A safety detection method and system for intelligent hands in robotics technology

Abstract

The present invention discloses a safety detection method and system for an intelligent hand of robotics technology, and specifically relates to the field of robotics technology. The present invention obtains appearance image information of the robot intelligent hand and records appearance defect data of the robot intelligent hand, and then analyzes the appearance structural safety of the robot intelligent hand, which can improve the accuracy of safety detection and reduce the application risks brought by appearance defects. The present invention detects the perception ability of the robot intelligent hand, calculates the perception recognition accuracy coefficient and the tactile sensor sensitivity coefficient based on the detection data, detects the grasping ability of the robot intelligent hand, and calculates the grasping success rate coefficient, average positioning accuracy coefficient, average grasping stability coefficient and maximum grasping force based on the detection data, which can improve the accuracy of the grasping ability detection of the robot intelligent hand, expand the detection range, reduce application risks, and ensure the safety quality of the application of the robot intelligent hand.

Description

Technical Field

[0001] The present invention relates to the field of robotics technology, and more particularly, to a method and system for detecting safety of an intelligent hand in robotics technology. Background Art

[0002] With the continuous advancement of science and technology and the evolving needs of society, artificial intelligence and robotics are becoming hot research areas worldwide. Flexible, high-load end-effectors with sensing capabilities, as the end-effectors of robots, can mimic the complex and dexterous manipulations of the human hand. They are a key development direction in the field of humanoid robotics and a key component in enabling dexterous manipulation. They are widely used in space exploration, industrial manufacturing, home services, logistics and transportation, security inspections, and other fields.

[0003] The existing robot intelligent hand safety detection method tests the robot intelligent hand's grasping success rate and obstacle avoidance ability after completing electromagnetic compatibility testing, electrical safety testing, and mechanical safety testing. If all tests pass, the robot intelligent hand continues to be used; otherwise, a safety alarm is sent to trigger the robot intelligent hand maintenance task.

[0004] There are still some problems with the existing safety detection methods for robot intelligent hands: the grasping success rate is only one of the data indicators of the grasping ability of robot intelligent hands, and it is rather one-sided to use it to evaluate the grasping ability of robot intelligent hands. We should start from multiple perspectives to improve the accuracy of the grasping ability detection of robot intelligent hands. When the robot grasps some objects with low structural strength and fragile objects, the end effector joints are also required to have tactile perception capabilities, and the perception and recognition accuracy of the robot intelligent hand should be supplemented by the detection. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method and system for intelligent hand safety detection in robotics technology to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for detecting safety of an intelligent hand in robotics technology, comprising the following steps:

[0007] S1: Set the appearance structure safety index, operation performance index and application safety index of the robot intelligent hand;

[0008] S2: After acquiring appearance image information of the robot intelligent hand, record appearance defect data of the robot intelligent hand, analyze the appearance structural safety of the robot intelligent hand based on the recorded appearance defect data, and calculate the appearance structural safety factor;

[0009] S3: Test the perception ability of the robot's intelligent hand and calculate the perception recognition accuracy coefficient and tactile sensor sensitivity coefficient based on the test data. Test the grasping ability of the robot's intelligent hand and calculate the grasping success rate coefficient, average positioning accuracy coefficient, average grasping stability coefficient, and maximum grasping force based on the test data.

[0010] S4: Analyze the actual operation performance coefficient of the robot intelligent hand based on the calculated robot intelligent hand perception ability related indicators, grasping ability related indicators and the set robot intelligent hand related operation performance indicators;

[0011] S5: Perform radiation interference detection, conducted interference detection, drive response detection, and safety device detection on the robot intelligent hand, record the number of qualified and unqualified items, and calculate the application safety factor based on the recorded data;

[0012] S6: Compare the appearance structure safety factor, actual operation performance factor, and application safety factor of the robot intelligent hand with the expected appearance structure safety factor, expected operation performance factor, and expected application safety factor, respectively, and output different instructions based on the comparison results;

[0013] S7: After receiving the instruction to evaluate the comprehensive application safety quality of the robot intelligent hand, the comprehensive application safety quality index of the robot intelligent hand is calculated based on the appearance structure safety factor, actual operation performance factor and application safety factor of the robot intelligent hand.

[0014] Preferably, the appearance structure safety index of the robot intelligent hand set in step S1 is the expected appearance structure safety factor, the operation performance index of the robot intelligent hand set in step S1 includes the expected perception recognition accuracy coefficient, the expected tactile sensor sensitivity coefficient, the expected grasping success rate coefficient, the expected average positioning accuracy coefficient, the expected average grasping stability coefficient, the expected maximum grasping force and the expected operation performance coefficient, and the application safety index of the robot intelligent hand set in step S1 is the expected application safety factor.

