Concrete leveling robot construction area segmentation method, equipment and medium

Abstract

The invention provides a concrete leveling robot construction area segmentation method and device and a medium, and relates to the technical field of concrete leveling robot construction.The method comprises the steps that an obstacle rectangular frame is arranged for each obstacle in a concrete leveling robot construction area; according to each barrier rectangular frame and the outer boundary of the construction area of the concrete leveling robot, performing construction sub-area division on the area outside the barrier rectangular frames in the construction area of the concrete leveling robot to obtain a plurality of construction sub-areas corresponding to the construction area of the concrete leveling robot; each side of each construction subarea is parallel to each side of the concrete leveling robot construction area; each construction sub-area does not contain an obstacle rectangular frame; according to the method, path repetition, construction omission and collision risks caused by irregular areas in the prior art are effectively avoided, meanwhile, the calculation complexity of path planning is reduced, and the construction efficiency and the leveling precision are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of concrete leveling robot construction technology, and in particular to a method for dividing the construction area of ​​a concrete leveling robot, an electronic device, and a storage medium. Background Technology

[0002] In the construction process of concrete screed robots, the reasonable division of the construction area is a key prerequisite for ensuring construction efficiency and screed quality. Especially when there are obstacles in the construction area, scientific area division is necessary to achieve obstacle avoidance and complete coverage of the construction area. In the existing technology, the construction area division of concrete screed robots is mostly done manually or by simple geometric division. The divided construction area often has an irregular shape, and the division boundary has poor adaptability to the robot's rectangular construction trajectory. At the same time, when dealing with obstacles, it is difficult to achieve precise obstacle enclosure and avoidance and regular division of construction sub-areas. This leads to cumbersome subsequent path planning process for the robot, and problems such as repeated construction paths and missed construction areas are prone to occur. This not only reduces construction efficiency, but may also cause insufficient screeding accuracy due to irregular area boundaries, or even the risk of collision between the robot and obstacles, seriously affecting the stability and safety of concrete screed construction. Summary of the Invention

[0003] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows: According to a first aspect of this application, a method for segmenting the construction area of ​​a concrete leveling robot is provided, the method comprising the following steps: S100, an obstacle rectangle is set for each obstacle in the construction area of ​​the concrete leveling robot; the construction area of ​​the concrete leveling robot is a rectangular area, and each side of the obstacle rectangle is parallel to each side of the construction area of ​​the concrete leveling robot; the obstacle rectangle completely covers the corresponding obstacle. S200: Based on each obstacle rectangle and the outer boundary of the concrete leveling robot construction area, the area outside the obstacle rectangle in the concrete leveling robot construction area is divided into several construction sub-regions, resulting in several construction sub-regions corresponding to the concrete leveling robot construction area; each construction sub-region is a rectangle, and each side of each construction sub-region is parallel to each side of the concrete leveling robot construction area; each construction sub-region does not contain an obstacle rectangle.

[0004] According to another aspect of this application, a non-transitory computer-readable storage medium is also provided, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above-described concrete leveling robot construction area segmentation method.

[0005] According to another aspect of this application, an electronic device is also provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0006] The present invention has at least the following beneficial effects: The concrete leveling robot construction area segmentation method of the present invention divides the area outside the construction area into several rectangular construction sub-areas by setting rectangular frames parallel to the boundary of the construction area for obstacles. This achieves precise obstacle enclosure and avoidance while ensuring the regularity and uniformity of the construction sub-areas. The rectangular structure is highly compatible with the construction trajectory of the concrete leveling robot, greatly simplifying the subsequent path planning process. It effectively avoids path repetition, construction omissions, and collision risks caused by irregular areas in the prior art, while reducing the computational complexity of path planning. It significantly improves construction efficiency and leveling accuracy, fundamentally solving the core pain point of poor adaptability between existing area segmentation and robot construction, and ensuring the stability and reliability of the construction process. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A flowchart of a concrete leveling robot construction area segmentation method provided in an embodiment of the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] It should be noted that, based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Furthermore, this device and / or practice the method can be implemented using other structures and / or functionalities besides one or more of the aspects set forth herein.

[0011] The following will refer to Figure 1The flowchart shown is a method for dividing the construction area of ​​a concrete leveling robot, which introduces a method for dividing the construction area of ​​a concrete leveling robot.

[0012] Example 1: The concrete leveling robot's method for dividing the construction area may include the following steps: S100, an obstacle rectangle is set for each obstacle in the construction area of ​​the concrete leveling robot; the construction area of ​​the concrete leveling robot is a rectangular area, and each side of the obstacle rectangle is parallel to each side of the construction area of ​​the concrete leveling robot; the obstacle rectangle completely covers the corresponding obstacle.

[0013] In this embodiment, obstacle recognition and contour extraction can be performed using the visual sensors (such as high-definition cameras and lidar) mounted on the concrete leveling robot or the preset CAD drawings of the construction area to identify all obstacles (such as steel bar piles, embedded parts, construction equipment, etc.) in the construction area and extract the actual contour coordinates of each obstacle (for example, the contour vertex coordinates of a certain L-shaped obstacle are (2.1m, 3.2m), (2.1m, 5.8m), (6.9m, 5.8m), (6.9m, 4.5m), (4.3m, 4.5m), (4.3m, 3.2m)).

[0014] Determine the boundary parameters of the obstacle rectangle. Establish a Cartesian coordinate system with the length of the construction area as the X-axis and the width as the Y-axis (origin at the top left corner of the construction area). For the outline coordinates of each obstacle, calculate its minimum (Xmin) and maximum (Xmax) values ​​in the X-axis direction and its minimum (Ymin) and maximum (Ymax) values ​​in the Y-axis direction. For example, for the L-shaped obstacle mentioned above, Xmin = 2.1m, Xmax = 6.9m, Ymin = 3.2m, and Ymax = 5.8m.

[0015] Using (Xmin, Ymin) as the bottom left vertex and (Xmax, Ymax) as the top right vertex, generate a rectangle parallel to the boundaries (X-axis, Y-axis) of the construction area. The four sides of this rectangle are parallel to the length and width of the construction area, respectively, and it completely encompasses all contour points of the obstacle (i.e., the coordinates of any vertex of the obstacle satisfy Xmin ≤ X ≤ Xmax and Ymin ≤ Y ≤ Ymax). If the obstacle is close to the boundary of the construction area, the rectangle can be generated to fit the outer boundary of the construction area (no extra redundant space is needed, avoiding wasted construction area).

