A laser-based navigation path planning control method and system

Abstract

The application discloses a laser navigation path planning control method and system, wherein the method comprises the following steps: acquiring the distance value of a robot and a calibration point based on a laser radar; determining the three-dimensional coordinates of the current robot according to the distance value of the robot and the calibration point; comparing and analyzing the three-dimensional coordinates of the robot with a preset running route to determine the running error value of the current robot; if the running error value of the robot is greater than a preset running error threshold value, generating running adjustment information; and adjusting the running of the current robot based on the running adjustment information. The application realizes the adjustment and planning of the running route by recognizing the calibration point through the laser radar, improves the anti-interference capability, and is suitable for the environments such as warehouses and workshops with unstable signals.

Description

Technical Field

[0001] This application relates to the field of robot navigation and automatic control technology, and more specifically, to a laser navigation path planning and control method and system. Background Technology

[0002] With the rapid development of automation technology, mobile robots and autonomous vehicles have shown great application potential in many fields such as warehousing and logistics and intelligent manufacturing. One of the core technologies for enabling these intelligent agents to operate autonomously is path planning and control technology. Existing path planning and control technology mainly relies on positioning systems, such as the Beidou system. However, when robots are used in environments such as workshops and warehouses where signals are unstable or poor, the operation of the corresponding robots may be interfered with. Summary of the Invention

[0003] To address at least one of the aforementioned technical problems, the present invention aims to provide a laser navigation path planning and control method and system that can improve the robot's anti-interference capability and is suitable for environments such as warehouses and workshops with unstable signals.

[0004] The first aspect of this invention provides a laser navigation path planning and control method, comprising:

[0005] Based on LiDAR, the distance between the robot and the calibration point is obtained;

[0006] The robot's current three-dimensional coordinates are determined based on the distance between the robot and the calibration point.

[0007] The robot's three-dimensional coordinates are compared and analyzed with the preset running route to determine the current running error value of the robot;

[0008] If the robot's operating error value is greater than the preset operating error threshold, operating adjustment information will be generated;

[0009] Based on the operational adjustment information, the current operation of the robot is adjusted.

[0010] This plan also includes:

[0011] Based on the preset control terminal, the robot is controlled to scan the entire operating area and obtain point cloud data of the entire operating area;

[0012] Based on the preset calibration points, a three-dimensional coordinate map of the entire operating area is constructed according to the point cloud data of the entire operating area;

[0013] The preset running route is mapped onto the three-dimensional coordinate graph to determine the three-dimensional coordinate points corresponding to the preset running route.

[0014] In this solution, the preset route acquisition step specifically includes:

[0015] Obtain the robot's destination and current coordinates;

[0016] Based on the robot's destination, current location coordinates, and the 3D coordinate map of the operating area, construct an initial set of routes;

[0017] The initial routes in the initial route set are preprocessed to obtain the preprocessed route set;

[0018] Extract the features of the preprocessed route and the corresponding feature values;

[0019] The feature values ​​of the preprocessed routes are comprehensively compared and analyzed to determine the evaluation score of the corresponding preprocessed routes.

[0020] Set the preprocessed route with the highest score as the preset running route.

[0021] In this solution, the step of constructing an initial route set based on the robot's destination, current location coordinates, and a 3D coordinate map of the operating area specifically includes:

[0022] Based on the three-dimensional coordinate map of the operating area, determine all the paths the robot can take within the operating area;

[0023] Extract the midpoints of all roads in the operating area and construct the calibrated driving routes for each road;

[0024] Starting from the robot's current position and ending at its destination, connect the marked travel routes along any of the roads it passes through to construct an initial route;

[0025] Combine all the initial routes to obtain the initial route set.

[0026] In this scheme, the step of preprocessing the initial routes in the initial route set to obtain the preprocessed route set specifically includes:

[0027] Based on preset distance values, calibration points are set at intervals along the calibrated driving route of the road;

[0028] Obtain the robot's driving speed and maximum angular velocity.