[0015] Preferably, the structural analysis process for the robot intelligent hand in step S2 is as follows:

[0016] S21. Use a camera to capture a full-view overall appearance image of the robot intelligent hand, use a weighing tool to weigh the disassembled robot intelligent hand structure, and obtain the size and shape information of the robot intelligent hand structure through the camera;

[0017] S22. Record the deformation area Ax, damage area As, and theoretical initial area Ac of the flexible end effector configured for the robot intelligent hand; record the number maz of correct sensor installation positions configured for the robot intelligent hand and the number mac of incorrect sensor installation positions; record the number mbz of structures of the robot intelligent hand with correct size, shape, and weight and the number mbc of structures with incorrect size, shape, or weight;

[0018] S23. Calculate the safety factor XA of the appearance structure of the robot intelligent hand. The specific formula is:

[0019]

[0020] Preferably, the steps for detecting the perception capability of the robot intelligent hand in step S3 are as follows:

[0021] S311. Construct a test platform with several objects of different shapes, sizes, and materials, and perform single-point sensing continuous testing on the tactile sensor using objects of known size, shape, and material;

[0022] S312, recording the number of correctly identified objects mct, the number of incorrectly identified objects mcf, the change value ΔP of the tactile sensor normal stress measurement range, the change value ΔR of the output resistance within the full-scale pressure test range, and the resistance value Rm output by the tactile sensor at maximum normal stress;

[0023] S313. Calculate the perception recognition accuracy coefficient βgz. The specific formula is: Calculate the sensitivity coefficient αmr of the tactile sensor. The specific formula is:

[0024] Preferably, the steps for detecting the grasping ability of the robot intelligent hand in step S3 are as follows:

[0025] S321. Perform a static grasping test and a dynamic grasping test on the robot intelligent hand, respectively. Set an expected grasping duration, start timing from the moment the object is grasped, and monitor the actual grasping duration. If the actual grasping duration is greater than or equal to the expected duration, the grasp is marked as successful; otherwise, the grasp is failed.

[0026] S322. Record the number of successful grasps mdt, the number of failed grasps mdf, the j-th grasping force Fwij during the i-th successful grasp, the duration Twij of the j-th grasping force during the i-th successful grasp, the expected grasping force center position coordinates (xmi, ymi, zmi) of the i-th successful grasp of the target object, the j-th actual grasping force center position coordinates (xcij, ycij, zcij) of the i-th successful grasp of the target object, and the appearance duration Tcij of the j-th actual grasping force center position coordinates of the i-th successful grasp of the target object;

[0027] S323. The grasping force corresponding to the maximum duration of the grasping force during the i-th successful grasp is marked as the stable grasping force during the i-th successful grasp, and is represented by Fwai. The duration of the maximum grasping force during the i-th successful grasp is marked as the stable grasping duration during the i-th successful grasp, and is represented by Twai. The actual grasping force center position coordinates corresponding to the maximum duration of the actual grasping force center position coordinates of the i-th successfully grasped target object are marked as the actual stable grasping force center position coordinates of the i-th successfully grasped target object, and are represented by (xwi, ywi, zwi). The duration of the maximum actual grasping force center position coordinates of the i-th successfully grasped target object is marked as the stable positioning duration of the i-th successfully grasped target object, and is represented by Twbi.

[0028] S324. Calculate the grasping success rate coefficient βwc. The specific formula is:

[0029] S325. Calculate the positioning accuracy coefficient αdi when the target object is successfully grasped for the i-th time. The specific formula is: Calculate the average positioning accuracy coefficient αdr. The specific formula is:

[0030] S326. Calculate the grasping stability coefficient αwi during the i-th successful grasping. The specific formula is: nwai is the grasping force during the i-th successful grasping, nwbi is the actual grasping force center position coordinates during the i-th successful grasping of the target object, and the average grasping stability coefficient αwr is calculated using the following formula:

[0031] S327. After integrating the stable grasping force during each successful grasp, the maximum value of the stable grasping force is extracted, which is the maximum grasping force Fwm of the robot intelligent hand.

[0032] Preferably, the calculation formula of the actual operation performance coefficient XC of the robot intelligent hand in step S4 is:

[0033]

[0034] Preferably, the detection steps in step S5 are as follows:

[0035] S51. Set the expected radiation emission intensity range of the robot intelligent hand, the expected amplitude range and frequency range of the electromagnetic interference signal, the expected response time of the moving parts, and the expected response time of the safety device;

[0036] S52. Place the robotic hand on a turntable in the anechoic chamber, place the receiving antenna in a specified position, and connect it to a spectrum analyzer. After setting the parameters and speed of the spectrum analyzer, start the robotic hand and start the spectrum analyzer to scan and record the radiation emission intensity at different frequencies and angles.