[0016] This step, by enclosing obstacles with standardized rectangular frames, firstly achieves precise obstacle positioning and boundary quantification, avoiding the problem of ambiguous avoidance ranges caused by irregular obstacle outlines in existing technologies. Secondly, the design of the obstacle rectangle frame being parallel to the construction area boundary provides a unified geometric benchmark for the subsequent rectangular sub-region segmentation of the S200, ensuring the adaptability of the area boundary and the obstacle enclosure frame during the segmentation process and avoiding irregular "dead zone areas." At the same time, the rectangular frame design that completely covers the obstacles eliminates the risk of collision between the robot and obstacles from the source, providing clear obstacle avoidance boundaries for subsequent path planning, significantly improving the safety of the construction process. Furthermore, the parametric generation method of the rectangle frame requires no manual intervention, adapting to the needs of automated construction and improving the efficiency of area preprocessing.

[0017] S200: Based on each obstacle rectangle and the outer boundary of the concrete leveling robot construction area, the area outside the obstacle rectangle in the concrete leveling robot construction area is divided into several construction sub-regions, resulting in several construction sub-regions corresponding to the concrete leveling robot construction area; each construction sub-region is a rectangle, and each side of each construction sub-region is parallel to each side of the concrete leveling robot construction area; each construction sub-region does not contain an obstacle rectangle.

[0018] Furthermore, step S200 includes the following steps: S210, select one of the length and width directions of the concrete leveling robot's construction area as the priority segmentation direction.

[0019] Furthermore, step S210 includes the following steps: S211, Obtain the distribution density ρ of all obstacle rectangles along the length direction within the construction area of ​​the concrete leveling robot. L and the distribution density ρ in the width direction W ; where ρ L ρ is the ratio of the total projected length of the obstacle rectangle along its length to the length of the area to be worked by the concrete leveling robot. W It is the ratio of the total projected width of the obstacle rectangle in the width direction to the width of the construction area of ​​the concrete leveling robot.

[0020] In this embodiment, the coordinate range of the construction area can be determined by taking the lower left corner of the concrete leveling robot construction area as the origin, the length direction as the X-axis, and the width direction as the Y-axis (e.g., if the construction area is 12m×8m, the coordinate range is X∈[0,12], Y∈[0,8]).

[0021] Key parameters of all obstacle rectangles within the construction area are calculated: ① Total projected length L_total: the sum of (Xmax - Xmin) of all obstacle rectangles (Xmax and Xmin are the maximum and minimum X-axis values ​​of a single obstacle rectangle); ② Total projected width W_total: the sum of (Ymax - Ymin) of all obstacle rectangles (Ymax and Ymin are the maximum and minimum Y-axis values ​​of a single obstacle rectangle). Then, the distribution density is calculated: ρ L =L_total / Length of construction area, ρ W =W_total / Width of construction area.

[0022] S212, if ρ L <ρ W If so, then the length direction is chosen as the priority segmentation direction.

[0023] If ρ L <ρ W (Obstacles are sparser along the length direction, resulting in a lower probability of crossing obstacles during segmentation), so the length direction (X-axis) is selected as the priority segmentation direction.

[0024] S213, if ρ W <ρ L If so, then the width direction is selected as the priority segmentation direction.

[0025] If ρ W <ρ L Select the width direction (Y-axis) as the priority segmentation direction.

[0026] S214, if ρ L =ρ W If the length or width direction is selected as the preferred segmentation direction, then the segmentation direction can be chosen.

[0027] If ρ L =ρ W You can randomly select the length or width direction as the priority segmentation direction.

[0028] This step determines the priority segmentation direction by quantifying the obstacle distribution density and robot construction efficiency, avoiding problems such as sub-region fragmentation and frequent cross-obstacle segmentation caused by random selection of segmentation direction in existing technologies. Prioritizing segmentation in directions with sparse obstacles can reduce irregular areas caused by obstacle interference during the segmentation process, laying a regular foundation for subsequent elongated area division and secondary segmentation. At the same time, it adapts to the characteristics of robot construction efficiency, improving the adaptability of area segmentation and subsequent construction from the source, reducing invalid operations, and improving the coherence of the overall construction process.

[0029] S220, along the determined priority segmentation direction, divides the area outside the obstacle rectangle within the construction area of ​​the concrete leveling robot into several elongated areas.

[0030] Based on the priority segmentation direction, the area outside the obstacle is divided into continuous elongated strip regions to ensure the continuity and regularity of the segmentation. The specific steps are as follows: Step 1: Determine the segmentation spacing. The segmentation spacing needs to be adapted to the minimum effective construction width of the concrete leveling robot (Wmin, which is equal to the robot's construction width ΔD, such as 1.5m). That is, the segmentation spacing ≥ Wmin, to avoid the robot being unable to construct effectively due to the subsequent sub-area width being too small.

[0031] Step 2: Divide the area into long strips along the preferred direction. Starting from the starting point of the preferred division direction (e.g., X=0 on the X-axis), draw dividing lines parallel to the preferred division direction at the set intervals. This will completely cover the area outside the obstacle rectangle within the construction area, forming several long strip-shaped regions. When dividing, avoid the area within the obstacle rectangle: if a dividing line passes through an obstacle rectangle, the dividing line should be interrupted within the corresponding interval of the obstacle rectangle, ensuring that the long strip-shaped region only contains continuous space without obstacles.

[0032] Step 3: Define the boundaries of the elongated regions. The length of each elongated region is parallel to the preferred segmentation direction, and the width is the set segmentation interval (the width of the edge elongated regions can be adaptively adjusted according to the remaining space of the construction area, but must be ≥Wmin), and the boundaries are all parallel to the boundaries of the construction area.

[0033] This step divides the area into elongated regions along the priority segmentation direction, achieving "preliminary regularization" of the barrier-free area by transforming large, irregular barrier-free areas into continuous or segmented elongated structures, reducing the complexity of subsequent secondary segmentation. The segmentation spacing is adapted to the robot's minimum effective construction width, ensuring that the width of the elongated regions meets the robot's construction needs and avoiding decreased construction efficiency or insufficient leveling accuracy due to overly narrow regions. At the same time, the boundaries of the elongated regions are parallel to the construction area, continuing the regularity of the overall region and providing a unified benchmark for subsequent vertical segmentation to generate rectangular sub-regions, effectively reducing the fragmentation of sub-regions after segmentation and improving the overall efficiency of region segmentation.

[0034] S230: For each elongated region, segment it along a direction perpendicular to the priority segmentation direction based on the distribution of the obstacle rectangles within the elongated region.

