Intelligent control method and system for obstacle avoidance sudden stop of AGV robot and medium

Abstract

The invention provides an intelligent control method and system for obstacle avoidance sudden stop of an AGV robot and a medium. The method comprises the following steps: carrying out obstacle avoidance sudden stop test on a preset AGV robot, collecting monitoring information, extracting operation characteristic data of the AGV robot, state characteristic data of an obstacle and environment interference characteristic data, obtaining design characteristic data, carrying out processing in combination with the operation characteristic data, obtaining a responsivity index, and carrying out real-time detection on the responsivity index. The method comprises the following steps: acquiring obstacle avoidance accuracy data and sudden stop precision data, processing to obtain an obstacle avoidance sudden stop accuracy index, performing processing according to operation characteristic data and state characteristic data, obtaining a safety distance conformity index, obtaining ground characteristic data, performing processing in combination with environment interference characteristic data, and obtaining an environment adaptability coefficient. And the responsivity index, the obstacle avoidance emergency stop accuracy index and the safety distance conformity index are combined for processing to obtain the obstacle avoidance emergency stop performance index, and corresponding adjustment is performed, so that the intelligent control technology of the obstacle avoidance emergency stop of the AGV robot is realized.

Description

Technical Field

[0001] The present application relates to the technical field of AGV robots, and more specifically, to an intelligent control method, system and medium for an AGV robot to avoid obstacles and stop suddenly. Background Art

[0002] With the continuous development of industrial automation, AGV robots have been widely used in logistics, manufacturing and other fields. However, in the actual operation process, AGV robots may encounter various obstacles, such as people, goods, equipment, etc. If obstacle avoidance and emergency stop cannot be performed in a timely and effective manner, collision accidents may occur, resulting in casualties and property losses.

[0003] Therefore, in order to improve the operation safety, reliability and intelligence level of AGV robots, it is of great practical significance to study an efficient and reliable intelligent control method for obstacle avoidance and emergency stop of AGV robots.

[0004] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0005] The purpose of the present application is to provide an intelligent control method, system and medium for the obstacle avoidance and emergency stop of an AGV robot. The method can perform an obstacle avoidance and emergency stop test on a preset AGV robot, extract, evaluate and comprehensively process the monitoring information corresponding to each obstacle avoidance and emergency stop test, and finally obtain the obstacle avoidance and emergency stop performance index of the AGV robot. The AGV robot can be adjusted accordingly according to the obstacle avoidance and emergency stop performance index, thereby realizing the technology of intelligent control of the obstacle avoidance and emergency stop of the AGV robot.

[0006] The present application also provides an intelligent control method for an AGV robot to avoid obstacles and stop suddenly, comprising the following steps:

[0007] Perform obstacle avoidance and emergency stop tests on the preset AGV robot;

[0008] Collect monitoring information corresponding to each obstacle avoidance emergency stop test, and extract the operation characteristic data of the AGV robot, the state characteristic data of the obstacle, and the environmental interference characteristic data during the obstacle avoidance emergency stop;

[0009] Acquire the design characteristic data of the AGV robot, and process it in combination with the operation characteristic data corresponding to each obstacle avoidance emergency stop test to obtain the responsiveness index of the AGV robot;

[0010] Obtaining obstacle avoidance accuracy data and emergency stop accuracy data of the AGV robot, and processing to obtain an obstacle avoidance emergency stop accuracy index of the AGV robot;

[0011] According to the running characteristic data of the AGV robot, combined with the state characteristic data of the obstacle, processing is performed to obtain a safety distance compliance index of the AGV robot;

[0012] Acquire ground characteristic data corresponding to each obstacle avoidance emergency stop test, and process the data in combination with the environmental interference characteristic data to obtain an environmental adaptability coefficient;

[0013] According to the responsiveness index, obstacle avoidance emergency stop accuracy index and safety distance compliance index, the obstacle avoidance emergency stop performance index of the AGV robot is obtained in combination with the environmental adaptability coefficient, and the AGV robot is adjusted accordingly according to the obstacle avoidance emergency stop performance index.

[0014] Optionally, in the intelligent control method for obstacle avoidance and emergency stop of the AGV robot described in the present application, the collecting of monitoring information corresponding to each obstacle avoidance and emergency stop test, and the extraction of operation characteristic data of the AGV robot, state characteristic data of obstacles, and environmental interference characteristic data during obstacle avoidance and emergency stop, include:

[0015] Collect monitoring information corresponding to each obstacle avoidance emergency stop test, including operation monitoring information of the AGV robot, obstacle status monitoring information, and environmental status information;

[0016] Extracting the operation characteristic data of the AGV robot during obstacle avoidance emergency stop, the state characteristic data of the obstacle, and the environmental interference characteristic data according to the monitoring information;

[0017] The operation characteristic data of the AGV robot includes robot linear velocity data, robot angular velocity data, robot linear acceleration data, robot angular acceleration data, robot distance measurement data and robot response time data;

[0018] The state characteristic data of the obstacle includes obstacle linear velocity data, obstacle angular velocity data, obstacle linear acceleration data and obstacle angular acceleration data;

[0019] The environmental interference characteristic data includes light intensity data, electromagnetic interference data and noise interference data.

[0020] Optionally, in the intelligent control method for obstacle avoidance and emergency stop of the AGV robot described in the present application, the step of acquiring the design characteristic data of the AGV robot and processing the data in combination with the operation characteristic data corresponding to each obstacle avoidance and emergency stop test to obtain the responsiveness index of the AGV robot includes:

[0021] Acquire the design characteristic data of the AGV robot, including the robot's maximum linear velocity data, the robot's maximum angular velocity data, the robot's maximum linear acceleration data, and the robot's maximum angular acceleration data;

[0022] The robot maximum linear velocity data, the robot maximum angular velocity data, the robot maximum linear acceleration data and the robot maximum angular acceleration data are processed in combination with the robot distance measurement data and the robot response time data to obtain the responsiveness index of the AGV robot.

[0023] Optionally, in the intelligent control method for obstacle avoidance and emergency stop of the AGV robot described in the present application, the obtaining of obstacle avoidance accuracy data and emergency stop precision data of the AGV robot, and processing to obtain the obstacle avoidance and emergency stop accuracy index of the AGV robot include:

[0024] Obtain the number of times the AGV robot successfully avoids the obstacle and the total number of times it encounters obstacles in the multiple obstacle avoidance emergency stop tests, and process to obtain the obstacle avoidance accuracy of the AGV robot;

[0025] Obtaining a preset robot distance measurement value corresponding to each obstacle avoidance emergency stop test, and processing the robot distance measurement data to obtain emergency stop accuracy data of the AGV robot;

[0026] The obstacle avoidance accuracy rate data and the emergency stop accuracy data are processed to obtain the obstacle avoidance emergency stop accuracy index of the AGV robot.

[0027] Optionally, in the intelligent control method for obstacle avoidance and emergency stop of the AGV robot described in the present application, the acquiring of ground characteristic data corresponding to each obstacle avoidance and emergency stop test, combined with the environmental interference characteristic data for processing, to obtain an environmental adaptability coefficient, includes:

[0028] Acquire ground characteristic data corresponding to each obstacle avoidance emergency stop test, including ground roughness data and ground flatness data;

[0029] The environmental adaptability coefficient is obtained by processing the ground roughness data and the ground flatness data in combination with the light intensity data, the electromagnetic interference data and the noise interference data.