[0037] S53. Connect the power cord of the device under test to the LISN. Connect the output of the LISN to a spectrum analyzer. After setting the parameters of the spectrum analyzer, start the robotic hand and start the spectrum analyzer to scan and record the amplitude and frequency of the electromagnetic interference signal conducted and emitted on the power cord.

[0038] S54, outputting different operation instructions to the robot intelligent hand, recording the time required for its moving parts to complete the instructions, and then calculating the average response time of the moving parts;

[0039] S55. Activate the safety device of the robot intelligent hand multiple times, record the response time of the safety device, and calculate the average response time of the safety device;

[0040] S56. Compare the detected radiation emission intensity, electromagnetic interference signal amplitude and frequency, average response time of the moving parts, and average response time of the safety device with the set expected values ​​respectively. If the radiation emission intensity, electromagnetic interference signal amplitude, and frequency are respectively within the corresponding set expected value ranges, the radiation interference test and the conducted interference test pass. If the average response time of the moving parts and the average response time of the safety device are respectively less than the corresponding set expected response time, the drive response test and the safety device test pass.

[0041] S57. Record the number of qualified items mha and the number of unqualified items mhb, and calculate the application safety factor XQ. The specific formula is:

[0042] Preferably, in step S6, when the three judgment results of the appearance structure safety factor of the robot intelligent hand is greater than or equal to the expected appearance structure safety factor, the actual operation performance coefficient is greater than or equal to the expected operation performance coefficient, and the application safety factor is greater than or equal to the expected application safety factor are all established, an instruction for evaluating the comprehensive application safety quality of the robot intelligent hand is output; when any one of the three judgment results of the appearance structure safety factor of the robot intelligent hand is less than the expected appearance structure safety factor, the actual operation performance coefficient is less than the expected operation performance coefficient, and the application safety factor is less than the expected application safety factor occurs, a third-level application safety alarm is output; if any two of the judgment results occur, a second-level application safety alarm is output; if all three judgment results occur, a first-level application safety alarm is output.

[0043] Preferably, the specific calculation formula of the comprehensive application safety quality index YA of the robot intelligent hand in step S7 is: YA=ln(XA+XC+XQ+1).

[0044] To achieve the above-mentioned object, the present invention provides the following technical solution: a robot technology intelligent hand safety detection system, which implements the above-mentioned robot technology intelligent hand safety detection method, comprising:

[0045] Basic setting module: used to set the appearance structure safety indicators, operation performance indicators and application safety indicators of the robot intelligent hand;

[0046] Appearance and structural safety detection module: After acquiring the appearance image information of the robot intelligent hand, it records the appearance defect data of the robot intelligent hand, analyzes the appearance and structural safety of the robot intelligent hand based on the recorded appearance defect data, and calculates the appearance and structural safety factor;

[0047] Operational performance testing module: This module tests the perception ability of the robot's intelligent hand and calculates the perception recognition accuracy coefficient and tactile sensor sensitivity coefficient based on the test data. It also tests the grasping ability of the robot's intelligent hand and calculates the grasping success rate coefficient, average positioning accuracy coefficient, average grasping stability coefficient, and maximum grasping force based on the test data.

[0048] Operational performance analysis module: Analyzes the actual operational performance coefficient of the robot hand based on the calculated perception ability related indicators, grasping ability related indicators and the set robot hand related operational performance indicators;

[0049] Application safety detection module: This module performs radiation interference detection, conducted interference detection, drive response detection, and safety device detection on the robot's intelligent hand, records the number of qualified and unqualified items, and calculates the application safety factor based on the recorded data;

[0050] Test result analysis module: compares the appearance structural safety factor, actual operational performance factor, and application safety factor of the robot intelligent hand with the expected appearance structural safety factor, expected operational performance factor, and expected application safety factor, and outputs different instructions based on the comparison results;

[0051] Security alert sending module: used to send level 1 application security alert, level 2 application security alert or level 3 application security alert;

[0052] Comprehensive application safety quality assessment module: Calculates the comprehensive application safety quality index of the robot intelligent hand based on its appearance and structural safety factor, actual operational performance factor, and application safety factor;

[0053] Database: used to store data information of all modules in the system.

[0054] Technical effects and advantages of the present invention:

[0055] The present invention obtains appearance image information of the robot intelligent hand and records appearance defect data of the robot intelligent hand, analyzes the appearance structural safety of the robot intelligent hand based on the recorded appearance defect data, can improve the accuracy of safety detection, and reduce the application risks brought by appearance defects; the present invention detects the perception ability of the robot intelligent hand, calculates the perception recognition accuracy coefficient and the tactile sensor sensitivity coefficient based on the detection data, detects the grasping ability of the robot intelligent hand, calculates the grasping success rate coefficient, the average positioning accuracy coefficient, the average grasping stability coefficient and the maximum grasping force based on the detection data, can improve the accuracy of the grasping ability detection of the robot intelligent hand and improve the operating performance; the present invention performs radiation interference detection, conduction interference detection, drive response detection and safety device detection on the robot intelligent hand respectively, expands the detection range, can reduce application risks, and improves the safety quality of the robot intelligent hand application. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A diagram showing the steps of the method of the present invention.

