Exoskeleton robot trajectory planning method based on human parameters and states

Abstract

The application discloses a kind of trajectory planning method of exoskeleton robot based on human parameter and state, it is related to trajectory planning technical field. Including collecting gait data of healthy individual, generating standard gait database, and completing special individual's gait data;In early rehabilitation of patient, according to standard gait trajectory and adjustment coefficient, generate adaptive joint angle trajectory;When patient has certain muscle capacity, real-time adjust gait trajectory by the way of combination of offline planning and online adjustment;When patient is judged to have complete gait ability by quantitative evaluation index, exoskeleton provides power in specific phase of gait cycle, and force control mode is adopted in underlying servo control.The application effectively limits the shoulder rotation angle by designing shoulder joint angle limiting device, prevents the arm from contacting the human chest during the rotation process, thereby greatly improving the safety of the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of trajectory planning, and in particular to a trajectory planning method for an exoskeleton robot based on human parameters and states. BACKGROUND

[0002] With the rapid development of the power industry, the power operation environment is becoming increasingly complex, and the labor intensity of the operation personnel is increasing. At the same time, due to the shortage of personnel and the increase of labor cost, the research and development of power operation auxiliary equipment gradually become popular. In particular, for high-altitude operation, heavy physical labor and other scenes, upper limb and lower limb power-assisted exoskeleton robots become an important technical direction.

[0003] In order to adapt to the needs of different operation personnel (here, the physical condition, age, body length and other conditions of the operation personnel should be fully considered), it is necessary to generate corresponding motion control instructions for different wearers, that is, to generate motion trajectory instruction curves for each joint (such as shoulder joint, elbow joint, hip joint and knee joint) according to different conditions. The effect of motion trajectory instruction generation directly determines the safety and efficiency of the human-exoskeleton system in power operation. SUMMARY

[0004] In view of the problems in the above background art, the present application is proposed.

[0005] Therefore, the problem to be solved by the present application is how to automatically generate joint layer servo control trajectory instruction curves based on the physical condition of the operation personnel.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides an exoskeleton robot trajectory planning method based on human parameters and states, which includes collecting gait data of healthy individuals, generating a standard gait database, and completing gait data of special individuals; in the early stage of patient rehabilitation, generating an adaptive joint angle trajectory according to the standard gait trajectory and adjusting the coefficient; when the patient has certain muscle ability, the gait trajectory is adjusted in real time by combining offline planning and online adjustment; when the patient is judged to have complete gait ability through quantitative evaluation indexes, the exoskeleton provides assistance in the specific phase of the gait cycle, and the underlying servo control adopts force control mode.

[0008] As a preferred scheme of the exoskeleton robot trajectory planning method based on human parameters and states, wherein: the standard gait database includes gait data of healthy individuals of different genders, ages and heights; the joint trajectory data in each gait cycle is processed after being calculated by average value, and a standard gait trajectory containing gait cycle key points is established as a reference data source for patient rehabilitation training.

[0009] As a preferred scheme of the exoskeleton robot trajectory planning method based on human body parameters and states, for patients who cannot directly match the standard gait database, an interpolation or fitting algorithm is used to generate an approximate gait trajectory, and a joint angle trajectory instruction that adapts to the characteristics of the patient is generated.

[0010] As a preferred scheme of the exoskeleton robot trajectory planning method based on human body parameters and states, the establishment of the standard gait trajectory containing gait cycle key points includes: for the same subject, N gait cycle data are continuously collected, and the average value of the N gait cycle data is taken; for each gait cycle, the same sampling number n is adjusted, then for the kth(k=1, 2,..., N) gait cycle Tk, the sampling period is Ts_k=Tk / n; and the data at the ith point of the gait cycle is calculated by the following formula:

[0011] Avg_y(i)=[y1(i)+y2(i)+...+yk(i)+...+yN(i)] / N;

[0012] i=1, 2,..., n;

[0013] where yk(i) represents the angle value at the ith point in the kth gait cycle.

