Control method of upper and lower limb integrated robot

Abstract

The invention provides a control method of an upper and lower limb integrated robot, which comprises the following steps of: realizing motion control of lower limbs according to data of force sensors on two sides of a hip joint; in the lower limb movement process, left and right leg recognition is performed; according to the left and right leg identification information and the lower limb movement speed, the angular velocity of upper limb shoulder and elbow joint movement is estimated, so that upper limb movement is controlled, and upper and lower limb movement coordination control is realized. Left and right leg recognition is carried out in the lower limb movement process, the angular speed of upper limb shoulder and elbow joint movement is estimated according to left and right leg recognition information and the lower limb movement speed, accordingly, upper limb movement is controlled, the upper limbs and the lower limbs of a patient can be trained at the same time, upper limb and lower limb coordinated movement is achieved, and a better rehabilitation training effect is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and in particular to a control method for an upper and lower limb integrated robot. Background Art

[0002] When walking, the human body mobilizes the bones, muscles and nervous system to cooperate and support each other to complete the walking movement.

[0003] At present, the upper and lower limb rehabilitation training robots basically adopt a separate control method or a pure passive control method (accepting the specified motion trajectory command). The separate control method means that although the mechanical structure of the upper and lower limbs is designed to be integrated, the movement of the upper and lower limbs is controlled separately, and the upper and lower limbs cannot be controlled at the same time, that is, the lower limbs cannot be trained when the upper limbs are trained, or the upper limbs cannot be trained when the lower limbs are trained. The consequence is that the actual rehabilitation effect is greatly reduced. Therefore, the method of separate control of the upper and lower limbs is not much different from the separate lower limb or upper limb training equipment. On the contrary, it greatly increases the cost without achieving the desired effect. The advantages of this method are: fast, simple, and realizes sports training. The disadvantage is that it is not a real upper and lower limb coordination training, and it has its form but not its spirit.

[0004] The pure passive control method is to use a passive control method for the upper and lower limb movements, that is, to design a motion trajectory for each joint, issue trajectory point command values, and let the upper and lower limb devices move according to the predetermined trajectory. Although this can achieve the motion control of the upper and lower limbs at the same time, the pure passive control method lacks interaction with the patient, the setting of the trajectory is good or bad, whether the patient's own joint range of motion is taken into account, and it is impossible for the patient to drive the equipment for training. The lack of active participation of the patient causes the entire integrated training to become a purely passive method and lack of coordinated training. Therefore, the advantage of this method is that it can train the upper and lower limbs at the same time and can exercise. The disadvantage is the lack of a detailed exercise plan, the patient cannot actively participate in the training, and the upper and lower limb coordination training effect is poor.

[0005] Therefore, how to achieve truly integrated rehabilitation training for the upper and lower limbs is a problem that needs to be solved at present. Summary of the invention

[0006] The purpose of the present invention is to provide a control method for an upper and lower limb integrated robot, which can realize true upper and lower limb integrated rehabilitation training.

[0007] In order to achieve the above-mentioned object, the present invention provides a control method for an upper and lower limb integrated robot, comprising: realizing motion control of the lower limbs according to force sensor data on both sides of the hip joint;

[0008] During lower limb movement, left and right leg identification is performed;

[0009] Based on the left and right leg identification information and the lower limb movement speed, the angular velocity of the upper limb shoulder and elbow joints is estimated, thereby controlling the movement of the upper limbs and achieving coordinated control of the upper and lower limb movements.

[0010] Among the optional solutions, the method for identifying the left and right legs includes: establishing a left and right leg identification model, taking normal human walking as a benchmark, designing different walking speeds, recording the beats of the left and right legs and the forces on both sides of the hip joints during walking, conducting a large number of walking experiments, and training the left and right leg identification model;

[0011] When the trainee is training, the left and right legs are identified during walking based on the trained left and right leg identification models and the real-time force sensor data on both sides of the hip joint.