[0057] Figure 2 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] like Figure 1 This embodiment provides a method for detecting safety of an intelligent hand in robotics technology, comprising the following steps:

[0060] S1: Set the appearance structure safety index, operation performance index and application safety index of the robot intelligent hand;

[0061] Furthermore, the appearance structure safety index of the robot intelligent hand set in step S1 is the expected appearance structure safety factor, the operation performance index of the robot intelligent hand set in step S1 includes the expected perception and recognition accuracy coefficient, the expected tactile sensor sensitivity coefficient, the expected grasping success rate coefficient, the expected average positioning accuracy coefficient, the expected average grasping stability coefficient, the expected maximum grasping force and the expected operation performance coefficient, and the application safety index of the robot intelligent hand set in step S1 is the expected application safety factor.

[0062] S2: After acquiring appearance image information of the robot intelligent hand, record appearance defect data of the robot intelligent hand, analyze the appearance structural safety of the robot intelligent hand based on the recorded appearance defect data, and calculate the appearance structural safety factor;

[0063] Furthermore, the structural analysis process of the robot intelligent hand in step S2 is as follows:

[0064] S21. Use a camera to capture a full-view overall appearance image of the robot intelligent hand, use a weighing tool to weigh the disassembled robot intelligent hand structure, and obtain the size and shape information of the robot intelligent hand structure through the camera;

[0065] S22. Record the deformation area Ax, damage area As, and theoretical initial area Ac of the flexible end effector configured for the robot intelligent hand; record the number maz of correct sensor installation positions configured for the robot intelligent hand and the number mac of incorrect sensor installation positions; record the number mbz of structures of the robot intelligent hand with correct size, shape, and weight and the number mbc of structures with incorrect size, shape, or weight;

[0066] S23. Calculate the safety factor XA of the appearance structure of the robot intelligent hand. The specific formula is:

[0067]

[0068] S3: Test the perception ability of the robot's intelligent hand and calculate the perception recognition accuracy coefficient and tactile sensor sensitivity coefficient based on the test data. Test the grasping ability of the robot's intelligent hand and calculate the grasping success rate coefficient, average positioning accuracy coefficient, average grasping stability coefficient, and maximum grasping force based on the test data.

[0069] Furthermore, the steps for detecting the perception capability of the robot intelligent hand in step S3 are as follows:

[0070] S311. Construct a test platform with several objects of different shapes, sizes, and materials, and perform single-point sensing continuous testing on the tactile sensor using objects of known size, shape, and material;

[0071] S312, recording the number of correctly identified objects mct, the number of incorrectly identified objects mcf, the change value ΔP of the tactile sensor normal stress measurement range, the change value ΔR of the output resistance within the full-scale pressure test range, and the resistance value Rm output by the tactile sensor at maximum normal stress;

[0072] S313. Calculate the perception recognition accuracy coefficient βgz. The specific formula is: Calculate the sensitivity coefficient αmr of the tactile sensor. The specific formula is:

[0073] Furthermore, the steps for detecting the grasping ability of the robot intelligent hand in step S3 are as follows:

[0074] S321. Perform a static grasping test and a dynamic grasping test on the robot intelligent hand, respectively. Set an expected grasping duration, start timing from the moment the object is grasped, and monitor the actual grasping duration. If the actual grasping duration is greater than or equal to the expected duration, the grasp is marked as successful; otherwise, the grasp is failed.

[0075] S322. Record the number of successful grasps mdt, the number of failed grasps mdf, the j-th grasping force Fwij during the i-th successful grasp, the duration Twij of the j-th grasping force during the i-th successful grasp, the expected grasping force center position coordinates (xmi, ymi, zmi) of the i-th successful grasp of the target object, the j-th actual grasping force center position coordinates (xcij, ycij, zcij) of the i-th successful grasp of the target object, and the appearance duration Tcij of the j-th actual grasping force center position coordinates of the i-th successful grasp of the target object;

[0076] S323. The grasping force corresponding to the maximum duration of the grasping force during the i-th successful grasp is marked as the stable grasping force during the i-th successful grasp, and is represented by Fwai. The duration of the maximum grasping force during the i-th successful grasp is marked as the stable grasping duration during the i-th successful grasp, and is represented by Twai. The actual grasping force center position coordinates corresponding to the maximum duration of the actual grasping force center position coordinates of the i-th successfully grasped target object are marked as the actual stable grasping force center position coordinates of the i-th successfully grasped target object, and are represented by (xwi, ywi, zwi). The duration of the maximum actual grasping force center position coordinates of the i-th successfully grasped target object is marked as the stable positioning duration of the i-th successfully grasped target object, and is represented by Twbi.

