An AGV robot operation control method and system based on machine vision

Abstract

The present invention discloses an AGV robot operation control method and system based on machine vision, which relates to the field of AGV robot technology. The method comprises: obtaining the operation road information of the AGV robot working environment and dividing it into several areas; determining the positions of all AGV robots; determining the initial congestion index of each area of ​​the operation road; identifying road obstacles in the operation road; determining the operation walking priority index of each area in the operation road; determining at least one walking route; calculating the priority value of each walking route; screening out the walking route with the largest priority value as the optimal walking route. The advantages of the present invention are: when performing AGV robot movement control, a comprehensive analysis of the overall working environment is achieved, the walking smoothness of the AGV robot is ensured, and the working efficiency of the AGV robot is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV robots, and in particular to an AGV robot operation control method and system based on machine vision. Background Art

[0002] The main function of AGV robots is to carry out automated mobile work. AGV robots automatically move to the target location through special landmark navigation to perform their work. In existing industrial automation designs, AGV robots are an important means of transporting between different production processes. In existing industrial designs, landmark navigation roads for AGV robots to walk on are usually marked in advance in the workshop. AGV robots carry out material flow by walking on the navigation roads.

[0003] However, in the existing technology, although AGV robots generally have 3D visual image acquisition modules, the 3D visual image acquisition of existing AGV robots is only used for path obstacle avoidance, and it is difficult to achieve an overall analysis of the AGV robot's working environment on the road. When performing AGV robot movement control, there is a lack of overall analysis, which affects the walking smoothness of the AGV robot and makes it difficult to maximize the work efficiency of the AGV robot. Summary of the Invention

[0004] In order to solve the above technical problems, a machine vision-based AGV robot operation control method and system are provided. This technical solution solves the problem in the above-mentioned existing technology that although AGV robots generally have 3D visual image acquisition modules, the 3D visual image acquisition of existing AGV robots is only used for path obstacle avoidance, and it is difficult to achieve an overall analysis of the AGV robot's working environment on the road. When performing AGV robot movement control, there is a lack of overall analysis, which affects the walking smoothness of the AGV robot and makes it difficult to maximize the work efficiency of the AGV robot.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A machine vision-based AGV robot operation control method, comprising:

[0007] Obtaining the working road information of the AGV robot working environment, and dividing the working road of the AGV robot working environment into several areas;

[0008] Based on the UWB positioning system, the positions of all AGV robots in the working area are determined;

[0009] Based on the positions of all AGV robots in the work area, determine the initial congestion index of each area of ​​the work road;

[0010] Identify obstacles on the working road based on the working road images collected by the visual devices of all AGV robots;

[0011] Determine the priority index for each area of ​​the work road based on the obstacles on the work road and the initial congestion index of each area of ​​the work road;

[0012] Determine an initial position of the AGV robot and a target position of the AGV robot, and determine at least one walking route based on working road information of the AGV robot's working environment;

[0013] Calculate the priority value of each walking route based on the walking priority index of each area in the working road;

[0014] The walking route with the largest priority value is selected as the optimal walking route, and control instructions are issued to control the AGV robot to walk along the optimal walking route.

[0015] Preferably, the UWB positioning system is used to determine the positions of all AGV robots in the working area, specifically including:

[0016] The AGV robot receives the UWB signal sent by the UWB positioning base station through the built-in UWB communication module, and solves the signal data contained in the UWB signal. At the same time, based on the solved signal data, it returns the position information data to the UWB positioning base station through the UWB signal;

[0017] Based on the time difference between the UWB signal transmission time and the time of receiving the UWB signal returned by the AGV robot, combined with the UWB signal propagation speed, the distance between the AGV robot and the UWB positioning base station is determined;

[0018] Determine the distance between the AGV robot and at least three UWB positioning base stations;

[0019] Draw a circle with the position of the UWB positioning base station as the center and the distance between the AGV robot and the UWB positioning base station as the radius, which is recorded as the positioning circle;

[0020] Determine the intersection of the positioning circles made by at least three UWB positioning base stations. The intersection is the position of the AGV robot.

