A one-way assistance control method and system for upper limb rehabilitation robot based on patient motor ability

Abstract

The application belongs to the field of upper limb rehabilitation robots, and discloses a one-way auxiliary control method and system for an upper limb rehabilitation robot based on the movement ability of a patient, which comprises the following steps: acquiring the reference trajectory, the end position and the end interaction force of a mechanical arm; determining the shortest distance and the reference trajectory target point; acquiring the normal interaction force and the tangential interaction force; determining the normal movement ability coefficient and the tangential movement ability coefficient; determining the normal constraint force and the tangential auxiliary force; converting the sum of the normal constraint force, the tangential auxiliary force and the end interaction force into a desired speed, and controlling the mechanical arm to move correspondingly according to the desired speed. The method can quickly adapt to the movement ability of the patient to adjust the auxiliary level, and provide one-way assistance according to the movement trend of the patient, gradually provide assistance when the movement trend of the patient is poor, and quickly reduce the assistance when the movement trend of the patient is good, so as to stimulate the patient to actively participate in the rehabilitation exercise. The application has a good guiding significance for the design of the auxiliary control of the upper limb rehabilitation robot.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of robot-assisted medical rehabilitation, and relates to a one-way assistance control method and system for upper limb rehabilitation robots based on patient motor ability. BACKGROUND

[0002] At present, the number of patients with upper limb motor dysfunction such as stroke and spinal cord injury is increasing sharply year by year all over the world. Although about two-thirds of stroke patients can survive, more than 80% of them may have hemiplegia, especially the upper limb motor function will be lost to a certain extent, which leads to difficulties in daily life and seriously affects the quality of life. These survivors need long-term physical therapy to recover daily life activities, so upper limb rehabilitation is particularly important.

[0003] In the process of upper limb rehabilitation training, compared with rehabilitation therapists, upper limb rehabilitation robots can provide more accurate rehabilitation training, and have great potential to improve upper limb motor function and daily life ability. Current upper limb rehabilitation robots are mainly divided into two categories: end traction type and exoskeleton type, among which end traction type rehabilitation robots can better complete rehabilitation training in daily life.

[0004] Clinical studies have also shown that in the process of rehabilitation training, the robot system should provide as little assistance as possible, and let the patient actively participate in the rehabilitation exercise, which is more helpful to induce neural plasticity and improve rehabilitation effect. Although the existing upper limb rehabilitation robot assistance control algorithm can help patients complete rehabilitation exercises, it still has some problems: (1) the current assistance control mainly provides assistance by manually adjusting the assistance level, and cannot well identify the patient's motor ability to provide corresponding assistance in real time; (2) the patient's movement trend is ignored, and additional assistance is still provided when the patient has a good movement trend, which is not conducive to the patient's active participation in the movement.

[0005] Therefore, based on the above problems of the existing assistance control, it is necessary to improve the traditional upper limb rehabilitation robot assistance control method to achieve the purpose of adjusting the assistance level in real time according to the patient's motor ability and promoting the patient's active participation in the movement. SUMMARY

[0006] In view of the problems and deficiencies in the prior art, the purpose of the present application is to provide a one-way assistance control method and system for upper limb rehabilitation robots based on patient motor ability.

[0007] To achieve the purpose of the application, the technical scheme adopted by the present application is as follows:

[0008] The present application provides a one-way assistance control method for upper limb rehabilitation robots based on patient motor ability, comprising the following steps:

[0009] S101, obtaining a reference trajectory, an end position and an end interaction force of a robot arm;

[0010] S102, determining a shortest distance from the end position to the reference trajectory and a corresponding reference trajectory target point according to the end position and the reference trajectory of the robot arm;

[0011] S103, obtaining a normal interaction force perpendicular to a direction of the reference trajectory and a tangent interaction force parallel to the direction of the reference trajectory according to the end interaction force and the reference trajectory of the robot arm;

[0012] S104, determining a normal motion capability coefficient including a one-way constraint coefficient according to the normal interaction force and the shortest distance;

[0013] S105, determining a tangent motion capability coefficient including a one-way auxiliary coefficient according to the tangent interaction force and a tangent interaction force conversion rate;

[0014] S106, determining a normal constraint force according to the normal motion capability coefficient and the shortest distance;

[0015] S107, determining a tangent auxiliary force according to the tangent motion capability coefficient;

[0016] S108, converting a sum of the normal constraint force, the tangent auxiliary force and the end interaction force of the robot arm into a desired velocity, and then controlling the robot arm to perform corresponding motion according to the desired velocity.

