Exoskeleton robot trajectory planning method based on human body parameters and states

Abstract

The invention discloses an exoskeleton robot trajectory planning method based on human body parameters and states, and relates to the technical field of trajectory planning. Comprising the steps of collecting gait data of healthy individuals, generating a standard gait database, and complementing gait data of special individuals; in the early rehabilitation stage of the patient, an adaptive joint angle track is generated according to the standard gait track and the adjustment coefficient; after the patient has certain muscle ability, the gait track is adjusted in real time in a mode of combining off-line planning and on-line adjustment; and when the patient is judged to have the complete gait ability through the quantitative evaluation index, the exoskeleton provides assisting power at a specific phase of a gait cycle, and bottom layer servo control adopts a force control mode. By designing the shoulder joint angle limiting device, the rotation angle of the shoulder is effectively limited, arms are prevented from making contact with the chest of the human body in the rotation process, and therefore the safety of a user is greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of trajectory planning, and in particular to an exoskeleton robot trajectory planning method based on human body parameters and states. Background Art

[0002] With the rapid development of the power industry, the power operation environment has become increasingly complex, the labor intensity of operators has continued to increase, and due to the shortage of personnel and the increase in labor costs, the research and development of auxiliary equipment for power operations has gradually become popular. Especially for scenes such as high-altitude operations and heavy physical labor, upper and lower limb assisted exoskeleton robots have become an important technical direction.

[0003] In order to meet the needs of different operators (the physical condition, age, limb length and other conditions of the operators should be fully considered here), it is necessary to generate corresponding motion control instructions for different wearer conditions, that is, it is necessary 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 operations. Summary of the invention

[0004] In view of the problems existing in the above background technology, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to automatically generate a joint-layer servo control trajectory command curve based on the physical state of the operator.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

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

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

[0009] As a preferred solution of the exoskeleton robot trajectory planning method based on human parameters and states according to the present invention, for patient individuals who cannot directly match the standard gait database, interpolation or fitting algorithms are used to generate approximate gait trajectories, and joint angle trajectory instructions adapted to the patient's characteristics are generated.

[0010] As a preferred solution of the exoskeleton robot trajectory planning method based on human parameters and states according to the present invention, the establishment of the standard gait trajectory including key points of the gait cycle includes: for the same subject, continuously collecting data of N gait cycles and taking the average value of the data of N gait cycles; for each gait cycle, adjusting to the same number of samples n, then for the k-th (k = 1, 2,..., N) gait cycle Tk, the sampling period is Ts_k = Tk / n; then the data at the i-th 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 i-th point in the k-th gait cycle.

[0014] As a preferred solution of the exoskeleton robot trajectory planning method based on human parameters and states according to the present invention, the generation of the adapted joint angle trajectory based on the standard gait trajectory and adjusting the coefficient includes: using the standard gait trajectory multiplied by the coefficient a value (0 < a <= 1) to obtain the joint angle as the instruction for the lower servo closed-loop control; the size of the a value is adjusted as the rehabilitation process progresses; the standard gait trajectory is retrieved and matched in the standard gait database according to the subject's parameters.

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

[0016] As a preferred solution of the exoskeleton robot trajectory planning method based on human body parameters and states described in the present invention, the adjustable gain admittance controller adjusts the relative angle according to the interaction force; the angular frequency and the attenuation coefficient are adjusted according to the human body comfort. The relevant formulas are as follows:

[0017]

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

[0019] In the second aspect, an embodiment of the present invention provides an exoskeleton robot trajectory planning system based on human body parameters and states, which includes: a healthy gait acquisition module, which is used to collect gait data of healthy individuals, establish a standard gait database, and complete the gait data of special individuals; an early rehabilitation gait generation module, which is used to generate an adaptive joint angle trajectory based on the standard gait trajectory and adjust the coefficient in the early stage of patient rehabilitation; a gait real-time adjustment module, which is used to adjust the gait trajectory in real time by combining offline planning and online adjustment when the patient has a certain muscle capacity; a gait assistance providing module, which is used to provide assistance at a specific phase of the gait cycle when the patient is judged to have complete gait ability through quantitative evaluation indicators, and the underlying servo control adopts a force control mode.

[0020] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the exoskeleton robot trajectory planning method based on human body parameters and states as described in the first aspect of the present invention are implemented.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the exoskeleton robot trajectory planning method based on human body parameters and states as described in the first aspect of the present invention are implemented.

