Navigation system, method and equipment of rehabilitation training vehicle and storage medium

Abstract

The invention provides a navigation system, method and device for a rehabilitation training vehicle and a storage medium, and the method comprises a data obtaining unit which is used for obtaining each signal point location of a current lighting lamp; the central processing unit is used for assigning signal values to the signal points, calculating position deviation based on the signal values, judging whether the position deviation is equal to a preset stable value or not, if yes, driving at the current double-wheel rotating speed, and if not, inputting the position deviation and the current double-wheel rotating speed into a pre-established adjusting model, and obtaining the double-wheel expected rotating speed through the adjusting model; and the servo driver is used for executing the double-wheel expected rotating speed. Whether magnetic induction is in a balanced state or not is indicated through the position deviation amount, when the state is unbalanced, the servo driver is adjusted through the adjusting model, and automatic judgment and automatic navigation of the state of the rehabilitation training vehicle are achieved.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a navigation system, method, device and storage medium of a rehabilitation training vehicle. Background Art

[0002] The rehabilitation training vehicle is a device specially designed for rehabilitation therapy, which assists patients in various training activities during the rehabilitation process. With the rapid development of the global industry, the application of rehabilitation training vehicles is becoming more and more extensive.

[0003] In the related technology, existing rehabilitation training vehicles often operate outdoors and require manual control of the movement of the rehabilitation training vehicle. If encountering bad weather, it is necessary to use it in an indoor simulated scene. However, when the patient wears VR glasses, the scene is virtualized and the manual control buttons become blurred in the virtual scene, which may cause the patient to be unable to walk normally. Therefore, a navigation solution is needed that can automatically sense the environment to guide the movement of the rehabilitation training vehicle.

[0004] Based on the above analysis of the development status of this technical field, the existing technology lacks a rehabilitation training vehicle that can be used in virtual reality or real environments and has automatic navigation. Summary of the invention

[0005] The object of the present invention is to provide a navigation system, method, device and storage medium for a rehabilitation training vehicle, aiming to solve the above-mentioned problems in the prior art.

[0006] According to a first aspect of an embodiment of the present invention, a navigation system for a rehabilitation training vehicle is provided, comprising:

[0007] A data acquisition unit, used to obtain the signal points of each currently lit light;

[0008] The central processing unit is used to assign a signal value to the signal point, calculate the position deviation based on the signal value, and determine whether the position deviation is equal to a preset stable value. If so, the vehicle is driven at the current dual-wheel speed; otherwise, the position deviation and the current dual-wheel speed are input into a pre-established adjustment model, and the desired dual-wheel speed is obtained through the adjustment model;

[0009] Servo drive for executing the desired rotation speed of the dual wheels.

[0010] According to a second aspect of an embodiment of the present invention, a navigation method for a rehabilitation training vehicle is provided, comprising:

[0011] Acquire the signal points of the currently lit lights through the data acquisition unit;

[0012] The signal point is assigned a signal value by a central processor, and the position deviation is calculated based on the signal value, and it is determined whether the position deviation is equal to a preset stable value. If it is equal, the vehicle is driven at the current dual-wheel speed; otherwise, the position deviation and the current dual-wheel speed are input into a pre-established adjustment model, and the desired dual-wheel speed is obtained through the adjustment model;

[0013] The desired rotation speed of the two wheels is implemented by the servo drive.

[0014] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the navigation method for a rehabilitation training vehicle provided in the second aspect of the present disclosure are implemented.

[0015] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which a program for implementing information transmission is stored, and when the program is executed by a processor, the steps of the navigation method for a rehabilitation training vehicle provided in the second aspect of the present disclosure are implemented.

[0016] The technical solution provided by the embodiment of the present invention includes the following beneficial effects: the point lights of the signal points are lit up to indicate that induction occurs with the magnetic navigation sensor on the training vehicle, and the signal points that are currently lit are related to the current position. These points surround the rehabilitation training vehicle, and the position deviation can indicate whether the magnetic induction is in a balanced state. When the state is unbalanced, the servo drive is adjusted using an adjustment model, thereby realizing automatic judgment of the state of the rehabilitation training vehicle and automatic navigation.

