CN113696177B - Control and evaluation method and system applied to exoskeleton robot - Google Patents
Control and evaluation method and system applied to exoskeleton robot Download PDFInfo
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- 238000011156 evaluation Methods 0.000 title claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 37
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- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1628—Programme controls characterised by the control loop
- B25J9/1653—Programme controls characterised by the control loop parameters identification, estimation, stiffness, accuracy, error analysis
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/0006—Exoskeletons, i.e. resembling a human figure
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
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Abstract
The invention discloses a control and evaluation method applied to an exoskeleton robot, which comprises the following steps: responding to the starting of the equipment, acquiring the start-end position data and the start-end speed data and carrying out standardization processing to obtain a standardized start-end position and a standardized start-end speed; obtaining ideal curve time by combining the movement limit data of the equipment driver based on the standardized start and end positions and the standardized speed; processing the ideal curve time to obtain a preset equipment motion track and an output value; acquiring relevant data of equipment movement in real time, wherein the relevant data of the movement at least comprises a moment and an angle; and comparing the preset motion track and output value of the equipment with the torque and angle to judge whether the equipment is abnormal or not. The method can judge whether the equipment movement is abnormal or not, if the equipment movement is not abnormal, the collected data can be processed, the processed data is drawn into a corresponding curve, and the curve is subjected to similarity judgment with the existing curve to calculate a corresponding focus.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a control and evaluation method and system applied to an exoskeleton robot.
Background
With the further development of artificial intelligence technology, more and more artificial intelligence devices are emerging, such as exoskeleton robots, public service robots, intelligent factory assembly robots, and the like. The artificial intelligence devices bring convenience to work and life of people. However, various exoskeleton robots face a plurality of technical points which need to be broken through urgently in application scenes, for example, in bedside rehabilitation scenes, under the condition of limiting the functions of exoskeleton drivers, the problem that power is blocked to cause unsmooth constant-speed motion output is solved.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a control and evaluation method and a system of an exoskeleton robot.
In order to solve the above technical problem, the present invention is provided with the following technical solutions, wherein a control and evaluation method applied to an exoskeleton robot includes the following steps:
responding to the starting of the equipment, acquiring the start-end position data and the start-end speed data and carrying out standardization processing to obtain a standardized start-end position and a standardized start-end speed;
obtaining ideal curve time by combining the movement limit data of the equipment driver based on the standardized start and end positions and the standardized speed;
processing the ideal curve time to obtain a preset equipment motion track and an output value;
acquiring relevant data of equipment movement in real time, wherein the relevant data of the equipment movement at least comprises the moment, angle and angular speed of the acquired data;
comparing the preset motion track and output value of the equipment with the torque and angle to judge whether the motion of the equipment is abnormal or not;
if the actual torque characteristic curve is normal, the actual torque characteristic curve is obtained through calculation of the motion related data, the actual torque characteristic curve is evaluated based on the existing standard torque characteristic curve, and the symptoms are calculated according to unreasonable data in the actual torque characteristic curve.
As an implementation manner, the calculating the actual torque characteristic curve through the motion related data includes the following steps:
the obtained motion related data are sorted and classified, unreasonable data are eliminated, and processed data are obtained;
obtaining total flexion work and total extension work, average flexion work and average extension work, maximum peak moment of flexion and maximum peak moment of extension, minimum peak moment of flexion and minimum peak moment of extension and maximum work of flexion and extension based on the processed data;
and obtaining an actual torque characteristic curve based on the data.
As an implementation manner, the ideal curve time is obtained by combining the motion limit data of the device driver based on the normalized start and end positions and the normalized speed, specifically:
obtaining a maximum speed, a minimum acceleration, a maximum acceleration, a minimum jerk and a maximum jerk based on the device driver motion limit data;
obtaining a speed change section time and a constant speed section time based on the maximum speed, the minimum acceleration, the maximum acceleration, the minimum jerk and the maximum jerk, wherein the speed change section time comprises an acceleration section time and a deceleration section time;
judging the constant speed section time, and correcting the speed change section time based on the judgment result to obtain a correction result;
the ideal curve time is obtained based on the correction result.