[0017] Preferably, in step S104, the normal motion capability coefficient is determined according to the normal interaction force and the shortest distance, specifically including:

[0018] determining a deviation index subjected to normalization processing:

[0019] where d is the shortest distance from the end position of the robot arm to the reference trajectory, R s is a virtual boundary threshold, R m is a deviation calculation maximum boundary, and e is the deviation index subjected to normalization processing;

[0020] determining a clamped normal interaction force:

[0021] where F ha is the normal interaction force, a value greater than 0 representing that the normal interaction force is away from the reference trajectory target point, and a value less than 0 representing that the normal interaction force tends to the reference trajectory target point; F haL represents the clamped normal interaction force after the normal interaction force is subjected to clamping processing, F ha_max is a maximum boundary in the clamping processing of the normal interaction force;

[0022] Based on the aforementioned amplitude-limiting normal interaction force, determine the trend coefficient of the normal interaction force moving away from the trajectory:

[0023]

[0024] Where dot is the vector pointing from the target point on the reference trajectory to the position of the robotic arm's end effector. The interaction force with the end effector of the robotic arm is directed inward. The vector product, NMT is the trend coefficient of the normal interaction force moving away from the target point of the reference trajectory;

[0025] Based on the aforementioned deviation index and the trend coefficient of the normal interaction force moving away from the reference trajectory target point, the normal state coefficient after moving average processing at time t is determined:

[0026] NCI t t represents the normal state coefficient after moving average processing at time t, and m and n are the number of frames in the time window for calculating the moving average.

[0027] Based on the aforementioned normal state coefficient, determine the normal motion capability coefficient:

[0028] K an =β t (K an_min +(K an_max -K an_min )·NCI t / 2),

[0029] Where K an β is the normal motion capability coefficient. t K is the unidirectional constraint coefficient. an_min K is the minimum normal motion capability coefficient. an_max This is the coefficient for maximum normal motion capability.

[0030] Preferably, the unidirectional constraint coefficient β is determined. t The method is as follows:

[0031] Preferably, determining the tangential motion capability coefficient based on the tangential interaction force and the tangential interaction force transformation rate in step S105 specifically includes:

[0032] Determine the limiting tangential interaction force:

[0033] Where F ht The tangential interaction force has a value greater than 0, indicating that the tangential interaction force and the reference trajectory move in the same direction; a value less than 0 indicates that the tangential interaction force and the reference trajectory move in opposite directions. F htL F represents the limited tangential interaction force after amplitude limiting processing.atmax It serves as the maximum tangential auxiliary force and also as the maximum boundary for amplitude limiting.

[0034] Determine the rate of change of the limiting tangential interaction force:

[0035] Where dF ht Let dF be the rate of change of the tangential interaction force. htL Represents the rate of change of tangential interaction force dF ht The clipped tangential interaction force transformation rate, dF, after clipping processing ht-max The maximum boundary during the limiting process of the tangential interaction force transformation rate;

[0036] Based on the amplitude-limiting tangential interaction force and the transformation rate of the amplitude-limiting tangential interaction force, the tangential state coefficients for moving average processing at time t are determined:

[0037]

[0038] TPI t The tangential state coefficient at time t is the moving average processed.

[0039] Based on the aforementioned tangential state coefficient, the tangential motion capability coefficient is determined as: K at =g t ·TPI t

[0040] Where K at g is the tangential motion capability coefficient. t is the one-way auxiliary coefficient for moving average processing at time t.

[0041] Preferably, the unidirectional auxiliary coefficient g for moving average processing at time t is... t Determined by the following method:

[0042]

[0043] Preferably, in step S106, the normal constraint force is determined based on the normal motion capability coefficient and the shortest distance, specifically as follows:

[0044]

[0045] in For normal constraint forces, the shortest distance is less than R. s For the region of free movement, the shortest distance d is greater than R. s For guiding purposes, This is the unit vector pointing from the robot's end effector position to the target point on the reference trajectory.

[0046] Preferably, the tangential auxiliary force is determined according to the tangential motion ability coefficient in step S107, and specifically:

[0047]

[0048] wherein is the tangential auxiliary force, is a tangential unit vector parallel to the reference trajectory at the position of the end of the mechanical arm.

[0049] Preferably, the desired velocity is converted from the normal constraint force, the tangential auxiliary force and the interaction force of the end of the mechanical arm in step S108, and the specific calculation model is wherein M is an inertia matrix, D is a damping matrix, K is a stiffness matrix, Fh is the interaction force measured by the six-dimensional force sensor, x d are the desired acceleration, velocity and position of the end of the mechanical arm, respectively, x a are the actual required acceleration, velocity and position of the end of the mechanical arm to be controlled, respectively; K(x a -x d is the elastic force generated by the robot.