[0022] The beneficial effects of the present invention are as follows: the present invention effectively limits the shoulder rotation angle by designing a shoulder joint angle limiter, preventing the arm from contacting the human chest during the rotation process, thereby greatly improving the safety of the user. Especially in scenes such as aerial work and heavy physical labor, it can effectively reduce accidental injuries caused by excessive rotation; the use of magnets and boss structures makes the installation and removal of the limit block very simple and can be completed without complex tools. This design not only reduces the difficulty of operation, but also reduces maintenance time, and improves the operability and reliability of the equipment; the present invention can automatically generate corresponding motion control instructions according to the physical condition, age, limb length and other conditions of the operator, ensuring 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 shapes and different needs, improving the versatility of the equipment; and by automatically generating motion trajectory command curves of each joint (such as shoulder joint, elbow joint, hip joint and knee joint), the present invention can effectively reduce the labor intensity of the operator and improve work efficiency. Especially in the power grid industry, it can significantly improve the completion speed of tasks such as aerial work and heavy physical labor; 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 respond to different usage 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 people's movement intentions in real time, making trajectory planning more accurate and efficient. The present invention is not only suitable for the power grid industry, but can also be extended to other fields that require high-intensity upper limb operations, such as construction, logistics handling, 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 Generate a strategy flow chart for trajectory planning based on rehabilitation level I.

[0025] Figure 2 It is a standard gait database of joint angles.

[0026] Figure 3 This is the block diagram of the admittance control system.

[0027] Figure 4 A block diagram of a new trajectory command curve is generated for online adjustment of the standard trajectory curve through the admittance control strategy.

[0028] Figure 5 The work done by muscles in each joint during walking

[0029] Figure 6 It is the torque change curve of each joint in the normal gait cycle of the subject.

[0030] Figure 7 It is a standard gait database of joint torques. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0034] Example 1

[0035] Reference Figure 1 to Figure 7 , which is the first embodiment of the present invention, and provides an exoskeleton robot trajectory planning method based on human body 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 trajectories of healthy individuals are collected to construct a standard gait database containing different genders, ages and heights to provide reference trajectories for different patients. The data of each gait cycle is post-processed with the average value of several sampling points to form a standard gait trajectory.

[0038] For patient features that cannot be directly matched, interpolation or fitting methods can be used to generate approximate gait trajectories in the database.

[0039] The gait trajectory command can be driven by a single hip joint, a single knee joint, or a single ankle joint according to the configuration of the exoskeleton's active drive joint structure. The trajectory command of the corresponding joint needs to be generated as the instruction of the joint servo drive. Considering the versatility, this patent takes the three-joint (hip, knee, ankle) active drive exoskeleton as an example. That is, it is necessary to generate three-joint trajectory commands.

[0040] The trajectory planning of exoskeleton walking can be divided into offline generation, online real-time generation, and a combination of the two. The offline method is generally generated by mathematical expressions and measured data. The online real-time generation mode relies on sensors (such as accelerometers, electromyography sensors, etc.) to predict people's movement intentions in real time.

[0041] The specific trajectory generation method to be used needs to be determined based on the subject's lower limb health status.

[0042] S1.1: Gait trajectories vary among individuals and are strongly correlated with human characteristics such as gender, age, and height.

[0043] The standard gait database is built according to the following structure based on gender, age, and height. The joint trajectories collected from people with normal gait are used as standard gait trajectory data.

[0044] For the subjects in each age range, those with a height of 150cm to 190cm were selected to walk at a constant speed with a standard gait on a horizontal road, and the trajectory curve of the subjects in the sagittal plane was recorded at the same time.

[0045] S1.2: The collected joint trajectory curve is intercepted from the right heel landing to the trajectory before the next right heel landing as a complete gait cycle T.

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

[0047] For each gait cycle, assume that 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.

[0048] The data at the i-th point in 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 i-th point in the k-th gait cycle.

[0052] The standard gait data of a certain subject (such as Zhang San, male, 20 years old, 150 cm tall) can be obtained from the above formula.

[0053] According to the above steps, continue to select subjects of each age group, a certain height and gender for experiments, collect and obtain their gait trajectory instructions, and store them in the database.