As an implementation manner, the correcting the shift stage time based on the determination result specifically includes:
and if the constant speed section time is smaller than the preset value, correcting the speed change section time, judging the speed change section time during correction, and correcting the speed change section time based on the judgment result.
In one embodiment, the output value includes an output position, a velocity, an acceleration, and a jerk, and the output position, the velocity, the acceleration, and the jerk are normalized to obtain a normalized output position, a normalized velocity, a normalized acceleration, and a normalized jerk.
As an implementation, the following steps are also included:
acquiring relevant information of a user, wherein the relevant information at least comprises a movement height;
and judging the user type based on the related information of the user, if the user is a new user, acquiring and storing the basic information of the new user, and if the user is an old user, executing a corresponding instruction.
As an implementation, the method further comprises the following steps:
and if the equipment is abnormal in motion, the equipment is stopped suddenly and alarm information is sent out.
A control and evaluation system applied to an exoskeleton robot comprises an acquisition processing module, a first processing module, a second processing module, a data acquisition module, a judgment module and a drawing calculation module;
the acquisition processing module is used for responding to the starting of the equipment, acquiring the start-end position data and the start-end speed data and carrying out standardization processing to obtain a standardized start-end position and a standardized start-end speed;
the first processing module is used for obtaining ideal curve time by combining the movement limit data of the equipment driver based on the standardized starting and ending positions and the standardized speed;
the second processing module is used for processing the ideal curve time to obtain a preset equipment motion track and an output value;
the data acquisition module is used for acquiring relevant data of equipment motion in real time, wherein the relevant data of the equipment motion at least comprises the moment, the angle and the angular speed of the acquired data;
the judging module is used for comparing the preset equipment motion track and output value with the torque and angle to judge whether the equipment motion is abnormal or not;
the drawing calculation module is configured to: if the actual moment characteristic curve is normal, the actual moment characteristic curve is obtained through calculation of motion related data, the actual moment characteristic curve is evaluated based on the existing standard moment characteristic curve, and the symptoms are calculated according to unreasonable data in the actual moment characteristic curve.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method steps of:
responding to the starting of the equipment, acquiring the start-end position data and the start-end speed data and carrying out standardization processing to obtain a standardized start-end position and a standardized start-end speed;
obtaining ideal curve time by combining the movement limit data of the equipment driver based on the standardized start and end positions and the standardized speed;
processing the ideal curve time to obtain a preset equipment motion track and an output value;
acquiring relevant data of equipment movement in real time, wherein the relevant data of the equipment movement at least comprises the moment, angle and angular speed of the acquired data;
comparing the preset motion track and output value of the equipment with the torque and angle to judge whether the motion of the equipment is abnormal or not;
if the actual torque characteristic curve is normal, the actual torque characteristic curve is obtained through calculation of the motion related data, the actual torque characteristic curve is evaluated based on the existing standard torque characteristic curve, and the symptoms are calculated according to unreasonable data in the actual torque characteristic curve.
A device for monitoring the status of a workflow, comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the method steps as follows when executing said computer program:
responding to the starting of the equipment, acquiring the starting and ending position data and the starting and ending speed data, and performing standardization processing to obtain a standardized starting and ending position and a standardized starting and ending speed;
obtaining ideal curve time by combining the movement limit data of the equipment driver based on the standardized start and end positions and the standardized speed;
processing the ideal curve time to obtain a preset equipment motion track and an output value;
acquiring relevant data of equipment movement in real time, wherein the relevant data of the equipment movement at least comprises the moment, angle and angular speed of the acquired data;
comparing the preset motion track and output value of the equipment with the torque and angle to judge whether the motion of the equipment is abnormal or not;
if the actual moment characteristic curve is normal, the actual moment characteristic curve is obtained through calculation of motion related data, the actual moment characteristic curve is evaluated based on the existing standard moment characteristic curve, and the symptoms are calculated according to unreasonable data in the actual moment characteristic curve.
