Low-cost robot high-computing-power simulation system

Abstract

The invention discloses a low-cost robot high-computing-power simulation system, and the system comprises an identity verification module which is used for verifying the identity of a user, and logging in the system after the verification succeeds; the motion control module is used for simulating motion control of the robot and simulating and controlling each motion muscle and joint of the robot; the instruction generation module is used for generating a control instruction after the movement of each movement joint and muscle of the robot is converted according to the movement requirement; the central processing module receives the control instruction and applies motion control to the motion control module; the feedback module is used for detecting the motion state of each motion muscle and joint of the robot, and the motion state comprises the motion speed, the motion direction and the motion distance; the data storage module is used for storing operation record information of the system and robot model information; according to the method, the cost is reduced: through a software simulation mode, the purchase and maintenance cost of entity hardware is avoided, and the research and development cost is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the field of robot simulation technology, and in particular to a low-cost robot high-computing power simulation system. Background Art

[0002] With the rapid development of intelligent manufacturing, autonomous driving, service robots and other fields, the complexity and functionality requirements of robot systems are increasing.

[0003] A robot is a machine device that performs work automatically. It can accept human commands, run pre-programmed programs, or act according to the principles and guidelines formulated by artificial intelligence technology. The core functions of a robot are:

[0004] A moving part, which is controlled by a driving source and is used to realize joint movement of the robot;

[0005] A control component is used to control the moving component and control the movement speed, position and time of the robot;

[0006] A central processing unit, which is used to analyze and process the data;

[0007] The feedback component collects the robot's motion data and feeds it back to the central processing unit to achieve closed-loop control.

[0008] The robot needs to be simulated, and simulation is used to simulate the robot's working accuracy and reliability in different environments. The traditional testing and verification methods based on physical hardware are not only costly and time-consuming, but also pose safety risks. Summary of the invention

[0009] In view of the defects in the prior art, the present invention provides a low-cost robot high-computing power simulation system;

[0010] It includes:

[0011] The identity verification module is used to verify the user's identity and log into the system after successful verification;

[0012] The motion control module is used to simulate the motion control of the robot and simulate each motion muscle and joint of the robot;

[0013] The command generation module converts the movement of each joint and muscle of the robot into control commands according to the movement requirements;

[0014] A central processing module receives control instructions and applies motion control to the motion control module;

[0015] A feedback module detects the motion state of each moving muscle and joint of the robot, wherein the motion state includes motion speed, motion direction and motion distance;

[0016] A data storage module, which is used to store the system's operation record information and robot model information;

[0017] Human-computer interaction interface, which includes input, output and display interfaces based on voice and control buttons.

[0018] Preferably, the identity verification module includes any one or more serial combinations of fingerprint verification, account-password verification, voiceprint verification, and iris verification.

[0019] Preferably: the operation logic of the low-cost robot high-computing power simulation system includes the following steps:

[0020] S1: First, input the motion requirement information of a robot through the human-computer interaction interface;

[0021] S2: The command generation module converts the motion demand information into motion control commands for muscles and joints based on the motion demand information and the robot's joint muscle information;

[0022] S3: The central processing module receives the motion control instruction, converts the control instruction into a drive signal and transmits it to the motion control module;

[0023] S4: The motion control module receives the driving signal and drives the corresponding muscles and joints to realize the simulated motion of the robot;

[0024] S5: The feedback module collects the motion information of the robot in simulation operation and feeds the motion information back to the central processing module;

[0025] S6: The central processing module compares the feedback information with the drive signal information and uses a compensation algorithm based on threshold control to perform subsequent simulation control;

[0026] S7: The data storage module stores the simulation records.

[0027] Preferably: in step S6, the compensation algorithm based on threshold control includes the following steps:

[0028] S61: Setting the offset threshold ;

[0029] S62: During the initial simulation, the drive signal value P and the feedback motion value are obtained. , and P, and Compare and then compensate according to the difference ΔP;

[0030] S63: In subsequent simulations, obtain the previous n drive signal values ​​P and feedback motion values The difference ΔP, ;

[0031] S64: Calculate the standard deviation of the m differences among the previous n times , is the mean of all ΔP, ,in is the driving signal value P and the feedback motion value of the ith time The difference between

[0032] S65: If S< , then the driving signal is not compensated this time, and control is performed based on the instructions generated by the instruction generation module;

[0033] S66: If S , the adjacent compensation algorithm is used to compensate the control signal.