[0050] The second aspect of the present application provides a one-way auxiliary control system of an upper limb rehabilitation robot based on patient motion ability, which comprises:

[0051] a position and reference trajectory acquisition module for acquiring the position of the end of the mechanical arm and the reference trajectory;

[0052] a reference trajectory target point and shortest distance determination module for determining the shortest distance reference trajectory target point from the position of the end of the mechanical arm to the parameter trajectory according to the position of the end of the mechanical arm and the reference trajectory obtained by the position and reference trajectory acquisition module;

[0053] an end interaction force acquisition module for acquiring the interaction force between the patient and the end of the mechanical arm;

[0054] a normal interaction force and tangential interaction force determination module for determining the normal interaction force perpendicular to the direction of the reference trajectory and the tangential interaction force parallel to the direction of the reference trajectory according to the interaction force of the end of the mechanical arm obtained by the end interaction force acquisition module and the reference trajectory obtained by the reference trajectory target point and shortest distance determination module;

[0055] a normal motion ability coefficient determination module for determining the normal motion ability coefficient according to the normal interaction force obtained by the normal interaction force and tangential interaction force determination module and the shortest distance obtained by the reference trajectory target point and shortest distance determination module;

[0056] A tangential motion capability coefficient determination module is configured to determine a tangential motion capability coefficient based on the normal interaction force and the tangential interaction force obtained by the normal interaction force and tangential interaction force determination module;

[0057] A normal constraint force determination module is configured to determine a normal constraint force acting on the end of the robot arm based on the normal motion capability coefficient obtained by the normal motion capability coefficient determination module, the shortest distance obtained by the shortest distance determination module, and the constraint boundary model;

[0058] A tangential auxiliary force determination module is configured to determine a tangential auxiliary force acting on the end of the robot arm based on the tangential motion capability coefficient obtained by the tangential motion capability coefficient determination module and a tangential auxiliary model;

[0059] A robot execution module is configured to convert the sum of the normal constraint force obtained by the normal motion capability coefficient determination module, the tangential auxiliary force obtained by the tangential auxiliary force determination module, and the robot end interaction force obtained by the robot end interaction force acquisition module into a desired velocity, and then control the robot to perform corresponding motion according to the desired velocity.

[0060] The third aspect of the present application provides an electronic device comprising a memory and a processor, wherein the memory is configured to store executable instructions capable of running on the processor, and the processor is configured to execute the steps in the method provided in the first aspect above when running the executable instructions

[0061] The above technical solution of the present application achieves (1) in the direction perpendicular to the target trajectory, when the patient moves away from the target trajectory, gradually provide the normal constraint force, when the patient tends to the target trajectory, quickly remove the normal constraint force, and do not provide the normal constraint force; (2) in the direction parallel to the target trajectory, when the patient is unable to move, gradually provide the tangential auxiliary force to drive the patient's arm to move, when the patient accelerates to follow the target trajectory, quickly remove the tangential auxiliary force, and do not provide the tangential auxiliary force, so that the robot system can provide smaller auxiliary force, adapt to different motion states of the patient, and stimulate the patient to actively participate in the motion.

[0062] Compared with the prior art, the present application has the following advantages:

[0063] (1) The present application adopts the moving average method to calculate the normal motion capability coefficient and the tangential motion capability coefficient, which can adjust the auxiliary level of the robot in real time according to the motion state of the patient in the last period, and prevent the sudden change of the auxiliary level caused by the sudden change of the patient's motion.

[0064] (2) The application additionally adds the patient's ability to tend to the target trajectory and the ability to accelerate to follow the target trajectory, in addition to introducing error indicators and tangential interaction force indicators in the direction perpendicular to the trajectory and parallel to the trajectory, respectively, to more accurately identify the patient's movement ability and provide more appropriate assistance.

[0065] (3) The application introduces a one-way constraint coefficient and a one-way assistance coefficient determined by the movement trend on the basis of identifying the patient's movement ability, which can quickly reduce the normal constraint force and the tangential assistance force when the patient tends to the target trajectory and accelerates, reducing the assistance of the robot and allowing the patient to actively participate in rehabilitation exercise. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 A flowchart of the one-way assistance control method of the upper limb rehabilitation robot based on the patient's movement ability of the application;

[0067] Figure 2 A schematic diagram of the determination of the virtual reference path and the reference trajectory target point and the decomposition plane of the interaction force at the end of the robot in the preferred embodiment of the application;

[0068] Figure 3 A schematic diagram of the normal constraint force and the tangential assistance force plane on the virtual reference path in the preferred embodiment of the application;

[0069] Figure 4 A schematic diagram of the quality-damping-stiffness model of the control method used by the robot in the preferred embodiment of the application;

[0070] Figure 5 The change trend of the shortest distance and the tangential constraint force in the experimental process of the preferred embodiment of the application;

[0071] Figure 6 The change trend of the normal movement ability in the experimental process of the preferred embodiment of the application;

[0072] Figure 7 The change trend of the tangential interaction force and the tangential assistance force in the experimental process of the preferred embodiment of the application;

[0073] Figure 8 The change trend of the tangential movement ability in the experimental process of the preferred embodiment of the application;

[0074] Figure 9 A schematic diagram of the structure of the one-way assistance system of the upper limb rehabilitation robot based on the patient's movement ability of the application. DETAILED DESCRIPTION

[0075] The purpose of this invention is to provide a one-way auxiliary control method and system for upper limb rehabilitation robots based on the patient's motor ability, so as to more accurately identify the patient's motor ability and trend to provide appropriate assistance, and quickly reduce the assistance when the patient has a good motor trend, thereby promoting the patient's active participation in rehabilitation training.