[0021] Preferably, determining the initial congestion index of each area of ​​the working road based on the positions of all AGV robots in the working area specifically includes:

[0022] Determine the road area of ​​each area of ​​the working road;

[0023] Obtain information about all AGV robots in each area, and determine the total area occupied by all AGV robots in each area based on the information about all AGV robots in each area;

[0024] Calculate the ratio of the total area occupied by all AGV robots in the area to the road area of ​​the area as the benchmark congestion index of the area;

[0025] Calculate the initial congestion index of each area of ​​the working road using the congestion calculation formula;

[0026] The congestion calculation formula is specifically:

[0027]

[0028] Where, is the initial congestion index of the jth area in the working road, is the benchmark congestion index in the jth area of ​​the working road, is the maximum value of the baseline congestion index in all areas, is the minimum value of the baseline congestion index of all areas, The total number of regions.

[0029] Preferably, the identifying of obstacles on the working road based on the working road images collected by the visual devices of all AGV robots specifically includes:

[0030] Acquire the collected working road image through visible light vision, and extract the red, green and blue color components of the pixel points in the working road image;

[0031] Obtain the weighted coefficients of the human eye sensitivity to red, green, and blue colors, substitute the red, green, and blue components of each pixel in the road image into the weighted average formula, and obtain the grayscale of each pixel in the working road image;

[0032] Determine the standard grayscale interval of the working road;

[0033] The working road image area outside the standard grayscale range of the working road image pixel points is recorded as the obstacle area;

[0034] Use infrared vision to determine whether there is a three-dimensional structure in the obstacle area. If so, it is determined that there is an obstacle in the obstacle area. If not, it is determined that there is no obstacle in the obstacle area.

[0035] Summarize the locations of obstacles in the movement of all AGV robots and determine whether at least two AGV robots recognize the existence of obstacles at the same location. If so, it is determined that there is a fixed obstacle at that location. If not, it is determined that there is no fixed obstacle at that location.

[0036] The weighted average formula is specifically:

[0037]

[0038] Where, is the grayscale of the pixel in row a and column b in the working road image, , , are the weighted coefficients of human eye sensitivity to red, green and blue respectively, , , The red, green and blue color components of the pixel in the a-th row and b-th column respectively.

[0039] Preferably, the determining of the working travel priority index of each area on the working road based on the road obstacles on the working road and the initial congestion index of each area on the working road specifically includes:

[0040] Based on the obstacles in the working road, determine the number of AGV robots that can be accommodated in parallel on each road section in the area;

[0041] Based on the maximum number of AGV robots that can be accommodated in parallel on each road section in the area and the total length of the working roads in the area, the road parallel fitting index of the area is calculated using the road fitting formula;

[0042] Based on the regional road parallel fitting index and the initial congestion index of the region, the operation walking priority index of each region is comprehensively calculated through a comprehensive evaluation formula;

[0043] The road fitting formula is specifically:

[0044]

[0045] In the road fitting formula, is the road parallel fitting index of the region, is the total length of the road section in the area that can accommodate a maximum of i AGV robots in parallel. is the maximum value of the maximum number of AGV robots that can be accommodated in parallel on each road section in the area. is the total length of the working roads in the area;

[0046] The comprehensive evaluation formula is specifically:

[0047]

[0048] Where, is the priority index of the operation walking in the jth area, is the road parallel fitting index of the jth region, Emphasize weights for road attributes. Emphasize weights for congestion attributes.

[0049] Preferably, the determining of the number of AGV robots that can be accommodated simultaneously in each road section in the area based on the road obstacles in the working road specifically includes:

[0050] The difference between the width of the road at the location of the fixed obstacle in the calculation area and the width of the road occupied by the fixed obstacle is recorded as the actual width of the road at that location;

[0051] Get the actual walking road width at each road section in the area;

[0052] Divide the actual walking road width by the walking width of the AGV robot and round down to the nearest integer to obtain the number of AGV robots that can be accommodated in parallel on the road section in the area.

[0053] Preferably, the calculation of the priority value of each walking route based on the working walking priority index of each area in the working road specifically includes:

[0054] Determine the length of the walking route through each area;

[0055] Multiply the length of the area through which the walking route passes by the operation walking priority index of the area to obtain the walking index of the walking route in the area;

[0056] The walking indices of all areas of the walking route are accumulated and summed to obtain the priority value of the walking route.