Due to the adoption of the technical scheme, the invention has the remarkable technical effects that:
the method can judge whether the equipment movement is abnormal or not, if the equipment movement is not abnormal, the collected data can be processed, the processed data is drawn into a corresponding curve, and the curve is subjected to similarity judgment with the existing curve to calculate a corresponding focus.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a detailed schematic of the process flow of the present invention;
FIG. 2 is a graph illustrating an embodiment;
fig. 3 is a schematic diagram of the overall structure of the system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and "a" and "an" typically include at least two, but do not exclude the presence of at least one.
The words "if", as used herein may be interpreted as "at \8230; \8230whenor" when 8230; \8230when or "in response to a determination" or "in response to a monitoring", depending on the context. Similarly, the phrase "if it is determined" or "if it is monitored (a stated condition or event)" may be interpreted as "when determining" or "in response to determining" or "when monitoring (a stated condition or event)" or "in response to monitoring (a stated condition or event)", depending on the context.
It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such good or system. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in articles of commerce or systems including such elements.
Example 1
A control and evaluation method for an exoskeleton robot, as shown in fig. 1, comprises the following steps:
s100, responding to the starting of the equipment, acquiring the start-end position data and the start-end speed data, and carrying out standardization processing to obtain a standardized start-end position and a standardized start-end speed;
s200, obtaining ideal curve time by combining the movement limit data of the equipment driver based on the standardized starting position and the standardized end position and the standardized speed;
s300, processing the ideal curve time to obtain a preset equipment motion track and an output value;
s400, acquiring relevant data of equipment movement in real time, wherein the relevant data of the movement at least comprises the moment, the angle and the angular speed of the acquired data;
s500, comparing the preset motion track and output value of the equipment with the torque and angle to judge whether the motion of the equipment is abnormal or not;
s600, if the actual moment characteristic curve is normal, calculating through the motion related data to obtain the actual moment characteristic curve, evaluating the actual moment characteristic curve based on the existing standard moment characteristic curve, and calculating the symptoms according to unreasonable data in the actual moment characteristic curve.
The whole control evaluation method can be divided into two large parts, wherein the first part is to judge whether the motion of the equipment is abnormal or not, and if the equipment is abnormal, the equipment is directly and emergently stopped and alarm information is sent out. If the equipment does not move abnormally, generating a real moment characteristic curve according to the collected data, comparing the real moment characteristic curve with the existing standard moment characteristic curve, judging whether the actual moment characteristic curve is different, and if so, calculating the corresponding symptoms by using the data at the different places.
The output value comprises an output position, a speed, an acceleration and a jerk, and the output position, the speed, the acceleration and the jerk are respectively subjected to standardization processing to obtain a standardized output position, a standardized speed, a standardized acceleration and a standardized jerk.
In one embodiment, the calculating the actual torque characteristic curve through the motion-related data includes the following steps:
the obtained motion related data are sorted and classified, unreasonable data are eliminated, and processed data are obtained; and obtaining total flexion work and total extension work, average flexion work and average extension work, maximum peak moment of flexion and maximum peak moment of extension, minimum peak moment of flexion and minimum peak moment of extension and maximum work of flexion and extension based on the processed data, wherein if the total flexion work is A, the total extension work is B, the average flexion work is C, and the average extension work is D, the total flexion-extension work ratio A/B, the total flexion-extension work sum is A + B, the average flexion-extension work ratio C/D, and the average flexion-extension work sum is C + D, and obtaining an actual moment characteristic curve based on the data.
