CN114700942A - Upper limb robot optimization method and device and upper limb robot - Google Patents
Upper limb robot optimization method and device and upper limb robot Download PDFInfo
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- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
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- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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- B25J9/00—Programme-controlled manipulators
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
The invention provides an upper limb robot optimization method, an upper limb robot optimization device and an upper limb robot, wherein the upper limb robot optimization method comprises the following steps: modeling the muscles and bones of the upper limbs based on three elements of a Hill equation and Hill muscles to obtain a musculoskeletal model; performing motion capture on an upper limb to obtain experimental data, and processing the experimental data by using a musculoskeletal model to obtain a terminal displacement track; equivalent simplification is carried out on the upper limb robot, and a kinematic model is obtained; processing the tail end displacement track by using a kinematic model, and performing reverse processing to obtain human-computer interaction force; simulating human-computer interaction force through a muscle-skeleton model to obtain a comfort level index; changing mechanical arm parameters of the mechanical arm through a genetic algorithm, and solving the maximum comfort degree of the mechanical arm; the mechanical arm parameters corresponding to the maximum comfort level serve as the optimization result of the upper limb robot, so that the ergonomic design of the mechanical arm is guaranteed, and the comfort level of the upper limb robot in use is also guaranteed.
Description
Technical Field
The invention relates to the technical field of mechanical arm design, in particular to an upper limb robot optimization method and device and an upper limb robot.
Background
In recent years, with the progress of aging society, patients suffering from motor dysfunction are increasing, people pay more and more attention to equipment for improving and treating the motor dysfunction, various medical upper limb robots have appeared at home and abroad, and the medical upper limb robot technology is gradually applied and advanced in the field of rehabilitation. The existing mechanism design of the medical upper limb robot only considers the mechanistic index to improve the mechanical quality of the robot as an optimization target, cannot represent the kinematics and dynamics characteristics in the interaction process of the robot mechanism and the human body, cannot represent the physiological and psychological comfort of the human body, can show that the mechanical quality of the robot is high but the comfort of a user is poor, so that the training will of the user is poor, and the rehabilitation training effect is poor.
Disclosure of Invention
The problem to be solved by the invention is how to improve the design of the upper limb robot.
In order to solve the above problems, the present invention provides an upper limb robot optimization method, including:
modeling the muscles and bones of the upper limb based on three elements of a Hill equation and Hill muscles to obtain a musculoskeletal model, wherein the musculoskeletal model is used for describing the physical characteristics and mechanical properties of the muscles of the upper limb during movement; performing motion capture on the upper limb to obtain experiment data, and processing the experiment data by using the musculoskeletal model to obtain a tail end displacement track, wherein the tail end displacement track comprises one of a tail end displacement track of the upper limb and a tail end displacement track of a mechanical arm; equivalent simplification is carried out on the upper limb robot, and a kinematic model is obtained; processing the tail end displacement track by using the kinematic model, and performing reverse processing to obtain a human-computer interaction force, wherein the human-computer interaction force is used for representing an interaction force generated between the upper limb and the mechanical arm; simulating the human-computer interaction force through the muscle skeleton model to obtain a comfort level index; changing mechanical arm parameters of the mechanical arm through a genetic algorithm, and solving the maximum comfort level of the mechanical arm, wherein the mechanical arm parameters comprise length and quality, and the maximum comfort level represents the maximum value which can be reached by the comfort level index on the premise of only changing the mechanical arm parameters; and taking the mechanical arm parameter corresponding to the maximum comfort level as an optimization result of the upper limb robot.
Optionally, the changing, by a genetic algorithm, a parameter of the mechanical arm, and the solving for the maximum comfort level of the mechanical arm includes:
determining boundary conditions of the mechanical arm parameters based on the working requirements of the mechanical arm, wherein the boundary conditions comprise the length and the mass of a first force arm, the length and the mass of a second force arm and the length and the mass of a third force arm; assigning values to the three force arms of the mechanical arm in the boundary condition through a genetic algorithm, and calculating the human-computer interaction force of the mechanical arm under different assignments to obtain the maximum human-computer interaction force; and extracting the maximum human-computer interaction force, and processing the maximum human-computer interaction force through the musculoskeletal model to obtain the maximum comfort level.
Optionally, the simulating the human-computer interaction force through the musculoskeletal model, and obtaining a comfort level index includes:
inputting the maximum equidistant force of the muscles, the moment arm of the muscles relative to each joint axis and the generalized force acting on each joint axis into the musculoskeletal model to calculate the activation values of the muscles, wherein the activation values are used for describing the activation condition of the upper limb muscles.
Optionally, said inputting into said musculoskeletal model a maximum equidistant force of a muscle, a moment arm of said muscle about each joint axis, and a generalized force acting on each joint axis to obtain an activation value of said muscle comprises:
calculating the activation value by the following formula:
wherein n represents n muscles involved in the musculoskeletal model, amRepresenting the activation level of the mth muscle in discrete time steps,
represents the maximum isometric force of the muscle m, rm,jRepresents the moment arm of the mth muscle about the jth joint axis, taujRepresenting a generalized force acting on the j-th joint axis.