[0012] In the optional scheme, the method for estimating the angular velocity of the shoulder and elbow joints of the upper limbs includes: establishing an upper limb swing speed model, the model input data includes force sensor data on both sides of the hip joint, left and right leg identification information, lower limb movement speed, upper limb shoulder angle position information, upper limb elbow angle position information; the model output is the angular velocity of the shoulder and elbow joints of the upper limbs;

[0013] According to the upper limb swing speed model, the fuzzy control method is used to select the fuzzy membership function, based on the standard trajectory database and the customized rule base, the inference machine mechanism is used to calculate the angular velocity of the shoulder and elbow joints of the upper limb.

[0014] In the optional scheme, the speed of the robot treadmill is calculated according to the following formula to achieve the motion control of the lower limbs;

[0015] F error =F Target -F Sensor

[0016]

[0017] Among them, F Target It indicates the target force, which is input manually; F Sensor It represents the combined force of the force sensors on the left and right sides. error Indicates the deviation between the target force and the force sensor data, V cmd Indicates the treadmill speed; K p , K i , K d They represent the proportional, integral and differential coefficients of the PID controller respectively. The integral term represents the error F over a period of time. error The cumulative value of the differential term represents the error F error The rate of change.

[0018] The beneficial effects of the present invention are:

[0019] The present invention identifies the left and right legs during the movement of the lower limbs, estimates the angular velocity of the shoulder and elbow joints of the upper limbs based on the left and right leg identification information and the movement speed of the lower limbs, thereby controlling the movement of the upper limbs. The present invention can train the patient's upper and lower limbs at the same time, realize coordinated movement of the upper and lower limbs, and achieve better rehabilitation training effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, in which like reference numerals generally represent like components.

[0021] Figure 1 Schematic diagram of the upper and lower limb algorithm model in one embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of the lower limb motion control principle in one embodiment of the present invention.

[0023] Figure 3 This is a basic flow chart of left and right leg identification in one embodiment of the present invention.

[0024] Figure 4 This is an upper limb swing speed model in one embodiment of the present invention.

[0025] Figure 5 This is a simulation diagram of left and right leg identification in one embodiment of the present invention.

[0026] Figure 6 This is a schematic diagram of the structure of an upper and lower limb integrated robot in one embodiment of the present invention. DETAILED DESCRIPTION

[0027] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. According to the following description and drawings, the advantages and features of the present invention will become clearer. However, it should be noted that the concept of the technical solution of the present invention can be implemented in a variety of different forms and is not limited to the specific embodiments described herein. The drawings are all in a very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0028] It should be understood that when an element or layer is referred to as being "on, adjacent to, connected to or coupled to other elements or layers, it may be directly on, adjacent to, connected to or coupled to other elements or layers, or there may be intervening elements or layers. In contrast, when an element is referred to as being "directly on, directly adjacent to, directly connected to or directly coupled to other elements or layers, there may be no intervening elements or layers. It should be understood that, although the terms first, second, third, etc. may be used to describe various elements, components, regions, layers and / or parts, these elements, components, regions, layers and / or parts should not be limited by these terms. These terms are only used to distinguish one element, component, region, layer or part from another element, component, region, layer or part. Therefore, without departing from the teachings of the present invention, the first element, component, region, layer or part discussed below may be represented as a second element, component, region, layer or part.

[0029] Spatially relative terms such as "under," "below," "below," "under," "above," "above," etc., may be used herein for ease of description to describe the relationship of an element or feature shown in the figures to other elements or features. It should be understood that in addition to the orientations shown in the figures, the spatially relative terms are intended to include different orientations of the device in use and operation. For example, if the device in the accompanying drawings is flipped, then the elements or features described as "under other elements" or "under" or "under" will be oriented as "on" the other elements or features. Therefore, the exemplary terms "under" and "under" may include both upper and lower orientations. The device may be oriented otherwise (rotated 90 degrees or other orientations) and the spatial descriptors used herein are interpreted accordingly.