The moment characteristic curve is a generic concept, and may be, for example, a extensor-flexor antagonistic contraction moment curve, a distal and proximal femur fracture moment curve, or other moment curves, and the curves are not limited herein.
Specific reference may be made to the following implementation:
the method comprises the steps of collecting original data such as time, angle, angular velocity and moment in the movement process, classifying the data and performing other preprocessing, eliminating unreasonable data to obtain desired data, and performing calculation integration, wherein the data in the following table are calculated to obtain a calculation result.
| Flexion | Stretch out | Ratio of elongation to yield | Total work of flexion and extension | |
| Gross work | A | B | A/B | A+B |
| Average work | C | D | C/D | C+D |
| Maximum peak moment (number of groups) | E1(N1) | F1(M1) | ||
| Minimum peak moment (number of groups) | E2(N2) | F2(M2) | ||
| Maximum work (the number of groups) | G(N3) | H(M3) |
The data are integrated into an actual torque characteristic curve, the specific integration process can be referred to in the prior art literature, whether the actual torque characteristic curve is different from the existing standard torque characteristic curve or not is judged, and possible symptoms are deduced based on difference points. In an embodiment, for example, images of a single period are analyzed, and the images may be generated through collected data, as shown in fig. 2, and a specific determination method may refer to an evaluation method of authentication such as an existing medical journal, which states that a certain type of specific moment curve may represent a certain joint lesion/muscle weakness pathology, which may be referred to in the prior art and will not be described herein again.
For example, in fig. 2, if there is an abnormality (sudden change) in the rising portion or an abnormality (distortion) in the falling portion in the three images, the number data is acquired, a statistical function of similarity such as euclidean distance, manhattan distance, jaccard coefficient, and pearson correlation is called, a determination threshold is set, and the curve similarity is determined, so that the symptom can be determined. Of course, if the lesion is drawn into other curves, the lesion may be processed in this manner, and the presence or absence of the lesion may be finally estimated.
In addition, in one embodiment, the collected data can be used to generate an extensor muscle antagonistic contraction moment curve or other curves, and the similarity between the actual curve and the existing standard curve is compared to finally deduce whether a lesion exists.
Specifically, the ideal curve time is obtained by combining the movement limit data of the device driver based on the normalized start-end position and the normalized speed, specifically:
obtaining a maximum speed, a minimum acceleration, a maximum acceleration, a minimum jerk and a maximum jerk based on the device driver motion limit data;
obtaining a speed change section time and a constant speed section time based on the maximum speed, the minimum acceleration, the maximum acceleration, the minimum jerk and the maximum jerk, wherein the speed change section time comprises an acceleration section time and a deceleration section time;
judging the constant speed section time, and correcting the speed change section time based on the judgment result to obtain a correction result;
the ideal curve time is obtained based on the correction result.
In another embodiment, the correcting the shift stage time based on the determination result specifically includes:
and if the constant speed section time is less than the preset value, correcting the speed change section time, judging the speed change section time during correction, and correcting the speed change section time based on a judgment result.
The specific process is as follows:
setting the drive limit value: the minimum jerk and the maximum jerk are opposite numbers, the minimum speed and the maximum speed are opposite numbers, then:
V min 0＝-Vmax0；a min 0＝-amax0；j min 0＝-jmax0
from the above parameters, normalized start and end position and velocity input initial values can be found, see the following process:
in order to unify the characteristics of the curve, a symbol variable alpha is defined for judging the increase and decrease of the position, the variable alpha can be understood as a drive state zone bit of the driver, and when the position of the end point is greater than the position of the start point, alpha =1, namely the driver rotates forwards; when the end position value is equal to the start position, α =0, i.e. the drive is stopped; when the end position value is less than the start position, α = -1, i.e. the drive is reversed, then there are:
the position and the speed of the starting point and the end point are standardized, and the following steps can be changed: p0= α P0, P1= α P1, V0= α V0, V1= α V1;
the limit speed, the acceleration and the jerk are transformed as follows:
the process of the accelerated phase time solution is as follows:
if (V max 1-V0) jmax1<amax1 2 Then, the first step is executed,
Ta＝2T j1 wherein j1 is changed into acceleration time and Ta acceleration section time length;
if (V max 1-V0) jmax1 is not satisfied<amax1 2 Then, if the number of the first time zone is less than the first threshold value,
the process of solving for the deceleration section time is as follows:
if (V max 1-V1) jmax1<amax1 2 Then, the first step is executed,
Td＝2T j2 wherein Tj2 is changed into deceleration time and Td deceleration section duration;
if not (V max 1-V1) jmax1<amax1 2 Then, then
The process of solving at constant speed section time is as follows:
tv =0, then
In this case, δ Δ can be understood as a correction variable for correcting the acceleration time and the deceleration time.