Optionally, after the inputting the maximum equidistant force of the muscle, the moment arm of the muscle about each joint axis and the generalized force acting on each joint axis into the musculoskeletal model to calculate the activation value of the muscle, the method further comprises:
obtaining muscle operability through the activation value; obtaining a human-computer efficacy index according to a preset working condition coefficient; obtaining the comfort index based on the muscle manipulability and the ergonomics index.
Optionally, the obtaining the comfort index based on the muscle operability and the ergonomics index comprises:
the comfort index is obtained by the following formula:
where ρ iso、ρsDenotes a predetermined inhibition index, κα、κeWhich represents a pre-set gain index of the gain,
representing the degree of muscle manipulability, ωeRepresenting the ergonomics index.
Optionally, the performing motion capture on the upper limb to obtain experimental data, and processing the experimental data using the musculoskeletal model to obtain the tip displacement trajectory includes:
performing motion capture on the upper limb to obtain motion capture data; scaling the motion capture data through a Scaling tool, and reversely obtaining joint kinematics data, wherein the joint kinematics data comprise a rotation angle of an arm, an adduction or abduction angle of a shoulder joint, an angle of the arm around the shoulder and an elbow bending angle; and simulating the joint kinematics data through the musculoskeletal model to obtain the terminal displacement track.
Optionally, the equivalently simplifying the upper limb robot, and obtaining the kinematic model includes:
and carrying out three-degree-of-freedom DH modeling on the upper limb robot to obtain the kinematic model.
Compared with the prior art, the upper limb musculoskeletal model and the mechanical arm kinematics model are obtained by respectively modeling the upper limb musculoskeletal and the mechanical arm in consideration of the comfort of a user in the training process, and the angle, the displacement and the stress data of the upper limb and the mechanical arm and the relationship between the upper limb and the mechanical arm can be respectively represented by the two models; the muscle skeleton model can simulate the experimental data of the upper limb, can process the human-computer interaction force obtained by the kinematics model to obtain a quantized comfort level index, finally changes the parameters of the mechanical arm through a genetic algorithm, solves the mechanical arm parameters corresponding to the maximum comfort level as an optimization result, and ensures that the optimal mechanical arm parameters are optimized by considering the activation condition of the upper limb mechanism skeleton in the training on the basis of considering the mechanical arm ergonomics.
In a second aspect, the present invention provides an upper limb robot optimization device, including:
the modeling device comprises a first modeling module, a second modeling module and a third modeling module, wherein the first modeling module is used for modeling the muscle and the skeleton of an upper limb based on a Hill equation and three elements of Hill muscle to obtain a musculoskeletal model, and the musculoskeletal model is used for describing the physical characteristics and the mechanical properties of the muscle of the upper limb during movement; a first processing module, configured to capture motion of the upper limb, obtain experimental data, and process the experimental data using the musculoskeletal model to obtain a terminal displacement trajectory, where the terminal displacement trajectory includes one of an upper limb terminal displacement trajectory and a mechanical arm terminal displacement trajectory; the second modeling module is used for carrying out equivalent simplification on the upper limb robot to obtain a kinematics model; the second processing module is used for processing the tail end displacement track by using the kinematic model and obtaining a human-computer interaction force through reverse processing, wherein the human-computer interaction force is used for representing an interaction force generated between the upper limb and the mechanical arm; the obtaining module is used for simulating the human-computer interaction force through the musculoskeletal model to obtain a comfort level index; the optimization module is used for changing mechanical arm parameters of the mechanical arm through a genetic algorithm and solving the maximum comfort degree of the mechanical arm, wherein the mechanical arm parameters comprise length and quality, and the maximum comfort degree represents the maximum value which can be reached by the comfort degree index on the premise of only changing the mechanical arm parameters; and the output module is used for taking the mechanical arm parameters corresponding to the maximum comfort level as the optimization result of the upper limb robot.
Compared with the prior art, the upper limb robot optimization device has the beneficial effects consistent with the upper limb robot optimization method, which are not described herein.
In a third aspect, the present invention provides an upper limb robot, comprising a computer readable storage medium storing a computer program and a processor, wherein the computer program is read by the processor and executed to implement the upper limb robot optimization method as described above.
Compared with the prior art, the upper limb robot of the invention has the beneficial effects consistent with the optimization method of the upper limb robot, and the detailed description is omitted here.
Drawings
Fig. 1 is a schematic flow chart of an upper limb robot optimization method according to an embodiment of the invention;
fig. 2 is a schematic flow chart of the upper limb robot optimization method according to the embodiment of the present invention after step S200 is refined;
fig. 3 is a flowchart illustrating a step S600 of an upper limb robot optimization method according to an embodiment of the present invention after refinement;
fig. 4 is a flowchart illustrating a step S500 of an upper limb robot optimization method according to an embodiment of the present invention after refinement;
FIG. 5 is a graph of comfort versus time prior to upper extremity robot optimization in accordance with an embodiment of the present invention;
fig. 6 is a diagram of comfort versus time after optimization of an upper limb robot in accordance with an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. While certain embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more complete and thorough understanding of the present invention. It should be understood that the drawings and the embodiments of the present invention are illustrative only and are not intended to limit the scope of the present invention.