[0030] The purpose of the terms used herein is only to describe specific embodiments and is not intended to be limiting of the present invention. When used herein, the singular forms "one", "an" and "said / the" are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "consisting of" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0031] Example 1

[0032] Reference Figures 1 to 6 This embodiment provides a control method for an upper and lower limb integrated robot, comprising:

[0033] The movement control of the lower limbs is achieved based on the force sensor data on both sides of the hip joint;

[0034] During lower limb movement, left and right leg identification is performed;

[0035] Based on the left and right leg identification information and the lower limb movement speed, the angular velocity of the upper limb shoulder and elbow joints is estimated, thereby controlling the movement of the upper limbs and achieving coordinated control of the upper and lower limb movements.

[0036] Specifically, the algorithm mainly includes four parts: lower limb motion control, left and right leg identification, upper limb swing speed model and upper limb motion control. First, the force sensors on both sides of the hip joint are used to realize the motion control of the lower limbs. The proportional-integral-differential control (PID controller) is used with the six-axis force sensor data to realize the motion of the lower limbs. Secondly, according to the force data on both sides of the hip joint, the support vector machine classification algorithm is used to identify whether the left leg or the right leg is moving during the action. Thirdly, the upper limb swing speed model is designed based on the relevant knowledge of fuzzy control, which can realize the motion of the lower limbs while quickly obtaining the motion speed of the upper limbs, so that the upper and lower limbs can keep consistent and coordinated. Finally, according to the information obtained from the swing speed model, the upper limb motion algorithm is designed to realize the motion control of the upper limbs and realize the coordinated control of the upper and lower limbs. Using this method, on the one hand, the movement of the upper and lower limbs can be realized, instead of simply realizing the innovation of the mechanical structure of the upper and lower limbs and ignoring the importance of equipment movement. On the other hand, it effectively solves the purely passive upper and lower limb movements, allowing patients to actively participate in the integrated training of the upper and lower limbs, which can better realize coordinated motion control and improve the rehabilitation effect. Therefore, this method can better achieve the effectiveness, flexibility, efficiency and safety of upper and lower limb equipment.

[0037] The structure of the whole system related algorithm is as follows Figure 1 As shown, the lower limb controller mainly realizes lower limb motion control and left and right leg identification, the upper limb controller mainly realizes upper limb motion control, and the upper limb swing speed model is realized based on two parts of data of upper limb and lower limb.

[0038] Reference Figure 2 , First of all, the lower limb motion control, which is based on the existing weight-loss gait training system, uses a PID controller to achieve motion control of the pelvic support mechanism and treadmill according to the data of the force sensors on both sides. According to the set target weight-loss force, the value comes from the setting of the host computer software, and the force sensor data on both sides of the pelvis are used as the actual feedback value. In the vertical direction, a PID controller is designed to control the speed of the pelvic support mechanism to achieve motion control of the pelvic support mechanism, and in the horizontal direction, a PID controller is also designed to control the speed of the treadmill to achieve motion control of the treadmill.

[0039] Reference Figure 3 , followed by left and right leg recognition. Through observation, it is found that during normal walking, the data changes of the left and right force sensors have certain regularity. Therefore, based on the walking of normal people, a large number of walking experiments are carried out, different walking speeds are designed, the beats of the left and right legs are recorded during walking, the force sensor data on the left and right sides are collected, and the support vector machine (SVM) method is used to realize the left and right leg recognition during walking. The training set data is the left and right force sensor data. The relevant parameters of the SVM model are set, and data training is performed. The best training model is obtained through cross-validation. Combined with the trained model, the new test data (new left and right force sensor data) is applied to obtain the desired data output result, that is, whether the left leg or the right leg is moving.