[0057] Furthermore, this solution proposes an AGV robot operation control system based on machine vision, which is used to implement the above-mentioned AGV robot operation control method based on machine vision, including:

[0058] An area division module is used to obtain the working road information of the AGV robot working environment and divide the working road of the AGV robot working environment into several area parts;

[0059] A positioning module, the positioning module being electrically connected to the area division module, and configured to determine the positions of all AGV robots within the working area based on a UWB positioning system, and to determine an initial congestion index for each area of ​​the working road based on the positions of all AGV robots within the working area;

[0060] An obstacle recognition module, which is used to identify obstacles on the working road based on the working road images collected by the visual devices of all AGV robots;

[0061] a road state analysis module, the road state analysis module being electrically connected to the area division module, the positioning module, and the obstacle identification module, and configured to determine a work travel priority index for each area of ​​the work road based on road obstacles and an initial congestion index for each area of ​​the work road;

[0062] A route planning module is electrically connected to the road state analysis module and the area division module. The route planning module is used to determine the initial position and target position of the AGV robot, determine at least one walking route based on the working road information of the AGV robot's working environment, and calculate the priority value of each walking route based on the working walking priority index of each area in the working road, screen out the walking route with the largest priority value as the optimal walking route, issue a control instruction, and control the AGV robot to walk along the optimal walking route.

[0063] Optionally, the positioning module includes:

[0064] A UWB system, comprising at least three UWB positioning base stations and a UWB communication module built into an AGV robot. The AGV robot receives UWB signals sent by the UWB positioning base stations through the built-in UWB communication module, calculates the signal data contained in the UWB signals, and returns position information data to the UWB positioning base stations via UWB signals based on the calculated signal data;

[0065] A signal analysis unit configured to determine the distance between the AGV robot and the UWB positioning base station based on a time difference between a UWB signal transmission time and a UWB signal reception time received from the AGV robot, combined with a UWB signal propagation speed;

[0066] The position determination unit is used to draw a circle with the position of the UWB positioning base station as the center and the distance between the AGV robot and the UWB positioning base station as the radius, which is recorded as the positioning circle. The intersection of the positioning circles made by at least three UWB positioning base stations is determined. The intersection is the position of the AGV robot.

[0067] Optionally, the route planning module includes:

[0068] A starting and ending point planning unit, which is used to determine the initial position and target position of the AGV robot, and determine at least one walking route based on the working road information of the AGV robot's working environment;

[0069] A route analysis and calculation unit, configured to calculate a priority value for each travel route based on a travel priority index for each area of ​​the work road;

[0070] The route determination unit is used to select the walking route with the largest priority value as the optimal walking route, issue a control instruction, and control the AGV robot to walk along the optimal walking route.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] The present invention proposes an AGV robot operation control scheme based on machine vision. The vision module is used to analyze the collected data of the specific road surface information of the navigation road when the robot is walking. The UWB positioning system is combined to analyze the aggregation state of all AGV robots in the working environment and the movement properties of the AGV robots in the working environment. Then, the movement of the AGV robot is comprehensively controlled in combination with the movement road data in the working environment. This method can effectively improve the adaptability of the AGV robot when moving in the working environment, thereby effectively improving the efficiency of the AGV robot when performing work. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a flow chart of the AGV robot operation control method based on machine vision proposed in the present invention;

[0074] Figure 2 Flowchart of the method for determining the positions of all AGV robots in a working area in the present invention;

[0075] Figure 3 A flow chart of a method for determining an initial congestion index for each area of ​​a working road in the present invention;

[0076] Figure 4 This is a flow chart of the method for identifying road obstacles on a working road in the present invention;

[0077] Figure 5 This is a flow chart of a method for determining the priority index of work travel in each area of ​​a work road in the present invention;

[0078] Figure 6 A flow chart of the method for determining the number of AGV robots that can be accommodated simultaneously in each road section in an area in the present invention;

[0079] Figure 7 This is a flow chart of the method for calculating the priority value of each walking route in the present invention. DETAILED DESCRIPTION

[0080] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0081] Reference Figure 1 As shown, a machine vision-based AGV robot operation control method includes:

[0082] Obtaining the working road information of the AGV robot working environment, and dividing the working road of the AGV robot working environment into several areas;

[0083] Based on the UWB positioning system, the positions of all AGV robots in the working area are determined;