[0035] For each elongated region, based on the distribution of internal obstacles, it is precisely divided along the vertical direction to achieve obstacle avoidance and sub-region rectangularization. The specific steps are as follows: Step 1: Extract obstacle information within the elongated region. For each elongated region, calculate the vertical (perpendicular to the priority segmentation direction) boundary coordinates of all obstacle rectangles contained within it (e.g., if the priority segmentation direction is the X-axis and the vertical direction is the Y-axis, extract the Ymin and Ymax of the obstacle rectangles).

[0036] Step 2: Determine the position of the vertical dividing line. Based on the boundary coordinates in the vertical direction, draw a vertical dividing line below the Ymin and above the Ymax of each obstacle rectangle (the dividing line is parallel to the vertical direction). Ensure that the dividing line does not pass through the obstacle rectangle and that the boundary of the sub-region formed after the division is parallel to the boundary of the construction area.

[0037] Step 3: Perform vertical segmentation. Divide the long strip area into multiple sub-regions along the vertical segmentation line. Each sub-region contains only the unobstructed space within the long strip area and is rectangular in shape (because the boundary of the long strip area is parallel to the X-axis and the vertical segmentation line is parallel to the Y-axis, they intersect to form a rectangle).

[0038] This step involves precise vertical segmentation of obstacles within a long, narrow area. Firstly, it enables accurate obstacle avoidance, ensuring that the segmented sub-regions do not contain any obstacle rectangles, fundamentally reducing the collision risk during robot construction. Secondly, the vertical segmentation lines are perpendicular to the priority segmentation direction, and their boundaries are parallel to the construction area, guaranteeing the rectangular structure of the segmented sub-regions. This perfectly matches the robot's rectangular construction trajectory, providing the optimal region shape for subsequent path planning (such as a bow-shaped path). Simultaneously, segmentation based on obstacle boundary coordinates avoids fragmentation caused by over-segmentation, ensuring reasonable sub-region sizes, improving the continuity and efficiency of robot construction, and solving the core problems of poor compatibility between segmented areas and obstacle avoidance, and poor robot construction adaptability in existing technologies.

[0039] S240, repeat step 230 until each construction sub-region obtained after segmentation is a rectangle and does not contain any obstacle rectangle; obtain several construction sub-regions corresponding to the construction area of ​​the concrete leveling robot.

[0040] This step, through repeated segmentation and rigorous verification, ensures that all construction sub-areas meet the core requirements of "rectangular + unobstructed," achieving the integrity and standardization of area segmentation. The specific steps are as follows: Step 1: Repeat vertical segmentation. Check each sub-region obtained after segmentation in S230. If a sub-region still contains some obstacle rectangles (e.g., due to the complex distribution of obstacles, the vertical segmentation did not completely avoid them), or if the sub-region is not rectangular (e.g., there are multiple scattered obstacles in a long strip region, resulting in an irregular region after one segmentation), return to S230 and continue segmenting in the vertical direction until the sub-region is completely free of obstacles and is rectangular.

[0041] Step 2: Comprehensive Verification. All segmented sub-regions undergo unified verification. Verification criteria include: ① Shape Verification: All four sides of each sub-region are parallel to the construction area boundary and form a closed rectangle; ② Obstacle Avoidance Verification: The sub-region does not contain any vertices or edges of obstacle rectangles; ③ Area Verification: The total area of ​​all construction sub-regions = construction area - sum of the areas of all obstacle rectangles, ensuring no areas are omitted or duplicated.

[0042] Step 3: Output the construction sub-regions. After successful verification, all rectangular sub-regions that meet the requirements are taken as the final construction sub-regions, completing the segmentation.

[0043] This step ensures the "rectangularization" and "obstacle-free" nature of the sub-regions through repeated segmentation, completely solving the problems of irregular segmentation and incomplete obstacle avoidance in existing technologies. The comprehensive verification process ensures the accuracy and completeness of the segmentation results from three dimensions: shape, obstacle avoidance, and area, avoiding incomplete construction due to omissions or reduced construction efficiency due to repetition. The final output of regular rectangular sub-regions not only provides the optimal adaptation shape for robot path planning, reducing the complexity of path calculation, but also ensures that the robot's construction trajectory in each sub-region is continuous and without backtracking, significantly improving leveling accuracy and construction efficiency. At the same time, it continues the obstacle avoidance logic of the overall solution, providing dual protection for the safety and stability of the construction process.

[0044] Furthermore, the length of the construction sub-region is greater than or equal to the minimum effective construction length of the concrete leveling robot, and the width is greater than or equal to the minimum effective construction width of the concrete leveling robot.

[0045] In this embodiment, the minimum effective construction length (the shortest distance that can guarantee leveling accuracy in a single continuous construction by the robot, which is determined by the operational stability of the leveling mechanism) and the minimum effective construction width (equal to the robot's leveling width, which is an inherent parameter) are first determined by the robot's factory parameters and on-site calibration. During segmentation, the dimensions of the sub-regions are checked in real time to ensure that the length is ≥ the minimum effective construction length and the width is ≥ the minimum effective construction width. If these conditions are not met, adjacent compliant sub-regions are merged or the segmentation spacing is adjusted.

[0046] Furthermore, the method also includes the following steps: S300: For any obstacle W in the construction area of ​​the concrete leveling robot, obtain the first preset value N=1.

[0047] For any obstacle W within the concrete leveling robot's construction area that has been bounded by an obstacle rectangle set by S100, the core parameters for detour construction are first initialized: the first preset value N=1 is set (N is the detour count counter, representing the current detour round N), and the distance rules for each detour round are preset (e.g., D). N+1 -D N =ΔD, where ΔD is the construction width of the concrete leveling robot, ensuring seamless connection between the construction areas traversed by adjacent wheels.

[0048] S310: Control the concrete leveling robot to circle around the outside of W, and obtain the coverage area G corresponding to the outer boundary of the construction area. N The distance between the concrete leveling robot and the outer side of W is the Nth preset distance D. N .

[0049] Furthermore, D N+1 -D N =ΔD; where ΔD is the construction width of the concrete leveling robot.

[0050] The concrete leveling robot is controlled to perform construction along a trajectory that "circles around the outside of obstacle W": the robot's travel path maintains a preset distance D from the outer edge of the rectangular frame corresponding to obstacle W. N (D) N When D is a positive number, such as when N=1 N =0.3m (adapted to the robot's obstacle avoidance safety distance), continuously constructing one circle along the perimeter of the rectangular frame to complete this round-trip construction. During construction, the positioning module (such as GPS, LiDAR) on the robot records the coordinates of the outer boundary of the construction trajectory in real time, and the closed area enclosed by the outer boundary is defined as the coverage area G of this construction. N (G) N The region is a ring-shaped or rectangular ring, and its inner boundary is the outer edge of the obstacle rectangle + D. N The outer boundary is the inner boundary + ΔD).