[0077] S324. Calculate the grasping success rate coefficient βwc. The specific formula is:

[0078] S325. Calculate the positioning accuracy coefficient αdi when the target object is successfully grasped for the i-th time. The specific formula is: Calculate the average positioning accuracy coefficient αdr. The specific formula is:

[0079] S326. Calculate the grasping stability coefficient αwi during the i-th successful grasping. The specific formula is: nwai is the grasping force during the i-th successful grasping, nwbi is the actual grasping force center position coordinates during the i-th successful grasping of the target object, and the average grasping stability coefficient αwr is calculated using the following formula:

[0080] S327. After integrating the stable grasping force during each successful grasp, the maximum value of the stable grasping force is extracted, which is the maximum grasping force Fwm of the robot intelligent hand.

[0081] S4: Analyze the actual operational performance coefficient of the robot hand based on the calculated perception-related indicators, grasping-related indicators and the set operational performance indicators of the robot hand;

[0082] Furthermore, the calculation formula of the actual operation performance coefficient XC of the robot intelligent hand in step S4 is:

[0083] S5: Perform radiation interference detection, conducted interference detection, drive response detection, and safety device detection on the robot intelligent hand, record the number of qualified and unqualified items, and calculate the application safety factor based on the recorded data;

[0084] Furthermore, the detection steps in step S5 are as follows:

[0085] S51. Set the expected radiation emission intensity range of the robot intelligent hand, the expected amplitude range and frequency range of the electromagnetic interference signal, the expected response time of the moving parts, and the expected response time of the safety device;

[0086] S52. Place the robotic hand on a turntable in the anechoic chamber, place the receiving antenna in a specified position and connect it to a spectrum analyzer. After setting the parameters and speed of the spectrum analyzer, start the robotic hand and start the spectrum analyzer to scan and record the radiation emission intensity at different frequencies and angles.

[0087] S53. Connect the power cord of the device under test to the LISN. Connect the output of the LISN to a spectrum analyzer. After setting the parameters of the spectrum analyzer, start the robotic hand and start the spectrum analyzer to scan and record the amplitude and frequency of the electromagnetic interference signal conducted and emitted on the power cord.

[0088] S54, outputting different operation instructions to the robot intelligent hand, recording the time required for its moving parts to complete the instructions, and then calculating the average response time of the moving parts;

[0089] S55. Activate the safety device of the robot intelligent hand multiple times, record the response time of the safety device, and calculate the average response time of the safety device;

[0090] S56. Compare the detected radiation emission intensity, electromagnetic interference signal amplitude and frequency, average response time of the moving parts, and average response time of the safety device with the set expected values ​​respectively. If the radiation emission intensity, electromagnetic interference signal amplitude, and frequency are respectively within the corresponding set expected value ranges, the radiation interference test and the conducted interference test pass. If the average response time of the moving parts and the average response time of the safety device are respectively less than the corresponding set expected response time, the drive response test and the safety device test pass.

[0091] S57. Record the number of qualified items mha and the number of unqualified items mhb, and calculate the application safety factor XQ. The specific formula is:

[0092] It should be specifically noted in this embodiment that the parameters of the spectrum analyzer during the radiated interference test include but are not limited to the center frequency, the scanning bandwidth, and the polarization mode.

[0093] S6: Compare the appearance structure safety factor, actual operation performance factor, and application safety factor of the robot intelligent hand with the expected appearance structure safety factor, expected operation performance factor, and expected application safety factor, respectively, and output different instructions based on the comparison results;

[0094] Furthermore, in step S6, when the three judgment results of the appearance structure safety factor of the robot intelligent hand is greater than or equal to the expected appearance structure safety factor, the actual operation performance coefficient is greater than or equal to the expected operation performance coefficient, and the application safety factor is greater than or equal to the expected application safety factor are all established, an instruction for evaluating the comprehensive application safety quality of the robot intelligent hand is output; when any one of the three judgment results of the appearance structure safety factor of the robot intelligent hand is less than the expected appearance structure safety factor, the actual operation performance coefficient is less than the expected operation performance coefficient, and the application safety factor is less than the expected application safety factor occurs, a third-level application safety alarm is output; if any two of the judgment results occur, a second-level application safety alarm is output; if all three judgment results occur, a first-level application safety alarm is output.

[0095] In this embodiment, it should be specifically noted that the urgency of the third-level application security alarm is lower than that of the second-level application security alarm, and the urgency of the second-level application security alarm is lower than that of the first-level application security alarm.