[0017] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 is a schematic diagram of a navigation system of a rehabilitation training vehicle according to an embodiment of the present invention;

[0020] Figure 2 is a schematic diagram of an automatic navigation system architecture according to an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of the overall navigation process of an embodiment of the present invention;

[0022] Figure 4 is a flow chart of a navigation method of a rehabilitation training vehicle according to an embodiment of the present invention;

[0023] Figure 5 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0025] System Example

[0026] According to an embodiment of the present invention, a navigation system for a rehabilitation training vehicle is provided. The navigation system for the rehabilitation training vehicle is applied in a virtual reality or real environment and is installed on the rehabilitation training vehicle. If it is in a virtual reality, the establishment of the virtual scene is completely consistent with the real environment, that is, obstacles in the virtual environment also exist at corresponding positions in the real environment. Figure 1 is a schematic diagram of a navigation system of a rehabilitation training vehicle according to an embodiment of the present invention. Figure 1 As shown, the navigation system of the rehabilitation training vehicle according to an embodiment of the present invention specifically includes:

[0027] The data acquisition unit 10 is used to obtain the signal points of the current lights, specifically for:

[0028] The signal points of each light being turned on due to magnetic field induction are obtained, wherein the magnetic field induction is the induction generated between the magnetic navigation sensor placed on the rehabilitation training vehicle and the magnetic strip on the driving path.

[0029] The signal points specifically include: I / O points. The signal points can be understood as actual physical points, which are similar to small light bulbs. At every moment when the rehabilitation training vehicle is running, there are multiple lighted signal points around the rehabilitation training vehicle. As the rehabilitation training vehicle moves, the I / O points closer to the vehicle light up, while the remaining points are off.

[0030] The central processor 12 is used to assign a signal value to the signal point, calculate the position deviation based on the signal value, and determine whether the position deviation is equal to a preset stable value. If it is equal, the vehicle drives at the current dual-wheel speed. Otherwise, the position deviation and the current dual-wheel speed are input into a pre-established adjustment model, and the desired dual-wheel speed is obtained through the adjustment model. Specifically, it is used to:

[0031] Based on the magnitude of the magnetic force induced by the magnetic field, each signal point is assigned a value through the built-in software. The magnetic force generated by the signal point on the right side of the rehabilitation training vehicle is assigned a positive value, and the magnetic force generated by the signal point on the left side is assigned a negative value. For example, if the magnitude of the magnetic force received by the left and right sides converted by the built-in software is 1, the assigned values ​​are -1 and +1 respectively;

[0032] The left and right sides are determined based on the center line of the rehabilitation training vehicle;

[0033] Calculate the average of each assigned value, and use the average as the position deviation K, that is, the average value of all the currently lit point assignments. The position deviation is equivalent to the relative position. In the embodiment of the present invention, the preset stability value is 0, because when the position deviation K is equal to 0, it means that the magnetic field strength felt by the magnetic navigation sensor on the left side is equal to the magnetic field strength felt on the right side. This symmetrical distribution indicates that the magnetic navigation sensor has not deviated from the center line, and also represents that the rehabilitation training vehicle is in a normal position and operates stably.

[0034] If the position deviation K is not equal to 0, it is necessary to calculate the expected rotation speed of the two wheels through the adjustment model. When the position deviation K is greater than 0, the training vehicle is offset to the right compared to the stable state, and the vehicle body needs to be adjusted to turn left. When the position deviation K is less than 0, the rehabilitation training vehicle is offset to the left compared to the stable state, and the vehicle body needs to be adjusted to turn right. When the position deviation K is equal to 0, no adjustment is required.

[0035] The regulation model is specifically used to:

[0036] Based on the position deviation K and the current dual-wheel speed, the dual-wheel desired speed is calculated by proportional-differential regulation PD method, and the position deviation K is substituted into formula 1 to solve the compensation speed, where the dual-wheel desired speed includes the left wheel desired speed and the right wheel desired speed:

[0037] e=S×[PD.Kp×K+PD.Kd×(K-Klast)] Formula 1;

[0038] Among them, e represents the compensation speed, PD.Kp represents the proportional coefficient, PD.Kd represents the differential time constant, K represents the position deviation, Klast represents the position deviation obtained last time, and S represents the linear running speed;