Judging the existence of the speed change section, which comprises the following specific steps:
if T is a orT d If the time is less than 0, correcting the speed change period time;
the process of correcting the missing of the acceleration section comprises the following steps:
the process of the missing correction of the deceleration section comprises the following steps:
the process of limiting acceleration and speed correction is as follows:
Alim＝jmax1T j1 ，Dlim＝-jmax1T j2 ，Vlim＝V0+(T a -T j1 )Alim＝V1-(T d -T j2 )Alim
the acceleration control system comprises an Alim acceleration section limit acceleration, a Dlim deceleration section limit acceleration, a Vlim limit speed, a Vmax1 maximum speed standard value, a Vmin1 minimum speed standard value, an amax1 maximum acceleration standard value, an amin1 minimum acceleration standard value, a jmax1 maximum acceleration standard value, a jmin1 minimum acceleration standard value, a starting point position P0, a starting point speed V0, an end point position P1 and end point speeds V1 and T, wherein the time length is the duration.
The process of the uniform speed change judgment is as follows: if T a ＜2T j orT d ＜2T j Correcting the speed of the non-uniform variable speed section, which is specifically as follows: amax1= γ amax, with0<Gamma < 1, modify gamma until T v >=0, i.e. T a >2*T j1 orT d >2*T j2 Wherein, gamma is a decay variable (decreasing from 1 to 0 regularly), and the decay is performed according to a specific proportion in each calculation, for example, gamma decreases by 0.01 in each calculation.
Trajectory equation calculations, where the acceleration segment is as follows:
(1)，t∈[0，T j1 ]
(2)，t∈[T j1 ，T a -T j1 ]
(3)，t∈[T a -T j1 ，T a ]
the uniform speed section comprises the following steps:
t∈[T a ，T a +T v ]
the deceleration section is as follows:
(1)t∈[T-T d ，T-T d +T j2 ]
(2)t∈[T-T d +T j2 ，T-T j2 ]
(3)t∈[T-T j2 ，T]
in the above several stages, T is the time point of the curve, pout is the position output value, vout is the speed output value, aout is the acceleration output value, jout is the jerk output value, alim acceleration limit acceleration, dlim deceleration limit acceleration, vlim limit speed, vmax1 maximum speed standard value, vmin1 minimum speed standard value, amax1 maximum acceleration standard value, amin1 minimum acceleration standard value, jmax1 maximum jerk standard value, jmin1 minimum jerk standard value, starting point position P0, starting point speed V0, ending point position P1, ending point speed V1, T is the duration.
And normalizing the output value of the normalized motion curve to output position, speed, acceleration and jerk, wherein Pout = alpha Pout, vout = alpha Vout, aout = alpha Aout, jout = alpha Jout, wherein Pout is a position output value, vout is a speed output value, aout is an acceleration output value, and Jout is a jerk output value.
In one embodiment, the method further comprises a process of determining the user information, and the specific steps may participate in the following processes:
acquiring relevant information of a user, wherein the relevant information at least comprises a movement height;
and judging the user type based on the related information of the user, if the user is a new user, acquiring and storing the basic information of the new user, and if the user is an old user, executing a corresponding instruction.