[0034] Preferably: the step S62 includes the following steps:

[0035] S621: If ΔP is less than , the control is precise and no compensation is required;

[0036] S622: If ΔP is greater than , then the compensation value is ΔP.

[0037] Preferably: in step S66, the logic of the proximity compensation algorithm is: , is the driving signal value P and the feedback motion value of the ith time The difference, for The weight of; that is, the control signal generated by the central processing module this time is , It is the driving signal value converted by the instruction generation module.

[0038] Preferably: in the step S66, , where n is a positive integer, is a constant coefficient.

[0039] Preferably: the storage logic of the data storage module is:

[0040] S71: Divide the data storage space into multiple independent storage areas;

[0041] S72: storing the robot data, the operation record data, and the simulation data in different areas;

[0042] S73: Encrypting each area by setting an encryption algorithm;

[0043] S74: The user's identity verification is successful and after decryption, the user can read, add or delete data in the corresponding area.

[0044] Preferably: in the step S73, the encryption algorithm adopts any one of symmetric encryption, asymmetric encryption, hash encryption and obfuscation encryption.

[0045] Preferably: in step S73, the encryption algorithm of each area is the same or different.

[0046] Preferably: in the step S73, the encryption method comprises the following steps:

[0047] S731: Combine all regions in pairs to form multiple groups The combination of represents the i-th region and the o-th region;

[0048] S732: Establish an encryption algorithm set, which includes n different encryption algorithms, and sort and number the n encryption algorithms;

[0049] S733: Normalize the data in the i and o regions so that the data is within the interval;

[0050] S734: Calculate the mean of the normalized data in regions i and o, and then round it to get , ;

[0051] S735: The sequence number in the encrypted set is The same encryption algorithm is used to encrypt the data in area o, and the serial number in the encryption set is The same encryption algorithm is used to encrypt data in the i zone.

[0052] Preferably: in the step S732, the encryption algorithm is:

[0053] ,in is the minimum value in the data, is the maximum value in the data;

[0054] , where e is the base of natural logarithms;

[0055] ,in is the minimum value in the data, is the maximum value in the data;

[0056] ,in ;

[0057] ,in , ;

[0058] Any combination of; and x is the data before encryption, For the encrypted data, is the i-th data before encryption, is the mean value of all data before encryption.

[0059] The beneficial effects of the present invention are embodied in:

[0060] 1. Reduce costs: Through software simulation, the purchase and maintenance costs of physical hardware are avoided, greatly reducing R&D costs.

[0061] 2. Improve efficiency: The simulation system supports rapid iteration and parallel computing, which can greatly shorten the R&D cycle and improve R&D efficiency.

[0062] 3. Improve safety: Simulation verification in a virtual environment avoids the safety risks that may occur during the testing of physical hardware.

[0063] 4. Optimize performance: Through data analysis and visualization tools, users can conduct detailed analysis and optimization of the performance of the robot system to improve the overall performance of the robot system.

[0064] 5. High data security: On the basis of data encryption, the encrypted areas are first associated, and then the data of the two related areas are used as the selection logic of the encryption algorithm, so that the encryption algorithm of a single area is uncertain and the encrypted data is also uncertain, thereby increasing the difficulty of cracking and increasing data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0066] Figure 1 is a structural diagram of the present invention;

[0067] Figure 2 It is the working logic diagram of the present invention. DETAILED DESCRIPTION

[0068] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0069] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0070] Embodiment 1:

[0071] A low-cost robot high-computing power simulation system, comprising:

[0072] The identity verification module is used to verify the user's identity and log into the system after successful verification;

[0073] The motion control module is used to simulate the motion control of the robot and simulate each motion muscle and joint of the robot;

[0074] The command generation module converts the movement of each joint and muscle of the robot into control commands according to the movement requirements;

[0075] A central processing module receives control instructions and applies motion control to the motion control module;

[0076] A feedback module detects the motion state of each moving muscle and joint of the robot, wherein the motion state includes motion speed, motion direction and motion distance;

[0077] A data storage module, which is used to store the system's operation record information and robot model information;

[0078] Human-computer interaction interface, which includes input, output and display interfaces based on voice and control buttons.

[0079] The identity verification module adopts fingerprint verification.