[0054] It should be noted that when selecting subjects, try to make the ages evenly distributed in the given age group, and the height does not necessarily follow the height gradient of 5 cm as follows.

[0055] In this stage, the bottom servo of the patient adopts the position control mode, so the reference instruction trajectory is the position instruction as above.

[0056] S2: In the early stage of the patient's rehabilitation, according to the standard gait trajectory and adjust the coefficient to generate an adapted joint angle trajectory to achieve the full drive of the exoskeleton for the patient's lower limbs.

[0057] For patients with no lower limb motor ability, it is generally adjusted from the standard gait curve of normal people similar to the patient's gender, age and height.

[0058] Regarding the adjustment method, the joint angle obtained by multiplying the standard gait trajectory by the coefficient a (0 < a <= 1) can be used as the instruction for the lower servo closed-loop control. That is, by using this training method, the exoskeleton has great rigidity and the patient's lower limbs are completely driven by the exoskeleton.

[0059] This training method is applicable to the early stage of rehabilitation training.

[0060] The value of a is adjusted by the rehabilitation physician according to the formulated rehabilitation plan as the rehabilitation process progresses. In order to exclude the subjectivity of the physician, the present invention constructs a mapping table using the quantitative evaluation index I and the coefficient a, and automatically calculates the coefficient a through the automatically calculated evaluation index I. That is, if the I value under complete paralysis is output, then a takes a very small value, such as a = 0.1. Larger values may cause secondary muscle strains.

[0061] Among them, the calculation method of the quantitative evaluation index I is given in another patent (a quantitative rehabilitation evaluation method for lower limbs using wearable sensors).

[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 not available in the database, it can be obtained by interpolating or fitting several adjacent trajectory curves.

[0063] The process of building the standard gait database is as Figure 2 shown.

[0064] S3: When the patient has a certain level of muscle capacity, the gait trajectory is adjusted in real time through a combination of 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 a certain level of muscle strength, it is determined whether he or she is suitable for entering the adaptive training mode.

[0066] The method of combining offline planning and online adjustment is adopted to adjust the gait trajectory in real time based on methods such as admittance control, so that the trajectory can adapt 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 to achieve better rehabilitation effects.

[0067] Specifically, the quantitative evaluation index I is used to determine whether the patient belongs to this category.

[0068] For such patients, their muscles have a certain degree of strength and they can move autonomously to a certain extent. If a completely passive training method is continued without considering the autonomous movement ability of the subject, the movement of the exoskeleton and the patient will be uncoordinated, and the best rehabilitation effect will not be achieved.

[0069] Therefore, for such patients, it is necessary to consider the patient's ability to move autonomously to adjust the trajectory curve. That is, the generation of the gait trajectory of such patients is completed by using offline planning plus online adjustment. Figure 4 To control the standard trajectory curve by the admittance control strategy Perform online adjustment to generate a new trajectory command curve q * Assuming that the expected interaction force F is equal to 0, if the patient is in a completely paralyzed state, that is, the lower limbs of the person cannot move, the human-computer interaction force feedback value is equal to 0, then the correction angle command q amt If the human-machine interaction force is also equal to 0, it will be converted to passive control. As long as the human-machine interaction force is not equal to 0, the standard trajectory command curve will be adjusted online in real time to take the patient's own movement ability into account.

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

[0071] like 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 an admittance controller can fully consider the influence of people in the control loop, improving safety and comfort; the relationship between the human-machine interaction force and the adjustment angle is set as a second-order system, and the parameters of the admittance controller are directly adjusted, which is convenient for debugging;

[0073] The angular frequency and attenuation coefficient can be adjusted appropriately according to human comfort. Compared with the commonly used parameter setting method, the parameter setting is faster. The relevant formula is as follows:

[0074]

[0075]

[0076] Among them, M d s is the joint angular acceleration, B d s is the joint angular velocity, K d is the angle coefficient, M d is the joint angle value, ω n The angular frequency for the interaction force and the adjustment angle response, is the attenuation coefficient.

[0077] At this stage, the patient's bottom servo adopts the inner loop 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 quantitative evaluation indicators, the exoskeleton provides assistance at specific phases of the gait cycle, and the underlying servo control adopts force control mode to improve the naturalness of gait and rehabilitation effect.

[0079] When the patient recovers to a near-normal state, the exoskeleton enters the power-assistance mode, supporting the patient to provide power assistance at specific gait phases. The power-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 power assistance at the right time.

