple second gait sub-trajectories are arranged along the time axis and connected sequentially to obtain the second gait trajectory.
[0077] In one embodiment of this disclosure, the first computing unit 23 is further configured to:
[0078] In response to the first difference being less than a first preset threshold, the first coefficient is determined as a preset positive correlation coefficient;
[0079] In response to the first difference being between a first set threshold and a second set threshold, the second coefficient is determined as a preset positive correlation coefficient;
[0080] In response to the first difference being greater than a second preset threshold, the third coefficient is determined as a preset positive correlation coefficient;
[0081] Among them, the first coefficient, the second coefficient, and the third coefficient decrease in that order.
[0082] In one embodiment of this disclosure, before outputting robot control commands based on the second gait trajectory, the command output unit 24 is specifically used for:
[0083] The angular velocity curve is obtained by differentiating the second gait trajectory.
[0084] Filter out abrupt change points on the angular velocity curve; wherein, the abrupt change point is a point whose difference from the previous angular velocity data is greater than an angular velocity threshold;
[0085] Based on the mutation point, anomalies in the second gait trajectory are determined; wherein, the anomalies are data points on the second gait trajectory that occur at the same time as the mutation point.
[0086] Remove the outlier from the second gait trajectory and refit the second gait trajectory based on the other data points besides the outlier.
[0087] In one embodiment of this disclosure, the instruction output unit 24 is further configured to:
[0088] Obtain the current walking data of the first target user;
[0089] Calculate the second difference between the current walking data and the target walking data; the target walking data is the data corresponding to the current moment in the second gait trajectory.
[0090] If the second difference is greater than the deviation threshold, then the robot control command is output based on the second difference.
[0091] In one embodiment of this disclosure, a second computing unit 25 is further included, for:
[0092] Acquire walking data from multiple secondary target users;
[0093] Draw the corresponding third gait trajectory based on the walking data of each second target user;
[0094] Multiple third-gait trajectories are fused to obtain the preset target gait trajectory.
[0095] See Figure 3 , Figure 3 This is a schematic block diagram of a robot provided according to an embodiment of this disclosure. Figure 3 The robot 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 stores computer programs, including program instructions. The processors 301 execute the program instructions stored in the memory 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules / units in the above-described device embodiments, such as... Figure 2 The functions of modules 21 to 24 are shown.
[0096] It should be understood that, in the embodiments of this disclosure, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.
[0097] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.
[0098] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.
[0099] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this disclosure can execute the implementation methods described in the first and second embodiments of the robot control method provided in the embodiments of this disclosure, or they can execute the robot implementation methods described in the embodiments of this disclosure, which will not be repeated here.
[0100] In another embodiment of this disclosure, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. The computer program can also instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.
[0101] The computer-readable storage medium can be an internal storage unit of the robot in any of the foregoing embodiments, such as the robot's hard drive or memory. The computer-readable storage medium can also be an external storage device of the robot, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the robot. Furthermore, the computer-readable storage medium can include both internal and external storage units of the robot. The computer-readable storage medium is used to store computer programs and other programs and data required by the robot. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.
[0102] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.
[0103] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the robot and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0104] In the several embodiments provided in this application, it should be understood that the disclosed robots and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.
[0105] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this disclosure, depending on actual needs.
[0106] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0107] The above are merely specific embodiments of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this disclosure, and these modifications or substitutions should all be covered within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.
Claims
1. A robot comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it performs the following steps: Obtain the historical walking data of the first target user; The oscillator model is trained based on the historical walking data of the first target user to obtain the first step state trajectory; The first gait trajectory is adjusted based on a preset target gait trajectory to obtain a second gait trajectory; Output robot control commands based on the second gait trajectory; The step of adjusting the first gait trajectory based on a preset target gait trajectory to obtain a second gait trajectory includes: The first gait trajectory and the target gait trajectory are divided according to the gait cycle to obtain multiple first gait sub-trajectories and multiple target gait sub-trajectories; Calculate the difference between the first step gait sub-trajectory and the target gait sub-trajectory corresponding to the first step gait sub-trajectory to obtain the first difference corresponding to the first step gait sub-trajectory; The preset positive correlation coefficient corresponding to each first-step sub-trajectory is determined based on the first difference corresponding to each first-step sub-trajectory; Each first-step sub-trajectory is adjusted based on a preset positive correlation coefficient corresponding to each first-step sub-trajectory to obtain multiple second-step sub-trajectories; Multiple second gait sub-trajectories are arranged along the time axis and connected sequentially to obtain the second gait trajectory.