[0030] Optionally, in the intelligent control method for obstacle avoidance and emergency stop of the AGV robot described in the present application, the responsiveness index, the obstacle avoidance and emergency stop accuracy index, and the safety distance compliance index are processed in combination with the environmental adaptability coefficient to obtain the obstacle avoidance and emergency stop performance index of the AGV robot, and the AGV robot is adjusted accordingly according to the obstacle avoidance and emergency stop performance index, including:

[0031] Obtain the obstacle avoidance emergency stop performance index of the AGV robot according to the responsiveness index, obstacle avoidance emergency stop accuracy index and safety distance compliance index in combination with the environmental adaptability coefficient;

[0032] Comparing the obstacle avoidance emergency stop performance index with a preset obstacle avoidance emergency stop performance value to obtain a comparison result;

[0033] Determine the obstacle avoidance and emergency stop capability of the AGV robot according to the comparison result;

[0034] If the obstacle avoidance and emergency stop capability of the AGV robot fails to reach the expected value, the AGV robot is adjusted accordingly.

[0035] In a second aspect, the present application provides an intelligent control system for an AGV robot to avoid obstacles and stop suddenly, the system comprising: a memory and a processor, the memory comprising a program of an intelligent control method for an AGV robot to avoid obstacles and stop suddenly, and the program of the intelligent control method for an AGV robot to avoid obstacles and stop suddenly implements the following steps when executed by the processor:

[0036] Perform obstacle avoidance and emergency stop tests on the preset AGV robot;

[0037] Collect monitoring information corresponding to each obstacle avoidance emergency stop test, and extract the operation characteristic data of the AGV robot, the state characteristic data of the obstacle, and the environmental interference characteristic data during the obstacle avoidance emergency stop;

[0038] Acquire the design characteristic data of the AGV robot, and process it in combination with the operation characteristic data corresponding to each obstacle avoidance emergency stop test to obtain the responsiveness index of the AGV robot;

[0039] Obtaining obstacle avoidance accuracy data and emergency stop accuracy data of the AGV robot, and processing to obtain an obstacle avoidance emergency stop accuracy index of the AGV robot;

[0040] According to the running characteristic data of the AGV robot, combined with the state characteristic data of the obstacle, processing is performed to obtain a safety distance compliance index of the AGV robot;

[0041] Acquire ground characteristic data corresponding to each obstacle avoidance emergency stop test, and process the data in combination with the environmental interference characteristic data to obtain an environmental adaptability coefficient;

[0042] According to the responsiveness index, obstacle avoidance emergency stop accuracy index and safety distance compliance index, the obstacle avoidance emergency stop performance index of the AGV robot is obtained in combination with the environmental adaptability coefficient, and the AGV robot is adjusted accordingly according to the obstacle avoidance emergency stop performance index.

[0043] Optionally, in the intelligent control system for obstacle avoidance and emergency stop of the AGV robot described in the present application, it is characterized in that the monitoring information corresponding to each obstacle avoidance and emergency stop test is collected, and the operation characteristic data of the AGV robot, the state characteristic data of the obstacle and the environmental interference characteristic data during the obstacle avoidance and emergency stop are extracted, including:

[0044] Collect monitoring information corresponding to each obstacle avoidance emergency stop test, including operation monitoring information of the AGV robot, obstacle status monitoring information, and environmental status information;

[0045] Extracting the operation characteristic data of the AGV robot during obstacle avoidance emergency stop, the state characteristic data of the obstacle, and the environmental interference characteristic data according to the monitoring information;

[0046] The operation characteristic data of the AGV robot includes robot linear velocity data, robot angular velocity data, robot linear acceleration data, robot angular acceleration data, robot distance measurement data and robot response time data;

[0047] The state characteristic data of the obstacle includes obstacle linear velocity data, obstacle angular velocity data, obstacle linear acceleration data and obstacle angular acceleration data;

[0048] The environmental interference characteristic data includes light intensity data, electromagnetic interference data and noise interference data.

[0049] Optionally, in the intelligent control system for obstacle avoidance and emergency stop of the AGV robot described in the present application, the acquiring of the design characteristic data of the AGV robot and processing in combination with the operation characteristic data corresponding to each obstacle avoidance and emergency stop test to obtain the responsiveness index of the AGV robot include:

[0050] Acquire the design characteristic data of the AGV robot, including the robot's maximum linear velocity data, the robot's maximum angular velocity data, the robot's maximum linear acceleration data, and the robot's maximum angular acceleration data;

[0051] The robot maximum linear velocity data, the robot maximum angular velocity data, the robot maximum linear acceleration data and the robot maximum angular acceleration data are processed in combination with the robot distance measurement data and the robot response time data to obtain the responsiveness index of the AGV robot.

[0052] In the third aspect, the present application also provides a computer-readable storage medium, which stores a program of an intelligent control method for obstacle avoidance and emergency stop of an AGV robot. When the program of the intelligent control method for obstacle avoidance and emergency stop of an AGV robot is executed by a processor, the steps of the intelligent control method for obstacle avoidance and emergency stop of an AGV robot as described in any of the above items are implemented.

[0053] As can be seen from the above, the intelligent control method, system and medium for the obstacle avoidance and emergency stop of the AGV robot disclosed in the present invention perform an obstacle avoidance and emergency stop test on the preset AGV robot, collect monitoring information corresponding to each obstacle avoidance and emergency stop test, and extract the operation characteristic data of the AGV robot during the obstacle avoidance and emergency stop, the state characteristic data of the obstacle and the environmental interference characteristic data, obtain the design characteristic data of the AGV robot, combine the operation characteristic data corresponding to each obstacle avoidance and emergency stop test for processing, obtain the responsiveness index of the AGV robot, obtain the obstacle avoidance accuracy data and the emergency stop accuracy data of the AGV robot, and process the obstacle avoidance and emergency stop data of the AGV robot. The accuracy index is processed according to the operation characteristic data of the AGV robot in combination with the state characteristic data of the obstacle to obtain the safety distance compliance index of the AGV robot. The ground characteristic data corresponding to each obstacle avoidance emergency stop test is obtained and processed in combination with the environmental interference characteristic data to obtain the environmental adaptability coefficient. According to the responsiveness index, the obstacle avoidance emergency stop accuracy index and the safety distance compliance index, the obstacle avoidance emergency stop performance index of the AGV robot is obtained in combination with the environmental adaptability coefficient. The AGV robot is adjusted accordingly according to the obstacle avoidance emergency stop performance index, thereby realizing the intelligent control technology of the AGV robot's obstacle avoidance emergency stop.

[0054] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A flow chart of an intelligent control method for an AGV robot to avoid obstacles and stop suddenly provided in an embodiment of the present application;

[0057] Figure 2 A flow chart of obtaining a responsiveness index for an intelligent control method for an AGV robot to avoid obstacles and stop suddenly provided in an embodiment of the present application;

[0058] Figure 3 A flow chart of obtaining an obstacle avoidance emergency stop accuracy index for an intelligent control method for an AGV robot obstacle avoidance emergency stop provided in an embodiment of the present application;

[0059] Figure 4 A flow chart of obtaining the environmental adaptability coefficient of the intelligent control method for the obstacle avoidance and emergency stop of the AGV robot provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0061] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0062] Please refer to Figure 1 , Figure 1 The flowchart of the intelligent control method of the AGV robot for obstacle avoidance and emergency stop in some embodiments of the present application. The intelligent control method of the AGV robot for obstacle avoidance and emergency stop is used in terminal devices, such as computers, mobile terminals, etc. The intelligent control method of the AGV robot for obstacle avoidance and emergency stop includes the following steps:

[0063] S11. Perform obstacle avoidance and emergency stop test on the preset AGV robot;