[0084] Based on the positions of all AGV robots in the work area, determine the initial congestion index of each area of ​​the work road;

[0085] Identify obstacles on the working road based on the working road images collected by the visual devices of all AGV robots;

[0086] Determine the priority index for each area of ​​the work road based on the obstacles on the work road and the initial congestion index of each area of ​​the work road;

[0087] Determine an initial position of the AGV robot and a target position of the AGV robot, and determine at least one walking route based on working road information of the AGV robot's working environment;

[0088] Calculate the priority value of each walking route based on the walking priority index of each area in the working road;

[0089] The walking route with the largest priority value is selected as the optimal walking route, and control instructions are issued to control the AGV robot to walk along the optimal walking route.

[0090] This solution uses the visual module to analyze the collected data of the specific road surface information of the navigation road when the robot is walking, and combines the UWB positioning system to analyze the aggregation status of all AGV robots in the working environment and the movement properties of the AGV robots in the working environment, and then combines the movement road data in the working environment to comprehensively control the movement of the AGV robots.

[0091] Reference Figure 2 As shown, the UWB-based positioning system determines the positions of all AGV robots in the working area, specifically including:

[0092] The AGV robot receives the UWB signal sent by the UWB positioning base station through the built-in UWB communication module, and solves the signal data contained in the UWB signal. At the same time, based on the solved signal data, it returns the position information data to the UWB positioning base station through the UWB signal;

[0093] Based on the time difference between the UWB signal transmission time and the time of receiving the UWB signal returned by the AGV robot, combined with the UWB signal propagation speed, the distance between the AGV robot and the UWB positioning base station is determined;

[0094] Determine the distance between the AGV robot and at least three UWB positioning base stations;

[0095] Draw a circle with the position of the UWB positioning base station as the center and the distance between the AGV robot and the UWB positioning base station as the radius, which is recorded as the positioning circle;

[0096] Determine the intersection of the positioning circles made by at least three UWB positioning base stations. The intersection is the position of the AGV robot.

[0097] This solution uses UWB communication positioning technology to perform high-precision positioning of all AGV robots in the working environment and can achieve high-precision positioning and distance measurement, thereby achieving high-precision positioning of AGV robots.

[0098] Reference Figure 3 As shown, the determination of the initial congestion index of each area of ​​the working road based on the positions of all AGV robots in the working area specifically includes:

[0099] Determine the road area of ​​each area of ​​the working road;

[0100] Obtain information about all AGV robots in each area, and determine the total area occupied by all AGV robots in each area based on the information about all AGV robots in each area;

[0101] Calculate the ratio of the total area occupied by all AGV robots in the area to the road area of ​​the area as the benchmark congestion index of the area;

[0102] Calculate the initial congestion index of each area of ​​the working road using the congestion calculation formula;

[0103] The congestion calculation formula is specifically:

[0104]

[0105] Where, is the initial congestion index of the jth area in the working road, is the benchmark congestion index in the jth area of ​​the working road, is the maximum value of the baseline congestion index in all areas, is the minimum value of the baseline congestion index of all areas, The total number of regions.

[0106] Since there are multiple AGV robots working and moving simultaneously in the workshop, this solution uses UWB technology to locate the position of each AGV robot in real time, and measures the road congestion status by the number of AGV robots in each area of ​​the working environment.

[0107] Reference Figure 4 As shown, the identification of obstacles on the working road based on the working road images collected by the visual devices of all AGV robots specifically includes:

[0108] Acquire the collected working road image through visible light vision, and extract the red, green and blue color components of the pixel points in the working road image;

[0109] Obtain the weighted coefficients of the human eye sensitivity to red, green, and blue colors, substitute the red, green, and blue components of each pixel in the road image into the weighted average formula, and obtain the grayscale of each pixel in the working road image;

[0110] Determine the standard grayscale interval of the working road;

[0111] The working road image area outside the standard grayscale range of the working road image pixel points is recorded as the obstacle area;

[0112] Use infrared vision to determine whether there is a three-dimensional structure in the obstacle area. If so, it is determined that there is an obstacle in the obstacle area. If not, it is determined that there is no obstacle in the obstacle area.