It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "alternative embodiments". Relevant definitions for other terms will be given in the following description. It should be noted that the terms "first", "second", and the like in the present invention are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in the present invention are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.
The upper limb robot provides quantitative repeatable function guide movement support required by nerve remodeling of a patient through mechanical mechanism assistance, and effectively promotes function remodeling, compensation and regeneration of a nervous system. The upper limb robot assists the patient to move the upper limb, so that the atrophy of muscles and joints can be effectively slowed down, and the active rehabilitation effect of 'thinking of walking in one' is realized.
Since the twentieth century, various upper limb robots have been developed at home and abroad to assist patients in performing rehabilitation exercises for the healthy and affected sides of the upper limbs, such as a two-arm training robot in which the healthy and affected side arms are mirror images of each other, a training robot having a single-side mechanical arm, and the like. On the mechanism design of the training robot, the mechanistic indexes are simply considered, for example, the working space of the mechanism is enlarged, so that the training robot can be completely adapted to the moving range of the upper limb; the operability and all isotropies of the robot are improved, so that the robot has the same performance in all angles and all action amplitudes. The indexes can only represent the kinematics and dynamics characteristics of the mechanism, cannot represent the kinematics and dynamics characteristics of a generalized closed-loop mechanism formed by an upper limb mechanism and an upper limb mechanism skeleton system in the interaction process of the robot mechanism and the upper limb, and cannot represent the physiological and psychological comfort in the interaction process. When a user actually trains through the training robot, there may be a case where the mechanical performance of the robot arm is good but the use experience is not high. Therefore, the invention establishes a coupling simulation platform by modeling the upper limb musculoskeletal and the mechanical arm of the upper limb robot, and establishes an evaluation index, namely a comfort index, when the mechanical arm of the upper limb robot and the upper limb interact by introducing the transfer characteristic of the upper limb robot and the upper limb robot in the man-machine interaction process. By optimizing the comfort level index, the upper limb robot is structurally optimized by using a multi-objective genetic algorithm, and the parameter design of the mechanical arm with higher comfort level is obtained.
As shown in fig. 1, an upper limb robot optimization method provided in an embodiment of the present invention includes:
and S100, modeling the muscle and the skeleton of the upper limb based on three elements of a Hill equation and Hill muscle to obtain a musculoskeletal model, wherein the musculoskeletal model is used for describing the physical characteristics and the mechanical properties of the muscle of the upper limb during movement.
The Hill muscle model is the most widely used mechanical model of muscle structure so far, and describes physical characteristics and mechanical properties of muscles through three basic units of a Contraction Element (CE), a Series Elastic Element (SE), and a Parallel Elastic Element (PE) corresponding to biological tissues of muscles.
The working characteristics of different muscles are different, the invention mainly aims at the shoulder, elbow, upper arm and lower arm of the upper limb to establish a musculoskeletal model, and describes the physical characteristics and mechanical properties of the muscles of the shoulder, elbow, upper arm and lower arm when the upper limb moves through the musculoskeletal model.
Preferably, the muscles and bones of the upper limbs are modeled using OpenSim.
OpenSim is open source software which is developed based on C + + and Java languages and is applied to muscle model development and biomechanical simulation and analysis.
When the OpenSim modeling method is used for analyzing the movement of the upper limb, a general model is firstly used for model optimization under the preset test condition, and an individualized model is established.
And S200, performing motion capture on the upper limb to obtain experiment data, and processing the experiment data by using the musculoskeletal model to obtain a tail end displacement track, wherein the tail end displacement track comprises one of the tail end displacement track of the upper limb and the tail end displacement track of the mechanical arm.
In one embodiment, the motion capture data of the upper limb is collected as experimental data, then the experimental data is input into a musculoskeletal model, and the displacement track of the end of the upper limb in the experiment is obtained through simulation of the musculoskeletal model. The wrist (or other position of the hand) is regarded as a mass point, and the displacement track of the mass point in the experiment is the tail end displacement track.
In the training process of the upper limb robot, the hand is connected with the tail end of the mechanical arm, so that the auxiliary force is applied to the mechanical arm, the hand is assisted to displace, and the displacement track of the tail end of the upper limb and the displacement track of the tail end of the mechanical arm can be used as tail end displacement tracks in the training process.
Specifically, the displacement trajectory of the upper limb terminal is the trajectory of the wrist center-terminal point; the tail end displacement track of the mechanical arm is the displacement track of a handle of the mechanical arm.
And step S300, equivalently simplifying the upper limb robot to obtain a kinematic model.
Optionally, the upper limb robot is subjected to three-degree-of-freedom DH modeling, so as to obtain the kinematic model.