[0096] S7: After receiving the instruction to evaluate the comprehensive application safety quality of the robot intelligent hand, the comprehensive application safety quality index of the robot intelligent hand is calculated based on the appearance structure safety factor, actual operation performance factor and application safety factor of the robot intelligent hand.

[0097] Furthermore, the specific calculation formula of the comprehensive application safety quality index YA of the robot intelligent hand in step S7 is: YA=ln(XA+XC+XQ+1).

[0098] It should be specifically noted in this embodiment that the expected values, set values, and preset values ​​used are all selected based on actual needs and are not limited to specific values ​​here.

[0099] like Figure 2 The embodiment shown provides an intelligent hand safety detection system for robot technology, including a basic setting module, an appearance and structure safety detection module, an operation performance detection module, an operation performance analysis module, an application safety detection module, a detection result analysis module, a security alarm sending module, a comprehensive application safety quality assessment module and a database. The basic setting module is connected to the operation performance analysis module and the detection result analysis module, the operation performance detection module is connected to the operation performance analysis module, the appearance and structure safety detection module, the operation performance analysis module, and the application safety detection module are connected to the detection result analysis module, the detection result analysis module is connected to the security alarm sending module and the comprehensive application safety quality assessment module, and all modules in the system are connected to the database.

[0100] The basic setting module is used to set the appearance structure safety index, operation performance index and application safety index of the robot intelligent hand;

[0101] The appearance structure safety detection module obtains appearance image information of the robot intelligent hand and records appearance defect data of the robot intelligent hand, analyzes the appearance structure safety of the robot intelligent hand based on the recorded appearance defect data, and calculates the appearance structure safety factor;

[0102] The operational performance detection module detects the perception ability of the robot intelligent hand, calculates the perception recognition accuracy coefficient and the tactile sensor sensitivity coefficient based on the detection data, and detects the grasping ability of the robot intelligent hand, calculates the grasping success rate coefficient, the average positioning accuracy coefficient, the average grasping stability coefficient and the maximum grasping force based on the detection data;

[0103] The operation performance analysis module analyzes the actual operation performance coefficient of the robot intelligent hand based on the calculated robot intelligent hand perception ability related indicators, grasping ability related indicators and the set robot intelligent hand related operation performance indicators;

[0104] The application safety detection module performs radiation interference detection, conducted interference detection, drive response detection, and safety device detection on the robot intelligent hand, records the number of qualified items and the number of unqualified items, and calculates the application safety factor based on the recorded data;

[0105] The detection result analysis module compares the appearance structure safety factor, actual operation performance factor, and application safety factor of the robot intelligent hand with the expected appearance structure safety factor, expected operation performance factor, and expected application safety factor, and outputs different instructions based on the comparison results;

[0106] The security alarm sending module is used to send a first-level application security alarm, a second-level application security alarm or a third-level application security alarm;

[0107] The comprehensive application safety quality evaluation module calculates the comprehensive application safety quality index of the robot intelligent hand based on the appearance structure safety factor, actual operation performance factor and application safety factor of the robot intelligent hand.

[0108] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting safety of an intelligent hand in robotics, characterized by: The following steps are involved: S1: Set the appearance structure safety index, operation performance index and application safety index of the robot intelligent hand; S2: After acquiring appearance image information of the robot intelligent hand, record appearance defect data of the robot intelligent hand, analyze the appearance structural safety of the robot intelligent hand based on the recorded appearance defect data, and calculate the appearance structural safety factor; S3: Test the perception ability of the robot's intelligent hand and calculate the perception recognition accuracy coefficient and tactile sensor sensitivity coefficient based on the test data. Test the grasping ability of the robot's intelligent hand and calculate the grasping success rate coefficient, average positioning accuracy coefficient, average grasping stability coefficient, and maximum grasping force based on the test data. S4: Analyze the actual operation performance coefficient of the robot intelligent hand based on the calculated robot intelligent hand perception ability related indicators, grasping ability related indicators and the set robot intelligent hand related operation performance indicators; S5: Perform radiation interference detection, conducted interference detection, drive response detection, and safety device detection on the robot intelligent hand, record the number of qualified and unqualified items, and calculate the application safety factor based on the recorded data; S6: Compare the appearance structure safety factor, actual operation performance factor, and application safety factor of the robot intelligent hand with the expected appearance structure safety factor, expected operation performance factor, and expected application safety factor, respectively, and output different instructions based on the comparison results; S7: After receiving the instruction to evaluate the comprehensive application safety quality of the robot intelligent hand, the comprehensive application safety quality index of the robot intelligent hand is calculated based on the appearance structure safety factor, actual operation performance factor and application safety factor of the robot intelligent hand.