2. The robot as described in claim 1, characterized in that, When determining the preset positive correlation coefficient corresponding to each first-step sub-trajectory based on the first difference corresponding to each first-step sub-trajectory, the processor performs the following steps: In response to the first difference being less than a first preset threshold, the first coefficient is determined as a preset positive correlation coefficient; In response to the first difference being between a first set threshold and a second set threshold, the second coefficient is determined as a preset positive correlation coefficient; In response to the first difference being greater than a second preset threshold, the third coefficient is determined as a preset positive correlation coefficient; Among them, the first coefficient, the second coefficient, and the third coefficient decrease in that order.
3. The robot as described in claim 1, characterized in that, The second gait trajectory includes a hip joint angle-time curve and a knee joint angle-time curve. Before outputting robot control commands based on the second gait trajectory, the processor performs the following steps: The angular velocity curve is obtained by differentiating the second gait trajectory. Filter out abrupt change points on the angular velocity curve; wherein, the abrupt change point is a point whose difference from the previous angular velocity data is greater than an angular velocity threshold; Based on the mutation point, anomalies in the second gait trajectory are determined; wherein, the anomalies are data points on the second gait trajectory that occur at the same time as the mutation point. Remove the outlier from the second gait trajectory and refit the second gait trajectory based on the other data points besides the outlier.
4. The robot as described in claim 1, characterized in that, When outputting robot control commands based on the second gait trajectory, the processor performs the following steps: Obtain the current walking data of the first target user; Calculate the second difference between the current walking data and the target walking data; the target walking data is the data corresponding to the current moment in the second gait trajectory. If the second difference is greater than the deviation threshold, then the robot control command is output based on the second difference.
5. The robot as described in claim 1, characterized in that, When the processor executes the computer program, it also performs the following steps: Acquire walking data from multiple secondary target users; Draw the corresponding third gait trajectory based on the walking data of each second target user; Multiple third-gait trajectories are fused to obtain the preset target gait trajectory.
6. The robot as described in claim 1, characterized in that, Also includes: Sensors are used to collect walking data from the first target user; An actuator is used to execute robot control commands output by the processor to drive the corresponding joints to rotate.
7. A robot control device, characterized in that, include: The data acquisition unit is used to acquire the historical walking data of the first target user; The data processing unit is used to train an oscillator model based on the historical walking data of the first target user to obtain the first step state trajectory; The first calculation unit is used to adjust the first gait trajectory based on a preset target gait trajectory to obtain a second gait trajectory; The instruction output unit is used to output robot control instructions based on the second gait trajectory; The first computing unit is specifically used for: The first gait trajectory and the target gait trajectory are divided according to the gait cycle to obtain multiple first gait sub-trajectories and multiple target gait sub-trajectories; Calculate the difference between the first step gait sub-trajectory and the target gait sub-trajectory corresponding to the first step gait sub-trajectory to obtain the first difference corresponding to the first step gait sub-trajectory; The preset positive correlation coefficient corresponding to each first-step sub-trajectory is determined based on the first difference corresponding to each first-step sub-trajectory; Each first-step sub-trajectory is adjusted based on a preset positive correlation coefficient corresponding to each first-step sub-trajectory to obtain multiple second-step sub-trajectories; Multiple second gait sub-trajectories are arranged along the time axis and connected sequentially to obtain the second gait trajectory.
8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it performs the following steps: Obtain the historical walking data of the first target user; The oscillator model is trained based on the historical walking data of the first target user to obtain the first step state trajectory; The first gait trajectory is adjusted based on a preset target gait trajectory to obtain a second gait trajectory; Output robot control commands based on the second gait trajectory; The step of adjusting the first gait trajectory based on a preset target gait trajectory to obtain a second gait trajectory includes: The first gait trajectory and the target gait trajectory are divided according to the gait cycle to obtain multiple first gait sub-trajectories and multiple target gait sub-trajectories; Calculate the difference between the first step gait sub-trajectory and the target gait sub-trajectory corresponding to the first step gait sub-trajectory to obtain the first difference corresponding to the first step gait sub-trajectory; The preset positive correlation coefficient corresponding to each first-step sub-trajectory is determined based on the first difference corresponding to each first-step sub-trajectory; Each first-step sub-trajectory is adjusted based on a preset positive correlation coefficient corresponding to each first-step sub-trajectory to obtain multiple second-step sub-trajectories; Multiple second gait sub-trajectories are arranged along the time axis and connected sequentially to obtain the second gait trajectory.