Example 2:
a control and evaluation system applied to an exoskeleton robot, as shown in fig. 3, includes an acquisition processing module 100, a first processing module 200, a second processing module 300, a data acquisition module 400, a determination module 500, and a plotting and calculating module 600;
the acquisition processing module 100, in response to the start of the device, acquires the start-end position data and the start-end speed data and performs standardization processing to obtain a standardized start-end position and a standardized start-end speed;
the first processing module 200, based on the normalized start and end positions and the normalized speed, combining the motion limit data of the device driver to obtain an ideal curve time;
the second processing module 300 is configured to process the ideal curve time to obtain a preset device motion trajectory and an output value;
the data acquiring module 400 is configured to acquire device motion-related data in real time, where the motion-related data at least includes a moment, an angle, and an angular velocity of the acquired data;
the judging module 500 is configured to compare a preset device motion track and output value with a torque and an angle, and judge whether the device is abnormal in motion;
the drawing calculation module 600 is configured to: if the actual moment characteristic curve is normal, the actual moment characteristic curve is obtained through calculation of motion related data, the actual moment characteristic curve is evaluated based on the existing standard moment characteristic curve, and the symptoms are calculated according to unreasonable data in the actual moment characteristic curve.
The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the foregoing method embodiment, which is not described herein again.
It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such good or system. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in articles of commerce or systems including such elements.
The foregoing description shows and describes several preferred embodiments of the invention, but as aforementioned, it is to be understood that the invention is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A control and evaluation method applied to an exoskeleton robot is characterized by comprising the following steps:
responding to the starting of the equipment, acquiring the starting and ending position data and the starting and ending speed data, and performing standardization processing to obtain a standardized starting and ending position and a standardized starting and ending speed;
obtaining ideal curve time based on the standardized start-end position and the standardized start-end speed by combining with the movement limit data of the equipment driver;
processing the ideal curve time to obtain a preset equipment motion track and an output value;
acquiring relevant data of equipment movement in real time, wherein the relevant data of the equipment movement at least comprises the moment, angle and angular speed of the acquired data;
comparing the preset motion track and output value of the equipment with the torque and angle to judge whether the motion of the equipment is abnormal or not;
if the actual moment characteristic curve is normal, calculating through motion related data to obtain an actual moment characteristic curve, evaluating the actual moment characteristic curve based on the existing standard moment characteristic curve, and calculating the focus according to unreasonable data in the actual moment characteristic curve;
the output value comprises an output position, a speed, an acceleration and a jerk, and the output position, the speed, the acceleration and the jerk are respectively subjected to standardization processing to obtain a standardized output position, a standardized starting and ending speed, a standardized acceleration and a standardized jerk.
2. The control and evaluation method for an exoskeleton robot as claimed in claim 1 wherein the calculation of the actual torque profile from the motion related data comprises the steps of:
the obtained motion related data are sorted and classified, unreasonable data are eliminated, and processed data are obtained;
obtaining total flexion work and total extension work, average flexion work and average extension work, maximum peak moment of flexion and maximum peak moment of extension, minimum peak moment of flexion and minimum peak moment of extension and maximum work of flexion and extension based on the processed data;
and obtaining an actual torque characteristic curve based on the data.
3. The control and assessment method applied to an exoskeleton robot as claimed in claim 1, wherein the ideal curve time is obtained based on the normalized start-end position and normalized start-end velocity in combination with the device driver motion limit data, specifically:
obtaining a maximum speed, a minimum acceleration, a maximum acceleration, a minimum jerk and a maximum jerk based on the device driver motion limit data;
obtaining a speed change section time and a constant speed section time based on the maximum speed, the minimum acceleration, the maximum acceleration, the minimum jerk and the maximum jerk, wherein the speed change section time comprises an acceleration section time and a deceleration section time;
judging the constant speed section time, and correcting the speed change section time based on the judgment result to obtain a correction result;
the ideal curve time is obtained based on the correction result.