[0113] Summarize the locations of obstacles in the movement of all AGV robots and determine whether at least two AGV robots recognize the existence of obstacles at the same location. If so, it is determined that there is a fixed obstacle at that location. If not, it is determined that there is no fixed obstacle at that location.

[0114] The weighted average formula is specifically:

[0115]

[0116] Where, is the grayscale of the pixel in row a and column b in the working road image, , , are the weighted coefficients of human eye sensitivity to red, green and blue respectively, , , The red, green and blue color components of the pixel in the a-th row and b-th column respectively.

[0117] It is understandable that in the working environment of the AGV robot, the moving path of the AGV robot is usually marked with special landmark navigation signs, that is, the working road of the AGV robot. In this solution, the areas in the working road of the AGV robot that do not conform to the special landmark navigation signs are identified by visible light recognition. At the same time, infrared vision is used to determine whether there are three-dimensional obstacles in the area that hinder the movement of the AGV robot, and the road status is evaluated in combination with the visual recognition results of multiple AGV robots.

[0118] Reference Figure 5 As shown, the determination of the working travel priority index of each area on the working road based on the road obstacles and the initial congestion index of each area on the working road specifically includes:

[0119] Based on the obstacles in the working road, determine the number of AGV robots that can be accommodated in parallel on each road section in the area;

[0120] Based on the maximum number of AGV robots that can be accommodated in parallel on each road section in the area and the total length of the working roads in the area, the road parallel fitting index of the area is calculated using the road fitting formula;

[0121] Based on the regional road parallel fitting index and the initial congestion index of the region, the operation walking priority index of each region is comprehensively calculated through a comprehensive evaluation formula;

[0122] The road fitting formula is specifically:

[0123]

[0124] In the road fitting formula, is the road parallel fitting index of the region, is the total length of the road section in the area that can accommodate a maximum of i AGV robots in parallel. is the maximum value of the maximum number of AGV robots that can be accommodated in parallel on each road section in the area. is the total length of the working roads in the area;

[0125] The comprehensive evaluation formula is specifically:

[0126]

[0127] Where, is the priority index of the operation walking in the jth area, is the road parallel fitting index of the jth region, Emphasize weights for road attributes. Emphasize weights for congestion attributes.

[0128] Reference Figure 6 As shown, the method of determining the number of AGV robots that can be accommodated in parallel on each road section in the area based on the road obstacles in the working road specifically includes:

[0129] The difference between the width of the road at the location of the fixed obstacle in the calculation area and the width of the road occupied by the fixed obstacle is recorded as the actual width of the road at that location;

[0130] Get the actual walking road width at each road section in the area;

[0131] Divide the actual walking road width by the walking width of the AGV robot and round down to the nearest integer to obtain the number of AGV robots that can be accommodated in parallel on the road section in the area.

[0132] Reference Figure 7 As shown, the calculation of the priority value of each walking route based on the work walking priority index of each area in the work road specifically includes:

[0133] Determine the length of the walking route through each area;

[0134] Multiply the length of the area through which the walking route passes by the operation walking priority index of the area to obtain the walking index of the walking route in the area;

[0135] The walking indices of all areas of the walking route are accumulated and summed to obtain the priority value of the walking route.

[0136] It is understandable that when planning roads, it is necessary to avoid areas with obstacles and a large number of AGV robots gathered on the road. Based on this, this solution will subtract the length of the path through each area and the operation walking priority index of each area. The operation walking priority index represents the existence of obstacles and the gathering of AGV robots in each area, which determines the walking speed of the AGV robot. The length of the path represents the length of the walking path, which determines the walking distance of the AGV robot. By comprehensively evaluating the priority value of each path, the optimal walking control of the AGV robot can be achieved.