[0064] S12, collecting monitoring information corresponding to each obstacle avoidance emergency stop test, and extracting the operation characteristic data of the AGV robot during the obstacle avoidance emergency stop, the state characteristic data of the obstacle, and the environmental interference characteristic data;

[0065] S13, obtaining the design characteristic data of the AGV robot, and processing the data in combination with the operation characteristic data corresponding to each obstacle avoidance emergency stop test to obtain the responsiveness index of the AGV robot;

[0066] S14, obtaining obstacle avoidance accuracy data and emergency stop accuracy data of the AGV robot, and processing to obtain an obstacle avoidance emergency stop accuracy index of the AGV robot;

[0067] S15, processing the running characteristic data of the AGV robot in combination with the state characteristic data of the obstacle to obtain a safety distance compliance index of the AGV robot;

[0068] S16, obtaining ground characteristic data corresponding to each obstacle avoidance emergency stop test, and processing the ground characteristic data in combination with the environmental interference characteristic data to obtain an environmental adaptability coefficient;

[0069] S17. According to the responsiveness index, obstacle avoidance emergency stop accuracy index and safety distance compliance index, the obstacle avoidance emergency stop performance index of the AGV robot is obtained in combination with the environmental adaptability coefficient, and the AGV robot is adjusted accordingly according to the obstacle avoidance emergency stop performance index.

[0070] It should be noted that with the continuous development of industrial automation, AGV robots have been widely used in logistics, manufacturing and other fields. However, in the actual operation process, AGV robots may encounter various obstacles, such as personnel, goods, equipment, etc. If obstacle avoidance and emergency stop cannot be performed in a timely and effective manner, collision accidents may occur, causing casualties and property losses. Therefore, in order to improve the operation safety, reliability and intelligence level of AGV robots, it is of great practical significance to study an efficient and reliable AGV robot obstacle avoidance and emergency stop intelligent control method. In order to solve the above problems, the technology of the present application provides an AGV robot obstacle avoidance and emergency stop intelligent control method. In this embodiment, firstly, an obstacle avoidance and emergency stop test is performed on the preset AGV robot, and the monitoring information corresponding to each obstacle avoidance and emergency stop test is collected. The operation characteristic data of the AGV robot during the obstacle avoidance and emergency stop, the state characteristic data of the obstacle, and the environmental interference characteristic data are extracted to obtain the design characteristic data of the AGV robot, and the operation characteristic data corresponding to each obstacle avoidance and emergency stop test are combined. Processing is performed to obtain the responsiveness index of the AGV robot, the obstacle avoidance accuracy data and the emergency stop precision data of the AGV robot, and the obstacle avoidance emergency stop accuracy index of the AGV robot is obtained. According to the operation characteristic data of the AGV robot, combined with the state characteristic data of the obstacle, processing is performed to obtain the safety distance compliance index of the AGV robot, and the ground characteristic data corresponding to each obstacle avoidance emergency stop test is obtained, and combined with the environmental interference characteristic data for processing to obtain the environmental adaptability coefficient. According to the responsiveness index, the obstacle avoidance emergency stop accuracy index and the safety distance compliance index, combined with the environmental adaptability coefficient, processing is performed to obtain the obstacle avoidance emergency stop performance index of the AGV robot, and the AGV robot is adjusted accordingly according to the obstacle avoidance emergency stop performance index, thereby realizing the technology of intelligent control of the obstacle avoidance emergency stop of the AGV robot.

[0071] According to an embodiment of the present invention, the monitoring information corresponding to each obstacle avoidance emergency stop test is collected, and the operation characteristic data of the AGV robot, the state characteristic data of the obstacle, and the environmental interference characteristic data are extracted during the obstacle avoidance emergency stop, including:

[0072] Collect monitoring information corresponding to each obstacle avoidance emergency stop test, including operation monitoring information of the AGV robot, obstacle status monitoring information, and environmental status information;

[0073] Extracting the operation characteristic data of the AGV robot during obstacle avoidance emergency stop, the state characteristic data of the obstacle, and the environmental interference characteristic data according to the monitoring information;

[0074] The operation characteristic data of the AGV robot includes robot linear velocity data, robot angular velocity data, robot linear acceleration data, robot angular acceleration data, robot distance measurement data and robot response time data;

[0075] The state characteristic data of the obstacle includes obstacle linear velocity data, obstacle angular velocity data, obstacle linear acceleration data and obstacle angular acceleration data;

[0076] The environmental interference characteristic data includes light intensity data, electromagnetic interference data and noise interference data.

[0077] It should be noted that the AGV robot is subjected to multiple obstacle avoidance emergency stop tests, and during each test, one or more of the factors such as the physical properties and motion state of the obstacle, ground conditions, environmental conditions and the operating state of the AGV robot are different. Then, the monitoring information corresponding to each obstacle avoidance emergency stop test is collected, including the operation monitoring information of the AGV robot, the state monitoring information of the obstacle and the environmental state information. According to the above information, the operation characteristic data of the AGV robot during the obstacle avoidance emergency stop, the state characteristic data of the obstacle and the environmental interference characteristic data are extracted, wherein the operation characteristic data include the robot linear velocity, the robot angular velocity, the robot linear acceleration, the robot angular acceleration, the robot distance measurement and the robot response time data; the state characteristic data of the obstacle include the obstacle linear velocity, the obstacle angular velocity, the obstacle linear acceleration and the obstacle angular acceleration data; the environmental interference characteristic data include the light intensity, electromagnetic interference and noise interference data.

[0078] Please refer to Figure 2 , Figure 2 The flowchart of the intelligent control method for the obstacle avoidance emergency stop of the AGV robot in some embodiments of the present application is to obtain the responsiveness index. According to the embodiment of the present invention, the design characteristic data of the AGV robot is obtained, and the operation characteristic data corresponding to each obstacle avoidance emergency stop test is processed to obtain the responsiveness index of the AGV robot, including:

[0079] S21, obtaining design characteristic data of the AGV robot, including maximum linear velocity data of the robot, maximum angular velocity data of the robot, maximum linear acceleration data of the robot, and maximum angular acceleration data of the robot;

[0080] S22. Processing the robot maximum linear velocity data, the robot maximum angular velocity data, the robot maximum linear acceleration data, and the robot maximum angular acceleration data in combination with the robot distance measurement data and the robot response time data to obtain a responsiveness index of the AGV robot.

[0081] It should be noted that each AGV robot has its own design parameters, which determine the responsiveness of the device to a certain extent. Therefore, the responsiveness index of the AGV robot is obtained based on the design characteristic data of the robot, including the robot's maximum linear velocity, the robot's maximum angular velocity, the robot's maximum linear acceleration, and the robot's maximum angular acceleration data, combined with the robot's distance measurement data and the robot's response time data. The robot's distance measurement data refers to the distance between the AGV robot and the obstacle.

[0082] The calculation formula of the responsiveness index is:

[0083]

[0084] Among them, X d is the responsiveness index, z a 、x s 、v e 、c r They are the robot's maximum linear velocity data, robot's maximum angular velocity data, robot's maximum linear acceleration data, and robot's maximum angular acceleration data, respectively. li 、s ji are the robot distance measurement data and robot response time data corresponding to the i-th obstacle avoidance emergency stop test in n obstacle avoidance emergency stop tests, δ is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset AGV robot monitoring platform).