[0076] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0077] Example 1

[0078] Figure 1 This is a schematic diagram of a one-way assisted control method for an upper limb rehabilitation robot based on the patient's motor ability, as provided in this embodiment. Figure 1 As shown, the one-way assisted control method for the upper limb rehabilitation robot based on the patient's motor ability specifically includes:

[0079] S101, Obtain the reference trajectory, end effector position, and end effector interaction force of the robotic arm:

[0080] The reference trajectory of the robotic arm is generated by the host computer's UnitV interface based on a pre-written algorithm and sent to the slave computer for confirmation. This trajectory is typically an arc, a straight line, or a fitted curve. In this embodiment, a circle with radius R is selected as the reference trajectory. Figure 2 As shown, the patient needs to grasp the end of the robotic arm and follow it clockwise along a circular trajectory. The end position P at time t... act (x a (t), y a (t), z a (t) can be obtained by kinematic forward kinematics calculation of the joint angles of the robotic arm; the interaction force at the end of the robotic arm can be measured by a force sensor installed at the end of the robotic arm; the coordinates of all points are relative to the base coordinate system of the robotic arm.

[0081] S102, Based on the end-effector position and reference trajectory, determine the shortest distance from the end-effector position to the reference trajectory and its corresponding target point on the reference trajectory:

[0082] Since the reference trajectory used in this embodiment is as follows: Figure 2 The circular trajectory shown is used as a reference trajectory for the target point P. N The position P of the robotic arm end effector act The center P of the circular trajectory o The shortest distance d between the intersection of the line and the circular trajectory is the position P of the robotic arm's end effector. act and reference trajectory target point P N For details on the distances between them, please refer to [link / reference]. Figure 2 .

[0083] S103, the normal interaction force perpendicular to the reference trajectory direction and the tangential interaction force parallel to the reference trajectory direction are obtained according to the mechanical arm end interaction force and the reference trajectory:

[0084] As shown in the formula (2), the normal interaction force F Figure 2 n perpendicular to the reference trajectory direction and the tangential interaction force F t parallel to the reference trajectory direction can be obtained according to the mechanical arm end interaction force F and the reference trajectory: The reference trajectory target point P N is directed to the end position P act , and the unit vector of the vector is u , and the mechanical arm end interaction force F is dot multiplied by u , that is, the normal interaction force F ha n perpendicular to the reference trajectory direction is obtained, and the direction is u For the tangential interaction force F , the unit vector u of the direction is perpendicular to the normal interaction force F n, and is the same as the reference trajectory movement direction, and the mechanical arm end interaction force F is dot multiplied by u , that is, the tangential interaction force F ht t parallel to the reference trajectory direction is obtained, and the direction is u

[0085] S104, the normal motion ability coefficient is determined according to the normal interaction force and the shortest distance, and the normal motion ability coefficient includes a one-way constraint coefficient:

[0086] The normal motion ability coefficient is determined by formula (1)-(6):

[0087] Firstly, the deviation index is obtained by using formula (1) for normalization processing:

[0088]

[0089] In formula (1), d is the shortest distance, R s is the virtual boundary threshold, R m is the deviation calculation maximum boundary, and e is the deviation index for normalization processing. The virtual boundary threshold R s and the deviation calculation maximum boundary R m in formula (1) can be set by the user, the larger the virtual boundary threshold R s , the larger the interval that the patient can freely move in the vertical direction, and the larger the R m , the slower the increase of the deviation index, and the normal constraint force will be reduced to a certain extent.

[0090] Then, the amplitude-limited normal interaction force is determined by formula (2):

[0091]

[0092] In formula (2), F ha is a normal interaction force decomposed from the interaction force at the end of the robot arm to a direction perpendicular to the reference trajectory, and a value greater than 0 represents that the normal interaction force is away from the target point of the reference trajectory, and a value less than 0 represents that the normal interaction force is towards the target point of the reference trajectory; F haL represents a limiting normal interaction force after limiting processing is performed on the normal interaction force F ha ; F ha_max is a maximum boundary in the limiting processing of the normal interaction force.

[0093] Based on the limiting normal interaction force, a trend coefficient of the normal interaction force away from the trajectory is determined by using formula (3):

[0094]

[0095] In formula (3), dot is a vector from the target point of the reference trajectory to the position of the end of the robot arm, and F is the vector product of the vector dot and the interaction force vector F of the end of the robot arm, and NMT is the trend coefficient of the normal interaction force away from the target point of the reference trajectory.