Optionally, the upper limb robot is equivalently simplified through Simulink/multibody, and an equivalent kinematic model is obtained.
DH modeling, a modeling method that is put forward by Denavit and Hartenberg, mainly use in robot kinematics, this kind of method sets up a coordinate system on each arm of force, realize the transformation of the coordinate on two arms of force through the homogeneous coordinate transformation, in the system that the multi-arm connects in series, use the homogeneous coordinate transformation many times, in order to set up the relation of the first and last coordinate system.
DH modeling is carried out on the mechanical arm, conversion between a handle relative coordinate system and a world coordinate system is studied, and the stress condition of the mechanical arm can be calculated through a displacement track.
In an embodiment, the three-degree-of-freedom mechanical arm is modeled, and the three force arms are respectively recorded as a first force arm, a second force arm and a third force arm because the mechanical arm has three force arms. The first force arm is used as the initial end of the mechanical arm, the third force arm is used as the tail end of the mechanical arm, and the tail end of the mechanical arm is also connected with a handle. And establishing a coordinate system by taking the initial end as an origin, and further establishing a transformation matrix of the handle coordinate system through a DH parameter of the mechanical arm.
And step S400, processing the tail end displacement track by using the kinematic model, and performing reverse processing to obtain human-computer interaction force, wherein the human-computer interaction force is used for representing interaction force generated between the upper limb and the mechanical arm.
And (3) acquiring the tail end displacement track obtained in the step (S300), and calculating through a kinematic model to obtain the human-computer interaction force, wherein the upper limb robot is connected through a binding band in the human-computer interaction process, so that the human-computer interaction force is added between the upper limb and the tail end of the mechanical arm to simulate the stress of the binding band.
In one embodiment, the upper extremity robot has two arms that are mirror images, one being the healthy side arm and the other being the affected side arm. The healthy side mechanical arm is used for being actively pulled by a normal upper limb, and the affected side mechanical arm is used for carrying out mirror image impedance motion on the healthy side mechanical arm through a preset algorithm after the detection mechanical arm is pulled, so as to assist the affected side upper limb to move. When the side-care mechanical arm is pulled by an upper limb to move on the side-care side of the mechanical arm, except for the inertia force generated by the movement of the mechanical arm, the contact force mainly comes from the self weight of the mechanical arm, and in the actual measurement, the component force of the stress of the binding band in the vertical direction is approximately equal to the self weight of the mechanical arm, so that the stress of the binding band can be approximately regarded as the man-machine interaction force.
And S500, simulating the human-computer interaction force through the muscle skeleton model to obtain a comfort level index.
And (3) simulating the human-computer interaction force by using a muscle skeleton model, and obtaining the activation value of each muscle of the upper limb under the human-computer interaction force. The activation value represents the degree of activation of the muscle, and is positively correlated with the muscle force. When the activation value is 1, it means that the muscle is completely activated; when the activation value is 0, it means that the muscle is not mobilized at all and is in a relaxed state.
After the mobilized degree of the muscle is obtained through the activation value, the flexibility of the muscle in the process of participating in movement is further obtained on the basis of the activation value, and then the upper limb comfort degree is obtained through the flexibility quantitative calculation under the preset working condition.
And S600, changing mechanical arm parameters of the mechanical arm through a genetic algorithm, and solving the maximum comfort level of the mechanical arm, wherein the mechanical arm parameters comprise length and quality, and the maximum comfort level represents the maximum value which can be reached by the comfort level index on the premise of only changing the mechanical arm parameters.
Specifically, the robot arm parameters include a mass and a height of the first moment arm, a mass and a height of the second moment arm, and a mass and a height of the third moment arm.
In one embodiment, the robot arm parameters are as shown in the following table:
| parameter(s) | First force arm | Second force arm | Third force arm |
| Height/length | 157.5mm | 492mm | 425mm |
| Quality of | 1.28kg | 0.762kg | 0.346kg |
Wherein, the first force arm is a shoulder joint, the second force arm is a big arm, and the third force arm is a small arm.
Optionally, the genetic algorithm is implemented using MATLAB, iterative optimization is performed on the height/length and mass of the mechanical arm, and mechanical arm parameters that enable the flexibility of muscles to be higher are calculated to obtain the maximum comfort level, so that the corresponding mechanical arm parameters under the comfort level are calculated.
And S700, taking the mechanical arm parameter corresponding to the maximum comfort level as an optimization result of the upper limb robot.
And taking the mechanical arm parameters corresponding to the maximum comfort level as a final optimization result, wherein the optimization result comprises the height and the mass of a first force arm, namely a shoulder joint, of the upper limb robot, the length and the mass of a second force arm, namely a large arm, and the length and the mass of a third force arm, namely a small arm.
As shown in fig. 6, in one embodiment, the optimized parameters of the robot arm using the optimization method of the present invention are shown in the following table:
| parameter(s) | First force arm | Second force arm | Third force arm |
| Height/length | 172.4716mm | 450.0484mm | 389.5152mm |
| Quality of | 1.48kg | 0.832kg | 0.3675kg |
Wherein the comfort level corresponding to the mechanical arm parameter is 0.2635.