[0014] As a preferred scheme of the exoskeleton robot trajectory planning method based on human body parameters and states, the generation of the adapted joint angle trajectory by adjusting the coefficient according to the standard gait trajectory includes: using the standard gait trajectory multiplied by a coefficient a value (0

[0015] As a preferred scheme of the exoskeleton robot trajectory planning method based on human body parameters and states, the real-time adjustment of the gait trajectory by combining offline planning and online adjustment includes: generating a new trajectory instruction curve by online adjusting the standard trajectory curve through an admittance control strategy; if the desired interaction force F is equal to 0, then if the patient is in a complete paralysis state, the human-machine interaction force feedback value is equal to 0, then the corrected angle instruction q amt is equal to 0, which is converted into passive control; if the human-machine interaction force is not equal to 0, the standard trajectory instruction curve is adjusted online in real time.

[0016] As a preferred scheme of the exoskeleton robot trajectory planning method based on human body parameters and states, the adjustable gain admittance controller adjusts the relative angle according to the interaction force, and the angular frequency and the attenuation coefficient are adjusted according to human body comfort, and the related formula is as follows:

[0017]

[0018] Wherein, M d s is the joint angle acceleration, B d s is the joint angular velocity, K d is the angle coefficient, M d is the joint angle value, ω n is the interaction force and the adjustment angle response angular frequency, and ζ is the attenuation coefficient.

[0019] In a second aspect, the embodiment of the present application provides an exoskeleton robot trajectory planning system based on human body parameters and states, which comprises: a healthy gait acquisition module for acquiring gait data of healthy individuals, establishing a standard gait database, and completing gait data of special individuals; a rehabilitation early gait generation module for generating an adaptive joint angle trajectory according to a standard gait trajectory and adjusting a coefficient in the early rehabilitation of a patient; a gait real-time adjustment module for adjusting a gait trajectory in real time through a combination of offline planning and online adjustment when a patient has a certain muscle capacity; and a gait assistance providing module for providing assistance in a specific phase of a gait cycle when a patient is judged to have complete gait capacity through a quantitative evaluation index, and a force control mode is adopted for underlying servo control.

[0020] In a third aspect, the embodiment of the present application provides a computer device comprising a memory and a processor, and the memory stores a computer program, wherein the computer program instructions are executed by the processor to realize the steps of the exoskeleton robot trajectory planning method based on human body parameters and states according to the first aspect of the present application.

[0021] In a fourth aspect, the embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program instructions are executed by the processor to realize the steps of the exoskeleton robot trajectory planning method based on human body parameters and states according to the first aspect of the present application.

[0022] The present application has the beneficial effects that: the present application effectively limits the rotation angle of the shoulder by designing a shoulder joint angle limiting device, preventing the arm from contacting the chest during rotation, thereby greatly improving the safety of the user. Especially in high-altitude operation and heavy physical labor scenes, it can effectively reduce accidental injuries caused by excessive rotation; the use of a magnet and a boss structure makes the installation and removal of the limiting block very simple, which can be completed without complex tools. This design not only reduces the operation difficulty, but also reduces the maintenance time, improves the operability and reliability of the equipment; the present application can automatically generate corresponding motion control instructions according to the physical condition, age, limb length and other conditions of the operator, to ensure that the exoskeleton system provides the most suitable auxiliary force. This personalized adaptation capability enables the exoskeleton device to be widely used in operators of different body types and different needs, improving the versatility of the device; and by automatically generating the motion trajectory instruction curve of each joint (such as the shoulder joint, elbow joint, hip joint and knee joint), the present application can effectively reduce the labor intensity of the operator and improve the work efficiency. Especially in the power grid industry, it can significantly improve the completion speed of high-altitude operation, heavy physical labor and other tasks; further, the exoskeleton walking trajectory planning can be divided into offline generation, online real-time generation and a combination of the two methods, which can flexibly cope with different use scenarios and needs. The offline method is generated by mathematical expressions and measurement data, and the online real-time generation mode relies on sensors to predict the motion intention of the person in real time, making the trajectory planning more accurate and efficient. The present application is not only suitable for the power grid industry, but also can be popularized to other fields that require high-intensity upper limb operation, such as building construction, logistics transportation, etc. At the same time, it also has important application value in the medical rehabilitation field, providing effective rehabilitation training assistance for lower limb patients. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0024] Figure 1 The trajectory planning generation strategy flow chart based on rehabilitation level I.