[0039] The differential time constant is used to describe the time characteristics of the differential control link in the PD control algorithm. The size of PD.Kp and PD.Kd is related to the actual speed parameter. The above two parameters are adaptively adjusted with different linear running speeds. In the embodiment of the present invention, PD.Kp and PD.Kd are both equal to 0.1;

[0040] Assume that yl represents the current left wheel speed, and yr represents the current right wheel speed;

[0041] If the position deviation is greater than the preset stability value, the rehabilitation training vehicle deviates to the right compared to the stable state, the expected speed of the left wheel is |yl-|e||, and the expected speed of the right wheel is |yr+|e||, that is, the left wheel needs to reduce a compensation speed and the right wheel needs to increase a compensation speed to achieve a left turn;

[0042] If the position deviation is less than the preset stability value, the rehabilitation training vehicle shifts to the left compared to the stable state, the expected speed of the left wheel is |yl+|e||, and the expected speed of the right wheel is |yr-|e||, that is, a compensation speed needs to be increased for the left wheel and a compensation speed needs to be reduced for the right wheel to achieve a right turn;

[0043] The PD controller uses proportional and differential control outputs. Overall, the position deviation K value can be used to determine the direction and degree of deviation of the training vehicle. By substituting the K value into the PD formula, the compensation speed can be obtained, which is used to compensate the left and right wheels respectively to achieve the purpose of dynamic correction. The PD method can directly obtain the expected dual-wheel speed based on the position deviation K and the current dual-wheel speed.

[0044] The servo driver 14 is used to execute the desired rotation speed of the two wheels, specifically for:

[0045] The left drive is used to adjust the rotation speed of the left wheel of the rehabilitation training vehicle;

[0046] The right drive is used to adjust the rotation speed of the right wheel of the rehabilitation training vehicle.

[0047] The above technical solutions of the embodiments of the present invention are illustrated with reference to the following drawings.

[0048] Figure 2 Schematic diagram of the automatic navigation system architecture of an embodiment of the present invention. Figure 2 As shown, the navigation system running on the rehabilitation robot in the embodiment of the present invention includes: a data acquisition unit, a central processing unit and a servo driver;

[0049] Figure 3 Schematic diagram of the overall navigation process of an embodiment of the present invention. Figure 3 As shown in the figure, the complete navigation process based on the automatic navigation system is demonstrated, which includes obtaining the signal point of the light, generating the position deviation, and when the position deviation is not equal to the preset stable value, using the proportional-differential adjustment method to calculate the expected speed, and finally realizing the navigation adjustment.

[0050] To sum up, in response to the existing problems, the navigation system of the rehabilitation training vehicle is invented, and the system is divided into a data acquisition unit, a central processing unit and a servo driver; the I / O signal points are obtained through the digital acquisition unit, and the point lights of the signal points are lit to indicate that they are inductively coupled with the magnetic navigation sensor on the training vehicle. The currently lit signal points are related to the current position, and these points are around the rehabilitation training vehicle; the built-in software in the central processing unit automatically calculates the assignment result of each signal point to indicate the magnitude of the magnetic force, and calculates the position deviation to indicate whether the magnetic induction is in a balanced state; when the state is unbalanced, the adjustment model is used, and the proportional-differential adjustment PD method reduces the amount of code for the internal logic in the rehabilitation training vehicle, and can dynamically solve the expected rotational speed of the two wheels directly based on the deviation; finally, the servo driver is adjusted to realize automatic navigation of the rehabilitation training vehicle.

[0051] Method Embodiment

[0052] According to an embodiment of the present invention, a navigation method for a rehabilitation training vehicle is provided. The navigation system of the rehabilitation training vehicle is applied in a virtual reality or real environment. Figure 4 FIG. 1 is a flow chart of a navigation method of a rehabilitation training vehicle according to an embodiment of the present invention. Figure 4 As shown, the navigation method of the rehabilitation training vehicle according to an embodiment of the present invention specifically includes:

[0053] In step S410, the data acquisition unit acquires the signal points of the currently lit lights, which specifically includes:

[0054] Obtaining the signal points of each light that is lit due to magnetic field induction, wherein the magnetic field induction is the induction generated between the magnetic navigation sensor placed on the rehabilitation training vehicle and the magnetic strip on the driving path;

[0055] The signal points specifically include: I / O points.