Optionally, the robotic arm parameters are optimized using genetic algorithms to target maximum human-machine interaction force.
According to the muscle activation value and the comfort level formula, the comfort level and the interaction force are in positive correlation, when the human-computer interaction force of a certain mechanical arm parameter is larger, the comfort level corresponding to the mechanical arm parameter is also larger, so that in one embodiment, the mechanical arm parameter is optimized by directly taking the maximum human-computer interaction force as a target, and the optimal mechanical arm parameter can be obtained.
Optionally, as shown in fig. 3, the changing, by a genetic algorithm, a parameter of the mechanical arm, and solving for the maximum comfort level of the mechanical arm includes:
step S601, determining boundary conditions of the mechanical arm parameters based on the working requirements of the mechanical arm, wherein the boundary conditions comprise the length and the mass of a first force arm, the length and the mass of a second force arm and the length and the mass of a third force arm.
In one embodiment, the boundary conditions of the robot arm parameters are determined based on the upper limb data of the user of the robot arm, in other words, the length/height and the ratio of the robot arm should correspond to the length and the ratio of the shoulder, the upper arm and the lower arm of the user, and if the ratio is particularly out of order, then the user must not fit the robot arm.
In another embodiment, the boundary condition of the robot arm parameter is determined based on the upper limb movement range of the user, i.e. the movement range of the robot arm can completely cover the movement range of the upper limb of the user through the design of the robot arm parameter. When the range of motion of the mechanical arm completely exceeds the range of motion of the user's upper limbs, there is a possibility that the user may feel uncomfortable.
In yet another embodiment, the boundary conditions are as shown in the following table:
| parameter(s) | First force arm | Second force arm | Third force arm |
| Height/length | 157.5±15mm | 492±50mm | 425±50mm |
| Quality of | 1.28±0.2kg | 0.762±0.07kg | 0.346±0.04kg |
Step S602, assigning values to the three force arms of the mechanical arm in the boundary condition through a genetic algorithm, and calculating the human-computer interaction force of the mechanical arm under different assignments to obtain the maximum human-computer interaction force.
The method comprises the steps of assigning an initial population of mechanical arm parameters, sequentially selecting, adaptively crossing, mutating and generating a new population, then calculating the fitness of the new population (namely, simulating the interaction force through a musculoskeletal model), finally judging whether the currently simulated interaction force is the maximum value or not, if so, taking the interaction force as the maximum interaction force, and recording mechanical arm parameters corresponding to the interaction force together.
Step S603, extracting the maximum human-computer interaction force, and processing the maximum human-computer interaction force through the musculoskeletal model to obtain the maximum comfort level.
Human-computer interaction force is processed through a muscle skeleton model to obtain an activation value, muscle operability is calculated through the activation value, a preset working condition coefficient is introduced, and then comfort level is quantized to obtain the maximum comfort level.
Optionally, inputting the maximum equidistant force of the muscle, the moment arm of the muscle about each joint axis and the generalized force acting on each joint axis into the musculoskeletal model to calculate the activation value of the muscle, wherein the activation value is used for describing the activation condition of the upper limb muscle.
And calculating the activation value of each muscle for describing the activation condition of the upper limb muscle.
In an embodiment, the activation values of the relevant muscles are calculated by simulation with StaticOptimization tool in OpenSim, which in this embodiment is ten in total.
In one embodiment, the upper limb is simulated by a musculoskeletal model, leaving four joints associated with extension movements of the upper limb, wherein elv _ angle represents anterior-posterior rotation of the arm, shoulder _ elv represents adduction and abduction of the shoulder, shoulder _ rot represents inward and outward rotation of the arm about the shoulder, and elbow _ flexion represents elbow flexion.
Optionally, the activation value is calculated by the following formula:
wherein n represents n muscles involved in the musculoskeletal model, amRepresenting the activation level of the mth muscle in discrete time steps,
represents the maximum isometric force of the muscle m, rm,jRepresents the moment arm of the mth muscle about the jth joint axis, and τ j represents the generalized force acting on the jth joint axis.
n represents the number of activated muscles in the musculoskeletal model, and n is a constant value of 10, i.e., ten muscles associated with upper limb extension movements. j represents the j-th joint axis, and in this embodiment, a total of 4 joints related to the extension movement of the upper limb are reserved, so j takes the value of [1, 4 ].
According to the three elements of the modeling theory Hill of the muscle and the Hill equation lMTIs the length of the muscle-tendon,/TIs the length of the tendon,. lMIs the length of muscle fiber, alpha is the pinnate angle, and there is a relation l between themMT＝lT+lM cosα，
Is the maximum tension in isometric contraction of muscle fibers and tendons.
Optionally, the activation value of the muscle is obtained every preset time.
In the simulation process of one period, because the simulation content includes the training process of the upper limbs and the mechanical arm, the interaction between the upper limbs and the mechanical arm changes continuously, and the activation value of the muscle changes along with the change of the interaction.