[0085] Please refer to Figure 3 , Figure 3 The flowchart of obtaining the obstacle avoidance emergency stop accuracy index of the intelligent control method for obstacle avoidance emergency stop of the AGV robot in some embodiments of the present application. According to the embodiment of the present invention, the obstacle avoidance accuracy data and emergency stop precision data of the AGV robot are obtained, and the obstacle avoidance emergency stop accuracy index of the AGV robot is obtained by processing, including:

[0086] S31, obtaining the number of times the AGV robot successfully avoids the obstacles and the total number of times it encounters obstacles in the multiple obstacle avoidance emergency stop tests, and processing to obtain the obstacle avoidance accuracy of the AGV robot;

[0087] S32, obtaining a preset robot distance measurement value corresponding to each obstacle avoidance emergency stop test, and processing the robot distance measurement data to obtain emergency stop accuracy data of the AGV robot;

[0088] S33. Process the obstacle avoidance accuracy data and the emergency stop accuracy data to obtain an obstacle avoidance emergency stop accuracy index of the AGV robot.

[0089] It should be noted that when an AGV robot encounters an obstacle, there are two ways to deal with it, obstacle avoidance and emergency stop. On the one hand, it is necessary to master the obstacle avoidance accuracy of the AGV robot, where the obstacle avoidance accuracy refers to the ratio of the number of times the AGV successfully avoids obstacles to the total number of times it encounters obstacles within a certain period of time. On the other hand, it is necessary to obtain the emergency stop accuracy of the AGV robot, where the emergency stop accuracy refers to the deviation between the actual stop position and the expected stop position when the AGV performs an emergency stop operation. Then, the obstacle avoidance accuracy data and the emergency stop accuracy data are comprehensively processed to obtain the obstacle avoidance emergency stop accuracy index of the AGV robot.

[0090] The calculation formula of the obstacle avoidance emergency stop accuracy index is:

[0091]

[0092] Among them, B z is the obstacle avoidance emergency stop accuracy index, Z j 、T d They are obstacle avoidance accuracy data and emergency stop accuracy data respectively, and ε1 and ε2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset AGV robot monitoring platform).

[0093] Please refer to Figure 4 , Figure 4 The flowchart of the intelligent control method for the AGV robot obstacle avoidance emergency stop in some embodiments of the present application is to obtain the environmental adaptability coefficient. According to the embodiment of the present invention, the ground characteristic data corresponding to each obstacle avoidance emergency stop test is obtained, and the environmental interference characteristic data is combined for processing to obtain the environmental adaptability coefficient, including:

[0094] S41, obtaining ground characteristic data corresponding to each obstacle avoidance emergency stop test, including ground roughness data and ground flatness data;

[0095] S42. Processing the ground roughness data and the ground flatness data in combination with the light intensity data, the electromagnetic interference data, and the noise interference data to obtain an environmental adaptability coefficient.

[0096] It should be noted that the obstacle avoidance and emergency stop capabilities of the AGV robot are also affected by ground conditions and environmental conditions, where ground conditions include ground roughness and ground flatness data, and environmental conditions include light intensity data, electromagnetic interference data, and noise interference data. The environmental adaptability coefficient is then obtained by processing the above data.

[0097] The calculation formula of the environmental adaptability coefficient is:

[0098]

[0099] Among them, H s is the environmental adaptability coefficient, b qi 、m wi are the ground roughness data and ground flatness data corresponding to the i-th obstacle avoidance emergency stop test in n obstacle avoidance emergency stop tests, respectively. hi , g pi 、a yi They are the light intensity data, electromagnetic interference data, and noise interference data corresponding to the ith obstacle avoidance emergency stop test in n obstacle avoidance emergency stop tests, and γ1, γ2, γ3, and γ4 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset AGV robot monitoring platform).

[0100] According to an embodiment of the present invention, the responsiveness index, the obstacle avoidance emergency stop accuracy index, and the safety distance compliance index are processed in combination with the environmental adaptability coefficient to obtain the obstacle avoidance emergency stop performance index of the AGV robot, and the AGV robot is adjusted accordingly according to the obstacle avoidance emergency stop performance index, including:

[0101] Obtain the obstacle avoidance emergency stop performance index of the AGV robot according to the responsiveness index, obstacle avoidance emergency stop accuracy index and safety distance compliance index in combination with the environmental adaptability coefficient;

[0102] Comparing the obstacle avoidance emergency stop performance index with a preset obstacle avoidance emergency stop performance value to obtain a comparison result;

[0103] Determine the obstacle avoidance and emergency stop capability of the AGV robot according to the comparison result;

[0104] If the obstacle avoidance and emergency stop capability of the AGV robot fails to reach the expected value, the AGV robot is adjusted accordingly.

[0105] It should be noted that understanding the obstacle avoidance and emergency stop performance of the AGV robot can better grasp its corresponding obstacle avoidance and emergency stop capabilities, and can also make corresponding adjustments to the AGV robot when the capabilities do not reach the expected value. Therefore, it is necessary to first process the obtained responsiveness index, obstacle avoidance and emergency stop accuracy index, and safety distance compliance index in combination with the environmental adaptability coefficient to obtain the obstacle avoidance and emergency stop performance index of the AGV robot;

[0106] Wherein, the calculation formula of the obstacle avoidance emergency stop performance index is:

[0107]

[0108] Among them, N l is the obstacle avoidance emergency stop performance index, X d , B z , A f , H s They are responsiveness index, obstacle avoidance emergency stop accuracy index, safety distance compliance index, and environmental adaptability coefficient. λ1, λ2, λ3, and μ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset AGV robot monitoring platform).

[0109] According to an embodiment of the present invention, it also includes:

[0110] According to the running characteristic data of the AGV robot, combined with the state characteristic data of the obstacle, the safety distance compliance index of the AGV robot is obtained, which specifically includes:

[0111] According to the robot linear velocity data, robot angular velocity data, robot linear acceleration data, robot angular acceleration data and robot distance measurement data, combined with the obstacle linear velocity data, obstacle angular velocity data, obstacle linear acceleration data and obstacle angular acceleration data, a safety distance compliance index of the AGV robot is obtained;

[0112] The calculation formula of the safety distance compliance index is:

[0113]

[0114] Among them, A f is the safety distance compliance index, q zi 、w xi 、e ci 、r vi 、j li are the robot linear velocity data, robot angular velocity data, robot linear acceleration data, robot angular acceleration data, and robot distance measurement data corresponding to the i-th obstacle avoidance emergency stop test in n obstacle avoidance emergency stop tests, t bi,y mi 、u oi 、p ki are the obstacle linear velocity data, obstacle angular velocity data, obstacle linear acceleration data, and obstacle angular acceleration data corresponding to the ith obstacle avoidance emergency stop test in n obstacle avoidance emergency stop tests, and σ1, σ2, σ3, σ4, and ζ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset AGV robot monitoring platform).

[0115] It should be noted that an ideal minimum safety distance needs to be maintained between the AGV robot and obstacles to ensure that no collision occurs under any circumstances. The safety distance compliance index is obtained based on the comprehensive processing of the robot distance measurement data of each test, the corresponding AGV robot operation characteristic data and the obstacle status characteristic data. This index can reflect the deviation between the actual safety distance and the ideal minimum safety distance when the AGV robot encounters obstacles during actual operation.