[0029] Based on the robot's travel speed and maximum angular velocity, determine the robot's minimum turning radius at the corner;

[0030] Extract the corners of any initial route from the initial route set;

[0031] Construct a circle with the minimum turning radius and connect With the corner point, the entire circle Along Move the line connecting the corner point a set distance d to obtain a circle. And make the corner point on the circle Above;

[0032] Extract two calibration points located only at adjacent corners in the initial route, and draw a line and a circle based on the corresponding calibration points. Tangents to a circle

[0033] Based on the two tangents to the circle and the circle The intersecting arcs are used to adjust the corners of the initial route. After traversing all the corners in the corresponding initial route, the preprocessed route is obtained.

[0034] The preprocessed routes are combined to obtain a set of preprocessed routes.

[0035] In this scheme, the step of comprehensively comparing and analyzing the feature values ​​of the preprocessed routes to determine the evaluation score of the corresponding preprocessed routes specifically includes:

[0036] Extract the feature values ​​of the preprocessed route, including the number of marked corners, the color of the marked corners, and the corresponding route length value;

[0037] The evaluation score for the impact of marked corners on the route is determined based on the number of marked corners and the color of the marked corners;

[0038] Based on the route length value, determine the assessment score for the impact of the route length value on the route;

[0039] Subtract the evaluation scores of the impact of marked corners and route length on the route from the preset ideal evaluation score to obtain the evaluation score of the route after preprocessing.

[0040] In this solution, the step of comparing and analyzing the robot's three-dimensional coordinates with the preset running path to determine the current running error value of the robot specifically includes:

[0041] Based on the robot's two-dimensional coordinates, a straight line is drawn perpendicular to the preset running path to obtain a perpendicular straight line;

[0042] Extract the length value of the corresponding vertical line and set it as the current robot's running distance error value;

[0043] Obtain the current direction of the robot's movement;

[0044] Based on the intersection of the vertical straight line and the preset running route, draw a straight line parallel to the current running direction of the robot, and set it as the first straight line;

[0045] Extract the angle between the first straight line and the preset running route, and set the angle value as the current robot's running angle error value.

[0046] A second aspect of the present invention provides a laser navigation path planning and control system, including a memory and a processor. The memory stores a laser navigation path planning and control method program, which, when executed by the processor, performs the following steps:

[0047] Based on LiDAR, the distance between the robot and the calibration point is obtained;

[0048] The robot's current three-dimensional coordinates are determined based on the distance between the robot and the calibration point.

[0049] The robot's three-dimensional coordinates are compared and analyzed with the preset running route to determine the current running error value of the robot;

[0050] If the robot's operating error value is greater than the preset operating error threshold, operating adjustment information will be generated;

[0051] Based on the operational adjustment information, the current operation of the robot is adjusted.

[0052] This plan also includes:

[0053] Based on the preset control terminal, the robot is controlled to scan the entire operating area and obtain point cloud data of the entire operating area;

[0054] Based on the preset calibration points, a three-dimensional coordinate map of the entire operating area is constructed according to the point cloud data of the entire operating area;

[0055] The preset running route is mapped onto the three-dimensional coordinate graph to determine the three-dimensional coordinate points corresponding to the preset running route.

[0056] In this solution, the preset route acquisition step specifically includes:

[0057] Obtain the robot's destination and current coordinates;

[0058] Based on the robot's destination, current location coordinates, and the 3D coordinate map of the operating area, construct an initial set of routes;

[0059] The initial routes in the initial route set are preprocessed to obtain the preprocessed route set;

[0060] Extract the features of the preprocessed route and the corresponding feature values;

[0061] The feature values ​​of the preprocessed routes are comprehensively compared and analyzed to determine the evaluation score of the corresponding preprocessed routes.

[0062] Set the preprocessed route with the highest score as the preset running route.