[0080] Embodiment 2:

[0081] A low-cost robot high-computing power simulation system, the difference between this embodiment and embodiment 1 is:

[0082] The identity authentication module adopts account-password authentication.

[0083] Embodiment 3:

[0084] A low-cost robot high-computing power simulation system, the difference between this embodiment and embodiment 1 is:

[0085] The identity verification module adopts a serial combination of fingerprint verification, account-password verification, voiceprint verification, and iris verification.

[0086] Embodiment 4:

[0087] A low-cost robot high-computing power simulation system. This embodiment makes the following improvements on the basis of embodiment 3:

[0088] The identity verification module includes any one or more serial combinations of fingerprint verification, account-password verification, voiceprint verification, and iris verification.

[0089] The operation logic of the low-cost robot high-computing power simulation system includes the following steps:

[0090] S1: First, input the motion requirement information of a robot through the human-computer interaction interface;

[0091] S2: The command generation module converts the motion demand information into motion control commands for muscles and joints based on the motion demand information and the robot's joint muscle information;

[0092] S3: The central processing module receives the motion control instruction, converts the control instruction into a drive signal and transmits it to the motion control module;

[0093] S4: The motion control module receives the driving signal and drives the corresponding muscles and joints to realize the simulated motion of the robot;

[0094] S5: The feedback module collects the motion information of the robot in simulation operation and feeds the motion information back to the central processing module;

[0095] S6: The central processing module compares the feedback information with the drive signal information and uses a compensation algorithm based on threshold control to perform subsequent simulation control;

[0096] S7: The data storage module stores the simulation records.

[0097] Embodiment 5:

[0098] A low-cost robot high-computing power simulation system. This embodiment makes the following improvements on the basis of embodiment 4:

[0099] In step S6, the compensation algorithm based on threshold control includes the following steps:

[0100] S61: Setting the offset threshold ;

[0101] S62: During the initial simulation, the drive signal value P and the feedback motion value are obtained. , and P, and Compare and then compensate according to the difference ΔP;

[0102] S63: In subsequent simulations, obtain the previous n drive signal values ​​P and feedback motion values The difference ΔP, ;

[0103] S64: Calculate the standard deviation of the m differences among the previous n times , is the mean of all ΔP, ,in is the driving signal value P and the feedback motion value of the ith time The difference between

[0104] S65: If S< , then the driving signal is not compensated this time, and control is performed based on the instructions generated by the instruction generation module;

[0105] S66: If S , the adjacent compensation algorithm is used to compensate the control signal.

[0106] Embodiment 6:

[0107] A low-cost robot high-computing power simulation system. This embodiment makes the following improvements on the basis of embodiment 5:

[0108] The step S62 includes the following steps:

[0109] S621: If ΔP is less than , the control is precise and no compensation is required;

[0110] S622: If ΔP is greater than , then the compensation value is ΔP.

[0111] Embodiment 7:

[0112] A low-cost robot high-computing power simulation system, comprising:

[0113] The identity verification module is used to verify the user's identity and log into the system after successful verification;

[0114] The motion control module is used to simulate the motion control of the robot and simulate each motion muscle and joint of the robot;

[0115] The command generation module converts the movement of each joint and muscle of the robot into control commands according to the movement requirements;

[0116] A central processing module receives control instructions and applies motion control to the motion control module;

[0117] A feedback module detects the motion state of each moving muscle and joint of the robot, wherein the motion state includes motion speed, motion direction and motion distance;

[0118] A data storage module, which is used to store the system's operation record information and robot model information;

[0119] Human-computer interaction interface, which includes input, output and display interfaces based on voice and control buttons.

[0120] The identity verification module includes any one or more serial combinations of fingerprint verification, account-password verification, voiceprint verification, and iris verification.

[0121] The operation logic of the low-cost robot high-computing power simulation system includes the following steps:

[0122] S1: First, input the motion requirement information of a robot through the human-computer interaction interface;

[0123] S2: The command generation module converts the motion demand information into motion control commands for muscles and joints based on the motion demand information and the robot's joint muscle information;

[0124] S3: The central processing module receives the motion control instruction, converts the control instruction into a drive signal and transmits it to the motion control module;

[0125] S4: The motion control module receives the driving signal and drives the corresponding muscles and joints to realize the simulated motion of the robot;

[0126] S5: The feedback module collects the motion information of the robot in simulation operation and feeds the motion information back to the central processing module;

[0127] S6: The central processing module compares the feedback information with the drive signal information and uses a compensation algorithm based on threshold control to perform subsequent simulation control;

[0128] S7: The data storage module stores the simulation records.