[0096] Based on the deviation index and the trend coefficient of the normal interaction force away from the trajectory, a normal state coefficient after moving average processing at t is determined by using formula (4):

[0097]

[0098] In formula (4), NCI t is the normal state coefficient after moving average processing at t, and m and n are the time window frame numbers of moving average calculation.

[0099] Based on the normal state coefficient, one-way constraint coefficient β t and normal motion ability coefficient K an are determined by using formula (5) and formula (6):

[0100]

[0101] K an =β t (K an_min +(K an_max -K an_min )·NCI t / 2) (6)

[0102] In formula (5), β t is the one-way constraint coefficient after moving average processing at t; and in formula (6), Kan K is the normal motion capability coefficient. an_min K is the minimum normal motion capability coefficient. an_max This represents the maximum normal motion capability coefficient. Normal motion capability coefficient K an The smaller the value, the better the patient's motor performance.

[0103] The number of frames m and n in the time window for calculating the moving average in formula (4) can also be set by the user. Smaller m and n can speed up the normal motion capability coefficient K. an The response can quickly change the normal constraint force, but sudden interactive disturbances can cause the normal constraint force to change too quickly, resulting in jitter. Therefore, m is usually set to 4 times n, so that the normal constraint force can be quickly reduced when the patient approaches the target trajectory.

[0104] In formula (2) F ha_max It can be set by the user, F ha_max This will affect the trend NMT and the one-way constraint coefficient β that are far from the trajectory. t The rate of change, F ha_max Small, NMT and β t The rate of change of the interaction force with the normal direction will increase; K an_min and K an_max Used to adjust K an The range of variation of the normal motion capability coefficient, that is, the degree of variation of the normal constraint force strength, can be set by the user.

[0105] When the patient moves toward the target trajectory, NMT and β t Even when the value is less than 1, the shortest distance d also decreases, but due to the one-way constraint coefficient β... t For the normal motion capability coefficient K an The impact is relatively large, the normal motion capability coefficient K an As the force decreases rapidly, the normal constraint force also decreases rapidly. The patient needs to return to the trajectory position through their own efforts. The robot provides almost no assistance to stimulate the patient to actively participate in the movement.

[0106] When the patient moves away from the target trajectory, NMT is greater than 1, β t The value equals 1; at this time, the one-way constraint coefficient β t It has no effect; the normal motion capability coefficient K an As the trajectory deviation and the trend of moving away from the trajectory gradually increase, the normal restraint force gradually increases, thereby restricting the patient's movement out of the trajectory.

[0107] S105, determine the tangential motion capability coefficient based on the tangential interaction force and the tangential interaction force transformation rate, wherein the tangential motion capability coefficient includes a unidirectional auxiliary coefficient:

[0108] The tangential motion capability coefficient can be determined using equations (7) to (11):

[0109] First, the limiting tangential interaction force is determined using the following equation (7):

[0110]

[0111] In equation (7), F ht The interaction force at the robotic arm's end effector is decomposed into a tangential interaction force parallel to the reference trajectory. A value greater than 0 indicates that the tangential interaction force moves in the same direction as the reference trajectory, while a value less than 0 indicates that the tangential interaction force moves in the opposite direction to the reference trajectory. F htL Represents the tangential interaction force F ht The limited tangential interaction force F after amplitude limiting processing atmax This is the maximum tangential auxiliary force, which also serves as the maximum boundary for amplitude limiting. Maximum tangential auxiliary force F atmax This indicates the maximum tangential assistance force that the robot can provide, which can be set according to the user's needs.

[0112] Then, the conversion rate of the limiting tangential interaction force is determined using the following equation (8):

[0113]

[0114] In equation (8), dF ht Let dF be the rate of change of the tangential interaction force. htL Represents the rate of change of tangential interaction force dF ht The clipped tangential interaction force transformation rate, dF, after clipping processing ht_max The maximum boundary during the limiting process of the tangential interaction force transformation rate.

[0115] Based on the aforementioned limiting tangential interaction force and the transformation rate of the limiting tangential interaction force, the tangential state coefficient TPI for moving average processing at time t is determined using equation (9). t :

[0116]

[0117] Based on the above tangential state coefficients, the unidirectional auxiliary coefficient g is determined using equations (10) and (11). t and tangential motion capability coefficient K at :

[0118]

[0119] K at =g t ·TPI t (11)

[0120] In equation (10), g t K is the one-way auxiliary coefficient for moving average processing at time t; in equation (11) at The tangential motion capability coefficient, K. an The smaller the value, the better the patient's motor performance. The above dF ht_max It can also be configured according to the user's own needs, dF ht_max The smaller the value, the higher the tangential motion index (TPI). t and unidirectional auxiliary coefficient g t The rate of change will accelerate, and the distance to the upper limit will decrease.