[0080] The underlying servo control adopts a force control mode, which outputs power according to the phase of the joints in the gait cycle to achieve a smoother walking experience.

[0081] Preferably, a quantitative evaluation index I is used to determine whether the patient has fully recovered.

[0082] For normal people, the main function of the exoskeleton is to assist. The timing of assisting is based on the biomechanical characteristics of the human body during gait, and the timing of assisting different joints at different phases of the gait cycle needs to be determined based on the work done by the muscles of each joint at different phases of the gait, such as Figure 5 shown.

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

[0084] Positive work is the energy that needs to be provided to complete the corresponding movement, and negative work is the absorbed energy. The role of the exoskeleton here is to replace and provide assistance at the moment when the muscles need to provide energy, thereby reducing muscle fatigue. As shown in the above figure, the phase points within the red box are the points where the corresponding joint motors need to provide assistance. Therefore, in order to accurately complete the assistance control in real time, it is necessary to detect the gait phase of the human-exoskeleton system in a timely manner. For example, the Hip joint assistance point of the right lower limb can provide assistance in two intervals of a gait cycle, that is, for a period of time starting from the beginning phase of double support with the right foot in front, and for a period of time starting when the right leg is about to enter the swing phase. There are many methods to achieve gait phase recognition, such as using the method of sole pressure sensors, the method based on IMU, etc. This patent will not describe them in detail.

[0085] Regarding the gait trajectory command, as described above, it is a torque curve, and the torque curve can be calibrated based on the torque curves of each joint in the normal human gait cycle. As follows Figure 6 Is the torque change curve of each joint in the normal gait cycle of a subject. The assistance command table can be constructed according to this torque curve and the required assistance ratio system C (0 <= C <= 1). It can also be seen from the vertical axis of the following figure that the joint torque is normalized in terms of period on the horizontal axis and in terms of weight on the vertical axis.

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

[0087] For normal people, the assistance command is torque, so the underlying servo control adopts a force control mode.

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

[0089] Furthermore, the present embodiment also provides an exoskeleton robot trajectory planning system based on human body parameters and states, including a healthy gait acquisition module, which is used to collect gait data of healthy individuals, establish a standard gait database, and complete the gait data of special individuals; an early rehabilitation gait generation module, which is used to generate an adaptive joint angle trajectory based on the standard gait trajectory and adjust the coefficient in the early stage of patient rehabilitation; a gait real-time adjustment module, which is used to adjust the gait trajectory in real time by combining offline planning and online adjustment when the patient has a certain muscle capacity; a gait assistance providing module, which is used to provide assistance at a specific phase of the gait cycle when the patient is judged to have complete gait ability through quantitative evaluation indicators, and the underlying servo control adopts a force control mode.

[0090] This embodiment also provides a computer device, which is suitable for the case of an 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 computer executable instructions to implement the exoskeleton robot trajectory planning method based on human body parameters and states as proposed in the above embodiment.

[0091] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via 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 the operation 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 an external terminal in a wired or wireless manner, and 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 may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0092] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the exoskeleton robot trajectory planning method based on human body parameters and states as proposed in the above embodiment is implemented.

[0093] In summary, the present invention effectively limits the shoulder rotation angle by designing a shoulder joint angle limiter, preventing the arm from contacting the human chest during the rotation process, thereby greatly improving the safety of the user. Especially in scenes such as aerial work and heavy physical labor, it can effectively reduce accidental injuries caused by excessive rotation; the use of magnets and boss structures makes the installation and disassembly of the limit block very simple and can be completed without complex tools. This design not only reduces the difficulty of operation, but also reduces maintenance time, and improves the operability and reliability of the equipment; the present invention can automatically generate corresponding motion control instructions according to the physical condition, age, limb length and other conditions of the operator, ensuring 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 shapes and different needs, improving the versatility of the equipment; and by automatically generating motion trajectory command curves of each joint (such as shoulder joint, elbow joint, hip joint and knee joint), the present invention can effectively reduce the labor intensity of the operator and improve work efficiency. Especially in the power grid industry, it can significantly improve the completion speed of tasks such as aerial work and heavy physical labor; 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 respond to different usage 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 people's movement intentions in real time, making trajectory planning more accurate and efficient. The present invention is not only suitable for the power grid industry, but can also be extended to other fields that require high-intensity upper limb operations, such as construction, logistics handling, 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] The second embodiment of the present invention provides a method for automatically generating a joint-layer servo control trajectory command curve based on the limb health status of the subject. The gait trajectory command can be a single hip joint drive, a single knee joint drive, or a single ankle joint drive according to the configuration of the exoskeleton active drive joint structure, and the trajectory command of the corresponding joint needs to be generated as the servo drive command of the joint part. Taking into account the versatility, this patent takes the three-joint (hip, knee, ankle) active drive exoskeleton as an example for explanation, that is, it is necessary to generate three-joint trajectory commands.