[0056] In step S420, the central processor assigns a signal value to the signal point, calculates the position deviation based on the signal value, and determines whether the position deviation is equal to a preset stable value. If it is equal, the vehicle drives at the current dual-wheel speed. Otherwise, the position deviation and the current dual-wheel speed are input into a pre-established adjustment model, and the desired dual-wheel speed is obtained through the adjustment model, which specifically includes:

[0057] Based on the magnitude of the magnetic force induced by the magnetic field, that is, the signal point at the corresponding position of the magnetic strip trigger sensor, each signal point is assigned a value through the built-in software, the magnetic force generated by the signal point on the right side of the rehabilitation training vehicle is assigned a positive value, and the magnetic force value generated by the signal point on the left side is assigned a negative value, and the average of each assigned value is calculated, and the average is used as the position deviation;

[0058] The PD regulation model specifically includes:

[0059] Based on the position deviation and the current dual-wheel speed, the dual-wheel desired speed is calculated by proportional-differential adjustment. The position deviation is substituted into Formula 1 to solve the compensation speed, where the dual-wheel desired speed includes the left wheel desired speed and the right wheel desired speed:

[0060] e=S×[PD.Kp×K+PD.Kd×(K-Klast)] Formula 1;

[0061] Among them, e represents the compensation speed, PD.Kp represents the proportional coefficient, PD.Kd represents the differential time constant, K represents the position deviation, Klast represents the position deviation obtained last time, and S represents the linear running speed;

[0062] If the position deviation is greater than the preset stability value, the rehabilitation training vehicle shifts to the right compared to the stable state, and the expected speed of the left wheel is |yl-|e||, and the expected speed of the right wheel is |yr+|e||. If the position deviation is less than the preset stability value, the rehabilitation training vehicle shifts to the left compared to the stable state, and the expected speed of the left wheel is |yl+|e||, and the expected speed of the right wheel is |yr-|e||, where yl represents the current left wheel speed and yr represents the current right wheel speed.

[0063] In step S410, the desired rotation speed of the two wheels is executed by the servo driver, specifically including:

[0064] The servo drive specifically includes:

[0065] The left drive is used to adjust the rotation speed of the left wheel of the rehabilitation training vehicle;

[0066] The right drive is used to adjust the rotation speed of the right wheel of the rehabilitation training vehicle.

[0067] To sum up, in response to the existing problems, a navigation method for a rehabilitation training vehicle is invented. The method is used in a corresponding system, and the system is divided into a data acquisition unit, a central processing unit and a servo driver. The I / O signal points are acquired through the digital acquisition unit, and the point lights of the signal points are lit to indicate that they are inductively coupled with the magnetic navigation sensor on the training vehicle. The currently lit signal points are related to the current position, and these points are around the rehabilitation training vehicle. The built-in software in the central processing unit automatically calculates the assignment result of each signal point to indicate the magnitude of the magnetic force, and calculates the position deviation to indicate whether the magnetic induction is in a balanced state. When the state is unbalanced, an adjustment model is used, and the proportional-differential adjustment PD method reduces the amount of code for the internal logic in the rehabilitation training vehicle, and can dynamically solve the expected rotational speed of the two wheels directly based on the deviation. Finally, the servo driver is adjusted to achieve automatic navigation of the rehabilitation training vehicle.

[0068] Electronic device embodiment

[0069] Figure 5Schematic diagram of an electronic device according to an embodiment of the present invention. The electronic device 500 may include at least one processor 510 and a memory 520. The processor 510 may execute instructions stored in the memory 520. The processor 510 is connected to the memory 520 through a data bus. In addition to the memory 520, the processor 510 may also be connected to an input device 530, an output device 540, and a communication device 550 through a data bus.

[0070] The processor 510 may be any conventional processor, such as a commercially available CPU. The processor may also include a graphics processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a system on chip (System on Chip, SOC), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or a combination thereof.

[0071] The memory 520 may be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0072] In the disclosed embodiment, executable instructions are stored in the memory 520, and the processor 510 can read the executable instructions from the memory 520 and execute the instructions to implement all or part of the steps of the navigation method of the rehabilitation training vehicle in any of the above exemplary embodiments.