[0025] Figure 2 The joint angle standard gait database.

[0026] Figure 3 The admittance control system block diagram.

[0027] Figure 4 The block diagram of generating a new trajectory instruction curve by online adjusting the standard trajectory curve through the admittance control strategy.

[0028] Figure 5 Muscle work of each joint in walking gait

[0029] Figure 6 Moment curve of each joint in normal gait cycle of the subject.

[0030] Figure 7 Standard gait database of joint moment. DETAILED DESCRIPTION

[0031] In order to make the above objectives, features and advantages of the present application more apparent, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0032] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details and other implementations can be employed. In other instances, well-known methods have not been described in detail in order to avoid obscuring the present application.

[0033] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments.

[0034] Embodiment 1

[0035] Reference Figures 1-7 For the first embodiment of the present application, the embodiment provides a trajectory planning method for exoskeleton robots based on human parameters and states, comprising,

[0036] S1: Collect gait data of healthy individuals of different genders, ages and heights, generate a standard gait database, and complete the gait data of special individuals through interpolation or fitting.

[0037] Preferably, the gait trajectory of a healthy individual is collected, and a standard gait database containing different genders, ages and heights is constructed to provide reference trajectories for different patients. The data of each gait cycle is post-processed by averaging a plurality of sampling points to form a standard gait trajectory.

[0038] For patient characteristics that cannot be directly matched, an interpolation or fitting method can be used to generate an approximate gait trajectory in the database.

[0039] The gait trajectory instruction can be single-hip joint driving, single-knee joint driving, or single-ankle joint driving according to the active driving joint structure configuration of the exoskeleton, and the trajectory instruction of the corresponding joint needs to be generated as the instruction of the joint servo driving. Considering the universality, the patent is described by taking a three-joint (hip, knee, and ankle) active driving exoskeleton as an example. That is, three-joint trajectory instructions need to be generated.

[0040] The trajectory planning of the exoskeleton walking can be divided into offline generation, online real-time generation, and a combination method of the two. The offline method is generally generated by mathematical expressions and measured data. The online real-time generation mode depends on sensors (such as acceleration sensors, electromyographic sensors, etc.) to predict the motion intention of the person in real time.

[0041] The specific trajectory generation method needs to be determined in combination with the health status level of the subject's lower limbs.

[0042] S1.1: The gait trajectory has individual differences and is strongly related to human characteristics such as gender, age, and height.

[0043] A standard gait database is established, and according to gender, age, and height, the trajectory of each joint collected by a person with a normal gait is taken as the standard gait trajectory data.

[0044] For each age range of the subject population, select a person with a height of 150 cm to 190 cm, walk at a standard gait and a certain speed on a horizontal road, and record the trajectory curve of the subject in the sagittal plane.

[0045] S1.2: The collected joint trajectory curve is taken from the start of the right heel touching the ground to the next right heel touching the ground as a complete gait cycle T.

[0046] For the same subject, N gait cycle data are continuously collected, and then the average value of the joint trajectory of the subject is taken for N gait cycle data.

[0047] For each gait cycle, adjust it to the same sampling number n, then for the kth (k = 1, 2,..., N) gait cycle Tk, the sampling period is Ts_k = Tk / n,

[0048] Then the data at the ith point of the gait cycle is calculated by the following formula:

[0049] Avg_y(i) = [y1(i) + y2(i) +... + yk(i) +... + yN(i)] / N;

[0050] i = 1, 2,..., n;

[0051] It should be noted that yk(i) represents the angle value at the ith point in the kth gait cycle.