[0051] S320, if G N If the obstruction rectangle corresponding to W is not completely covered, then obtain N=N+1; proceed to S310; where, D N <D N+1 Otherwise, exit the current processing.

[0052] After construction is completed, the coverage area G is verified by coordinate comparison. N Does it completely cover the obstacle rectangle corresponding to obstacle W? (This is related to setting G...) N The boundary coordinates of G are superimposed with the boundary coordinates of the obstacle rectangle for analysis. NThe inner boundary of the boundary completely encloses the outer edge of the obstacle rectangle (i.e., all vertices of the obstacle rectangle are within G). N (within the inner boundary range), and G N If GN covers the entire area to be constructed around the obstacle rectangle without missing any obstacles, then the coverage is considered complete, and the current obstacle W's processing flow is skipped, moving on to the next obstacle; if GN does not completely cover the obstacle rectangle (e.g., one side of the obstacle rectangle is not covered by GN), then the coverage is considered complete. N If there is no coverage or a construction blind spot), then update the parameter N=N+1 (enter the next round of detour construction), maintaining the distance increment rule of ΔD (D N+1 =D N +ΔD), return to step S310 and repeat the detour construction until G N Completely cover the rectangle containing the obstacle.

[0053] This supplementary step, through a "multi-round incremental bypass construction" design, precisely solves the problems of blind spots and incomplete coverage around obstacles in existing technologies: First, using the rectangular frame of the obstacle as a reference, through D... N The incremental setup ensures a seamless transition between the coverage areas of each round of construction and the previous round, preventing gaps or missed areas; secondly, G N The coverage integrity judgment mechanism can dynamically adjust the number of detours, ensuring that all areas around the obstacle to be constructed are covered without wasting resources due to over-construction. At the same time, the detour distance is bound to the robot's construction width ΔD, making the coverage area of ​​the detour construction complementary to the previously segmented rectangular construction sub-areas, completely filling the construction gaps between the obstacle and the sub-areas, and ensuring that the leveling and coverage of the entire construction area is without dead corners. In addition, the trajectory design of the robot detouring along the outside of the obstacle continues the regular logic of the obstacle rectangle and the construction area being parallel, avoiding the decrease in leveling accuracy caused by irregular detours, further improving the consistency and stability of construction quality, and providing a guarantee for the integrity of the subsequent overall path planning.

[0054] S300-S320, as supplementary construction steps following the sub-regional division of S100-S240, forms a complete solution of "main area segmentation construction + special detour construction around obstacles": S100-S240 solves the regular segmentation and construction of large areas outside obstacles, while S300-S320 specifically handles the special areas around obstacles. The two work together to ensure full coverage of the construction area, retaining the efficiency of rectangular sub-regional construction while making up for the shortcomings of construction around obstacles through special detours, making the overall technical solution more practical and complete.

[0055] Furthermore, the method also includes the following steps: S400 plans the construction path of the concrete leveling robot based on the location relationship of each construction sub-area.

[0056] In this embodiment, step S400 can be implemented using the method described in Embodiment 2.

[0057] Example 2: After obtaining the construction sub-region corresponding to the concrete leveling robot's construction area based on the above embodiment 1, path planning can be performed using the following method: Q100: Obtain the first construction sub-regions closest to the preset starting point, the second construction sub-regions closest to the preset ending point, and the remaining undetermined construction sub-regions within the construction area of ​​the concrete leveling robot; the construction area of ​​the concrete leveling robot includes several construction sub-regions.

[0058] In this embodiment, the starting point of the concrete leveling robot's construction area is the starting point of the concrete leveling robot's work, and the ending point is the point where the concrete leveling robot stops after completing its work; the starting point and the ending point are preset points; the planar coordinates of the preset starting point (such as the robot's initial parking position S(0,0)) and the preset ending point (such as the parking point E(12,8) after construction) are obtained, and the "reference coordinates" of all construction sub-areas are obtained at the same time - the geometric center of the sub-area (such as the intersection of the diagonals of the rectangular sub-area) is used as the distance calculation benchmark (the coordinates of the entry point can also be selected to adapt to the subsequent path connection).

[0059] Using the Euclidean distance formula, the straight-line distances between the reference coordinates of each construction sub-region and S and E are calculated, resulting in a "Sub-region-Starting Point Distance Table" and a "Sub-region-Ending Point Distance Table". These are sorted in ascending order of distance. The first X sub-regions (where X is a preset value, such as 3, to balance computational load and optimality) closest to S are selected as the first construction sub-region; the first Y sub-regions (such as 3) closest to E are selected as the second construction sub-regions; the remaining sub-regions are assigned to undetermined construction sub-regions. If a sub-region is closest to both S and E (e.g., a sub-region near the middle of the path), it can be assigned to both the first and second sub-regions, improving the flexibility of the combination.

[0060] This step solves the problem of "disconnection between the beginning and end of the path and the actual start and end points" in the existing technology by quantifying the distance to filter related sub-regions. It avoids the robot having to make long-distance empty movements from the starting point to the first construction sub-region, or invalid movements from the last sub-region to the end point. At the same time, the "multiple candidates" (X, Y≥2) filtering logic provides a basis for multiple combinations of Q200 in the future, breaks the path limitations caused by a single beginning and end sub-region, and improves the path optimization space from the source.

[0061] Q200 combines any first construction sub-region and any second construction sub-region to obtain several construction sub-region groups.

[0062] For example: the first construction sub-region list = {a,b}, the first construction sub-region list = {d,c}, combined to obtain 4 construction sub-region groups: HA1={a,d}, HA2={a,c}, HA3={b,d}, HA4={b,c}.

[0063] This step generates multiple sets of "start-end" pairs through full combination, which solves the drawback of existing technology that "a single path candidate cannot guarantee the optimality" - existing solutions often fix the first and last construction sub-areas, which can easily lead to a lengthy overall path due to improper selection of the start and end; while this solution constructs a "path candidate pool" through multiple combinations, providing sufficient samples for the optimal selection of Q600, ensuring that the final target path is globally optimal rather than locally optimal.

[0064] Q300: For any construction sub-region group HA, take the first construction sub-region in HA as the starting construction sub-region, and determine several intermediate construction sub-regions in sequence from the construction sub-regions other than HA within the construction area of ​​the concrete leveling robot.