2. The method for detecting safety of an intelligent hand in robotics technology according to claim 1, wherein: The appearance structure safety index of the robot intelligent hand set in step S1 is the expected appearance structure safety factor. The operation performance index of the robot intelligent hand set in step S1 includes the expected perception recognition accuracy coefficient, the expected tactile sensor sensitivity coefficient, the expected grasping success rate coefficient, the expected average positioning accuracy coefficient, the expected average grasping stability coefficient, the expected maximum grasping force and the expected operation performance coefficient. The application safety index of the robot intelligent hand set in step S1 is the expected application safety factor.

3. The method for detecting safety of an intelligent hand in robotics technology according to claim 1, wherein: The structural analysis process of the robot intelligent hand in step S2 is as follows: S21. Use a camera to capture a full-view overall appearance image of the robot intelligent hand, use a weighing tool to weigh the disassembled robot intelligent hand structure, and obtain the size and shape information of the robot intelligent hand structure through the camera; S22. Record the deformation area Ax, damage area As, and theoretical initial area Ac of the flexible end effector configured for the robot intelligent hand; record the number maz of correct sensor installation positions configured for the robot intelligent hand and the number mac of incorrect sensor installation positions; record the number mbz of structures of the robot intelligent hand with correct size, shape, and weight and the number mbc of structures with incorrect size, shape, or weight; S23. Calculate the safety factor XA of the appearance structure of the robot intelligent hand. The specific formula is:

4. The method for detecting safety of an intelligent hand in robotics according to claim 1, wherein: The steps for detecting the perception capability of the robot intelligent hand in step S3 are as follows: S311. Construct a test platform with several objects of different shapes, sizes, and materials, and perform single-point sensing continuous testing on the tactile sensor using objects of known size, shape, and material; S312, recording the number of correctly identified objects mct, the number of incorrectly identified objects mcf, the change value ΔP of the tactile sensor normal stress measurement range, the change value ΔR of the output resistance within the full-scale pressure test range, and the resistance value Rm output by the tactile sensor at maximum normal stress; S313. Calculate the perception recognition accuracy coefficient βgz. The specific formula is: Calculate the sensitivity coefficient αmr of the tactile sensor. The specific formula is:

5. The method for detecting safety of an intelligent hand in robotics according to claim 1, wherein: The steps for detecting the grasping ability of the robot intelligent hand in step S3 are as follows: S321. Perform a static grasping test and a dynamic grasping test on the robot intelligent hand, respectively. Set an expected grasping duration, start timing from the moment the object is grasped, and monitor the actual grasping duration. If the actual grasping duration is greater than or equal to the expected duration, the grasp is marked as successful; otherwise, the grasp is failed. S322. Record the number of successful grasps mdt, the number of failed grasps mdf, the j-th grasping force Fwij during the i-th successful grasp, the duration Twij of the j-th grasping force during the i-th successful grasp, the expected grasping force center position coordinates (xmi, ymi, zmi) of the i-th successful grasp of the target object, the j-th actual grasping force center position coordinates (xcij, ycij, zcij) of the i-th successful grasp of the target object, and the appearance duration Tcij of the j-th actual grasping force center position coordinates of the i-th successful grasp of the target object; S323. The grasping force corresponding to the maximum duration of the grasping force during the i-th successful grasp is marked as the stable grasping force during the i-th successful grasp, and is represented by Fwai. The duration of the maximum grasping force during the i-th successful grasp is marked as the stable grasping duration during the i-th successful grasp, and is represented by Twai. The actual grasping force center position coordinates corresponding to the maximum duration of the actual grasping force center position coordinates of the i-th successfully grasped target object are marked as the actual stable grasping force center position coordinates of the i-th successfully grasped target object, and are represented by (xwi, ywi, zwi). The duration of the maximum actual grasping force center position coordinates of the i-th successfully grasped target object is marked as the stable positioning duration of the i-th successfully grasped target object, and is represented by Twbi. S324. Calculate the grasping success rate coefficient βwc. The specific formula is: S325. Calculate the positioning accuracy coefficient αdi when the target object is successfully grasped for the i-th time. The specific formula is: Calculate the average positioning accuracy coefficient αdr. The specific formula is: S326. Calculate the grasping stability coefficient αwi during the i-th successful grasping. The specific formula is: nwai is the grasping force during the i-th successful grasping, nwbi is the actual grasping force center position coordinates during the i-th successful grasping of the target object, and the average grasping stability coefficient αwr is calculated using the following formula: S327. After integrating the stable grasping force during each successful grasp, the maximum value of the stable grasping force is extracted, which is the maximum grasping force Fwm of the robot intelligent hand.