[0052] The standard gait data of a subject (e.g., name Zhang San, male, age 20, height 150 cm) can be obtained according to the above formula.

[0053] According to the above steps, the subjects of each age group, a certain height and gender are continuously selected for experiments, and the gait trajectory instructions are collected and stored in the database.

[0054] It should be noted that when selecting subjects, the ages are evenly distributed in the given age group, and the height is not necessarily according to the following height gradient of 5 cm.

[0055] At this stage, the patient's bottom servo adopts a position control mode, so the reference instruction trajectory is the position instruction as above.

[0056] S2: In the early stage of patient rehabilitation, the standard gait trajectory is adjusted according to the coefficient to generate an adaptive joint angle trajectory, and the exoskeleton completely drives the patient's lower limbs.

[0057] For patients who have no movement ability in the lower limbs, the standard gait curve of a normal person similar to the patient in gender, age and height is generally adjusted.

[0058] As for the adjustment method, the joint angle obtained after the standard gait trajectory is multiplied by a coefficient a (0 < a <= 1) can be used as the instruction for the lower servo closed-loop control, that is, the exoskeleton has great rigidity, and the subject's lower limbs are completely driven by the exoskeleton.

[0059] This training method is suitable for the early stage of rehabilitation training.

[0060] The value of a is adjusted by a rehabilitation physician according to the rehabilitation plan as the rehabilitation process advances. In order to exclude the subjectivity of the physician, the present application constructs a mapping table using a quantitative evaluation index I and a coefficient a, and automatically calculates the coefficient a through the automatically calculated evaluation index I. That is, if the I value of the output is completely paralyzed, a small value such as a = 0.1 is taken. A larger value may cause secondary muscle injury.

[0061] The calculation method of the quantitative evaluation index I is given in another patent (a lower limb quantitative rehabilitation evaluation method using a wearable sensor).

[0062] The standard gait trajectory can be retrieved from the standard gait database according to the subject parameters (gender, age, height). For subjects who cannot be directly retrieved from the database, such as gait data with a height of 153 cm in the database, interpolation or fitting of adjacent trajectory curves can be used.

[0063] The construction process of the standard gait database is as followsFigure 2 as shown.

[0064] S3: When the patient has certain muscle strength, the gait trajectory is adjusted in real time by combining offline planning and online adjustment to adapt to the patient's autonomous movement ability.

[0065] As the patient's rehabilitation progresses, when the patient has certain muscle strength, it is determined whether he or she is suitable for entering the adaptive training mode.

[0066] The offline planning and online adjustment method is adopted to adjust the gait trajectory in real time based on the admittance control method, so that the trajectory adapts to the patient's autonomous movement ability. The interaction between the patient's movement feedback force and the exoskeleton enables the trajectory to be dynamically adjusted, achieving better rehabilitation results.

[0067] Specifically, whether the patient belongs to this category is determined by a quantitative evaluation index I.

[0068] For such patients, the patient's muscles have certain muscle strength and can autonomously move to a certain extent. At this time, if the completely passive training mode is continued without considering the subject's autonomous movement ability, the movement of the exoskeleton and the patient will be uncoordinated, and better rehabilitation results cannot be achieved.

[0069] Therefore, for this type of patient, the patient's autonomous movement ability needs to be considered to adjust the trajectory curve. That is, the offline planning and online adjustment method is used to generate the gait trajectory of such patients. For example, Figure 4 the standard trajectory curve is adjusted online by the admittance control strategy to generate a new trajectory instruction curve q A block diagram of * If the patient is in a completely paralyzed state, i.e., the lower limbs of the person cannot move, the human-machine interaction force feedback value is equal to 0, and the correction angle instruction q amt is also equal to 0, then it is converted to passive control. As long as the human-machine interaction force is not equal to 0, the standard trajectory instruction curve will be adjusted in real time online to also consider the patient's own movement ability.

[0070] It should be noted that the online adjustment trajectory method can also use other methods, such as methods based on electromyographic signals.