[0065] Furthermore, step Q300 includes the following steps: Q310, obtain HA = (HA1, HA2), where HA1 is the first construction sub-region and HA2 is the second construction sub-region; and obtain each construction sub-region other than HA1 and HA2 within the construction area of ​​the concrete leveling robot, to obtain the first remaining construction sub-region list A = (A1, A2, ..., A...). i A n ), i=1,2,…,n; A i Let be the i-th construction sub-region outside of HA1 and HA2 within the construction area of ​​the concrete leveling robot, and n be the number of construction sub-regions outside of HA1 and HA2 within the construction area of ​​the concrete leveling robot.

[0066] In this embodiment, the entry and exit points for each construction sub-region are fixed points. The entry and exit points for each construction sub-region can be determined through the following steps: Q31. For any construction sub-region GE, traverse the 0-180° angle range according to the preset step size, with each angle corresponding to a path direction baseline.

[0067] In this embodiment, because the path direction is symmetrical (e.g., the path directions of 30° and 210° are completely consistent, only the travel directions are opposite), the 180° range covers all independent path directions, which can reduce the amount of calculation by 50%. Angle values ​​are selected according to a fixed step size (e.g., 5°, 10°, the smaller the step size, the more refined the direction, and the greater the amount of calculation), forming an angle set (e.g., {0°, 5°, 10°, ..., 175°, 180°} when the step size is 5°). Each angle value corresponds to a path direction baseline - the extension direction of the baseline is consistent with the angle value, and subsequent scan lines will be generated in the "vertical direction" parallel to the baseline (e.g., for a 30° baseline, the scan lines are arranged along the 120° direction), ensuring that the scan lines are parallel and cover the sub-region at the same angle.

[0068] By traversing the balanced path from a limited angle, the space and computational efficiency are optimized, avoiding the path coverage blind spots or frequent turning problems caused by fixing a single direction (such as only along the length direction) in the existing technology.

[0069] Q32, project GE onto the axis perpendicular to the baseline of each path direction, and obtain the minimum projection value minp, the maximum projection value maxp, and the projection span span = maxp - minp.

[0070] Connect the four vertices of the construction sub-region GE (rectangle) in clockwise / counterclockwise order to form a closed quadrilateral (e.g., GE is a 3m×2m rectangle with vertex coordinates (2,3), (5,3), (5,5), (2,5)); for each angle θ, take its vertical direction as the projection axis u (e.g., θ=30°, projection axis u=30°+90°=120°); project all vertices of the closed polygon onto the u axis; iterate through all projection values, take the minimum value as minp and the maximum value as maxp, and the projection span span=maxp-minp (span represents the "effective coverage length" of the sub-region on the projection axis, which determines the number of scan lines).

[0071] For example: baseline θ = 0° (horizontal direction), projection axis u = 90° (vertical direction); vertex projection p i =x i ×cos90°+y i ×sin90°=y i Therefore, minp=3 (y=3), maxp=5 (y=5), and span=5-3=2m.

[0072] Q33. Calculate the number of scan lines num_lines = roundup(span / PATH_WIDTH) + 1 based on the preset path width PATH_WIDTH; roundup() is the preset round-up function.

[0073] The value of PATH_WIDTH is equal to the construction width of the concrete leveling robot, ensuring seamless connection of the construction areas of adjacent scan lines.

[0074] Example: Continuing with the Q32 scenario, PATH_WIDTH=1.2m; span=2m, span / PATH_WIDTH≈1.667, roundup(1.667)=2; num_lines=2+1=3 lines. The scan lines are arranged horizontally (vertical to the baseline θ=0°) with a spacing of 1.2m, covering y=3.6m, 4.2m, and 4.8m, completely covering the range of span=2m.

[0075] Q34. Concatenate path segments according to the rule of even-numbered rows in ascending order and odd-numbered rows in descending order to form a bow-shaped path without repetition or omission.

[0076] Starting from minp+PATH_WIDTH / 2 (e.g., minp=3 in Q32, 3+1.2 / 2=3.6m), generate num_lines scan lines at PATH_WIDTH intervals, with each scan line extending beyond the sub-region (ensuring intersection with the boundary). Calculate the intersection point of each scan line with the sub-region boundary using a line segment intersection algorithm. After removing duplicate intersection points, if the number of intersection points is ≥2, sort them according to the path direction. Incorporate the sorted intersection points into PATH_WIDTH / 2 (to prevent the robot edge from exceeding the sub-region). Concatenate path segments according to the rule of "even-numbered rows in ascending order, odd-numbered rows in descending order"—add path points for intersections of even-indexed scan lines (0, 2, 4...) in "start point → end point" order, and add them for odd-indexed scan lines (1, 3, 5...) in "end point → start point" order. Connect the end points and start points of adjacent scan lines directly to form a bow-shaped path without repetition or omission.

[0077] For example: GE rectangular sub-region, θ=0° baseline; the intersection points of the 3 scan lines (index 0, 1, 2) are sorted as follows: Scan line 0 (y=3.6): (2,3.6)→(5,3.6) (ascending order); Scan line 1 (y=4.2): (5,4.2)→(2,4.2) (reverse order); Scan line 2 (y=4.8): (2,4.8)→(5,4.8) (ascending order); The spliced ​​path is: (2,3.6)→(5,3.6)→(5,4.2)→(2,4.2)→(2,4.8)→(5,4.8), forming a standard bow shape.

[0078] Q35. Determine the overall weight of each path based on the coverage, total length, and number of turns for each path direction.

[0079] Indicator Quantitative Calculation: Coverage fraction: A rasterization algorithm is used to rasterize the sub-region and the path into 10mm×10mm pixel masks respectively. Coverage fraction = (number of intersection pixels between the path and the sub-region / total number of pixels in the sub-region) × 100% (target ≥ 99.5%). Total path length L: The sum of the straight-line distances of all path segments (unit: m); Number of turns C: The number of times the angle between adjacent path segments in the statistical path is ≠ 180° (the number of turns for a bow-shaped path is usually "number of scan lines - 1", and the number of turns varies depending on the direction). Weighted scoring rules (weights can be adjusted according to construction needs, the core principle is "quality first"): Indicator standardization: Convert each indicator into a score of 0-100 (e.g., 99.5% coverage gets 100 points, and 20 points are deducted for every 0.1% decrease; the shorter the path length, the higher the score, and the fewer the number of turns, the higher the score). Overall score = Coverage score × 60% + Path length score × 30% + Number of turns score × 10% (Coverage has the highest weight to ensure no omissions in construction); Overall weight = Overall score (the higher the score, the higher the weight).

[0080] The three-dimensional indicators correspond to "construction quality baseline, efficiency core requirement, and stability auxiliary guarantee," which solves the problems of "only looking at coverage and ignoring efficiency" or "only looking at length and ignoring coverage" in existing technologies.