[0063] One or more technical solutions proposed in this application have at least the following technical effects:

[0064] This application uses the robot's built-in laser device to construct the three-dimensional coordinates of the entire operating area, and determines the position of the corresponding robot and the running error of the preset running route through calibration points. It does not require the configuration of a positioning device and is suitable for environments such as warehouses and workshops with unstable signals. Attached Figure Description

[0065] Figure 1 A flowchart of a laser navigation path planning and control method according to the present invention is shown;

[0066] Figure 2 This diagram illustrates the preprocessing of the initial route according to the present invention.

[0067] Figure 3 A block diagram of a laser navigation path planning control system according to the present invention is shown. Detailed Implementation

[0068] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0069] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0070] Figure 1 A flowchart of a laser navigation path planning and control method according to the present invention is shown.

[0071] like Figure 1 As shown, this invention discloses a laser navigation path planning and control method, comprising:

[0072] S101, based on LiDAR, obtains the distance value between the robot and the calibration point;

[0073] S102, Determine the current three-dimensional coordinates of the robot based on the distance between the robot and the calibration point;

[0074] S103, compare and analyze the robot's three-dimensional coordinates with the preset running route to determine the current running error value of the robot;

[0075] S104, If the robot's running error value is greater than the preset running error threshold, then running adjustment information is generated;

[0076] S105, Based on the operation adjustment information, adjust the current operation of the robot.

[0077] According to an embodiment of the present invention, at least two laser points are set on the robot, such as a binocular laser device. The distance between the robot and the calibration point is determined by the laser radar. Then, the angle between the corresponding lasers is determined according to their setting positions. The position of the robot relative to the calibration point can be determined by trigonometric functions. Finally, the two-dimensional coordinates of the calibration point in the three-dimensional coordinate graph of the entire operating area are extracted to determine the two-dimensional coordinates of the robot.

[0078] According to an embodiment of the present invention, it further includes:

[0079] Based on the preset control terminal, the robot is controlled to scan the entire operating area and obtain point cloud data of the entire operating area;

[0080] Based on the preset calibration points, a three-dimensional coordinate map of the entire operating area is constructed according to the point cloud data of the entire operating area;

[0081] The preset running route is mapped onto the three-dimensional coordinate graph to determine the three-dimensional coordinate points corresponding to the preset running route.

[0082] According to an embodiment of the present invention, before the robot works, the robot is manually controlled to scan the entire operating area. The point cloud data of the entire operating area is constructed by scanning the point cloud data. When the road surface of the operating area is a flat road surface, the height value of the point on the preset driving route is zero. The three-dimensional coordinate points on the preset driving route can also be represented by two-dimensional coordinate points. For example, the three-dimensional coordinate point (10,10,0) can be modified to the two-dimensional coordinate point (10,10).

[0083] According to an embodiment of the present invention, the preset route acquisition step specifically includes:

[0084] Obtain the robot's destination and current coordinates;

[0085] Based on the robot's destination, current location coordinates, and the 3D coordinate map of the operating area, construct an initial set of routes;

[0086] The initial routes in the initial route set are preprocessed to obtain the preprocessed route set;

[0087] Extract the features of the preprocessed route and the corresponding feature values;

[0088] The feature values ​​of the preprocessed routes are comprehensively compared and analyzed to determine the evaluation score of the corresponding preprocessed routes.

[0089] Set the preprocessed route with the highest score as the preset running route.

[0090] According to an embodiment of the present invention, the step of constructing an initial route set based on the robot's destination, current position coordinates, and a three-dimensional coordinate map of the operating area specifically includes:

[0091] Based on the three-dimensional coordinate map of the operating area, determine all the paths the robot can take within the operating area;

[0092] Extract the midpoints of all roads in the operating area and construct the calibrated driving routes for each road;

[0093] Starting from the robot's current position and ending at its destination, connect the marked travel routes along any of the roads it passes through to construct an initial route;

[0094] Combine all the initial routes to obtain the initial route set.