[0121] When the patient's tangential interaction force is small and the movement is accelerated, the unidirectional auxiliary coefficient g t and tangential state coefficient TPI t Less than 1, but because the moving average time window m is greater than n, the one-way auxiliary coefficient g t The rate of change will be faster, at which point the tangential motion capability coefficient K... at The rapid reduction leads to a rapid decrease in tangential assist force, providing no assistance and allowing the patient to actively follow the trajectory through their own efforts, thus stimulating the patient to actively participate in the movement.

[0122] When the patient decelerates or is unable to move, the one-way assist coefficient g t Equal to 1, unidirectional auxiliary coefficient g t It has no effect; the tangential motion capability coefficient K at The value of the tangential interaction force is changed only according to the value of the tangential auxiliary force, and then the value of the tangential auxiliary force is slowly adjusted to drive the patient's arm to perform rehabilitation exercises.

[0123] S106, Determine the normal constraint force based on the normal motion capability coefficient, the shortest path, and the constraint boundary model:

[0124] The constraint boundary model is determined using equation (12):

[0125]

[0126] In formula (12) For normal constraint force, from 0 to R s The region is a free-movement area, greater than R. s For the guiding area, the constraint force is varied using a quadratic curve. As the shortest path d increases, the normal constraint force increases faster and faster, thus restricting the patient's movement out of the trajectory. Let the unit vector of the robot's end effector position point to the target point of the reference trajectory be the region of the trajectory where the normal constraint force is located, as shown in the figure. Figure 3 As shown.

[0127] S107, the tangential auxiliary force is determined based on the tangential motion capability coefficient and the tangential auxiliary model, specifically including:

[0128] The tangential auxiliary model is determined using equation (13);

[0129]

[0130] In formula (13) As a tangential auxiliary force, Let be the tangential unit vector at the end effector position of the robotic arm that is parallel to the reference trajectory, and let be the region of the trajectory where the tangential auxiliary force is located. Figure 3 As shown.

[0131] S108 converts the sum of the current normal constraint force, tangential auxiliary force, and end effector interaction force of the robotic arm into the desired velocity, and then controls the robotic arm to perform corresponding movements based on the desired velocity:

[0132] The control algorithm for the interaction between the robotic arm and the patient is admittance control. The end effector force can be measured by a force sensor installed at the end of the robotic arm. The normal constraint force and the tangential auxiliary force can be calculated using formulas (12) and (13). The sum of the normal constraint force, the tangential auxiliary force, and the end effector force is input to... Figure 4 In the control system shown, the desired speed of the robotic arm's end effector is obtained, and its calculation model is as follows:

[0133]

[0134] Figure 4 In the above formula, M is the inertia matrix, D is the damping matrix, K is the stiffness matrix, and F... h The interaction force is measured by a six-dimensional force sensor. x d Given the desired acceleration, velocity, and position of the robotic arm's end effector. x a For the actual end-effector acceleration, velocity, and position that need to be controlled; K(x) a -x d The elastic force generated by the robot is defined here as K(x). a -x d The value is 0, due to the normal constraint force F. a and tangential auxiliary force F t Replace K(x) a -x d It produces the effect of elastic force.

[0135] Finally, the control method of this invention was implemented in a robot system, and the experimental results were obtained, as shown in the figure. Figures 5 to 8 As shown.

[0136] Figure 5 This example illustrates the changing trends of the shortest distance and tangential constraint force during the experiment. Figure 6 This shows the trend of change in normal motion capability during the experiment in this embodiment. From... Figure 5 and Figure 6 As can be seen from the analysis of normal motion, within the time frame of 0 to 25 seconds, the patient actively follows the trajectory, occasionally exhibiting significant deviations, but the normal constraint force does not change considerably, and the normal motion capability coefficient fluctuates within the range of 50 to 200. Between 25 and 47 seconds, the patient actively moves away from the trajectory, resulting in a larger deviation. At this point, the normal constraint gradually increases, restricting the patient's movement outside the trajectory, with the normal motion capability coefficient reaching a maximum of 400, significantly increasing the constraint. Furthermore, as the patient moves towards the target trajectory at 47 seconds, the normal motion capability coefficient decreases rapidly, and the normal constraint force also decreases rapidly, providing almost no force during this process, allowing the patient to actively participate in the movement. Between 47 and 70 seconds, the patient completely relaxes their arm; at this time, the robotic system provides a certain degree of normal constraint force in the normal direction to overcome the arm's gravity.