[0040] Reference Figure 4 , and then the upper limb swing speed model, which is based on fuzzy control theory to estimate the speed of upper limb swing. The input of fuzzy control mainly includes the left force sensor data, the right force sensor data, the treadmill walking speed, the shoulder angle position information, and the elbow angle position information. The shoulder angular velocity information and elbow angular velocity information are calculated through the fuzzy rule base. The input data includes the force sensor data on the left and right sides, the treadmill speed, the shoulder angle position information, and the elbow position information. Based on the input data, a suitable membership function is selected for fuzzy processing. Based on the existing shoulder and elbow standard trajectory database and the set fuzzy rule base, a corresponding inference engine mechanism is established to defuzzify the results to obtain the actual control output. The output result is the shoulder angular velocity information and the elbow angular velocity information.

[0041] Finally, the upper limb motion control is sent to the upper limb controller based on the shoulder and elbow angular velocity information obtained earlier to achieve upper limb motion control. During the entire motion process, the velocity value of the upper limb shoulder and elbow joint motion is estimated in real time based on the changes in the force sensor data on the left and right sides, so that the velocity control of the upper limb shoulder and elbow joint can be achieved to achieve integrated upper and lower limb motion control, thereby achieving coordinated upper and lower limb motion.

[0042] Therefore, based on the above content, the algorithm for realizing integrated human-machine control of upper and lower limbs mainly includes the above four modules. The specific implementation steps of the algorithm of each module are summarized as follows:

[0043] Step 1: Lower limb motion control: According to the force sensor data on the left and right sides of the hip joint, design the corresponding PID controller, set the appropriate PID controller parameters, use the vertical force to achieve the motion control of the pelvic support mechanism, and use the horizontal force to achieve the motion control of the treadmill.

[0044] In this embodiment, the speed of the robot treadmill is calculated according to the following formula, thereby realizing the motion control of the lower limbs;

[0045] F error =F Target -F Sensor

[0046]

[0047] Among them, F Target It indicates the target force, which is input manually; F Sensor It represents the combined force of the force sensors on the left and right sides. error Indicates the deviation between the target force and the force sensor data, V cmd Indicates the treadmill speed; K p , K i , K d They represent the proportional, integral and differential coefficients of the PID controller respectively. The integral term represents the error F over a period of time. error The cumulative value of the differential term represents the error F error The rate of change.

[0048] Step 2: Left and right leg identification: Based on the input left and right force sensor data, support vector machine (SVM) is used to realize left and right leg identification. The appropriate kernel function is selected, the corresponding loss function is designed, and the appropriate training model is determined through cross-validation, so as to complete left and right leg identification based on different force sensor data.

[0049] In this embodiment, a polynomial equation kernel function is selected, and its expression is basically as follows:

[0050] K(xy)=(x*y+c) d

[0051] Among them, x and y represent the input force sensor data, c represents the constant term, a fixed value. The actual application is based on the last debugged data. d represents the polynomial dimension.

[0052] Step 3: Upper limb swing speed model: Comprehensively consider some operating data of the upper and lower limbs, such as the force sensor data on the left and right sides, the treadmill running speed, and the shoulder and elbow angle position information, use the fuzzy control method, select the appropriate fuzzy membership function, based on the standard trajectory database information and self-established rule base, use the inference machine mechanism, and thus calculate the appropriate control output, the angular velocity of the shoulder and elbow joints.

[0053] The entire fuzzy processing process uses a triangle membership function, and the basic formula is as follows:

[0054]

[0055] Where x represents the left or right force sensor data, and a, b, and c represent the set thresholds, which are obtained by empirical methods.

[0056] Step 4: Upper limb motion control: Based on the operations in the previous three steps, the acquired shoulder and elbow joint angular velocity information is sent to the upper limb motion controller to realize the motion control of the upper limb, and finally complete the combined control of the upper and lower limbs, thereby realizing the integrated movement of the upper and lower limbs and achieving coordinated motion control.

[0057] Based on the method proposed in this design, some simple simulations and tests were carried out, and some simulation results were initially obtained. The simulation results are as follows Figure 5 As shown, the blue curve represents the processed unilateral force sensor data information, and the red curve represents the left and right leg identification information, where "1" represents the right leg and "0" represents the left leg information.