[0081] S36: The starting point of the path with the highest comprehensive weight is taken as the entry point of the corresponding construction sub-region, and the ending point of the path is taken as the exit point of the corresponding construction sub-region.

[0082] The path with the highest overall weight (score) is selected as the target construction path for GE. The starting point of the target path (the starting intersection of the first path segment) is defined as the entry point of GE (for the robot to enter from the previous sub-area), and the ending point of the target path (the ending intersection of the last path segment) is defined as the exit point of GE (for the robot to move to the next sub-area). The coordinates of the entry point and exit point are bound to the sub-area GE and synchronized to the path planning system to provide a benchmark for the connection between subsequent sub-areas.

[0083] In this embodiment, a closed loop of "angle traversal → quantitative indicators → weighted scoring" transforms the selection of connection nodes from "experience-based judgment" to "data-driven" approaches. This ensures that connection nodes in different sub-regions are adapted to the robot's construction characteristics, avoiding connection gaps caused by manual intervention. The "no repetition, no omission" characteristic of the bow-shaped path adapts to the regular shape of rectangular sub-regions, and the optimization of PATH_WIDTH / 2 ensures that the robot does not exceed the boundaries. Multi-dimensional weighted selection takes into account "complete coverage, shortest path, and fewest turns," ensuring that the path meets construction quality requirements while improving robot operation efficiency. The entry / exit point is directly associated with the beginning and end of the optimal path, and the path direction is linked to the connection distance of adjacent sub-regions (e.g., in the example, the entry point is close to the left sub-region), reducing the robot's turning adjustment time between sub-regions and mitigating the risk of decreased leveling accuracy due to poor connection.

[0084] Q311, obtain the second preset value M=1.

[0085] The second preset value M=1 is set. The core meaning of M is "the Mth intermediate construction sub-region after HA1" - by counting by sequence number, the intermediate sub-regions are recursively pushed in the order of "1→2→...→M" to avoid the path order being disordered.

[0086] Q312, obtain the minimum distance D among the distances between the departure point of HA1 and the entry point of each construction sub-region in A. MIN，M .

[0087] Starting from the departure point of HA1 (or the departure point of the (M-1)th intermediate sub-region if M > 1), and taking each A in list A as the starting point... i The entry point is the endpoint; the distance is calculated using the Euclidean distance formula.

[0088] The "starting point" for distance calculation is dynamically updated with M: when M=1, the starting point is the departure point of HA1; when M=2, the starting point is the departure point of the first intermediate sub-region; when M=k, the starting point is the departure point of the (k-1)th intermediate sub-region, realizing "continuous recursive connection".

[0089] Q313, D MIN，M The corresponding construction sub-region is determined as the Mth intermediate construction sub-region after HA1, and D is set as follows: MIN，M The corresponding construction sub-region is removed from A.

[0090] Set A corresponding to D_MIN,M i Directly define it as "the Mth intermediate construction sub-region after HA1" (e.g., when M=1, D MIN,1 Corresponding to A i =B, then B is the first intermediate sub-region); the already determined A iPermanently remove from list A—to avoid repeatedly selecting the same sub-region during subsequent recursion, ensuring that all intermediate sub-regions are not repeated or omitted.

[0091] Q314, if nM ≤ roundup(n / 2) or D MIN，M The distance DQ between the exit point of the corresponding construction sub-region and the entry point of HA2. M If ≤D1, then exit the current processing and obtain several intermediate construction sub-regions corresponding to HA1; otherwise, obtain M=M+1 and enter Q312; D1 is the first preset distance threshold, and roundup() is the preset rounding up function.

[0092] In this embodiment, judgment condition 1: nM≤roundup(n / 2) is used to avoid the first half of the path "over-occupying" the middle sub-region, resulting in too few connectable sub-regions in the second half (Q400) and causing path breaks; judgment condition 2: DQ M If the first half of the path is close to the termination anchor point HA2, stop the recursion to avoid the first half and the second half of the path "intersecting and overlapping" and reduce invalid backtracking.

[0093] The value of D1 needs to be adapted to the characteristics of robot construction. It is usually set to "robot construction width × 2" (e.g., if the construction width is 1.2m, D1 = 2.4m), or set to other values ​​to ensure that the connection distance between the end point of the first half and HA2 is reasonable, while avoiding the distance being too close, which would result in no intermediate sub-area to plan in the second half.

[0094] In this embodiment, by using "minimum distance filtering," the connection distance between each intermediate sub-region and the preceding region is minimized. Compared to randomly selecting sub-regions, this reduces the total length of the first half of the path by 15% to 30%, thus reducing the time spent on ineffective robot movement. Condition 1, "half-quantity control of remaining sub-regions," ensures that the number of intermediate sub-regions in the first and second halves is roughly equal, avoiding frequent turning caused by "overly dense" paths. Condition 2, "DQM≤D1," ensures that the distance between the endpoint of the first half and HA2 is within the robot's controllable movement range. Combined with the regular shape of rectangular sub-regions, this avoids the risk of path deviation or collision due to excessive distance during connection.

[0095] Q400, taking the second construction sub-region in HA as the terminated construction sub-region, determines several intermediate construction sub-regions from the remaining second construction sub-regions and the remaining undetermined construction sub-regions in a sequential order.

[0096] Q400 is the "second half convergence link" of the path planning. It takes HA2 (the second construction sub-region and the path end anchor point) as the core and determines the intermediate sub-region through "reverse recursion + nearest connection", forming a closed loop with the "forward recursion" of Q300.

[0097] Furthermore, step Q400 includes the following steps: Q410, obtain each construction sub-region within the concrete leveling robot's construction area, excluding HA1, HA2, and several intermediate construction sub-regions corresponding to HA1, to obtain the second remaining construction sub-region list B = (B1, B2, ..., B...). j B m ), j=1,2,…,m; B j Let be the j-th construction sub-region within the construction area of ​​the concrete leveling robot, excluding HA1, HA2, and several intermediate construction sub-regions corresponding to HA1, and m be the number of construction sub-regions within the construction area of ​​the concrete leveling robot, excluding HA1, HA2, and several intermediate construction sub-regions corresponding to HA1.

[0098] Define the "planned area": ​​This includes three parts—the starting anchor point HA1 in Q300, all intermediate construction sub-areas corresponding to HA1, and the ending anchor point HA2 (which needs to be reserved as the path endpoint and is not included in the intermediate sub-areas for the time being); Extract the "unplanned area": ​​Traverse all sub-areas of the construction area, remove the above "planned area", and sort the remaining sub-areas according to the coordinate order of "from right to left and from bottom to top" (or the same sorting rule as Q300, which does not affect the distance filtering), forming the second list of remaining construction sub-areas B.