[0095] It should be noted that the initial route is based on the center line of each road. The robot connects the center lines of the roads it passes through, and the starting point and the ending point are used as endpoints to construct the initial route. At the intersection, the center lines of the corresponding two roads are extended to intersect, thus ensuring the continuity of the initial route.

[0096] Figure 2 A schematic diagram of the preprocessing of the initial route according to the present invention is shown.

[0097] According to an embodiment of the present invention, the step of preprocessing the initial routes in the initial route set to obtain a preprocessed route set specifically includes:

[0098] Based on preset distance values, calibration points are set at intervals along the calibrated driving route of the road;

[0099] Obtain the robot's driving speed and maximum angular velocity.

[0100] Based on the robot's travel speed and maximum angular velocity, determine the robot's minimum turning radius at the corner;

[0101] Extract the corners of any initial route from the initial route set;

[0102] Construct a circle with the minimum turning radius and connect With the corner point, the entire circle Along Move the line connecting the corner point a set distance d to obtain a circle. And make the corner point on the circle Above;

[0103] Extract two calibration points located only at adjacent corners in the initial route, and draw a line and a circle based on the corresponding calibration points. Tangents to a circle

[0104] Based on the two tangents to the circle and the circle The intersecting arcs are used to adjust the corners of the initial route. After traversing all the corners in the corresponding initial route, the preprocessed route is obtained.

[0105] The preprocessed routes are combined to obtain a set of preprocessed routes.

[0106] It should be noted that, as Figure 2 As shown, calibration points 1-4 are all points on the calibrated driving route of the road. Calibration points 2 and 3 are not on the same road and therefore are not constrained by the preset distance value. Calibration points 2 and 3 are calibration points at corresponding corners, while calibration points 1 and 4 are two calibration points adjacent to only one corner on the initial route. Dividing the robot's driving speed by the maximum angular velocity value yields the minimum turning radius R. Let A be the intersection of the inner edges of the two corresponding roads. And A, to obtain the line , obtain the line Harmony The intersection point, wherein the set distance value d is the line Harmony The distance from the intersection point to intersection point A.

[0107] Further, extract the circle The angle between the tangent and the initial route in the corresponding road is considered reasonable if the angle is less than or equal to a preset angle threshold. If the angle is greater than the preset angle threshold, then the next adjacent calibration point away from the corner is extracted from the calibration point as the base point, and then a line and circle are drawn from the next adjacent calibration point as the base point. The tangent lines are identified, and the angle between the corresponding tangent lines and the initial route in the corresponding road is extracted. This angle is then compared with a preset angle threshold. The process continues until an angle less than or equal to the preset angle threshold exists, or until all calibration points in the corresponding road reach the circle. Until the angle between the tangent and the initial route of the road is greater than a preset angle threshold, the corresponding corner position is marked, and the distance from the last calibration point to the circle is recorded. The angle between the tangent and the initial route of the road, based on the last calibration point to the circle. The size of the angle between the tangent and the initial route of the road determines the marker color, where the larger the angle, the darker the marker color.

[0108] Furthermore, when the circle When the turning point extends beyond the outer edge of the road, the current turning position is marked; the minimum turning radius is adjusted based on the preset turning radius revision value to obtain the turning radius revision value, and then a new circle is constructed based on the turning radius. If the new circle If the circle still extends beyond the outer edge of the road, continue adjusting the turning radius using the preset turning radius revision value until the newly constructed circle is reached. The turning radius should not exceed the outer edge of the road. The corner position should be marked, and the marking color should be set according to the number of times the turning radius has been revised. The more times the turning radius has been revised, the darker the corresponding marking color should be. When the number of times the turning radius has been revised exceeds the preset revision threshold, the revision of the turning radius should be stopped, and the turning radius of the last revision should be set as the final turning radius.