[0116] According to an embodiment of the present invention, it also includes:

[0117] Monitor the running status information of the AGV robot in real time, and extract the real-time running characteristic data of the AGV robot when it senses the obstacle, including the real-time linear velocity data of the robot, the real-time angular velocity data of the robot, the real-time linear acceleration data of the robot, the real-time angular acceleration data of the robot, and the real-time distance measurement data of the robot;

[0118] Obtaining corresponding real-time state characteristic data of the obstacle, including real-time linear velocity data of the obstacle, real-time angular velocity data of the obstacle, real-time linear acceleration data of the obstacle, and real-time angular acceleration data of the obstacle;

[0119] According to the real-time operation characteristic data and the real-time state characteristic data, combined with the environmental adaptability coefficient, processing is performed to obtain an obstacle avoidance emergency stop controllability index;

[0120] Comparing the obstacle avoidance emergency stop controllability index with a preset obstacle avoidance emergency stop controllability threshold to obtain a comparison result;

[0121] If the obstacle avoidance emergency stop controllability index is greater than or equal to the preset obstacle avoidance emergency stop controllability threshold, commanding the AGV robot to perform obstacle avoidance work;

[0122] Otherwise, command the AGV robot to perform an emergency stop;

[0123] The calculation formula of the obstacle avoidance emergency stop controllability index is:

[0124]

[0125] Among them, K dis the obstacle avoidance emergency stop controllability index, q Z 、w X 、e C 、r V 、j L They are the robot’s real-time linear velocity data, robot’s real-time angular velocity data, robot’s real-time linear acceleration data, robot’s real-time angular acceleration data, and robot’s real-time distance measurement data, respectively. B ,y M 、u O 、p K They are respectively the obstacle real-time linear velocity data, obstacle real-time angular velocity data, obstacle real-time linear acceleration data and obstacle real-time angular acceleration data, H s is the environmental adaptability coefficient, τ1, τ2, τ3, τ4, and ρ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset AGV robot monitoring platform).

[0126] It should be noted that during the operation of the AGV robot, emergency situations often occur. If obstacle avoidance is chosen at this time, not only will it fail to successfully avoid the obstacle, but it may also bring more serious consequences. Therefore, it is crucial to quickly and accurately judge whether the AGV robot should take obstacle avoidance measures or emergency stop measures. Therefore, it is necessary to process the real-time operation characteristic data of the AGV robot and the real-time status characteristic data of the corresponding obstacles in combination with the corresponding environmental adaptability coefficient to obtain the obstacle avoidance emergency stop controllability index, and then determine the corresponding measures based on the comparison relationship between the obstacle avoidance emergency stop controllability index and the preset obstacle avoidance emergency stop controllability threshold.

[0127] In a second aspect, the present invention further discloses an intelligent control system for an AGV robot to avoid obstacles and stop suddenly, comprising a memory and a processor, wherein the memory comprises an intelligent control method program for an AGV robot to avoid obstacles and stop suddenly, and when the intelligent control method program for an AGV robot to avoid obstacles and stop suddenly is executed by the processor, the following steps are implemented:

[0128] Perform obstacle avoidance and emergency stop tests on the preset AGV robot;

[0129] Collect monitoring information corresponding to each obstacle avoidance emergency stop test, and extract the operation characteristic data of the AGV robot, the state characteristic data of the obstacle, and the environmental interference characteristic data during the obstacle avoidance emergency stop;

[0130] Acquire the design characteristic data of the AGV robot, and process it in combination with the operation characteristic data corresponding to each obstacle avoidance emergency stop test to obtain the responsiveness index of the AGV robot;

[0131] Obtaining obstacle avoidance accuracy data and emergency stop accuracy data of the AGV robot, and processing to obtain an obstacle avoidance emergency stop accuracy index of the AGV robot;

[0132] According to the running characteristic data of the AGV robot, combined with the state characteristic data of the obstacle, processing is performed to obtain a safety distance compliance index of the AGV robot;

[0133] Acquire ground characteristic data corresponding to each obstacle avoidance emergency stop test, and process the data in combination with the environmental interference characteristic data to obtain an environmental adaptability coefficient;

[0134] According to the responsiveness index, obstacle avoidance emergency stop accuracy index and safety distance compliance index, the obstacle avoidance emergency stop performance index of the AGV robot is obtained in combination with the environmental adaptability coefficient, and the AGV robot is adjusted accordingly according to the obstacle avoidance emergency stop performance index.

[0135] It should be noted that with the continuous development of industrial automation, AGV robots have been widely used in logistics, manufacturing and other fields. However, in the actual operation process, AGV robots may encounter various obstacles, such as personnel, goods, equipment, etc. If obstacle avoidance and emergency stop cannot be performed in a timely and effective manner, collision accidents may occur, causing casualties and property losses. Therefore, in order to improve the operation safety, reliability and intelligence level of AGV robots, it is of great practical significance to study an efficient and reliable AGV robot obstacle avoidance and emergency stop intelligent control method. In order to solve the above problems, the technology of the present application provides an AGV robot obstacle avoidance and emergency stop intelligent control method. In this embodiment, firstly, an obstacle avoidance and emergency stop test is performed on the preset AGV robot, and the monitoring information corresponding to each obstacle avoidance and emergency stop test is collected. The operation characteristic data of the AGV robot during the obstacle avoidance and emergency stop, the state characteristic data of the obstacle, and the environmental interference characteristic data are extracted to obtain the design characteristic data of the AGV robot, and the operation characteristic data corresponding to each obstacle avoidance and emergency stop test are combined. Processing is performed to obtain the responsiveness index of the AGV robot, the obstacle avoidance accuracy data and the emergency stop precision data of the AGV robot, and the obstacle avoidance emergency stop accuracy index of the AGV robot is obtained. According to the operation characteristic data of the AGV robot, combined with the state characteristic data of the obstacle, processing is performed to obtain the safety distance compliance index of the AGV robot, and the ground characteristic data corresponding to each obstacle avoidance emergency stop test is obtained, and combined with the environmental interference characteristic data for processing to obtain the environmental adaptability coefficient. According to the responsiveness index, the obstacle avoidance emergency stop accuracy index and the safety distance compliance index, combined with the environmental adaptability coefficient, processing is performed to obtain the obstacle avoidance emergency stop performance index of the AGV robot, and the AGV robot is adjusted accordingly according to the obstacle avoidance emergency stop performance index, thereby realizing the technology of intelligent control of the obstacle avoidance emergency stop of the AGV robot.

[0136] According to an embodiment of the present invention, the monitoring information corresponding to each obstacle avoidance emergency stop test is collected, and the operation characteristic data of the AGV robot, the state characteristic data of the obstacle, and the environmental interference characteristic data are extracted during the obstacle avoidance emergency stop, including:

[0137] Collect monitoring information corresponding to each obstacle avoidance emergency stop test, including operation monitoring information of the AGV robot, obstacle status monitoring information, and environmental status information;

[0138] Extracting the operation characteristic data of the AGV robot during obstacle avoidance emergency stop, the state characteristic data of the obstacle, and the environmental interference characteristic data according to the monitoring information;

[0139] The operation characteristic data of the AGV robot includes robot linear velocity data, robot angular velocity data, robot linear acceleration data, robot angular acceleration data, robot distance measurement data and robot response time data;

[0140] The state characteristic data of the obstacle includes obstacle linear velocity data, obstacle angular velocity data, obstacle linear acceleration data and obstacle angular acceleration data;

[0141] The environmental interference characteristic data includes light intensity data, electromagnetic interference data and noise interference data.

[0142] It should be noted that the AGV robot is subjected to multiple obstacle avoidance emergency stop tests, and during each test, one or more of the factors such as the physical properties and motion state of the obstacle, ground conditions, environmental conditions and the operating state of the AGV robot are different. Then, the monitoring information corresponding to each obstacle avoidance emergency stop test is collected, including the operation monitoring information of the AGV robot, the state monitoring information of the obstacle and the environmental state information. According to the above information, the operation characteristic data of the AGV robot during the obstacle avoidance emergency stop, the state characteristic data of the obstacle and the environmental interference characteristic data are extracted, wherein the operation characteristic data include the robot linear velocity, the robot angular velocity, the robot linear acceleration, the robot angular acceleration, the robot distance measurement and the robot response time data; the state characteristic data of the obstacle include the obstacle linear velocity, the obstacle angular velocity, the obstacle linear acceleration and the obstacle angular acceleration data; the environmental interference characteristic data include the light intensity, electromagnetic interference and noise interference data.