4. The control and evaluation method for an exoskeleton robot as claimed in claim 3 wherein the speed change period is modified based on the determination result, specifically:
and if the constant speed section time is smaller than the preset value, correcting the speed change section time, judging the speed change section time during correction, and correcting the speed change section time based on the judgment result.
5. The method for control and evaluation of an exoskeleton robot as claimed in claim 1 further comprising the steps of:
acquiring relevant information of a user, wherein the relevant information at least comprises a movement height;
and judging the user type based on the relevant information of the user, if the user is a new user, acquiring and storing the basic information of the new user, and if the user is an old user, executing a corresponding instruction.
6. The method for control and evaluation of an exoskeleton robot as claimed in claim 1 further comprising the steps of:
and if the equipment is abnormal in motion, the equipment is stopped suddenly and alarm information is sent out.
7. A control and evaluation system applied to an exoskeleton robot is characterized by comprising an acquisition processing module, a first processing module, a second processing module, a data acquisition module, a judgment module and a drawing calculation module;
the acquisition processing module is used for responding to the starting of the equipment, acquiring the start-end position data and the start-end speed data and carrying out standardization processing to obtain a standardized start-end position and a standardized start-end speed;
the first processing module is used for obtaining ideal curve time by combining the motion limit data of the equipment driver based on the standardized start-end position and the standardized start-end speed;
the second processing module is used for processing the ideal curve time to obtain a preset equipment motion track and an output value;
the data acquisition module is used for acquiring relevant data of equipment motion in real time, wherein the relevant data of the equipment motion at least comprises the moment, the angle and the angular speed of the acquired data;
the judging module is used for comparing a preset device motion track, an output value, a moment and an angle to judge whether the motion of the device is abnormal or not, wherein the output value comprises an output position, a speed, an acceleration and a jerk, and respectively standardizing the output position, the speed, the acceleration and the jerk to obtain a standardized output position, a standardized starting and ending speed, a standardized acceleration and a standardized jerk;
the drawing calculation module is configured to: if the actual moment characteristic curve is normal, calculating through the motion related data to obtain an actual moment characteristic curve, evaluating the actual moment characteristic curve based on the existing standard moment characteristic curve, and calculating the focus according to unreasonable data in the actual moment characteristic curve.
8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 6.
9. A device for monitoring the status of a workflow, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any one of claims 1 to 6 when executing the computer program.
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| US7217247B2 (en) * | 2002-09-23 | 2007-05-15 | Honda Giken Kogyo Kabushiki Kaisha | Gravity compensation method in a human assist system and a human assist system with gravity compensation control |
| CN105050563B (en) * | 2013-03-14 | 2019-01-22 | 埃克苏仿生公司 | Power orthopedic system for the rehabilitation of cooperative ground |
| KR20160016925A (en) * | 2013-05-31 | 2016-02-15 | 프레지던트 앤드 펠로우즈 오브 하바드 칼리지 | Soft exosuit for assistance with human motion |
| US10426637B2 (en) * | 2015-05-11 | 2019-10-01 | The Hong Kong Polytechnic University | Exoskeleton ankle robot |
| KR102510874B1 (en) * | 2017-06-15 | 2023-03-16 | 삼성전자주식회사 | Method for walking assist, and devices operating the same |
| CN112405504B (en) * | 2020-12-08 | 2022-02-22 | 杭州程天科技发展有限公司 | Exoskeleton robot |
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Denomination of invention: Control and evaluation methods and systems applied to exoskeleton robots Granted publication date: 20221227 Pledgee: Guotou Taikang Trust Co.,Ltd. Pledgor: HANGZHOU CHENGTIAN TECHNOLOGY DEVELOPMENT Co.,Ltd. Registration number: Y2024980004893 |