[0099] Q411, obtain the third preset value K=1.

[0100] The third preset value K=1 is set, where K means "the Kth intermediate construction sub-region before HA2" - corresponding to M (the sequence number after HA1) in Q300, forming a complete sequence chain of "HA1→M1→M2→...→K2→K1→HA2" to ensure linear path progression.

[0101] Q412, obtain the minimum distance H among the distances between the entry point of HA2 and the exit point of each construction sub-region in B. MIN，K .

[0102] Starting from the entry point of HA2 (since HA2 is the end point of the path, the robot needs to enter HA2 from the last intermediate sub-region, so the connection reference is the entry point of HA2), and using each B in list B... j The departure point is the destination (B) j The departure point is the "exit" of its path, which facilitates direct connection to the "entry" of HA2; the Euclidean distance formula is used to maintain consistency with Q300 and ensure unified calculation logic.

[0103] Q413, H MIN，K The corresponding construction sub-region is determined as the Kth intermediate construction sub-region before HA2, and H is set as follows: MIN，K The corresponding construction sub-region is removed from B.

[0104] H MIN,K Corresponding B j Defined as "the Kth intermediate construction sub-region before HA2" (e.g., when K=1, H MIN,1 Corresponding to B j =CE, then CE is the intermediate sub-region immediately adjacent to HA2); the already determined B j Permanently remove from list B—to avoid repeated selections during subsequent reverse recursion, ensuring that list B shrinks as K increases until the termination condition is met.

[0105] Q414, if H MIN，K The distance HQ between the entry point and the exit point of HA2 in the corresponding construction sub-region. K If ≤D2, then exit the current processing and obtain several intermediate construction sub-regions corresponding to HA2; otherwise, obtain K=K+1 and enter Q412; D2 is the second preset distance threshold.

[0106] In this embodiment, the two conditions of Q300 focus on "balancing the lengths of the first and second halves", while the single condition of Q414 focuses on "rapidly converging to the first half of the path" - since the length of the first half has been controlled by Q314, the second half only needs to ensure that the sub-regions recursively in the reverse direction can connect with the last intermediate sub-region of the first half, without the need for additional length balancing, which simplifies the calculation while ensuring closed loop.

[0107] The second preset distance threshold D2 has the same value logic as D1 (adapting to the characteristics of robot construction). It is usually "robot construction width × 1.5" (e.g., if the construction width is 1.2m, D2 = 1.8m). This ensures "bidirectional connection" between the reverse intermediate sub-region and HA2 (connecting the entry point of HA2 while maintaining a reasonable distance from the exit point of HA2 to avoid path congestion).

[0108] The reverse recursion starts from HA2, and the forward recursion starts from HA1. The two connect naturally in the intermediate region, avoiding path breaks where the first and second halves are separate systems, ensuring coverage of all intermediate sub-regions. The "proximity connection" logic minimizes the connection distance between each sub-region in the second half of the path and its preceding sub-region. Compared to randomly selecting sub-regions, the total path length in the second half can be reduced by 10% to 25%, minimizing unnecessary robot movement. Sub-regions are selected based on the entry point of HA2, ensuring that the connection distance between the last intermediate sub-region and HA2 is ≤ H. MIN,1 By combining the constraints of D2, the "entry-exit" of HA2 is smoothly connected with the intermediate sub-region, avoiding frequent turning of the robot near the end point anchor.

[0109] Furthermore, after step Q400 and before step Q500, the method further includes the following steps: Q420: If the number of identified intermediate construction sub-regions is NUM1 = NUM ​​- 2, proceed to Q500; otherwise, obtain each construction sub-region not identified as an intermediate construction sub-region, resulting in a list of undetermined construction sub-regions C = (C1, C2, ..., C...). p C q ), p=1,2,…,q; C p Let q be the p-th construction sub-region that has not been identified as an intermediate construction sub-region, and let NUM be the number of unidentified intermediate construction sub-regions.

[0110] In this embodiment, if the number of intermediate construction sub-regions NUM1 = NUM-2, it means that all undetermined construction sub-regions have been determined; otherwise, there are undetermined undetermined construction sub-regions.

[0111] Q421, obtain the fourth preset value R=1.

[0112] Q422, if R≤q, then the construction sub-region in C that is closest to the exit point of the last intermediate construction sub-region corresponding to HA1 is determined as the intermediate construction sub-region corresponding to HA1; and the construction sub-region is removed from C; obtain R=R+1, and proceed to Q423; otherwise, proceed to Q500.

[0113] End, the departure point of the last intermediate construction sub-region corresponding to Q300 300 As the connection benchmark; iterate through the undetermined list C and calculate each C... p Entry point and End 300 The Euclidean distance is chosen, and the shortest distance is C. p As the "newly added intermediate construction sub-area corresponding to HA1" (the end of the access Q300 path).

[0114] Q423, if R≤q, then determine the construction sub-region in C that is closest to the entry point of the last intermediate construction sub-region corresponding to HA2 as the intermediate construction sub-region corresponding to HA2; and remove the construction sub-region from C; obtain R=R+1, and proceed to Q422; otherwise, proceed to Q500.

[0115] Starting from the entry point of the last intermediate construction sub-region corresponding to Q400 400 As a baseline for connection; traverse the undetermined list C (the sub-regions completed by Q422 have been removed), and calculate each C p departure point and start 400 The Euclidean distance is chosen, and the shortest distance is C. p As the "newly added intermediate construction sub-area corresponding to HA2" (the front end of the access Q400 path).

[0116] This step forces the completion of all uncovered sub-regions, completely eliminating construction omissions. Compared to existing technologies with "no completion mechanism," the coverage accuracy is improved to 100%. During completion, the end departure point of Q300 and the front entry point of Q400 are used as references to ensure that the connection distance between the completed sub-region and the preceding and following paths is minimized. Compared to "random completion," the connection distance of the completed area is shortened by an average of 40% to 60%, reducing unnecessary robot movement. The alternating logic of completing one sub-region in the forward direction and one in the reverse direction ensures that the number of sub-regions in the first and second halves of the path is roughly balanced, avoiding frequent turning or decreased construction efficiency caused by an excessively long path.

[0117] Q500 connects the last determined intermediate construction sub-region in Q300 with the last determined construction sub-region in Q400 to obtain the construction path of the concrete leveling robot corresponding to HA.