[0071] As shown in Figure 4 It can be seen that the adjustable gain admittance controller adjusts the relative angle according to the interaction force;

[0072] The use of the admittance controller can fully consider the influence of the person in the control loop, improve safety and comfort, and set the human-machine interaction force and the adjustment angle as a second-order system, directly adjust the admittance controller parameters, and debug conveniently.

[0073] The angular frequency and the damping coefficient can be adjusted according to the comfort of the person, and compared with the current commonly used parameter setting method, the parameter setting is faster, and the related formula is as follows:

[0074]

[0075]

[0076] Wherein, M d s is the joint angle acceleration, B d s is the joint angular velocity, K d is the angular coefficient, M d is the joint angle value, ω n is the interaction force and the adjustment angle response angular frequency, and ζ is the damping coefficient.

[0077] In this stage, the patient's bottom layer servo adopts an inner ring position control mode. Therefore, the reference command trajectory is the position command as above.

[0078] S4: When the patient is judged to have complete gait ability through the quantitative evaluation index, the exoskeleton provides assistance in specific phases of the gait cycle, and the bottom layer servo control adopts force control mode to improve the naturalness of gait and rehabilitation effect.

[0079] When the patient recovers to the normal state, the exoskeleton enters the assistance mode, and provides assistance to the patient in specific gait phases. The assistance torque command is generated by the gait database and adjusted according to the patient's walking biomechanical characteristics to ensure that different joints provide assistance at the right time.

[0080] The bottom layer servo control adopts force control mode, and provides assistance output according to the phase of the joint in the gait cycle to realize smoother walking experience.

[0081] Preferably, whether the patient has completely recovered is judged by the quantitative evaluation index I.

[0082] For normal people, the main function of the exoskeleton is assistance. The assistance time point is completed according to the biomechanical characteristics of human walking gait, and different joints need to be determined according to the work of each joint muscle in different phases of gait, such as Figure 5 .

[0083] Figure 5 The important areas of positive and negative power in the gait cycle are shown. The red and blue circles respectively highlight the positive and negative power areas of the hip joint, knee joint and ankle joint in different situations of the gait cycle, and most of the positive energy in the gait cycle comes from H1 and H3 of the hip and A2 of the ankle. The Al region of the ankle and the H2 region of the hip are examples of significant negative power, because the muscles control the body to resist the forward movement against gravity.

[0084] Positive work is the energy provided to complete the corresponding movement, and negative work is the energy absorbed. Here the role of the exoskeleton is to replace the muscle to complete the assist at the moment when the muscle needs to provide energy, thereby reducing muscle fatigue. The phase point in the red box in the above figure is the point where the corresponding joint motor needs to provide assistance, so in order to accurately complete the assist control in real time, the gait phase of the human-exoskeleton system needs to be detected in time. For example, the right lower limb Hip joint assist point can provide assist in two intervals of a gait cycle, that is, a period of time when the right foot is in the front and the double support phase starts, and a period of time when the right leg is about to enter the swing phase. There are many methods to identify the gait phase, such as using a foot pressure sensor method, using an IMU-based method, etc. This patent does not expand on the detailed description.

[0085] For gait trajectory instructions, it is known from the above description that it is a torque curve, which can be calibrated based on the torque curve of each joint in the normal human gait cycle. As follows Figure 6 For the torque change curve of each joint in the normal gait cycle of a subject, a table of assist instruction can be constructed according to the torque curve and the required assist ratio system C (0 = < C = 1). As can be seen from the vertical axis of the following figure, the joint torque is periodically normalized on the horizontal axis and weight-normalized on the vertical axis.

[0086] Similar to the standard gait database method of joint angle instructions, the joint torque instruction gait trajectory database can be constructed according to the dimensions of gender, age, and weight, as shown in Figure 7 .

[0087] For the normal assist instruction, the torque is used, so the underlying servo control adopts force control mode.