[0058] This embodiment designs a corresponding motion control algorithm, with the force sensor data on both sides of the hip joint as the core, which can realize the motion control of the lower limb rehabilitation robot on the one hand, and can realize the identification of the left and right legs on the other hand, and drive the upper limb robot according to the status of the left and right legs to realize the motion control of the upper limbs. It can train the upper and lower limbs of the patient at the same time, realize the coordinated movement of the upper and lower limbs, and thus achieve better rehabilitation training effects.

[0059] The above control method can be applied to the control of unilateral upper limb robotic arms as well as the control of bilateral upper limb robotic arms.

[0060] Reference Figure 6 The upper and lower limb integrated robot using the control method of this embodiment may include: a lower limb training system 1, an upper limb training system 2 and a control module; the lower limb training system 1 includes a pelvic support mechanism, the pelvic support mechanism is used to support the hip joints on both sides of the trainee, and a force sensor 3 is provided at the position where the pelvic support mechanism contacts the trainee's hip joints; the upper limb training system 2 is connected to the pelvic support mechanism; the control module controls the lower limb training system 1 to work (control the speed of the treadmill) according to the data of the force sensor 3, and in the process of lower limb movement, the control module performs left and right leg identification, monitors the lower limb movement speed, and estimates the angular velocity of the upper limb shoulder and elbow joint movement, thereby controlling the upper limb training system 2 to work and realize the coordinated control of the upper and lower limb movement of the trainee. The mechanical arm of the upper limb training system of the robot can be either unilateral or bilateral.

[0061] The above description is only a description of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention. Any changes or modifications made by a person skilled in the art in the field of the present invention based on the above disclosure shall fall within the scope of protection of the claims.

Claims

1. A control method for an upper and lower limb integrated robot, characterized in that: include: The movement control of the lower limbs is achieved based on the force sensor data on both sides of the hip joint; During lower limb movement, left and right leg identification is performed; Based on the left and right leg identification information and the lower limb movement speed, the angular velocity of the upper limb shoulder and elbow joints is estimated, thereby controlling the movement of the upper limbs and achieving coordinated control of the upper and lower limb movements.

2. The control method of the upper and lower limb integrated robot according to claim 1, characterized in that: Methods for identifying left and right legs include: Establish a left-right leg recognition model, take normal human walking as a benchmark, design different walking speeds, record the rhythm of the left and right legs and the forces on both sides of the hip joints during walking, conduct a large number of walking experiments, and train the left-right leg recognition model; When the trainee is training, the left and right legs are identified during walking based on the trained left and right leg identification models and the real-time force sensor data on both sides of the hip joint.

3. The control method of the upper and lower limb integrated robot according to claim 1, characterized in that: Methods for estimating the angular velocity of the upper limb shoulder and elbow joints include: An upper limb swing speed model is established. The model input data includes the force sensor data on both sides of the hip joint, the left and right leg identification information, the lower limb movement speed, the upper limb shoulder angle position information, and the upper limb elbow angle position information; the model output is the angular velocity of the shoulder and elbow joints of the upper limbs; According to the upper limb swing speed model, the fuzzy control method is used to select the fuzzy membership function, based on the standard trajectory database and the customized rule base, the inference machine mechanism is used to calculate the angular velocity of the shoulder and elbow joints of the upper limb.

4. The control method of the upper and lower limb integrated robot according to claim 1, characterized in that: The speed of the robot treadmill is calculated according to the following formula to achieve motion control of the lower limbs; F error =F Target -F Sensor Among them, F Target It indicates the target force, which is input manually; F Sensor It represents the combined force of the force sensors on the left and right sides. error Indicates the deviation between the target force and the force sensor data, V cmd Indicates the treadmill speed; K p , K i , K d They represent the proportional, integral and differential coefficients of the PID controller respectively. The integral term represents the error F over a period of time. error The cumulative value of the differential term represents the error F error The rate of change.

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