[0118] First half end node: Extract the departure point of the "last determined intermediate construction sub-region" in Q300. This sub-region includes the last intermediate sub-region determined by forward recursion in Q300, or the last forward intermediate sub-region added after completion in Q422 (the completion logic prioritizes connecting to the end of the first half, so the last sub-region after completion is still the end of the first half). The starting node of the second half: Extract the entry point of the "last determined intermediate construction sub-region" in Q400. It should be clarified here that Q400 is a reverse recursion (derived from HA2 to the middle). The order of the determined intermediate sub-regions is "the Kth one before HA2 → the (K-1)th one → ... → the 1st one". Therefore, the "last determined intermediate construction sub-region" is actually the first sub-region closest to the first half of the Q400 path chain (including the reverse intermediate sub-region completed by Q423). Its entry point is the key benchmark connecting the first half.

[0119] Q600 determines the target construction path from the construction paths corresponding to each construction sub-area group.

[0120] Furthermore, step Q610 includes the following steps: Q611, for any construction path FH, obtain the total length S of FH itself. FH And the distance YH between the departure point of the second-to-last construction sub-region of FH and the entry point of the last construction sub-region.

[0121] Q622, If YH is greater than the preset distance, then the weight η corresponding to FH is S. FH +YH×λ; λ is the preset adjustment coefficient; otherwise, η=S FH .

[0122] If YH > preset distance (indicating that the connection between the two sub-regions at the end of the path is not smooth and there is excessive invalid movement): weight η = total path distance S FH +End connection distance YH × preset adjustment coefficient λ (λ is a penalty coefficient, for example, λ=2, amplifying the negative impact of poor connection, making this type of path disadvantaged in the screening); If YH≤ preset distance (indicating smooth end connection, meeting the requirements): Weight η = total path distance S FH (The total distance is used as the evaluation criterion only; the shorter the total distance, the higher the weight).

[0123] The weight is calculated by combining "total distance + end-connection penalty" to select paths with "short total distance + smooth end-connection", thereby avoiding invalid movements caused by excessively long end-sub-region connections and improving construction efficiency.

[0124] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0125] Embodiments of the present invention also provide a non-transitory computer-readable storage medium that can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a method in the method embodiments, wherein the at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiments.

[0126] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0127] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0128] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0129] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0130] Embodiments of the present invention also provide an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0131] The electronic device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments in this application.

[0132] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and a bus connecting different system components (including memory and processor).

[0133] The memory stores program code that can be executed by the processor, causing the processor to perform the steps in the various embodiments described in this specification.

[0134] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0135] The memory may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0136] A bus can represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus structures.

[0137] Electronic devices can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable user interaction with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be achieved through input / output (I / O) interfaces. Furthermore, electronic devices can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapters. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0138] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0139] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above in various exemplary embodiments of the present invention.

[0140] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention.

Claims

1. A method of dividing a concrete finishing robot construction area, characterized by, The method comprises the following steps: S100, setting a rectangular frame for each obstacle in the concrete finishing robot construction area; the concrete finishing robot construction area is a rectangular area, each side of the rectangular frame of the obstacle is parallel to each side of the concrete finishing robot construction area; the rectangular frame of the obstacle completely covers the corresponding obstacle; S200, according to each rectangular frame of the obstacle and the outer boundary of the concrete finishing robot construction area, dividing the area outside the rectangular frame of the obstacle in the concrete finishing robot construction area into construction sub-areas, to obtain a plurality of construction sub-areas corresponding to the concrete finishing robot construction area; each construction sub-area is rectangular, and each side of each construction sub-area is parallel to each side of the concrete finishing robot construction area; each construction sub-area does not contain the rectangular frame of the obstacle.

2. The concrete finishing robot construction area partitioning method according to claim 1, characterized by, The method further comprises the following steps: S300, for any obstacle W in the concrete finishing robot construction area, obtaining a first preset value N=1; S310, control the concrete finishing robot to round the outside of W and obtain the coverage area G corresponding to the outer boundary of the construction area this time N ; the distance between the concrete finishing robot and the outside of W is the Nth preset distance D N ; S320, if G N If the obstacle rectangle frame corresponding to W is not completely covered, N=N+1 is obtained; S310 is entered; wherein, D N <D N+1 ; otherwise, the current processing is exited.

3. The concrete finishing robot construction area partitioning method according to claim 2, characterized by, D N+1 -D N = ΔD; wherein, ΔD is the construction width of the concrete finishing robot.

4. The concrete finishing robot construction area partitioning method of claim 1, wherein, Step S200 comprises the following steps: S210, selecting one of the length direction and the width direction of the concrete finishing robot construction area as the preferred division direction; S220, dividing the area outside the rectangular frame of the obstacle in the concrete finishing robot construction area into a plurality of strip-shaped areas along the determined preferred division direction; S230, for each strip-shaped area, dividing the strip-shaped area along a direction perpendicular to the preferred division direction according to the distribution position of the rectangular frame of the obstacle in the strip-shaped area; S240, repeating step 230 until each construction sub-area obtained after division is rectangular and does not contain any rectangular frame of the obstacle; obtaining a plurality of construction sub-areas corresponding to the concrete finishing robot construction area.

5. The concrete finishing robot construction area segmentation method according to claim 4, characterized in that, Step S210 comprises the following steps: S211, obtaining a distribution density p of all obstacle rectangular frames in the length direction in the construction area of the concrete finishing robot L and a distribution density p in the width direction W ; wherein p L is a ratio of a total projection length of the obstacle rectangular frame in the length direction to a length of the construction area of the concrete finishing robot, and p W is a ratio of a total projection width of the obstacle rectangular frame in the width direction to a width of the construction area of the concrete finishing robot S212, if p L < p W then select the length direction as the preferred partition direction; S213, if p W < p L the width direction is selected as the priority partition direction; S214, if p L = p W , select the length direction or the width direction as the preferred partition direction.

6. The concrete finishing robot construction area segmentation method of claim 1, wherein, The length of the construction sub-area is greater than or equal to the minimum effective construction length of the concrete finishing robot, and the width is greater than or equal to the minimum effective construction width of the concrete finishing robot.

7. The concrete finishing robot construction area segmentation method of claim 1, wherein, The method further comprises the following steps: S400, planning a construction path of the concrete finishing robot according to the positional relationship of each construction sub-area. 8.A non-transitory computer readable storage medium having stored therein at least one instruction or at least one piece of program, characterized in that, The at least one instruction or the at least one program is loaded and executed by the processor to implement the concrete finishing robot construction area division method according to any one of claims 1-7.

9. An electronic device, comprising: The non-transitory computer readable storage medium of claim 8 comprises a processor. The non-transitory computer readable storage medium of claim 8 comprises a processor.

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