[0073] Computer Readable Storage Medium Embodiments

[0074] In addition to the above-mentioned methods and devices, an exemplary embodiment of the present disclosure may also be a computer program product or a computer-readable storage medium storing the computer program product. The computer product includes computer program instructions, and the computer program instructions can be executed by a processor to implement all or part of the steps described in the navigation method of the rehabilitation training vehicle in any of the above-mentioned exemplary embodiments.

[0075] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages ​​and scripting languages ​​(e.g., Python). The program code may be executed entirely on the user computing device, partially on the user computing device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0076] Computer readable storage media can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of readable storage media include: a static random access memory (SRAM) with one or more wires electrically connected, an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk, or any suitable combination of the above.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A navigation system for a rehabilitation training vehicle, characterized in that: include: A data acquisition unit, used to obtain the signal points of each currently lit light; a central processing unit, configured to assign a signal value to the signal point, calculate a position deviation based on the signal value, determine whether the position deviation is equal to a preset stable value, and if so, drive at the current dual-wheel speed; otherwise, input the position deviation and the current dual-wheel speed into a pre-established adjustment model, and obtain the desired dual-wheel speed through the adjustment model; A servo drive is used to execute the desired rotation speed of the dual wheels.

2. The method according to claim 1, characterized in that The navigation system of the rehabilitation training vehicle is applied in virtual reality or real environment.

3. The method according to claim 1, characterized in that The signal points specifically include: I / O points.

4. The method according to claim 1, characterized in that: The data acquisition unit is specifically used for: The signal points where the lights are turned on due to magnetic field induction are obtained, wherein the magnetic field induction is the induction generated between the magnetic navigation sensor placed on the rehabilitation training vehicle and the magnetic strip on the driving path.

5. The method according to claim 4, characterized in that The central processing unit is specifically used for: Based on the magnitude of the magnetic force induced by the magnetic field, each signal point is assigned a value through the built-in software. The magnetic force generated by the signal point on the right side of the rehabilitation training vehicle is assigned a positive value, and the magnetic force generated by the signal point on the left side is assigned a negative value. The average of each assigned value is calculated and the average is used as the position deviation.

6. The method according to claim 1, characterized in that The regulation model is specifically used for: Based on the position deviation and the current dual-wheel speed, the dual-wheel desired speed is calculated by proportional-differential regulation, and the position deviation is substituted into Formula 1 to solve the compensation speed, wherein the dual-wheel desired speed includes the left wheel desired speed and the right wheel desired speed: e=S×[PD.Kp×K+PD.Kd×(K-Klast)] Formula 1; Among them, e represents the compensation speed, PD.Kp represents the proportional coefficient, PD.Kd represents the differential time constant, K represents the position deviation, Klast represents the position deviation obtained last time, and S represents the linear running speed; If the position deviation is greater than the preset stability value, the rehabilitation training vehicle shifts to the right compared to the stable state, the expected speed of the left wheel is |yl-|e||, and the expected speed of the right wheel is |yr+|e||; if the position deviation is less than the preset stability value, the rehabilitation training vehicle shifts to the left compared to the stable state, the expected speed of the left wheel is |yl+|e||, and the expected speed of the right wheel is |yr-|e||, wherein yl represents the current left wheel speed, and yr represents the current right wheel speed.

7. The method according to claim 1, characterized in that The servo driver specifically comprises: The left drive is used to adjust the rotation speed of the left wheel of the rehabilitation training vehicle; The right drive is used to adjust the rotation speed of the right wheel of the rehabilitation training vehicle.

8. A navigation method for a rehabilitation training vehicle, characterized in that: The navigation system for the rehabilitation training vehicle according to any one of claims 1 to 7 comprises: Acquire the signal points of the currently lit lights through the data acquisition unit; Assigning a signal value to the signal point through a central processor, calculating a position deviation based on the signal value, judging whether the position deviation is equal to a preset stable value, and if so, driving at the current dual-wheel speed; otherwise, inputting the position deviation and the current dual-wheel speed into a pre-established adjustment model, and obtaining the desired dual-wheel speed through the adjustment model; The desired rotation speed of the dual wheels is implemented by a servo drive.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the navigation method for a rehabilitation training vehicle as claimed in claim 8 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by the processor, the steps of the navigation method for the rehabilitation training vehicle as claimed in claim 8 are implemented.

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