In one embodiment, the activation values of the muscles are obtained every preset time, for example, every 1 second, the activation values of ten relevant muscles are obtained through the above formula for calculating the activation values, and then the activation values are subjected to subsequent processing to obtain comfort level indexes at different times in the interaction process.
Optionally, as shown in fig. 4, after the inputting the maximum equidistant force of the muscle, the moment arm of the muscle about each joint axis and the generalized force acting on each joint axis into the musculoskeletal model to calculate the activation value of the muscle, the method further comprises:
and step S510, obtaining muscle operability through the activation value.
And calculating the manipulability of the muscle through the activation value of the muscle, wherein the manipulability represents the flexibility of the muscle.
In one embodiment, the muscle operability is calculated by the following equation:
wherein, ω isαWhich is indicative of the rate of muscle transfer,
indicates the degree of muscle manipulation, omegaαAnd
respectively all omega in the working spaceαAnd
of (2), thus max { omega [ # }αAnd
is the human-specific maximum Muscle Information Transfer Rate (MiTR) and muscle information operability (MiM).
And step S511, obtaining a human-machine efficacy index according to a preset working condition coefficient.
The ergonomics index is calculated by the following formula:
wherein, ω iseRepresenting an ergonomic index, represented by the coefficient reAnd (6) determining.
reIs a stepwise linear score which is inversely proportional to ergonomic satisfaction, from 1 (satisfactory working posture) to 15 (high risk of injury). Is usually acceptable (r)e1) or low risk (r)e2, 3) may or may not require correction, in one embodiment, reAt low or medium riskAnd taking out 3.
Step S512, obtaining the comfort index based on the muscle operability and the ergonomics index.
Finally, the comfort level index is calculated by the human-computer efficacy index and the muscle operability, and the comfort level is quantified by the method, so that the calculation and optimization of a more comfortable mechanical arm of the upper limb robot are ensured.
The comfort level index is obtained by fusing the muscle operability (namely the flexibility degree of the upper limb muscles) for measuring the muscle activity and the index for measuring the human engineering-human-machine efficiency index.
Optionally, step S512 includes:
the comfort index is obtained by the following formula:
wherein the content of the first and second substances,
representing the comfort index, poAnd ρsDenotes a predetermined inhibition index, καAnd kappaeRepresenting gain indices related to both muscle transfer rate and muscle manipulability, both values being preset values,
representing the degree of muscle manipulability, ωeRepresenting the ergonomics index.
ρoAnd ρsWhether the joint has interference or not and whether the joint has obstruction or not in the process of movement are related, in the embodiment, because the joint has no interference or obstruction, rho iso、ρsThe values are all preset to be 1.
When tasks involving greater force/acceleration, καIs more easily captured by the muscle transmission rate, so kappaαTaking a larger value between 0 and 1, otherwise more easily captured by muscle manipulability, then κeTake a larger value between 0 and 1, so in this example, καAnd kappaeAll are 0.5.
In one embodiment, as shown in fig. 5, the comfort level corresponding to the initial parameters of the mechanical arm is 0.2507. Comfort represents the minimum of comfort during movement of the robotic arm.
Alternatively, as shown in fig. 2, step S200 includes:
step S201, performing motion capture on the upper limb, and obtaining motion capture data.
Step S202, Scaling the motion capture data through a Scaling tool, and reversely obtaining joint kinematics data, wherein the joint kinematics data comprise a rotation angle of an arm, an adduction or abduction angle of a shoulder joint, an angle of the arm around the shoulder and an elbow bending angle.
In one embodiment, the motion capture data of a normal person is collected, and the Scaling tool is used for Scaling the model, wherein the Scaling proportion is determined by requirements.
The motion conditions of muscles and bones of the upper limb are simulated through the scaled model, and joint kinematics data, namely the rotation angle of the arm, the adduction or abduction angle of the shoulder joint, the angle of the arm around the shoulder and the elbow bending angle, are obtained. The present invention relates to the extension of the upper limbs
Step S203, simulating the joint kinematic data through the muscle skeleton model to obtain the tail end displacement track.
The motion angles and amplitudes of the four joints are simulated through the muscle skeleton model, and the motion condition of the upper limb muscle skeleton at the angles is simulated, so that a displacement track is obtained and is used as an input value of a kinematic model of the mechanical arm in the next step.