6. The method for detecting safety of an intelligent hand in robotics according to claim 1, wherein: The calculation formula for the actual operation performance coefficient XC of the robot intelligent hand in step S4 is:

7. The method for detecting safety of an intelligent hand in robotics technology according to claim 1, characterized in that: The detection steps in step S5 are as follows: S51. Set the expected radiation emission intensity range of the robot intelligent hand, the expected amplitude range and frequency range of the electromagnetic interference signal, the expected response time of the moving parts, and the expected response time of the safety device; S52. Place the robotic hand on a turntable in the anechoic chamber, place the receiving antenna in a specified position, and connect it to a spectrum analyzer. After setting the parameters and speed of the spectrum analyzer, start the robotic hand and start the spectrum analyzer to scan and record the radiation emission intensity at different frequencies and angles. S53. Connect the power cord of the device under test to the LISN. Connect the output of the LISN to a spectrum analyzer. After setting the parameters of the spectrum analyzer, start the robotic hand and start the spectrum analyzer to scan and record the amplitude and frequency of the electromagnetic interference signal conducted and emitted on the power cord. S54, outputting different operation instructions to the robot intelligent hand, recording the time required for its moving parts to complete the instructions, and then calculating the average response time of the moving parts; S55. Activate the safety device of the robot intelligent hand multiple times, record the response time of the safety device, and calculate the average response time of the safety device; S56. Compare the detected radiation emission intensity, electromagnetic interference signal amplitude and frequency, average response time of the moving parts, and average response time of the safety device with the set expected values ​​respectively. If the radiation emission intensity, electromagnetic interference signal amplitude, and frequency are respectively within the corresponding set expected value ranges, the radiation interference test and the conducted interference test pass. If the average response time of the moving parts and the average response time of the safety device are respectively less than the corresponding set expected response time, the drive response test and the safety device test pass. S57. Record the number of qualified items mha and the number of unqualified items mhb, and calculate the application safety factor XQ. The specific formula is:

8. The method for detecting safety of an intelligent hand in robotics according to claim 1, wherein: In step S6, when the three judgment results of the appearance structure safety factor of the robot intelligent hand is greater than or equal to the expected appearance structure safety factor, the actual operation performance coefficient is greater than or equal to the expected operation performance coefficient, and the application safety factor is greater than or equal to the expected application safety factor are all established, an instruction for evaluating the comprehensive application safety quality of the robot intelligent hand is output; when any one of the three judgment results of the appearance structure safety factor of the robot intelligent hand is less than the expected appearance structure safety factor, the actual operation performance coefficient is less than the expected operation performance coefficient, and the application safety factor is less than the expected application safety factor occurs, a third-level application safety alarm is output; if any two of the three judgment results occur, a second-level application safety alarm is output; if all three judgment results occur, a first-level application safety alarm is output.

9. The method for detecting safety of an intelligent hand in robotics according to claim 1, wherein: The specific calculation formula of the comprehensive application safety quality index YA of the robot intelligent hand in step S7 is: YA=ln(XA+XC+XQ+1).

10. A robot technology intelligent hand safety detection system, implementing a robot technology intelligent hand safety detection method according to any one of claims 1 to 9, characterized in that: include: Basic setting module: used to set the appearance structure safety indicators, operation performance indicators and application safety indicators of the robot intelligent hand; Appearance and structural safety detection module: After acquiring the appearance image information of the robot intelligent hand, it records the appearance defect data of the robot intelligent hand, analyzes the appearance and structural safety of the robot intelligent hand based on the recorded appearance defect data, and calculates the appearance and structural safety factor; Operational performance testing module: This module tests the perception ability of the robot's intelligent hand and calculates the perception recognition accuracy coefficient and tactile sensor sensitivity coefficient based on the test data. It also tests the grasping ability of the robot's intelligent hand and calculates the grasping success rate coefficient, average positioning accuracy coefficient, average grasping stability coefficient, and maximum grasping force based on the test data. Operational performance analysis module: Analyzes the actual operational performance coefficient of the robot hand based on the calculated perception ability related indicators, grasping ability related indicators and the set robot hand related operational performance indicators; Application safety detection module: This module performs radiation interference detection, conducted interference detection, drive response detection, and safety device detection on the robot's intelligent hand, records the number of qualified and unqualified items, and calculates the application safety factor based on the recorded data; Test result analysis module: compares the appearance structural safety factor, actual operational performance factor, and application safety factor of the robot intelligent hand with the expected appearance structural safety factor, expected operational performance factor, and expected application safety factor, and outputs different instructions based on the comparison results; Security alert sending module: used to send level 1 application security alert, level 2 application security alert or level 3 application security alert; Comprehensive application safety quality assessment module: Calculates the comprehensive application safety quality index of the robot intelligent hand based on its appearance and structural safety factor, actual operation performance factor, and application safety factor.

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