[0109] Furthermore, the maximum angular velocity value is multiplied by the last revised turning radius to obtain the maximum speed value during the turn, and the maximum speed value during the turn is set as the current turning speed value; when the robot enters the turning arc, it decelerates to the turning speed value to operate.

[0110] It should be noted that each time the turning radius is revised, the corresponding number of turning radius revisions is incremented, and the turning radius revision includes the revision of the minimum turning radius.

[0111] According to an embodiment of the present invention, the step of comprehensively comparing and analyzing the feature values ​​of the preprocessed route to determine the evaluation score of the corresponding preprocessed route specifically includes:

[0112] Extract the feature values ​​of the preprocessed route, including the number of marked corners, the color of the marked corners, and the corresponding route length value;

[0113] The evaluation score for the impact of marked corners on the route is determined based on the number of marked corners and the color of the marked corners;

[0114] Based on the route length value, determine the assessment score for the impact of the route length value on the route;

[0115] Subtract the evaluation scores of the impact of marked corners and route length on the route from the preset ideal evaluation score to obtain the evaluation score of the route after preprocessing.

[0116] It should be noted that different colors of marked corners are assigned different scores in advance. The scores of all marked corners on the preprocessed route are accumulated, and then the scores of all corresponding marked corners are subtracted from the preset total score to obtain the evaluation score of the marked corner's impact on the route. The entire set of preprocessed routes is traversed to determine the length of all preprocessed routes, and the evaluation score of the route length's impact on the route is set as P, with the formula as follows: ,in This represents the evaluation score of the impact of the preprocessed route length on the route. This represents the maximum route length value in the preprocessed route set. This represents the minimum route length value in the preprocessed route set. This represents the length of route i after preprocessing.

[0117] According to an embodiment of the present invention, the step of comparing and analyzing the robot's three-dimensional coordinates with a preset running path to determine the current running error value of the robot specifically includes:

[0118] Based on the robot's two-dimensional coordinates, a straight line is drawn perpendicular to the preset running path to obtain a perpendicular straight line;

[0119] Extract the length value of the corresponding vertical line and set it as the current robot's running distance error value;

[0120] Obtain the current direction of the robot's movement;

[0121] Based on the intersection of the vertical straight line and the preset running route, draw a straight line parallel to the current running direction of the robot, and set it as the first straight line;

[0122] Extract the angle between the first straight line and the preset running route, and set the angle value as the current robot's running angle error value.

[0123] It should be noted that the operating error values ​​include operating distance error values ​​and operating angle error values. If either operating error value is greater than a preset operating error threshold, operating adjustment information is generated; for example, if the distance error value is greater than a preset distance error threshold, distance adjustment information is generated; if the angle error value is greater than a preset angle error threshold, angle adjustment information is generated. The operating error thresholds include distance error thresholds and angle error thresholds; the operating adjustment information includes angle adjustment information and distance adjustment information.

[0124] Furthermore, if two robots are moving towards each other, the weight or task weight values ​​of the two robots are obtained. The robot with the higher weight or task weight value is set as the priority robot, and the robot with the lower weight or task weight value is set as the avoidance robot. When the distance between the two robots is within the set avoidance distance value, the avoidance robot adjusts its current running angle by a preset avoidance angle to achieve avoidance.

[0125] Figure 3 A block diagram of a laser navigation path planning control system according to the present invention is shown.

[0126] A second aspect of the present invention provides a laser navigation path planning and control system, including a memory and a processor. The memory stores a laser navigation path planning and control method program, which, when executed by the processor, performs the following steps:

[0127] Based on LiDAR, the distance between the robot and the calibration point is obtained;

[0128] The robot's current three-dimensional coordinates are determined based on the distance between the robot and the calibration point.

[0129] The robot's three-dimensional coordinates are compared and analyzed with the preset running route to determine the current running error value of the robot;

[0130] If the robot's operating error value is greater than the preset operating error threshold, operating adjustment information will be generated;

[0131] Based on the operational adjustment information, the current operation of the robot is adjusted.