[0129] In step S6, the compensation algorithm based on threshold control includes the following steps:

[0130] S61: Setting the offset threshold ;

[0131] S62: During the initial simulation, the drive signal value P and the feedback motion value are obtained. , and P, and Compare and then compensate according to the difference ΔP;

[0132] S63: In subsequent simulations, obtain the previous n drive signal values ​​P and feedback motion values The difference ΔP, ;

[0133] S64: Calculate the standard deviation of the m differences among the previous n times , is the mean of all ΔP, ,in is the driving signal value P and the feedback motion value of the ith time The difference between

[0134] S65: If S< , then the driving signal is not compensated this time, and control is performed based on the instructions generated by the instruction generation module;

[0135] S66: If S , the adjacent compensation algorithm is used to compensate the control signal.

[0136] The step S62 includes the following steps:

[0137] S621: If ΔP is less than , the control is precise and no compensation is required;

[0138] S622: If ΔP is greater than , then the compensation value is ΔP.

[0139] In step S66, the logic of the proximity compensation algorithm is: , is the driving signal value P and the feedback motion value of the ith time The difference, for The weight of; that is, the control signal generated by the central processing module this time is , It is the driving signal value converted by the instruction generation module.

[0140] In the step S66, , where n is a positive integer, is a constant coefficient.

[0141] Embodiment 8:

[0142] A low-cost robot high-computing power simulation system. This embodiment makes the following improvements on the basis of embodiment 7:

[0143] The storage logic of the data storage module is:

[0144] S71: Divide the data storage space into multiple independent storage areas;

[0145] S72: storing the robot data, the operation record data, and the simulation data in different areas;

[0146] S73: Encrypting each area by setting an encryption algorithm;

[0147] S74: The user's identity verification is successful and after decryption, the user can read, add or delete data in the corresponding area.

[0148] In the step S73, the encryption algorithm adopts any one of symmetric encryption, asymmetric encryption, hash encryption, and obfuscated encryption.

[0149] In step S73, the encryption algorithm of each area is the same.

[0150] Embodiment 9:

[0151] A low-cost robot high-computing power simulation system, the difference between this embodiment and embodiment 8 is:

[0152] In step S73, the encryption algorithm for each area is different.

[0153] Embodiment 10:

[0154] A low-cost robot high-computing power simulation system. This embodiment makes the following improvements on the basis of embodiment 9:

[0155] In step S73, the encryption method includes the following steps:

[0156] S731: Combine all regions in pairs to form multiple groups The combination of represents the i-th region and the o-th region;

[0157] S732: Establish an encryption algorithm set, which includes n different encryption algorithms, and sort and number the n encryption algorithms;

[0158] S733: Normalize the data in the i and o regions so that the data is within the interval;

[0159] S734: Calculate the mean of the normalized data in regions i and o, and then round it to get , ;

[0160] S735: The sequence number in the encrypted set is The same encryption algorithm is used to encrypt the data in area o, and the serial number in the encryption set is The same encryption algorithm is used to encrypt data in the i zone.

[0161] In the step S732, the encryption algorithm is:

[0162] ,in is the minimum value in the data, is the maximum value in the data;

[0163] , where e is the base of natural logarithms;

[0164] ,in is the minimum value in the data, is the maximum value in the data;

[0165] ,in ;

[0166] ,in , ;

[0167] Any combination of; and x is the data before encryption, For the encrypted data, is the i-th data before encryption, is the mean value of all data before encryption.

[0168] The present invention, on the basis of data encryption, first associates the encryption areas, and then uses the two mutually associated area data as the selection logic of the encryption algorithm, so that the encryption algorithm of a single area is uncertain and the encrypted data is also uncertain, thereby increasing the difficulty of cracking and increasing data security.