A complete embodiment of the invention comprises: firstly establishing a musculoskeletal model of an upper limb, specifically using a general model of OpenSim, then performing model Scaling through a Scaling tool under a test condition of mechanical arm rehabilitation, establishing a personalized model as the musculoskeletal model, and reserving four joints (elv _ angle, short _ elv, short _ rot and elbow _ flex) related to the extension and the movement of the upper limb. And then establishing a kinematics model of the healthy side of the upper limb robot through Simulink/multi body, wherein the model parameters comprise the length and the mass of the shoulder joint, the upper arm and the lower arm. The experimental data are obtained by a motion capture system, the kinematics data of four joints are obtained through a reverse kinematics tool, and the tail end displacement track is obtained through OpenSim simulation kinematics data, wherein the tail end displacement track can be the displacement track of a wrist center-tail end point or the displacement track of a handle at the tail end of the mechanical arm (the wrist center is connected with the tail end of the mechanical arm through a binding band in the experiment and can be regarded as a point). The end displacement trajectory is then processed by a multibody (or MATLAB) to obtain human-computer interaction forces. The method comprises the steps of utilizing a StaticOptimization tool in OpenSim to simulate and calculate activation values of ten relevant muscles, calculating muscle transmission rate and muscle operability for measuring muscle flexibility through the activation values, further introducing an ergonomic index, and obtaining a comfort index through the muscle transmission rate and the muscle operability for representing muscle flexibility and combining the ergonomic index for measuring an ergonomic standard. And then, changing the parameters of the mechanical arm to calculate the maximum comfort level index through a genetic algorithm, and correspondingly obtaining the optimal mechanical arm parameters.
Another embodiment of the present invention provides an upper limb robot optimization device, including:
the modeling device comprises a first modeling module, a second modeling module and a third modeling module, wherein the first modeling module is used for modeling the muscle and the skeleton of an upper limb based on three elements of a Hill equation and Hill muscle to obtain a musculoskeletal model, and the musculoskeletal model is used for describing the physical characteristics and the mechanical properties of the muscle of the upper limb during movement;
a first processing module, configured to capture motion of the upper limb, obtain experimental data, and process the experimental data using the musculoskeletal model to obtain a terminal displacement trajectory, where the terminal displacement trajectory includes one of an upper limb terminal displacement trajectory and a mechanical arm terminal displacement trajectory;
the second modeling module is used for carrying out equivalent simplification on the upper limb robot to obtain a kinematics model;
the second processing module is used for processing the tail end displacement track by using the kinematic model and obtaining a human-computer interaction force through reverse processing, wherein the human-computer interaction force is used for representing an interaction force generated between the upper limb and the mechanical arm;
the obtaining module is used for simulating the human-computer interaction force through the musculoskeletal model to obtain a comfort level index;
the optimization module is used for changing mechanical arm parameters of the mechanical arm through a genetic algorithm and solving the maximum comfort degree of the mechanical arm, wherein the mechanical arm parameters comprise length and quality, and the maximum comfort degree represents the maximum value which can be reached by the comfort degree index on the premise of only changing the mechanical arm parameters;
and the output module is used for taking the mechanical arm parameters corresponding to the maximum comfort level as the optimization result of the upper limb robot.
Compared with the prior art, the upper limb robot optimization device has the beneficial effects consistent with the upper limb robot optimization method, which are not described herein.
A further embodiment of the present invention provides an upper limb robot, including a computer readable storage medium storing a computer program and a processor, where the computer program is read by the processor and executed to implement the upper limb robot optimization method as described above.
Compared with the prior art, the upper limb robot of the invention has the beneficial effects consistent with the optimization method of the upper limb robot, and the detailed description is omitted here.
An electronic device that can be a server or a client of the present invention, which is an example of a hardware device that can be applied to aspects of the present invention, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
The upper limb robot includes a computing unit that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) or a computer program loaded from a storage unit into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, the ROM, and the RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like. In this application, the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
Although the present disclosure has been described above, the scope of the present disclosure is not limited thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present disclosure, and these changes and modifications are intended to be within the scope of the present disclosure.
Claims (10)
1. An upper limb robot optimization method, characterized in that an upper limb robot includes a robot arm, the upper limb robot optimization method comprising:
modeling the muscles and bones of the upper limb based on three elements of a Hill equation and Hill muscles to obtain a musculoskeletal model, wherein the musculoskeletal model is used for describing the physical characteristics and mechanical properties of the muscles of the upper limb during movement;
performing motion capture on the upper limb to obtain experiment data, and processing the experiment data by using the musculoskeletal model to obtain a tail end displacement track, wherein the tail end displacement track comprises one of a tail end displacement track of the upper limb and a tail end displacement track of a mechanical arm;
equivalent simplification is carried out on the upper limb robot, and a kinematic model is obtained;
processing the tail end displacement track by using the kinematic model, and performing reverse processing to obtain a human-computer interaction force, wherein the human-computer interaction force is used for representing an interaction force generated between the upper limb and the mechanical arm;
simulating the human-computer interaction force through the muscle skeleton model to obtain a comfort level index;
changing mechanical arm parameters of the mechanical arm through a genetic algorithm, and solving the maximum comfort level of the mechanical arm, wherein the mechanical arm parameters comprise length and quality, and the maximum comfort level represents the maximum value which can be reached by the comfort level index on the premise of only changing the mechanical arm parameters;
and taking the mechanical arm parameter corresponding to the maximum comfort level as an optimization result of the upper limb robot.