[0132] This plan also includes:

[0133] Based on the preset control terminal, the robot is controlled to scan the entire operating area and obtain point cloud data of the entire operating area;

[0134] Based on the preset calibration points, a three-dimensional coordinate map of the entire operating area is constructed according to the point cloud data of the entire operating area;

[0135] The preset running route is mapped onto the three-dimensional coordinate graph to determine the three-dimensional coordinate points corresponding to the preset running route.

[0136] In this solution, the preset route acquisition step specifically includes:

[0137] Obtain the robot's destination and current coordinates;

[0138] Based on the robot's destination, current location coordinates, and the 3D coordinate map of the operating area, construct an initial set of routes;

[0139] The initial routes in the initial route set are preprocessed to obtain the preprocessed route set;

[0140] Extract the features of the preprocessed route and the corresponding feature values;

[0141] The feature values ​​of the preprocessed routes are comprehensively compared and analyzed to determine the evaluation score of the corresponding preprocessed routes.

[0142] Set the preprocessed route with the highest score as the preset running route.

[0143] This invention discloses a laser navigation path planning and control method and system. The method includes: acquiring the distance between a robot and a calibration point using a laser radar; determining the robot's current three-dimensional coordinates based on the distance between the robot and the calibration point; comparing and analyzing the robot's three-dimensional coordinates with a preset running route to determine the robot's current running error value; if the robot's running error value is greater than a preset running error threshold, generating running adjustment information; and adjusting the robot's operation based on the running adjustment information. This invention uses laser radar to identify calibration points to achieve running route adjustment and planning, improving anti-interference capabilities and making it suitable for environments with unstable signals, such as warehouses and workshops.