[0137] Furthermore, based on the same inventive concept as the above-mentioned machine vision-based AGV robot operation control method, this solution proposes a machine vision-based AGV robot operation control system, including:

[0138] An area division module is used to obtain the working road information of the AGV robot working environment and divide the working road of the AGV robot working environment into several area parts;

[0139] A positioning module, the positioning module being electrically connected to the area division module, and configured to determine the positions of all AGV robots within the working area based on a UWB positioning system, and to determine an initial congestion index for each area of ​​the working road based on the positions of all AGV robots within the working area;

[0140] An obstacle recognition module, which is used to identify obstacles on the working road based on the working road images collected by the visual devices of all AGV robots;

[0141] a road state analysis module, the road state analysis module being electrically connected to the area division module, the positioning module, and the obstacle identification module, and configured to determine a work travel priority index for each area of ​​the work road based on road obstacles and an initial congestion index for each area of ​​the work road;

[0142] A route planning module is electrically connected to the road state analysis module and the area division module. The route planning module is used to determine the initial position and target position of the AGV robot, determine at least one walking route based on the working road information of the AGV robot's working environment, and calculate the priority value of each walking route based on the working walking priority index of each area in the working road, screen out the walking route with the largest priority value as the optimal walking route, issue a control instruction, and control the AGV robot to walk along the optimal walking route.

[0143] The positioning module includes:

[0144] A UWB system, comprising at least three UWB positioning base stations and a UWB communication module built into an AGV robot. The AGV robot receives UWB signals sent by the UWB positioning base stations through the built-in UWB communication module, calculates the signal data contained in the UWB signals, and returns position information data to the UWB positioning base stations via UWB signals based on the calculated signal data;

[0145] A signal analysis unit configured to determine the distance between the AGV robot and the UWB positioning base station based on a time difference between a UWB signal transmission time and a UWB signal reception time received from the AGV robot, combined with a UWB signal propagation speed;

[0146] The position determination unit is used to draw a circle with the position of the UWB positioning base station as the center and the distance between the AGV robot and the UWB positioning base station as the radius, which is recorded as the positioning circle. The intersection of the positioning circles made by at least three UWB positioning base stations is determined. The intersection is the position of the AGV robot.

[0147] The route planning module includes:

[0148] A starting and ending point planning unit, which is used to determine the initial position and target position of the AGV robot, and determine at least one walking route based on the working road information of the AGV robot's working environment;

[0149] A route analysis and calculation unit, configured to calculate a priority value for each travel route based on a travel priority index for each area of ​​the work road;

[0150] The route determination unit is used to select the walking route with the largest priority value as the optimal walking route, issue a control instruction, and control the AGV robot to walk along the optimal walking route.

[0151] The process of using the above system is as follows:

[0152] Step 1: The area division module obtains the working road information of the AGV robot working environment and divides the working road of the AGV robot working environment into several area parts;

[0153] Step 2: The positioning module calls the UWB system, and the AGV robot receives the UWB signal sent by the UWB positioning base station through the built-in UWB communication module, and solves the signal data contained in the UWB signal. At the same time, based on the solved signal data, the position information data is returned to the UWB positioning base station through the UWB signal. Then, the signal analysis unit determines the distance between the AGV robot and the UWB positioning base station based on the time difference between the sending time of the UWB signal and the receiving time of the UWB signal returned by the AGV robot, combined with the UWB signal propagation speed. Then, the position determination unit is used to draw a circle with the position of the UWB positioning base station as the center and the distance between the AGV robot and the UWB positioning base station as the radius, which is recorded as the positioning circle. The intersection of the positioning circles made by at least three UWB positioning base stations is determined. The intersection is the position of the AGV robot. The positioning module determines the initial congestion index of each area of ​​the working road based on the positions of all AGV robots in the working area;

[0154] Step 3: The obstacle recognition module identifies obstacles on the working road based on the working road images collected by the visual devices of all AGV robots;

[0155] Step 4: The road state analysis module determines the work travel priority index of each area on the work road based on the road obstacles on the work road and the initial congestion index of each area on the work road;

[0156] Step 5: The route planning module determines the initial position and target position of the AGV robot, determines at least one walking route based on the working road information of the AGV robot's working environment, and calculates the priority value of each walking route based on the working walking priority index of each area in the working road. The walking route with the largest priority value is selected as the optimal walking route, and a control instruction is issued to control the AGV robot to walk along the optimal walking route.

[0157] In summary, the advantages of the present invention are: when performing AGV robot movement control, a comprehensive analysis of the entire working environment is achieved, the walking smoothness of the AGV robot is ensured, and the working efficiency of the AGV robot is maximized.