[0137] Figure 7 The diagram shows the changing trends of the tangential interaction force and the tangential auxiliary force during the experiment in this embodiment. Figure 8 This shows the trend of tangential motion capability during the experiment in this embodiment. From... Figure 7 and Figure 8 As can be seen from the analysis of tangential motion, during the period from 0 to 47 seconds, the patient actively follows the reference trajectory in a direction parallel to the trajectory, and the robot system does not provide tangential assistance force, resulting in a very small tangential motion capability coefficient. During the period from 47 to 70 seconds, the patient completely relaxes their arm, at which point the robot system provides a certain tangential assistance force in the tangential direction, driving the patient's arm to move. Furthermore, at 70 seconds, the patient actively accelerates to follow the reference trajectory, at which point the tangential assistance force decreases rapidly, and the tangential motion capability coefficient also decreases rapidly, providing almost no assistance, allowing the patient to actively participate in the movement.

[0138] In summary, the present invention provides a one-way assisted control method and system for upper limb rehabilitation robots based on the patient's motor ability. This method can more accurately identify the patient's motor ability and trends, provide appropriate assistance, adapt to different motor situations, and quickly reduce assistance when there is a good motor trend, thereby promoting the patient's active participation in rehabilitation training.

[0139] Example 2

[0140] A one-way assistive control system for an upper limb rehabilitation robot based on the patient's motor ability, the structural schematic diagram of which is shown below. Figure 9 As shown, it includes:

[0141] The position and reference trajectory acquisition module is used to acquire the end position and reference trajectory of the robotic arm;

[0142] The reference trajectory target point and shortest distance determination module is used to determine the shortest distance reference trajectory target point from the end position of the robotic arm to the parameter trajectory based on the position and the reference trajectory obtained by the reference trajectory module;

[0143] The end effector force acquisition module is used to acquire the interaction force between the patient and the end effector of the robotic arm;

[0144] The normal interaction force and tangential interaction force determination module is used to determine the normal interaction force perpendicular to the reference trajectory direction and the tangential interaction force parallel to the reference trajectory direction based on the end-effector interaction force obtained by the end-effector interaction force acquisition module and the reference trajectory obtained by the reference trajectory target point and shortest distance determination module.

[0145] The normal motion capability coefficient determination module is used to determine the normal motion capability coefficient based on the normal interaction force obtained by the normal interaction force and tangential interaction force determination module and the shortest distance obtained by the reference trajectory target point and shortest distance determination module.

[0146] The tangential motion capability coefficient determination module is used to determine the tangential motion capability coefficient based on the tangential interaction force and tangential interaction force transformation rate obtained by the normal interaction force and tangential interaction force determination module.

[0147] The normal constraint force determination module is used to determine the normal constraint force acting on the end effector of the robotic arm based on the normal motion capability coefficient obtained by the normal motion capability coefficient determination module, the reference trajectory target point, the shortest distance obtained by the shortest distance determination module, and the constraint boundary model.

[0148] The tangential auxiliary force determination module is used to determine the tangential auxiliary force acting on the end effector of the robotic arm based on the tangential motion capability coefficient obtained by the tangential motion capability coefficient determination module and the tangential auxiliary model.

[0149] The robotic arm execution module is used to convert the sum of the normal constraint force obtained by the normal motion capability coefficient determination module, the tangential auxiliary force obtained by the tangential auxiliary force determination module, and the robotic arm end interaction force obtained by the end interaction force acquisition module into the desired speed, and then control the robotic arm to perform corresponding movements based on the desired speed.

[0150] Example 3

[0151] An electronic device includes a memory and a processor, the memory being used to store executable instructions that can run on the processor, the processor being used to execute, when running the executable instructions, a one-way assisted control method for an upper limb rehabilitation robot based on patient motor ability as described in Embodiment 1.

[0152] The above embodiments are specific implementations of the present invention, but the implementation of the present invention is not limited to the above embodiments. Any other combination, change, modification, substitution, or simplification that does not exceed the design concept of the present invention shall fall within the protection scope of the present invention.