[0144] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0145] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0146] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0147] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0148] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A laser navigation path planning and control method, characterized in that, include: Based on LiDAR, the distance between the robot and the calibration point is obtained; The robot's current three-dimensional coordinates are determined based on the distance between the robot and the calibration point. The robot's three-dimensional coordinates are compared and analyzed with the preset running route to determine the current running error value of the robot; If the robot's operating error value is greater than the preset operating error threshold, operating adjustment information will be generated; Based on the operational adjustment information, the current operation of the robot is adjusted; Also includes: Based on the preset control terminal, the robot is controlled to scan the entire operating area and obtain point cloud data of the entire operating area; Based on the preset calibration points, a three-dimensional coordinate map of the entire operating area is constructed according to the point cloud data of the entire operating area; The preset running route is mapped onto the three-dimensional coordinate graph to determine the three-dimensional coordinate points corresponding to the preset running route; The preset route acquisition steps specifically include: Obtain the robot's destination and current coordinates; Based on the robot's destination, current location coordinates, and the 3D coordinate map of the operating area, construct an initial set of routes; The initial routes in the initial route set are preprocessed to obtain the preprocessed route set; Extract the features of the preprocessed route and the corresponding feature values; The feature values ​​of the preprocessed routes are comprehensively compared and analyzed to determine the evaluation score of the corresponding preprocessed routes. Set the preprocessed route with the highest score as the preset running route; The step of preprocessing the initial routes in the initial route set to obtain the preprocessed route set specifically includes: Based on preset distance values, calibration points are set at intervals along the calibrated driving route of the road; Obtain the robot's driving speed and maximum angular velocity. Based on the robot's travel speed and maximum angular velocity, determine the robot's minimum turning radius at the corner; Extract the corners of any initial route from the initial route set; Construct a circle with the minimum turning radius and connect With the corner point, the entire circle Along Move the line connecting the corner point to the circle by a set distance d to obtain the circle. And make the corner point on the circle Above; Extract two calibration points located only at adjacent corners in the initial route, and draw a line and a circle based on the corresponding calibration points. Tangents to a circle Based on the two tangents to the circle and the circle The intersecting arcs are used to adjust the corners of the initial route. After traversing all the corners in the corresponding initial route, the preprocessed route is obtained. The preprocessed routes are combined to obtain a set of preprocessed routes; Also includes: extracting circles The angle between the tangent and the initial route in the corresponding road is considered reasonable if the angle is less than or equal to a preset angle threshold. If the angle is greater than the preset angle threshold, then the next adjacent calibration point away from the corner is extracted from the calibration point as the base point, and then a line and circle are drawn from the next adjacent calibration point as the base point. The tangent lines are identified, and the angle between the corresponding tangent lines and the initial route in the corresponding road is extracted. This angle is then compared with a preset angle threshold. The process continues until an angle less than or equal to the preset angle threshold exists, or until all calibration points in the corresponding road reach the circle. The angle between the tangent and the initial route of the road is greater than a preset angle threshold. Also includes: When the circle When the turning point extends beyond the outer edge of the road, the current turning position is marked; the minimum turning radius is adjusted based on the preset turning radius revision value to obtain the turning radius revision value, and then a new circle is constructed based on the turning radius. If the new circle If the circle still extends beyond the outer edge of the road, continue adjusting the turning radius using the preset turning radius revision value until the newly constructed circle is reached. Do not exceed the outer edge of the road; mark the corner position and set the mark color according to the number of times the turning radius is revised. The more times the turning radius is revised, the darker the corresponding mark color. When the number of times the turning radius is revised is greater than the preset revision threshold, stop revising the turning radius and set the turning radius of the last revision as the final turning radius. The step of comprehensively comparing and analyzing the feature values ​​of the preprocessed routes to determine the evaluation score of the corresponding preprocessed routes specifically includes: Extract the feature values ​​of the preprocessed route, including the number of marked corners, the color of the marked corners, and the corresponding route length value; The evaluation score for the impact of marked corners on the route is determined based on the number of marked corners and the color of the marked corners; Based on the route length value, determine the assessment score for the impact of the route length value on the route; Subtract the evaluation scores of the impact of marked corners and route length on the route from the preset ideal evaluation score to obtain the evaluation score of the route after preprocessing.

2. The laser navigation path planning and control method according to claim 1, characterized in that, The step of constructing an initial route set based on the robot's destination, current location coordinates, and a 3D coordinate map of the operating area specifically includes: Based on the three-dimensional coordinate map of the operating area, determine all the paths the robot can take within the operating area; Extract the midpoints of all roads in the operating area and construct the calibrated driving routes for each road; Starting from the robot's current position and ending at its destination, connect the marked travel routes along any of the roads it passes through to construct an initial route; Combine all the initial routes to obtain the initial route set.

3. The laser navigation path planning and control method according to claim 1, characterized in that, The step of comparing and analyzing the robot's three-dimensional coordinates with the preset running route to determine the current running error value of the robot specifically includes: Based on the robot's two-dimensional coordinates, a straight line is drawn perpendicular to the preset running path to obtain a perpendicular straight line; Extract the length value of the corresponding vertical line and set it as the current robot's running distance error value; Obtain the current direction of the robot's movement; Based on the intersection of the vertical straight line and the preset running route, draw a straight line parallel to the current running direction of the robot, and set it as the first straight line; Extract the angle between the first straight line and the preset running route, and set the angle value as the current robot's running angle error value.