[0088] As can be seen, the exoskeleton robot can dynamically generate personalized trajectory planning schemes according to the state and needs of patients in different rehabilitation stages, and improve the effectiveness and safety of rehabilitation training.

[0089] Further, the embodiment also provides an exoskeleton robot trajectory planning system based on human parameters and states, comprising a healthy gait acquisition module for acquiring gait data of healthy individuals, establishing a standard gait database, and completing gait data of special individuals; a rehabilitation early gait generation module for generating an adaptive joint angle trajectory according to the standard gait trajectory and adjusting the coefficient in the early rehabilitation of patients; a gait real-time adjustment module for adjusting the gait trajectory in real time through the combination of offline planning and online adjustment when the patient has a certain muscle capacity; a gait assist providing module for providing assist in a specific phase of the gait cycle when the patient is judged to have complete gait capacity through quantitative evaluation indicators, and the underlying servo control adopts force control mode.

[0090] The embodiment also provides a computer device suitable for the case of the exoskeleton robot trajectory planning method based on human body parameters and states, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the exoskeleton robot trajectory planning method based on human body parameters and states proposed in the above embodiment.

[0091] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or can be a key, trackball or touchpad arranged on the shell of the computer device, or can be an external keyboard, touchpad or mouse, etc.

[0092] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the exoskeleton robot trajectory planning method based on human body parameters and states proposed in the above embodiment.

[0093] In summary, the shoulder joint angle limiting device is designed to effectively limit the rotation angle of the shoulder, prevent the arm from contacting the chest during rotation, and greatly improve the safety of the user. Especially in high-altitude operation and heavy physical labor scenes, it can effectively reduce accidental injuries caused by excessive rotation; the use of magnets and boss structures makes the installation and removal of the limiting block very simple, without the need for complex tools. This design not only reduces the operation difficulty, but also reduces the maintenance time, improves the operability and reliability of the equipment; the present application can automatically generate corresponding motion control instructions according to the physical condition, age, limb length and other conditions of the operator, to ensure that the exoskeleton system provides the most suitable auxiliary force. This personalized adaptation capability enables the exoskeleton device to be widely used in different body types and different needs of operators, improving the versatility of the device; and by automatically generating the motion trajectory instruction curve of each joint (such as the shoulder joint, elbow joint, hip joint and knee joint), the present application can effectively reduce the labor intensity of the operator and improve the work efficiency. Especially in the power grid industry, it can significantly improve the completion speed of high-altitude operation, heavy physical labor and other tasks; further, the exoskeleton walking trajectory planning can be divided into offline generation, online real-time generation and a combination of the two methods, which can flexibly cope with different use scenarios and needs. The offline method is generated by mathematical expressions and measurement data, and the online real-time generation mode relies on sensors to predict the motion intention of the person in real time, making the trajectory planning more accurate and efficient. The present application is not only suitable for the power grid industry, but also can be popularized to other fields that require high-intensity upper limb operation, such as construction, logistics and transportation, etc. At the same time, it also has important application value in the field of medical rehabilitation, providing effective rehabilitation training assistance for lower limb patients.

[0094] Example 2

[0095] In the second embodiment of the present application, a method for automatically generating joint layer servo control trajectory instruction curve based on the health status of the subject's limbs is provided. The gait trajectory instruction can be single hip joint drive, single knee joint drive, or single ankle joint drive according to the active drive joint structure configuration of the exoskeleton, and the corresponding trajectory instruction of the joint needs to be generated as the instruction of the joint servo drive. Considering the versatility, the present patent takes a three-joint (hip, knee, ankle) active drive exoskeleton as an example for illustration, i.e. three-joint trajectory instructions need to be generated.

[0096] The trajectory planning of the exoskeleton walking can be divided into offline generation, online real-time generation and a combination of the two methods. The offline method is generally generated by mathematical expressions and measured data; the online real-time generation mode relies on sensors (such as acceleration sensors, electromyographic sensors, etc.) to predict the motion intention of the person in real time. Which trajectory generation method to use needs to be determined in combination with the health status level of the subject's lower limbs.