[0158] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A machine vision-based AGV robot operation control method, characterized in that: include: Obtaining the working road information of the AGV robot working environment, and dividing the working road of the AGV robot working environment into several areas; Based on the UWB positioning system, the positions of all AGV robots in the working area are determined; Based on the positions of all AGV robots in the work area, determine the initial congestion index of each area of ​​the work road; Identify obstacles on the working road based on the working road images collected by the visual devices of all AGV robots; Determine the priority index for each area of ​​the work road based on the obstacles on the work road and the initial congestion index of each area of ​​the work road; Determine an initial position of the AGV robot and a target position of the AGV robot, and determine at least one walking route based on working road information of the AGV robot's working environment; Calculate the priority value of each walking route based on the walking priority index of each area in the working road; Filter out the walking route with the largest priority value as the optimal walking route, issue control instructions, and control the AGV robot to walk along the optimal walking route; The determining of the working travel priority index for each area on the working road based on the road obstacles and the initial congestion index of each area on the working road specifically includes: Based on the obstacles in the working road, determine the number of AGV robots that can be accommodated in parallel on each road section in the area; Based on the maximum number of AGV robots that can be accommodated in parallel on each road section in the area and the total length of the working roads in the area, the road parallel fitting index of the area is calculated using the road fitting formula; Based on the regional road parallel fitting index and the initial congestion index of the region, the operation walking priority index of each region is comprehensively calculated through a comprehensive evaluation formula; The road fitting formula is specifically: In the road fitting formula, is the road parallel fitting index of the region, is the total length of the road section in the area that can accommodate a maximum of i AGV robots in parallel. is the maximum value of the maximum number of AGV robots that can be accommodated in parallel on each road section in the area. is the total length of the working roads in the area; The comprehensive evaluation formula is specifically: Where, is the priority index of the operation walking in the jth area, is the road parallel fitting index of the jth region, Emphasize weights for road attributes. Emphasize the weight of congestion attributes, is the initial congestion index of the jth area in the working road.

2. The AGV robot operation control method based on machine vision according to claim 1 is characterized in that: The UWB positioning system is used to determine the positions of all AGV robots in the working area, including: The AGV robot receives the UWB signal sent by the UWB positioning base station through the built-in UWB communication module, and solves the signal data contained in the UWB signal. At the same time, based on the solved signal data, it returns the position information data to the UWB positioning base station through the UWB signal; Based on the time difference between the UWB signal transmission time and the time of receiving the UWB signal returned by the AGV robot, combined with the UWB signal propagation speed, the distance between the AGV robot and the UWB positioning base station is determined; Determine the distance between the AGV robot and at least three UWB positioning base stations; Draw a circle with the position of the UWB positioning base station as the center and the distance between the AGV robot and the UWB positioning base station as the radius, which is recorded as the positioning circle; Determine the intersection of the positioning circles made by at least three UWB positioning base stations. The intersection is the position of the AGV robot.

3. The AGV robot operation control method based on machine vision according to claim 2 is characterized in that: Determining the initial congestion index of each area of ​​the working road based on the positions of all AGV robots in the working area specifically includes: Determine the road area of ​​each area of ​​the working road; Obtain information about all AGV robots in each area, and determine the total area occupied by all AGV robots in each area based on the information about all AGV robots in each area; Calculate the ratio of the total area occupied by all AGV robots in the area to the road area of ​​the area as the benchmark congestion index of the area; Calculate the initial congestion index of each area of ​​the working road using the congestion calculation formula; The congestion calculation formula is specifically: Where, is the initial congestion index of the jth area in the working road, is the benchmark congestion index in the jth area of ​​the working road, is the maximum value of the baseline congestion index in all areas, is the minimum value of the baseline congestion index of all areas, The total number of regions.

4. The AGV robot operation control method based on machine vision according to claim 3 is characterized in that: The identification of obstacles on the working road based on the working road images collected by the visual devices of all AGV robots specifically includes: Acquire the collected working road image through visible light vision, and extract the red, green and blue color components of the pixel points in the working road image; Obtain the weighted coefficients of the human eye sensitivity to red, green, and blue colors, substitute the red, green, and blue components of each pixel in the road image into the weighted average formula, and obtain the grayscale of each pixel in the working road image; Determine the standard grayscale interval of the working road; The working road image area outside the standard grayscale range of the working road image pixel points is recorded as the obstacle area; Use infrared vision to determine whether there is a three-dimensional structure in the obstacle area. If so, it is determined that there is an obstacle in the obstacle area. If not, it is determined that there is no obstacle in the obstacle area. Summarize the locations of obstacles in the movement of all AGV robots and determine whether at least two AGV robots recognize the existence of obstacles at the same location. If so, it is determined that there is a fixed obstacle at that location. If not, it is determined that there is no fixed obstacle at that location. The weighted average formula is specifically: Where, is the grayscale of the pixel in row a and column b in the working road image, , , are the weighted coefficients of human eye sensitivity to red, green and blue respectively, , , The red, green and blue color components of the pixel in the a-th row and b-th column respectively.