4. A laser-guided path planning and control system, characterized in that, The system includes a memory and a processor. The memory stores a program for a laser navigation path planning and control method. When the processor executes the program, the laser navigation path planning and control method performs the following steps: Based on LiDAR, the distance between the robot and the calibration point is obtained; The robot's current three-dimensional coordinates are determined based on the distance between the robot and the calibration point. The robot's three-dimensional coordinates are compared and analyzed with the preset running route to determine the current running error value of the robot; If the robot's operating error value is greater than the preset operating error threshold, operating adjustment information will be generated; Based on the operational adjustment information, the current operation of the robot is adjusted; Also includes: Based on the preset control terminal, the robot is controlled to scan the entire operating area and obtain point cloud data of the entire operating area; Based on the preset calibration points, a three-dimensional coordinate map of the entire operating area is constructed according to the point cloud data of the entire operating area; The preset running route is mapped onto the three-dimensional coordinate graph to determine the three-dimensional coordinate points corresponding to the preset running route; The preset route acquisition steps specifically include: Obtain the robot's destination and current coordinates; Based on the robot's destination, current location coordinates, and the 3D coordinate map of the operating area, construct an initial set of routes; The initial routes in the initial route set are preprocessed to obtain the preprocessed route set; Extract the features of the preprocessed route and the corresponding feature values; The feature values ​​of the preprocessed routes are comprehensively compared and analyzed to determine the evaluation score of the corresponding preprocessed routes. Set the preprocessed route with the highest score as the preset running route; The step of preprocessing the initial routes in the initial route set to obtain the preprocessed route set specifically includes: Based on preset distance values, calibration points are set at intervals along the calibrated driving route of the road; Obtain the robot's driving speed and maximum angular velocity. Based on the robot's travel speed and maximum angular velocity, determine the robot's minimum turning radius at the corner; Extract the corners of any initial route from the initial route set; Construct a circle with the minimum turning radius and connect With the corner point, the entire circle Along Move the line connecting the corner point to the circle by a set distance d to obtain the circle. And make the corner point on the circle Above; Extract two calibration points located only at adjacent corners in the initial route, and draw a line and a circle based on the corresponding calibration points. Tangents to a circle Based on the two tangents to the circle and the circle The intersecting arcs are used to adjust the corners of the initial route. After traversing all the corners in the corresponding initial route, the preprocessed route is obtained. The preprocessed routes are combined to obtain a set of preprocessed routes; Also includes: extracting circles The angle between the tangent and the initial route in the corresponding road is considered reasonable if the angle is less than or equal to a preset angle threshold. If the angle is greater than the preset angle threshold, then the next adjacent calibration point away from the corner is extracted from the calibration point as the base point, and then a line and circle are drawn from the next adjacent calibration point as the base point. The tangent lines are identified, and the angle between the corresponding tangent lines and the initial route in the corresponding road is extracted. This angle is then compared with a preset angle threshold. The process continues until an angle less than or equal to the preset angle threshold exists, or until all calibration points in the corresponding road reach the circle. The angle between the tangent and the initial route of the road is greater than a preset angle threshold. Also includes: When the circle When the turning point extends beyond the outer edge of the road, the current turning position is marked; the minimum turning radius is adjusted based on the preset turning radius revision value to obtain the turning radius revision value, and then a new circle is constructed based on the turning radius. If the new circle If the circle still extends beyond the outer edge of the road, continue adjusting the turning radius using the preset turning radius revision value until the newly constructed circle is reached. Do not exceed the outer edge of the road; mark the corner position and set the mark color according to the number of times the turning radius is revised. The more times the turning radius is revised, the darker the corresponding mark color. When the number of times the turning radius is revised is greater than the preset revision threshold, stop revising the turning radius and set the turning radius of the last revision as the final turning radius. The step of comprehensively comparing and analyzing the feature values ​​of the preprocessed routes to determine the evaluation score of the corresponding preprocessed routes specifically includes: Extract the feature values ​​of the preprocessed route, including the number of marked corners, the color of the marked corners, and the corresponding route length value; The evaluation score for the impact of marked corners on the route is determined based on the number of marked corners and the color of the marked corners; Based on the route length value, determine the assessment score for the impact of the route length value on the route; Subtract the evaluation scores of the impact of marked corners and route length on the route from the preset ideal evaluation score to obtain the evaluation score of the route after preprocessing.

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