Claims

1. A one-way auxiliary control system for an upper limb rehabilitation robot based on the patient's motor ability, characterized in that, The following steps are adopted: S101, obtain the reference trajectory, end-effector position, and end-effector interaction force of the robotic arm; S102, determine the shortest distance from the end position to the reference trajectory and the target point of the reference trajectory based on the end position of the robotic arm and the reference trajectory; S103, based on the end effector force of the robotic arm and the reference trajectory, obtain the normal interaction force perpendicular to the reference trajectory direction and the tangential interaction force parallel to the reference trajectory direction; S104, determine the normal motion capability coefficient based on the normal interaction force and the shortest distance, wherein the normal motion capability coefficient includes a unidirectional constraint coefficient; S105, determine the tangential motion capability coefficient based on the tangential interaction force and the tangential interaction force transformation rate, wherein the tangential motion capability coefficient includes a unidirectional auxiliary coefficient; S106, determine the normal constraint force based on the normal motion capability coefficient and the shortest distance; S107, Determine the tangential auxiliary force based on the tangential motion capability coefficient; S108, the sum of the normal constraint force, the tangential auxiliary force and the interaction force at the end of the robotic arm is converted into the desired speed, and then the robotic arm is controlled to perform corresponding movements according to the desired speed; Step S104, which determines the normal motion capability coefficient based on the normal interaction force and the shortest distance, specifically includes: Determine the deviation index for normalization: in This represents the shortest distance from the end effector position of the robotic arm to the reference trajectory. For virtual boundary thresholds, Calculate the maximum boundary for the deviation. The deviation index is used for normalization. Determine the amplitude-limiting normal interaction force: in The normal interaction force is greater than 0, which means that the normal interaction force is moving away from the target point of the reference trajectory, and less than 0 means that the normal interaction force is moving towards the target point of the reference trajectory. This represents the amplitude-limited normal interaction force after amplitude-limiting processing. The maximum boundary during the amplitude limiting process of the normal interaction force; Based on the aforementioned amplitude-limiting normal interaction force, determine the trend coefficient of the normal interaction force moving away from the reference trajectory target point: in Let be the vector pointing from the target point on the reference trajectory to the position of the robotic arm's end effector. Interaction force vector with the end effector of the robotic arm The vector product, The trend coefficient of the normal interaction force moving away from the target point of the reference trajectory; Based on the deviation index and the trend coefficient of the normal interaction force moving away from the reference trajectory target point, determine Normal state coefficients after moving average processing at any given time: in for Normal state coefficients after moving average processing at any given time. and The number of frames in the time window used to calculate the moving average; Based on the aforementioned normal state coefficient, determine the normal motion capability coefficient: in The normal motion capability coefficient, For unidirectional constraint coefficients, The minimum normal motion capability coefficient, This is the coefficient for maximum normal motion capability.

2. The one-way auxiliary control system for the upper limb rehabilitation robot based on patient motor ability according to claim 1, characterized in that, The one-way constraint coefficient Determined by the following method: .

3. The one-way auxiliary control system for the upper limb rehabilitation robot based on patient motor ability according to claim 1, characterized in that, Step S105, which determines the tangential motion capability coefficient based on the tangential interaction force and the tangential interaction force transformation rate, specifically includes: Determine the limiting tangential interaction force: in This represents the tangential interaction force. A value greater than 0 indicates that the tangential interaction force moves in the same direction as the reference trajectory, while a value less than 0 indicates that the tangential interaction force moves in the opposite direction to the reference trajectory. This represents the limited tangential interaction force after amplitude limiting processing. It serves as the maximum tangential auxiliary force and also as the maximum boundary for amplitude limiting. Determine the rate of change of the limiting tangential interaction force: in The rate of change of the tangential interaction force. This represents the limited tangential interaction force transformation rate after limiting the tangential interaction force transformation rate. The maximum boundary during the limiting process of the tangential interaction force transformation rate; Based on the amplitude-limiting tangential interaction force and the amplitude-limiting tangential interaction force transformation rate, determine Tangential state coefficients subjected to moving average processing at all times : Based on the tangential state coefficient, determine the tangential motion capability coefficient: in The coefficient for tangential motion capability. for A one-way auxiliary coefficient that is constantly processed by moving average.

4. The one-way auxiliary control system for the upper limb rehabilitation robot based on the patient's motor ability according to claim 3, characterized in that, One-way auxiliary coefficient for constant moving average processing Determined by the following method: 。 5. The one-way auxiliary control system for the upper limb rehabilitation robot based on patient motor ability according to claim 4, characterized in that, In step S106, the normal constraint force is determined based on the normal motion capability coefficient and the shortest distance, specifically as follows: in For normal constraint force, the shortest distance is less than For the region of free movement, the shortest distance d is greater than As a guiding area, This is the unit vector pointing from the robot's end effector position to the target point on the reference trajectory.

6. The one-way auxiliary control system for the upper limb rehabilitation robot based on patient motor ability according to claim 5, characterized in that, In step S107, the tangential auxiliary force is determined based on the tangential motion capability coefficient, specifically as follows: in As a tangential auxiliary force, It is the tangential unit vector at the end position of the robotic arm that is parallel to the reference trajectory.

7. The one-way auxiliary control system for the upper limb rehabilitation robot based on the patient's motor ability as described in claim 6, characterized in that, In step S108, the sum of the normal constraint force, the tangential auxiliary force, and the interaction force at the end of the robotic arm is converted into the desired velocity. The specific calculation model is as follows: + + ,in The inertia matrix, Here is the damping matrix. Here is the stiffness matrix. The interaction force is measured by a six-dimensional force sensor. , , The desired acceleration, velocity, and position of the robotic arm's end effector are listed in order. , , The following are the actual robotic arm end-effector acceleration, velocity, and position that need to be controlled, in order: The elastic force generated for the robot.

Images