5. The AGV robot operation control method based on machine vision according to claim 4 is characterized in that: The method of determining the number of AGV robots that can be accommodated simultaneously in each road section in the area based on the road obstacles in the working road specifically includes: The difference between the width of the road at the location of the fixed obstacle in the calculation area and the width of the road occupied by the fixed obstacle is recorded as the actual width of the road at that location; Get the actual walking road width at each road section in the area; Divide the actual walking road width by the walking width of the AGV robot and round down to the nearest integer to obtain the number of AGV robots that can be accommodated in parallel on the road section in the area.

6. The AGV robot operation control method based on machine vision according to claim 5 is characterized in that: The calculation of the priority value of each walking route based on the work walking priority index of each area in the work road specifically includes: Determine the length of the walking route through each area; Multiply the length of the area through which the walking route passes by the operation walking priority index of the area to obtain the walking index of the walking route in the area; The walking indices of all areas of the walking route are accumulated and summed to obtain the priority value of the walking route.

7. An AGV robot operation control system based on machine vision, characterized in that: The method for controlling an AGV robot operation based on machine vision according to any one of claims 1 to 6 comprises: An area division module is used to obtain the working road information of the AGV robot working environment and divide the working road of the AGV robot working environment into several area parts; A positioning module, the positioning module being electrically connected to the area division module, and configured to determine the positions of all AGV robots within the working area based on a UWB positioning system, and to determine an initial congestion index for each area of ​​the working road based on the positions of all AGV robots within the working area; An obstacle recognition module, which is used to identify obstacles on the working road based on the working road images collected by the visual devices of all AGV robots; a road state analysis module, the road state analysis module being electrically connected to the area division module, the positioning module, and the obstacle identification module, and configured to determine a work travel priority index for each area of ​​the work road based on road obstacles and an initial congestion index for each area of ​​the work road; A route planning module is electrically connected to the road state analysis module and the area division module. The route planning module is used to determine the initial position and target position of the AGV robot, determine at least one walking route based on the working road information of the AGV robot's working environment, and calculate the priority value of each walking route based on the working walking priority index of each area in the working road, screen out the walking route with the largest priority value as the optimal walking route, issue a control instruction, and control the AGV robot to walk along the optimal walking route.

8. The AGV robot operation control system based on machine vision according to claim 7, characterized in that: The positioning module includes: A UWB system, comprising at least three UWB positioning base stations and a UWB communication module built into an AGV robot. The AGV robot receives UWB signals sent by the UWB positioning base stations through the built-in UWB communication module, calculates the signal data contained in the UWB signals, and returns position information data to the UWB positioning base stations via UWB signals based on the calculated signal data; A signal analysis unit configured to determine the distance between the AGV robot and the UWB positioning base station based on a time difference between a UWB signal transmission time and a UWB signal reception time received from the AGV robot, combined with a UWB signal propagation speed; The position determination unit is used to draw a circle with the position of the UWB positioning base station as the center and the distance between the AGV robot and the UWB positioning base station as the radius, which is recorded as the positioning circle. The intersection of the positioning circles made by at least three UWB positioning base stations is determined. The intersection is the position of the AGV robot.

9. The AGV robot operation control system based on machine vision according to claim 8, characterized in that: The route planning module includes: A starting and ending point planning unit, which is used to determine the initial position and target position of the AGV robot, and determine at least one walking route based on the working road information of the AGV robot's working environment; A route analysis and calculation unit, configured to calculate a priority value for each travel route based on a travel priority index for each area of ​​the work road; The route determination unit is used to select the walking route with the largest priority value as the optimal walking route, issue a control instruction, and control the AGV robot to walk along the optimal walking route.

Images