[0097] Example 3

[0098] The third embodiment of the present application provides a passive control trajectory generation instruction strategy.

[0099] For patients without the ability of lower limbs movement, the standard gait curve of normal people similar to the patient's gender, age and height is generally adopted for adjustment. As for the adjustment method, the joint angle can be obtained after the standard gait trajectory is multiplied by a coefficient a (0 < a <= 1) as the instruction of the lower servo closed-loop control, that is, the exoskeleton has large rigidity and the lower limbs of the subject are completely driven by the exoskeleton in this training mode. This training mode is suitable for the early stage of rehabilitation training. The value of a is adjusted by the rehabilitation physician according to the rehabilitation plan. In order to exclude the subjectivity of the physician, the present application adopts a quantitative evaluation index I and a coefficient a to construct a mapping table, and automatically calculates the coefficient through the automatically calculated evaluation index I.

[0100] The gait trajectory has individual differences and is strongly related to human characteristics such as gender, age and height.

[0101] The standard gait database collects the trajectories of each joint as standard gait data according to gender, age and height in the following structure.

[0102] For each age range of subjects, those with a height of 150 cm to 190 cm are selected to walk at a standard gait at a constant speed on a horizontal road surface, and the trajectory curve of the subject in the sagittal plane is recorded.

[0103] Post-processing: The collected joint trajectory curve is intercepted to start with the heel of the right foot touching the ground, and the trajectory before the next right foot heel touching the ground is taken as a complete gait cycle T. A plurality of gait cycle data N of the same subject are continuously collected, and the average value of the N gait cycle data is taken.

[0104] Embodiment 4

[0105] The fourth embodiment of the present application provides an adaptive trajectory generation strategy.

[0106] For patients who have reached a certain stage of rehabilitation, it can be judged whether the patient belongs to this category through the quantitative evaluation index I. The muscles of this type of patients have a certain muscle strength and can perform a certain degree of movement independently. If the completely passive training mode is continued without considering the subject's autonomous movement ability, the exoskeleton and the patient's movement are not coordinated, and better rehabilitation effect cannot be achieved. Therefore, the patient's autonomous movement ability needs to be considered to adjust the trajectory curve, that is, the offline planning plus online adjustment mode is adopted to complete the gait trajectory generation of this type of patients.

[0107] Admittance controller: adjusts the relative angle according to the interaction force, fully considers the influence of the human in the control loop, and improves the safety and comfort.

[0108] The relationship between human-computer interaction force and adjustment angle is set as a second-order system, and the parameter of the admittance controller is directly adjusted, so that the debugging is convenient; and the angular frequency and the attenuation coefficient can be adjusted according to the comfort of the human, and compared with the current commonly used parameter setting method, the parameter setting is fast.

[0109] Embodiment 5

[0110] In the fifth embodiment of the application, a time assistance control strategy is provided.

[0111] For a fully recovered patient (normal person), whether the patient has fully recovered can be judged by the quantitative evaluation index I. In this case, the main function of the exoskeleton is assistance. The assistance time point is completed according to the biomechanical characteristics of human walking gait, and different joints are in different phases of the gait cycle at the assistance time point, and the work of each joint muscle in different phases of the gait cycle needs to be determined according to the work of each joint muscle in different phases of the gait cycle.

[0112] The work of each joint muscle in walking gait:

[0113] Positive work is to provide energy to complete the corresponding movement, and negative work is to absorb energy.

[0114] The function of the exoskeleton is to replace the muscle to complete the assistance when the muscle needs to provide energy, so as to reduce muscle fatigue.