[0169] 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 by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A low-cost robot high-computing power simulation system, characterized by: It includes: The identity verification module is used to verify the user's identity and log into the system after successful verification; The motion control module is used to simulate the motion control of the robot and simulate each motion muscle and joint of the robot; The command generation module converts the movement of each joint and muscle of the robot into control commands according to the movement requirements; A central processing module receives control instructions and applies motion control to the motion control module; A feedback module detects the motion state of each moving muscle and joint of the robot, wherein the motion state includes motion speed, motion direction and motion distance; A data storage module, which is used to store the system's operation record information and robot model information; Human-computer interaction interface, which includes input, output and display interfaces based on voice and control buttons.

2. A low-cost robot high-computing power simulation system according to claim 1, characterized in that: The identity verification module includes any one or more serial combinations of fingerprint verification, account-password verification, voiceprint verification, and iris verification.

3. A low-cost robot high-computing power simulation system according to claim 1, characterized in that: The operation logic of the low-cost robot high-computing power simulation system includes the following steps: S1: First, input the motion requirement information of a robot through the human-computer interaction interface; S2: The command generation module converts the motion demand information into motion control commands for muscles and joints based on the motion demand information and the robot's joint muscle information; S3: The central processing module receives the motion control instruction, converts the control instruction into a drive signal and transmits it to the motion control module; S4: The motion control module receives the driving signal and drives the corresponding muscles and joints to realize the simulated motion of the robot; S5: The feedback module collects the motion information of the robot in simulation operation and feeds the motion information back to the central processing module; S6: The central processing module compares the feedback information with the drive signal information and uses a compensation algorithm based on threshold control to perform subsequent simulation control; S7: The data storage module stores the simulation records.

4. A low-cost robot high-computing power simulation system according to claim 3, characterized in that: In step S6, the compensation algorithm based on threshold control includes the following steps: S61: Setting the offset threshold ; S62: During the initial simulation, the drive signal value P and the feedback motion value are obtained. , and P, and Compare and then compensate according to the difference ΔP; S63: In subsequent simulations, obtain the previous n drive signal values ​​P and feedback motion values The difference ΔP, ; S64: Calculate the standard deviation of the m differences among the previous n times , is the mean of all ΔP, ,in is the driving signal value P and the feedback motion value of the ith time The difference between S65: If S< , then the driving signal is not compensated this time, and control is performed based on the instructions generated by the instruction generation module; S66: If S , the adjacent compensation algorithm is used to compensate the control signal.

5. A low-cost robot high-computing power simulation system according to claim 4, characterized in that: The step S62 includes the following steps: S621: If ΔP is less than , the control is precise and no compensation is required; S622: If ΔP is greater than , then the compensation value is ΔP.

6. A low-cost robot high-computing power simulation system according to claim 5, characterized in that: In step S66, the logic of the proximity compensation algorithm is: , is the driving signal value P and the feedback motion value of the ith time The difference, for The weight of; that is, the control signal generated by the central processing module this time is , It is the driving signal value converted by the instruction generation module.

7. A low-cost robot high-computing power simulation system according to claim 6, characterized in that: In the step S66, , where n is a positive integer, is a constant coefficient.

8. A low-cost robot high-computing power simulation system according to claim 7, characterized in that: The storage logic of the data storage module is: S71: Divide the data storage space into multiple independent storage areas; S72: storing the robot data, the operation record data, and the simulation data in different areas; S73: Encrypting each area by setting an encryption algorithm; S74: The user's identity verification is successful and after decryption, the user can read, add or delete data in the corresponding area.

9. A low-cost robot high-computing power simulation system according to claim 8, characterized in that: In step S73, the encryption method includes the following steps: S731: Combine all regions in pairs to form multiple groups The combination of represents the i-th region and the o-th region; S732: Establish an encryption algorithm set, which includes n different encryption algorithms, and sort and number the n encryption algorithms; S733: Normalize the data in the i and o regions so that the data is within the interval; S734: Calculate the mean of the normalized data in regions i and o, and then round it to get , ; S735: The sequence number in the encrypted set is The same encryption algorithm is used to encrypt the data in area o, and the serial number in the encryption set is The same encryption algorithm is used to encrypt data in the i zone.

10. A low-cost robot high-computing power simulation system according to claim 9, characterized in that: In the step S732, the encryption algorithm is: ,in is the minimum value in the data, is the maximum value in the data; , where e is the base of natural logarithms; ,in is the minimum value in the data, is the maximum value in the data; ,in ; ,in , ; Any combination of; and x is the data before encryption, For the encrypted data, is the i-th data before encryption, is the mean value of all data before encryption.

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