[0096] Trajectory planning for exoskeleton walking can be divided into offline generation, online real-time generation, and a combination of the two. The offline method is generally generated by mathematical expressions and measured data; the online real-time generation mode relies on sensors (such as accelerometers, electromyography sensors, etc.) to predict people's movement intentions in real time. The specific trajectory generation method to be used needs to be determined in combination with the health status of the subject's lower limbs.

[0097] Example 3

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

[0099] For patients with no lower limb motor ability, it is generally obtained by adjusting the standard gait curve of normal people with similar gender, age, and height to the patient. Regarding the adjustment method, the joint angle can be obtained by multiplying the standard gait trajectory by a coefficient a (0 < a <= 1) as the instruction for the lower servo closed-loop control. That is, by training in this way, the exoskeleton has great rigidity, and the lower limbs of the subject are completely driven by the exoskeleton. This training method is applicable to the early stage of rehabilitation training. The value of a is adjusted by the rehabilitation physician according to the formulated rehabilitation plan as the rehabilitation process progresses. To exclude the subjectivity of the physician, this invention constructs a mapping table using the quantitative evaluation index I and the coefficient a, and automatically calculates the coefficient through the automatically calculated evaluation index I.

[0100] There are individual differences in gait trajectories, which have a strong correlation with human characteristics such as gender, age, and height.

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

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

[0103] Post-processing: The collected joint trajectory curve is intercepted starting from the right heel touching the ground, and the trajectory before the next right heel touches the ground is used as a complete gait cycle T. The gait cycle data N of the same subject is continuously collected, and the average value of the N gait cycle data is taken.

[0104] Embodiment 4

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

[0106] For patients who have recovered to a certain stage, it can be judged whether the patient belongs to this category through the quantitative evaluation index I. The muscles of this type of patient have a certain muscle strength and can perform a certain degree of movement independently. If the completely passive training method is continued without considering the subject's autonomous movement ability, the exoskeleton and the patient's movement will be不协调, and a better rehabilitation effect cannot be achieved. Therefore, it is necessary to consider the subject's autonomous movement ability to adjust the trajectory curve, that is, to complete the gait trajectory generation of such patients by using the off-line planning plus on-line adjustment method.

[0107] Admittance controller: Adjust the relative angle according to the interaction force, fully considering the influence of people in the control loop, and improving safety and comfort.

[0108] The relationship between the human-computer interaction force and the adjustment angle is set as a second-order system, and the parameters of the admittance controller are directly adjusted, which is convenient for debugging; the angular frequency and attenuation coefficient can be appropriately adjusted according to human comfort. Compared with the currently commonly used parameter setting methods, the parameter setting is fast.

[0109] Example 5

[0110] The fifth embodiment of the present invention provides a power assist control strategy.

[0111] For fully recovered patients (normal people), the quantitative evaluation index I can be used to determine whether the patient has fully recovered. In this case, the main function of the exoskeleton is to assist. The assisting time point is completed according to the biomechanical characteristics of the human body during gait. Different joints are at different phases of the gait cycle at the assisting time point, which needs to be determined according to the work done by each joint muscle at different phases of the gait.

[0112] The work done by muscles in each joint during walking gait:

[0113] Positive work is the energy required to complete the corresponding movement, and negative work is the absorption of energy.

[0114] The function of the exoskeleton is to provide assistance when muscles are needed to provide energy, thereby reducing muscle fatigue.