[0143] According to an embodiment of the present invention, the step of acquiring the design characteristic data of the AGV robot and processing the design characteristic data corresponding to each obstacle avoidance emergency stop test to obtain the responsiveness index of the AGV robot includes:

[0144] Acquire the design characteristic data of the AGV robot, including the robot's maximum linear velocity data, the robot's maximum angular velocity data, the robot's maximum linear acceleration data, and the robot's maximum angular acceleration data;

[0145] The robot maximum linear velocity data, the robot maximum angular velocity data, the robot maximum linear acceleration data and the robot maximum angular acceleration data are processed in combination with the robot distance measurement data and the robot response time data to obtain the responsiveness index of the AGV robot.

[0146] It should be noted that each AGV robot has its own design parameters, which determine the responsiveness of the device to a certain extent. Therefore, the responsiveness index of the AGV robot is obtained based on the design characteristic data of the robot, including the robot's maximum linear velocity, the robot's maximum angular velocity, the robot's maximum linear acceleration, and the robot's maximum angular acceleration data, combined with the robot's distance measurement data and the robot's response time data. The robot's distance measurement data refers to the distance between the AGV robot and the obstacle.

[0147] The calculation formula of the responsiveness index is:

[0148]

[0149] Among them, X d is the responsiveness index, z a 、x s 、v e 、c r They are the robot's maximum linear velocity data, robot's maximum angular velocity data, robot's maximum linear acceleration data, and robot's maximum angular acceleration data, respectively. li 、s ji are the robot distance measurement data and robot response time data corresponding to the i-th obstacle avoidance emergency stop test in n obstacle avoidance emergency stop tests, δ is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset AGV robot monitoring platform).

[0150] According to an embodiment of the present invention, the step of obtaining the obstacle avoidance accuracy data and the emergency stop accuracy data of the AGV robot and processing to obtain the obstacle avoidance emergency stop accuracy index of the AGV robot includes:

[0151] Obtain the number of times the AGV robot successfully avoids the obstacle and the total number of times it encounters obstacles in the multiple obstacle avoidance emergency stop tests, and process to obtain the obstacle avoidance accuracy of the AGV robot;

[0152] Obtaining a preset robot distance measurement value corresponding to each obstacle avoidance emergency stop test, and processing the robot distance measurement data to obtain emergency stop accuracy data of the AGV robot;

[0153] The obstacle avoidance accuracy rate data and the emergency stop accuracy data are processed to obtain the obstacle avoidance emergency stop accuracy index of the AGV robot.

[0154] It should be noted that when an AGV robot encounters an obstacle, there are two ways to deal with it, obstacle avoidance and emergency stop. On the one hand, it is necessary to master the obstacle avoidance accuracy of the AGV robot, where the obstacle avoidance accuracy refers to the ratio of the number of times the AGV successfully avoids obstacles to the total number of times it encounters obstacles within a certain period of time. On the other hand, it is necessary to obtain the emergency stop accuracy of the AGV robot, where the emergency stop accuracy refers to the deviation between the actual stop position and the expected stop position when the AGV performs an emergency stop operation. Then, the obstacle avoidance accuracy data and the emergency stop accuracy data are comprehensively processed to obtain the obstacle avoidance emergency stop accuracy index of the AGV robot.

[0155] The calculation formula of the obstacle avoidance emergency stop accuracy index is:

[0156]

[0157] Among them, B z is the obstacle avoidance emergency stop accuracy index, Z j , T d They are obstacle avoidance accuracy data and emergency stop accuracy data respectively, and ε1 and ε2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset AGV robot monitoring platform).

[0158] According to an embodiment of the present invention, the acquiring of ground characteristic data corresponding to each obstacle avoidance emergency stop test and processing in combination with the environmental interference characteristic data to obtain an environmental adaptability coefficient includes:

[0159] Acquire ground characteristic data corresponding to each obstacle avoidance emergency stop test, including ground roughness data and ground flatness data;

[0160] The environmental adaptability coefficient is obtained by processing the ground roughness data and the ground flatness data in combination with the light intensity data, the electromagnetic interference data and the noise interference data.

[0161] It should be noted that the obstacle avoidance and emergency stop capabilities of the AGV robot are also affected by ground conditions and environmental conditions, where ground conditions include ground roughness and ground flatness data, and environmental conditions include light intensity data, electromagnetic interference data, and noise interference data. The environmental adaptability coefficient is then obtained by processing the above data.

[0162] The calculation formula of the environmental adaptability coefficient is:

[0163]

[0164] Among them, H s is the environmental adaptability coefficient, b qi 、m wi are the ground roughness data and ground flatness data corresponding to the i-th obstacle avoidance emergency stop test in n obstacle avoidance emergency stop tests, respectively. hi , g pi 、a yi They are the light intensity data, electromagnetic interference data, and noise interference data corresponding to the ith obstacle avoidance emergency stop test in n obstacle avoidance emergency stop tests, and γ1, γ2, γ3, and γ4 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset AGV robot monitoring platform).

[0165] According to an embodiment of the present invention, the responsiveness index, the obstacle avoidance emergency stop accuracy index, and the safety distance compliance index are processed in combination with the environmental adaptability coefficient to obtain the obstacle avoidance emergency stop performance index of the AGV robot, and the AGV robot is adjusted accordingly according to the obstacle avoidance emergency stop performance index, including:

[0166] Obtain the obstacle avoidance emergency stop performance index of the AGV robot according to the responsiveness index, obstacle avoidance emergency stop accuracy index and safety distance compliance index in combination with the environmental adaptability coefficient;

[0167] Comparing the obstacle avoidance emergency stop performance index with a preset obstacle avoidance emergency stop performance value to obtain a comparison result;

[0168] Determine the obstacle avoidance and emergency stop capability of the AGV robot according to the comparison result;

[0169] If the obstacle avoidance and emergency stop capability of the AGV robot fails to reach the expected value, the AGV robot is adjusted accordingly.

[0170] It should be noted that understanding the obstacle avoidance and emergency stop performance of the AGV robot can better grasp its corresponding obstacle avoidance and emergency stop capabilities, and can also make corresponding adjustments to the AGV robot when the capabilities do not reach the expected value. Therefore, it is necessary to first process the obtained responsiveness index, obstacle avoidance and emergency stop accuracy index, and safety distance compliance index in combination with the environmental adaptability coefficient to obtain the obstacle avoidance and emergency stop performance index of the AGV robot;

[0171] Wherein, the calculation formula of the obstacle avoidance emergency stop performance index is:

[0172]

[0173] Among them, Nl is the obstacle avoidance emergency stop performance index, X d , B z , A f , H s They are responsiveness index, obstacle avoidance emergency stop accuracy index, safety distance compliance index, and environmental adaptability coefficient. λ1, λ2, λ3, and μ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset AGV robot monitoring platform).