2. The upper limb robot optimization method according to claim 1, wherein the changing of the arm parameters of the mechanical arm by the genetic algorithm to solve the maximum comfort of the mechanical arm comprises:
determining boundary conditions of the mechanical arm parameters based on the working requirements of the mechanical arm, wherein the boundary conditions comprise the length and the mass of a first force arm, the length and the mass of a second force arm and the length and the mass of a third force arm;
assigning values to the three force arms of the mechanical arm in the boundary condition through a genetic algorithm, and calculating the human-computer interaction force of the mechanical arm under different assignments to obtain the maximum human-computer interaction force;
and extracting the maximum human-computer interaction force, and processing the maximum human-computer interaction force through the musculoskeletal model to obtain the maximum comfort level.
3. The upper limb robot optimization method according to claim 1 or 2, wherein the simulating the human-computer interaction force through the musculoskeletal model to obtain a comfort index comprises:
inputting the maximum equidistant force of the muscles, the moment arm of the muscles relative to each joint axis and the generalized force acting on each joint axis into the musculoskeletal model to calculate the activation value of the muscles, wherein the activation value is used for describing the activation condition of the upper limb muscles.
4. The upper limb robot optimization method of claim 3, wherein the calculating the activation value of the muscle by inputting the maximum equidistant force of the muscle, the moment arm of the muscle about each joint axis and the generalized force acting on each joint axis into the musculoskeletal model comprises:
calculating the activation value by the following formula:
wherein n represents n muscles involved in the musculoskeletal model, amRepresenting the activation level of the mth muscle in discrete time steps,
representing the maximum isometric force of the muscle m,rm,jrepresents the moment arm, tau, of the mth muscle about the jth joint axisjRepresenting a generalized force acting on the j-th joint axis.
5. The upper limb robot optimization method according to claim 2, further comprising, after the calculating the activation value of the muscle by inputting the maximum equidistant force of the muscle, the moment arm of the muscle with respect to each joint axis, and the generalized force acting on each joint axis into the musculoskeletal model:
obtaining muscle operability through the activation value;
obtaining a human-computer efficacy index according to a preset working condition coefficient;
obtaining the comfort index based on the muscle manipulability and the ergonomics index.
6. The upper extremity robot optimization method according to claim 2, wherein said obtaining the comfort index based on the muscle operability and the ergonomics index comprises:
the comfort index is obtained by the following formula:
wherein the content of the first and second substances,
representing the comfort index, po、ρsDenotes a predetermined inhibition index, κα、κeWhich represents a pre-set gain index of the gain,
representing the degree of muscle manipulability, ωeRepresenting the ergonomics index.
7. The upper limb robot optimization method according to claim 2, wherein the performing motion capture on the upper limb to obtain experimental data, and the processing the experimental data using the musculoskeletal model to obtain the tip displacement trajectory comprises:
performing motion capture on the upper limb to obtain motion capture data;
scaling the motion capture data through a Scaling tool, and reversely obtaining joint kinematics data, wherein the joint kinematics data comprise a rotation angle of an arm, an adduction or abduction angle of a shoulder joint, an angle of the arm around the shoulder and an elbow bending angle;
and simulating the joint kinematics data through the musculoskeletal model to obtain the terminal displacement track.
8. The upper limb robot optimization method according to claim 2, wherein the equivalent simplification of the upper limb robot to obtain the kinematic model includes:
and carrying out three-degree-of-freedom DH modeling on the upper limb robot to obtain the kinematic model.
9. An upper limb robot optimizing device, comprising:
the modeling device comprises a first modeling module, a second modeling module and a third modeling module, wherein the first modeling module is used for modeling the muscle and the skeleton of an upper limb based on three elements of a Hill equation and Hill muscle to obtain a musculoskeletal model, and the musculoskeletal model is used for describing the physical characteristics and the mechanical properties of the muscle of the upper limb during movement;
a first processing module, configured to perform motion capture on the upper limb, obtain experimental data, and process the experimental data using the musculoskeletal model to obtain a terminal displacement trajectory, where the terminal displacement trajectory includes one of an upper limb terminal displacement trajectory and a mechanical arm terminal displacement trajectory;
the second modeling module is used for carrying out equivalent simplification on the upper limb robot to obtain a kinematic model;
the second processing module is used for processing the tail end displacement track by using the kinematic model and obtaining a human-computer interaction force through reverse processing, wherein the human-computer interaction force is used for representing an interaction force generated between the upper limb and the mechanical arm;
the obtaining module is used for simulating the human-computer interaction force through the muscle skeleton model to obtain a comfort level index;
the optimization module is used for changing mechanical arm parameters of the mechanical arm through a genetic algorithm and solving the maximum comfort degree of the mechanical arm, wherein the mechanical arm parameters comprise length and quality, and the maximum comfort degree represents the maximum value which can be reached by the comfort degree index on the premise of only changing the mechanical arm parameters;
and the output module is used for taking the mechanical arm parameters corresponding to the maximum comfort level as the optimization result of the upper limb robot.
10. An upper extremity robot, characterized in that it comprises a processor and a computer readable storage medium storing a computer program which, when read and executed by said processor, implements the upper extremity robot optimization method according to any one of claims 1 to 8.
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