[0115] The torque curve can be calibrated based on the torque curves of each joint in a normal human gait cycle. The following is a curve of torque changes of each joint in a normal gait cycle of a subject, and an assistance instruction table can be constructed based on this.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention 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 invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A trajectory planning method for an exoskeleton robot based on human body parameters and states, characterized in that: including Collecting gait data of healthy individuals, generating a standard gait database, and supplementing the gait data of special individuals; In the early stage of patient rehabilitation, generating an adapted joint angle trajectory according to the standard gait trajectory and adjusting coefficients; When the patient has a 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 indicators, the exoskeleton provides assistance at specific phases of the gait cycle, and the underlying servo control adopts a force control mode.

2. The exoskeleton robot trajectory planning method based on human body parameters and states as claimed in claim 1, characterized in that: The standard gait database includes collecting gait data of healthy individuals of different genders, ages, and heights; The joint trajectory data within each gait cycle is processed through average value calculation to establish a standard gait trajectory containing key points of the gait cycle, which serves as a reference data source for patient rehabilitation training.

3. The exoskeleton robot trajectory planning method based on human body parameters and states as claimed in claim 2, characterized in that: For patient individuals who cannot directly match the standard gait database, interpolation or fitting algorithms are used to generate an approximate gait trajectory and generate a joint angle trajectory instruction adapted to the patient's characteristics.

4. The exoskeleton robot trajectory planning method based on human body parameters and states as claimed in claim 3, characterized in that: The establishment of the standard gait trajectory containing key points of the gait cycle includes: For the same subject, continuously collect gait cycle data for N times and take the average value of the N gait cycle data; For each gait cycle, adjust it to the same number of samples n. Then, for the k-th (k = 1, 2,..., N) gait cycle Tk, the sampling period is Ts_k = Tk / n; Then, the data at the i-th point of the gait cycle is calculated by the following formula: Avg_y(i) = [y1(i) + y2(i) +... + yk(i) +... + yN(i)] / N i = 1, 2,..., n where yk(i) represents the angle value at the i-th point in the k-th gait cycle.

5. The exoskeleton robot trajectory planning method based on human body parameters and states as claimed in claim 4, characterized in that: The generation of an adapted joint angle trajectory according to the standard gait trajectory and adjusting coefficients includes: Multiplying the standard gait trajectory by the coefficient a value (0 < a <= 1) to obtain the joint angle as the instruction for the lower servo closed-loop control; The magnitude of the a value is adjusted as the rehabilitation process progresses; The standard gait trajectory is retrieved and matched in the standard gait database according to the subject parameters.

6. The exoskeleton robot trajectory planning method based on human body parameters and states as claimed in claim 5, characterized in that: The real-time adjustment of the gait trajectory by combining offline planning and online adjustment includes: Online adjustment of the standard trajectory curve through the admittance control strategy to generate a new trajectory instruction curve; If the expected interaction force F is equal to 0, then if the patient is in a completely paralyzed state, the human-computer interaction force feedback value is equal to 0, then the correction angle command q amt Equal to 0, converted to passive control; If the human-machine interaction force is not equal to 0, the standard trajectory instruction curve is adjusted online in real time.

7. The exoskeleton robot trajectory planning method based on human body parameters and states as claimed in claim 6, characterized in that: The adjustable gain admittance controller adjusts the relative angle according to the interaction force; The angular frequency and attenuation coefficient are adjusted according to human comfort, and the relevant formulas are as follows: in, is the joint angular acceleration, is the joint angular velocity, K d is the angle coefficient, M d is the joint angle value, ω n The angular frequency for the interaction force and the adjustment angle response, is the attenuation coefficient.

8. An exoskeleton robot trajectory planning system based on human body parameters and states, based on the exoskeleton robot trajectory planning method based on human body parameters and states according to any one of claims 1 to 7, characterized in that: also including A healthy gait acquisition module for collecting gait data of healthy individuals, establishing a standard gait database, and supplementing the gait data of special individuals; A gait generation module in the initial stage of rehabilitation for generating an adapted joint angle trajectory according to the standard gait trajectory and adjusting coefficients in the early stage of patient rehabilitation; A gait real-time adjustment module for adjusting the gait trajectory in real time by combining offline planning and online adjustment when the patient has a certain muscle ability; The gait assistance providing module is used to provide assistance at a specific phase of the gait cycle when the patient is judged to have complete gait ability through quantitative evaluation indicators. The underlying servo control adopts a force control mode.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the exoskeleton robot trajectory planning method based on human body parameters and states as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the exoskeleton robot trajectory planning method based on human body parameters and states as described in any one of claims 1 to 7 are implemented.

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