[0174] According to an embodiment of the present invention, it also includes:

[0175] According to the running characteristic data of the AGV robot, combined with the state characteristic data of the obstacle, the safety distance compliance index of the AGV robot is obtained, which specifically includes:

[0176] According to the robot linear velocity data, robot angular velocity data, robot linear acceleration data, robot angular acceleration data and robot distance measurement data, combined with the obstacle linear velocity data, obstacle angular velocity data, obstacle linear acceleration data and obstacle angular acceleration data, a safety distance compliance index of the AGV robot is obtained;

[0177] The calculation formula of the safety distance compliance index is:

[0178]

[0179] Among them, A f is the safety distance compliance index, q zi 、w xi 、e ci 、r vi 、j li are the robot linear velocity data, robot angular velocity data, robot linear acceleration data, robot angular acceleration data, and robot distance measurement data corresponding to the i-th obstacle avoidance emergency stop test in n obstacle avoidance emergency stop tests, t bi ,y mi 、u oi 、p ki are the obstacle linear velocity data, obstacle angular velocity data, obstacle linear acceleration data, and obstacle angular acceleration data corresponding to the ith obstacle avoidance emergency stop test in n obstacle avoidance emergency stop tests, and σ1, σ2, σ3, σ4, and ζ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset AGV robot monitoring platform).

[0180] It should be noted that an ideal minimum safety distance needs to be maintained between the AGV robot and obstacles to ensure that no collision occurs under any circumstances. The safety distance compliance index is obtained based on the comprehensive processing of the robot distance measurement data of each test, the corresponding AGV robot operation characteristic data and the obstacle status characteristic data. This index can reflect the deviation between the actual safety distance and the ideal minimum safety distance when the AGV robot encounters obstacles during actual operation.

[0181] According to an embodiment of the present invention, it also includes:

[0182] Monitor the running status information of the AGV robot in real time, and extract the real-time running characteristic data of the AGV robot when it senses the obstacle, including the real-time linear velocity data of the robot, the real-time angular velocity data of the robot, the real-time linear acceleration data of the robot, the real-time angular acceleration data of the robot, and the real-time distance measurement data of the robot;

[0183] Acquire corresponding real-time state characteristic data of the obstacle, including real-time linear velocity data of the obstacle, real-time angular velocity data of the obstacle, real-time linear acceleration data of the obstacle, and real-time angular acceleration data of the obstacle;

[0184] According to the real-time operation characteristic data and the real-time state characteristic data, combined with the environmental adaptability coefficient, processing is performed to obtain an obstacle avoidance emergency stop controllability index;

[0185] Comparing the obstacle avoidance emergency stop controllability index with a preset obstacle avoidance emergency stop controllability threshold to obtain a comparison result;

[0186] If the obstacle avoidance emergency stop controllability index is greater than or equal to the preset obstacle avoidance emergency stop controllability threshold, commanding the AGV robot to perform obstacle avoidance work;

[0187] Otherwise, command the AGV robot to perform an emergency stop;

[0188] The calculation formula of the obstacle avoidance emergency stop controllability index is:

[0189]

[0190] Among them, K d is the obstacle avoidance emergency stop controllability index, q Z 、w X 、e C 、r V 、j L They are the robot’s real-time linear velocity data, robot’s real-time angular velocity data, robot’s real-time linear acceleration data, robot’s real-time angular acceleration data, and robot’s real-time distance measurement data, respectively. B ,y M、u O 、p K They are respectively the obstacle real-time linear velocity data, obstacle real-time angular velocity data, obstacle real-time linear acceleration data and obstacle real-time angular acceleration data, H s is the environmental adaptability coefficient, τ1, τ2, τ3, τ4, and ρ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset AGV robot monitoring platform).

[0191] It should be noted that during the operation of the AGV robot, emergency situations often occur. If obstacle avoidance is chosen at this time, not only will it fail to successfully avoid the obstacle, but it may also bring more serious consequences. Therefore, it is crucial to quickly and accurately judge whether the AGV robot should take obstacle avoidance measures or emergency stop measures. Therefore, it is necessary to process the real-time operation characteristic data of the AGV robot and the real-time status characteristic data of the corresponding obstacles in combination with the corresponding environmental adaptability coefficient to obtain the obstacle avoidance emergency stop controllability index, and then determine the corresponding measures based on the comparison relationship between the obstacle avoidance emergency stop controllability index and the preset obstacle avoidance emergency stop controllability threshold.

[0192] The third aspect of the present invention provides a readable storage medium, which stores a program of an intelligent control method for an AGV robot to avoid obstacles and stop suddenly. When the program of the intelligent control method for an AGV robot to avoid obstacles and stop suddenly is executed by a processor, the steps of the intelligent control method for an AGV robot to avoid obstacles and stop suddenly as described in any one of the above items are implemented.

[0193] The intelligent control method, system and medium for the obstacle avoidance and emergency stop of an AGV robot disclosed in the present invention perform an obstacle avoidance and emergency stop test on a preset AGV robot, collect monitoring information corresponding to each obstacle avoidance and emergency stop test, extract the operation characteristic data of the AGV robot during the obstacle avoidance and emergency stop, the state characteristic data of the obstacle and the environmental interference characteristic data, obtain the design characteristic data of the AGV robot, process the operation characteristic data corresponding to each obstacle avoidance and emergency stop test, obtain the responsiveness index of the AGV robot, obtain the obstacle avoidance accuracy rate data and the emergency stop precision data of the AGV robot, and obtain the obstacle avoidance and emergency stop accuracy of the AGV robot through processing. Index, according to the operation characteristic data of the AGV robot, combined with the state characteristic data of the obstacle for processing, the safety distance compliance index of the AGV robot is obtained, the ground characteristic data corresponding to each obstacle avoidance emergency stop test is obtained, combined with the environmental interference characteristic data for processing, and the environmental adaptability coefficient is obtained. According to the responsiveness index, the obstacle avoidance emergency stop accuracy index and the safety distance compliance index, combined with the environmental adaptability coefficient for processing, the obstacle avoidance emergency stop performance index of the AGV robot is obtained, and the AGV robot is adjusted accordingly according to the obstacle avoidance emergency stop performance index, thereby realizing the technology of intelligent control of the obstacle avoidance emergency stop of the AGV robot.

[0194] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0195] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0196] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0197] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0198] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. An intelligent control method for an AGV robot to avoid obstacles and stop suddenly, characterized in that: The following steps are involved: Perform obstacle avoidance and emergency stop tests on the preset AGV robot; Collect monitoring information corresponding to each obstacle avoidance emergency stop test, and extract the operation characteristic data of the AGV robot, the state characteristic data of the obstacle, and the environmental interference characteristic data during the obstacle avoidance emergency stop; Acquire the design characteristic data of the AGV robot, and process it in combination with the operation characteristic data corresponding to each obstacle avoidance emergency stop test to obtain the responsiveness index of the AGV robot; Obtaining obstacle avoidance accuracy data and emergency stop accuracy data of the AGV robot, and processing to obtain an obstacle avoidance emergency stop accuracy index of the AGV robot; According to the running characteristic data of the AGV robot, combined with the state characteristic data of the obstacle, processing is performed to obtain a safety distance compliance index of the AGV robot; Acquire ground characteristic data corresponding to each obstacle avoidance emergency stop test, and process the data in combination with the environmental interference characteristic data to obtain an environmental adaptability coefficient; According to the responsiveness index, obstacle avoidance emergency stop accuracy index and safety distance compliance index, combined with the environmental adaptability coefficient, the obstacle avoidance emergency stop performance index of the AGV robot is obtained, and the AGV robot is adjusted accordingly according to the obstacle avoidance emergency stop performance index.