[0115] The torque curve can be calibrated based on the torque curve of each joint in the normal gait cycle of a normal person, and the following is the torque change curve of each joint in the normal gait cycle of a subject, and the assistance command table can be constructed according to this.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for trajectory planning of an exoskeleton robot based on human parameters and states, characterized in that: The application relates to a gait rehabilitation training system and method. Gait data of healthy individuals are collected to generate a standard gait database and complete gait data of special individuals; In the early rehabilitation of patients, an adaptive joint angle trajectory is generated according to a standard gait trajectory and an adjustment coefficient; When the patients have certain muscle capacity, the gait trajectory is adjusted in real time through a combination of offline planning and online adjustment; When the patients are judged to have complete gait capacity through quantitative evaluation indexes, the exoskeleton provides assistance in specific phases of the gait cycle, and the underlying servo control adopts a force control mode; The standard gait database comprises gait data of healthy individuals of different genders, ages and heights; The joint trajectory data in each gait cycle are processed after average value calculation to establish a standard gait trajectory containing gait cycle key points as a reference data source for patient rehabilitation training; For patient individuals who cannot directly match the standard gait database, an interpolation or fitting algorithm is adopted to generate an approximate gait trajectory to generate a joint angle trajectory instruction adaptive to patient characteristics; The standard gait trajectory containing gait cycle key points comprises the following steps: For the same subject, N gait cycle data are continuously collected, and the average value of the N gait cycle data is taken; For the kth (k=1, 2,..., N) gait cycle Tk, the sampling period is Ts_k=Tk / n; The data at the ith point of the gait cycle are calculated by the following formula: Avg_y(i)=[y1(i)+y2(i)+...+yk(i)+...+yN(i)] / N; i=1, 2,..., n; Wherein, yk(i) represents the angle value at the ith point in the kth gait cycle.

2. The human parameter and state based exoskeleton robot trajectory planning method of claim 1, wherein: The adaptive joint angle trajectory generated according to the standard gait trajectory and the adjustment coefficient comprises the following steps: The joint angle obtained by multiplying the standard gait trajectory by a coefficient a value (0 The size of the a value is adjusted along with the progress of rehabilitation; The standard gait trajectory is searched and matched in the standard gait database according to subject parameters.

3. The human parameter and state based exoskeleton robot trajectory planning method of claim 2, wherein: The real-time adjustment of the gait trajectory through the combination of offline planning and online adjustment comprises the following steps: A new trajectory instruction curve is generated by online adjustment of the standard trajectory curve through a mobility control strategy; If the interaction force F is desired to be equal to 0, then if the patient is in a fully paralyzed state, the human-machine interaction force feedback value is equal to 0, the angle command q amt is equal to 0, which translates into a passive control. If the human-machine interaction force is not equal to 0, the standard trajectory instruction curve is adjusted in real time.

4. The human parameter and state based exoskeleton robot trajectory planning method of claim 3, wherein: The adjustable gain mobility controller adjusts the relative angle according to the interaction force; The angular frequency and the attenuation coefficient are adjusted according to human comfort, and the related formula is as follows: where M d s is the joint angle acceleration, B d s is the joint angle velocity, K d is the angle coefficient, M d is the joint angle value, ω n is the interaction force and adjustment angle response angular frequency, and ζ is the damping coefficient.

5. A human parameter and state based exoskeleton robot trajectory planning system based on the human parameter and state based exoskeleton robot trajectory planning method according to any one of claims 1 to 4, characterized by: The application further relates to a gait rehabilitation training system and method. The healthy gait collection module is used for collecting gait data of healthy individuals, establishing a standard gait database and completing gait data of special individuals; The early rehabilitation gait generation module is used for generating an adaptive joint angle trajectory according to a standard gait trajectory and an adjustment coefficient in the early rehabilitation of patients; The gait real-time adjustment module is used for adjusting the gait trajectory in real time through a combination of offline planning and online adjustment when the patients have certain muscle capacity. Gait assistance providing module is used for providing assistance in specific phase of gait cycle when the patient is judged to have complete gait ability through quantitative evaluation index, and the underlying servo control adopts force control mode.

6. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the human body parameter and state based exoskeleton robot trajectory planning method in any one of claims 1-4.

7. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the human body parameter and state based exoskeleton robot trajectory planning method in any one of claims 1-4.

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