2. The intelligent control method for the AGV robot obstacle avoidance emergency stop according to claim 1 is characterized in that: The monitoring information corresponding to each obstacle avoidance emergency stop test is collected, and the operation characteristic data of the AGV robot, the state characteristic data of the obstacle, and the environmental interference characteristic data are extracted during the obstacle avoidance emergency stop, including: Collect monitoring information corresponding to each obstacle avoidance emergency stop test, including operation monitoring information of the AGV robot, obstacle status monitoring information, and environmental status information; Extracting the operation characteristic data of the AGV robot during obstacle avoidance emergency stop, the state characteristic data of the obstacle, and the environmental interference characteristic data according to the monitoring information; The operation characteristic data of the AGV robot includes robot linear velocity data, robot angular velocity data, robot linear acceleration data, robot angular acceleration data, robot distance measurement data and robot response time data; The state characteristic data of the obstacle includes obstacle linear velocity data, obstacle angular velocity data, obstacle linear acceleration data and obstacle angular acceleration data; The environmental interference characteristic data includes light intensity data, electromagnetic interference data and noise interference data.

3. The intelligent control method for the AGV robot obstacle avoidance emergency stop according to claim 2 is characterized in that: The acquiring of the design characteristic data of the AGV robot and processing in combination with the operation characteristic data corresponding to each obstacle avoidance emergency stop test to obtain the responsiveness index of the AGV robot include: Acquire the design characteristic data of the AGV robot, including the robot's maximum linear velocity data, the robot's maximum angular velocity data, the robot's maximum linear acceleration data, and the robot's maximum angular acceleration data; The robot maximum linear velocity data, the robot maximum angular velocity data, the robot maximum linear acceleration data and the robot maximum angular acceleration data are processed in combination with the robot distance measurement data and the robot response time data to obtain the responsiveness index of the AGV robot.

4. The intelligent control method for the AGV robot obstacle avoidance emergency stop according to claim 3 is characterized in that: The obtaining of the obstacle avoidance accuracy data and the emergency stop accuracy data of the AGV robot and processing to obtain the obstacle avoidance emergency stop accuracy index of the AGV robot includes: Obtain the number of times the AGV robot successfully avoids the obstacle and the total number of times it encounters obstacles in the multiple obstacle avoidance emergency stop tests, and process to obtain the obstacle avoidance accuracy of the AGV robot; Obtaining a preset robot distance measurement value corresponding to each obstacle avoidance emergency stop test, and processing the robot distance measurement data to obtain emergency stop accuracy data of the AGV robot; The obstacle avoidance accuracy rate data and the emergency stop accuracy data are processed to obtain the obstacle avoidance emergency stop accuracy index of the AGV robot.

5. The intelligent control method for obstacle avoidance and emergency stop of the AGV robot according to claim 4 is characterized in that: The acquiring of the ground characteristic data corresponding to each obstacle avoidance emergency stop test and processing the ground characteristic data in combination with the environmental interference characteristic data to obtain the environmental adaptability coefficient includes: Acquire ground characteristic data corresponding to each obstacle avoidance emergency stop test, including ground roughness data and ground flatness data; The environmental adaptability coefficient is obtained by processing the ground roughness data and the ground flatness data in combination with the light intensity data, the electromagnetic interference data and the noise interference data.

6. The intelligent control method for the AGV robot obstacle avoidance emergency stop according to claim 5 is characterized in that: The step of processing the responsiveness index, the obstacle avoidance emergency stop accuracy index, and the safety distance compliance index in combination with the environmental adaptability coefficient to obtain the obstacle avoidance emergency stop performance index of the AGV robot, and adjusting the AGV robot accordingly according to the obstacle avoidance emergency stop performance index includes: Obtain the obstacle avoidance emergency stop performance index of the AGV robot according to the responsiveness index, obstacle avoidance emergency stop accuracy index and safety distance compliance index in combination with the environmental adaptability coefficient; Comparing the obstacle avoidance emergency stop performance index with a preset obstacle avoidance emergency stop performance value to obtain a comparison result; Determine the obstacle avoidance and emergency stop capability of the AGV robot according to the comparison result; If the obstacle avoidance and emergency stop capability of the AGV robot fails to reach the expected value, the AGV robot is adjusted accordingly.

7. An intelligent control system for an AGV robot to avoid obstacles and stop suddenly, characterized in that: The system includes: a memory and a processor, wherein the memory includes a program of an intelligent control method for an AGV robot to avoid obstacles and stop suddenly, and when the program of the intelligent control method for an AGV robot to avoid obstacles and stop suddenly is executed by the processor, the following steps are implemented: Perform obstacle avoidance and emergency stop tests on the preset AGV robot; Collect monitoring information corresponding to each obstacle avoidance emergency stop test, and extract the operation characteristic data of the AGV robot, the state characteristic data of the obstacle, and the environmental interference characteristic data during the obstacle avoidance emergency stop; Acquire the design characteristic data of the AGV robot, and process it in combination with the operation characteristic data corresponding to each obstacle avoidance emergency stop test to obtain the responsiveness index of the AGV robot; Obtaining obstacle avoidance accuracy data and emergency stop accuracy data of the AGV robot, and processing to obtain an obstacle avoidance emergency stop accuracy index of the AGV robot; According to the running characteristic data of the AGV robot, combined with the state characteristic data of the obstacle, processing is performed to obtain a safety distance compliance index of the AGV robot; Acquire ground characteristic data corresponding to each obstacle avoidance emergency stop test, and process the data in combination with the environmental interference characteristic data to obtain an environmental adaptability coefficient; According to the responsiveness index, obstacle avoidance emergency stop accuracy index and safety distance compliance index, combined with the environmental adaptability coefficient, the obstacle avoidance emergency stop performance index of the AGV robot is obtained, and the AGV robot is adjusted accordingly according to the obstacle avoidance emergency stop performance index.

8. The intelligent control system for the AGV robot obstacle avoidance and emergency stop according to claim 7, characterized in that: The monitoring information corresponding to each obstacle avoidance emergency stop test is collected, and the operation characteristic data of the AGV robot, the state characteristic data of the obstacle, and the environmental interference characteristic data are extracted during the obstacle avoidance emergency stop, including: Collect monitoring information corresponding to each obstacle avoidance emergency stop test, including operation monitoring information of the AGV robot, obstacle status monitoring information, and environmental status information; Extracting the operation characteristic data of the AGV robot during obstacle avoidance emergency stop, the state characteristic data of the obstacle, and the environmental interference characteristic data according to the monitoring information; The operation characteristic data of the AGV robot includes robot linear velocity data, robot angular velocity data, robot linear acceleration data, robot angular acceleration data, robot distance measurement data and robot response time data; The state characteristic data of the obstacle includes obstacle linear velocity data, obstacle angular velocity data, obstacle linear acceleration data and obstacle angular acceleration data; The environmental interference characteristic data includes light intensity data, electromagnetic interference data and noise interference data.

9. The intelligent control system for the AGV robot obstacle avoidance and emergency stop according to claim 8, characterized in that: The acquiring of the design characteristic data of the AGV robot and processing in combination with the operation characteristic data corresponding to each obstacle avoidance emergency stop test to obtain the responsiveness index of the AGV robot include: Acquire the design characteristic data of the AGV robot, including the robot's maximum linear velocity data, the robot's maximum angular velocity data, the robot's maximum linear acceleration data, and the robot's maximum angular acceleration data; The robot maximum linear velocity data, the robot maximum angular velocity data, the robot maximum linear acceleration data and the robot maximum angular acceleration data are processed in combination with the robot distance measurement data and the robot response time data to obtain the responsiveness index of the AGV robot.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes an intelligent control method program for the AGV robot to avoid obstacles and stop suddenly. When the intelligent control method program for the AGV robot to avoid obstacles and stop suddenly is executed by the processor, the steps of the intelligent control method for the AGV robot to avoid obstacles and stop suddenly as described in any one of claims 1 to 6 are implemented.

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