Concrete placing boom path planning method and device

Abstract

The present application relates to the technical field of construction engineering machinery, and discloses a path planning method and device for a concrete placer. The method includes: obtaining boundary information and obstacle position information of a construction area; dividing the construction area into multiple sub-areas; performing sub-area sorting to obtain an optimal sub-area sorting scheme; respectively determining the path access order corresponding to each sub-area; when there are obstacles in a sub-area, adjusting the path corresponding to the sub-area to obtain the final paths in multiple sub-areas; connecting the final paths in multiple sub-areas in sequence according to the optimal sub-area sorting scheme to determine the construction path of the concrete placer; determining the movement speed of the concrete placer according to the pouring volume of the path area; and determining the concrete placing scheme of the concrete placer. The present application determines the optimal sub-area sorting and the path sorting corresponding to the sub-areas to determine the optimal final construction path of the concrete placer, realizes the co-evolution of path planning, and greatly improves the path planning efficiency and execution flexibility of the concrete placer in a complex environment.

Description

Technical Field

[0001] This application relates to the technical field of construction engineering machinery, and more particularly to a path planning method and device for a concrete placer. Background Art

[0002] In highly complex operation scenarios such as construction and the automatic control of concrete placers, path planning, as a core element for the efficient operation of equipment, faces many severe challenges. The concrete placer often needs to perform concrete placing operations in a construction environment with randomly distributed and complex steel bars (as placing obstacles) and irregular spaces. Therefore, traditional path planning algorithms often have difficulty effectively coping with these complex conditions. The distribution of obstacles is random and complex. The concrete placer must accurately avoid obstacles on the premise of ensuring full coverage of the entire construction area to prevent collisions. Summary of the Invention

[0003] Embodiments of this application provide a path planning method and device for a concrete placer, which determine the optimal sub-region sorting and the corresponding path sorting for each sub-region to determine the optimal final construction path of the concrete placer, realize the co-evolution of path planning, and greatly improve the path planning efficiency and execution flexibility of the concrete placer in a complex environment.

[0004] In a first aspect, embodiments of this application provide a path planning method for a concrete placer, which obtains the boundary information of the target construction area and the position information of target obstacles according to the construction map of the area to be concreted; divides the target construction area into multiple sub-regions according to the boundary information and the position information of target obstacles; performs region sorting on the multiple sub-regions to obtain an optimal sub-region sorting scheme; respectively determines the path access order corresponding to each sub-region; takes a sub-region as the target sub-region, and when there are target obstacles in the target sub-region, adjusts the path access order corresponding to the target sub-region according to the position information of the target obstacles. After adjusting the path access order corresponding to each target sub-region, update the path access order corresponding to each sub-region to obtain the final path within the multiple sub-regions; connect the final paths within the multiple sub-regions in sequence according to the optimal sub-region sorting scheme to determine the construction path of the concrete placer; determine the movement speed of the concrete placer according to the construction path of the concrete placer and the amount of concrete pouring within the coverage of the construction path; determine the concrete placing scheme of the concrete placer according to the construction path of the concrete placer and the movement speed of the concrete placer.

[0005] In one embodiment, the performing region sorting on the multiple sub-regions to obtain an optimal sub-region sorting scheme includes:

[0006] Define the objective function for region sorting, where the minimum value of the objective function indicates the least number of path intersections;

[0007] Initialize the number of sub-region sorting schemes and the number of iterations;

[0008] Generate multiple initial sub-region sorting schemes according to the number of the sub-region sorting schemes, and generate an initial random sub-region sorting for each initial sub-region sorting scheme;

[0009] Initialize a priority matrix, where the priority matrix includes the path priorities between any two sub-regions, and the path priority represents the priority of moving from one sub-region to another sub-region;

[0010] Perform heuristic search update on each initial sub-region sorting scheme according to the priority matrix and the number of iterations to obtain multiple updated sub-region sorting schemes;

[0011] After obtaining the multiple updated sub-region sorting schemes, determine the optimal sub-region sorting scheme.

[0012] In one embodiment, the performing heuristic search update on each initial sub-region sorting scheme according to the priority matrix and the number of iterations to obtain multiple updated sub-region sorting schemes includes:

[0013] Calculate the probability of moving from one sub-region to another sub-region in each sub-region sorting scheme according to the priority matrix;

[0014] Update each sub-region sorting scheme according to the probability and the number of iterations to obtain multiple updated sub-region sorting schemes.

[0015] In one embodiment, the respectively determining the path access order corresponding to each sub-region includes:

[0016] Take one sub-region as the target sub-region in the sorting order of the optimal sub-region sorting scheme;

[0017] Determine the path access starting point of the target sub-region;

[0018] Determine the path access ending point of the target sub-region according to the relative position of the next sub-region of the target sub-region;

[0019] Determine the optimal step length of the target sub-region;

[0020] Determine the path access order corresponding to the target sub-region according to the path access starting point, the path access ending point and the optimal step length of the target sub-region;

[0021] After completing the determination of the path access order corresponding to each target sub-region, determine the path access order corresponding to each sub-region.

[0022] In one embodiment, determining the path access starting point of the target sub-region includes:

[0023] If the target sub-region is the first region in the optimal sub-region sorting scheme, then determine the vertex at the upper left corner of the target sub-region as the path access starting point of the target sub-region;

[0024] If the target sub-region is not the first region in the optimal sub-region sorting scheme, then determine the point in the target sub-region that is closest to the path access end point of the previous sub-region of the target sub-region as the path access starting point of the target sub-region, where the previous sub-region of the target sub-region is the sub-region in the previous order of the target sub-region in the optimal sub-region sorting scheme.

[0025] In one embodiment, determining the path access end point of the target sub-region according to the relative position of the next sub-region of the target sub-region includes:

[0026] Obtain the center point coordinates of the next sub-region of the target sub-region, where the next sub-region of the target sub-region is the sub-region in the next order of the target sub-region in the optimal sub-region sorting scheme;

[0027] Obtain the coordinates of the optional end points, where the optional end points are the three vertices of the target sub-region excluding the path access starting point;

[0028] Calculate the distances between the coordinates of the optional end points and the center point coordinates of the next sub-region respectively according to a preset function to obtain an end point estimation function;

[0029] Minimize the end point estimation function to solve the coordinates of the optimal end point, and determine the optimal end point as the path access end point of the target sub-region.

[0030] In one embodiment, determining the optimal step size of the target sub-region includes:

[0031] Initialize the value range of the step size of the target sub-region, take values for the step size within the range, and construct a value group of the step size;

[0032] Randomly select an initial step size from the value group;

[0033] Construct a fitness function according to the coverage rate of the path constructed by the step size and the distance between the path access end point of the target sub-region and the starting point of the next sub-region of the target sub-region;

[0034] Iteratively optimize the value of the initial step size until the value of the fitness function corresponding to the step size reaches a preset condition, and determine the optimal step size of the target sub-region.

[0035] In one embodiment, the position information of the target obstacle includes the center coordinates and the radius. When there is a target obstacle in the target sub-region, adjusting the path access order corresponding to the target sub-region according to the position information of the target obstacle includes:

[0036] Obtain the center coordinates and the radius of the target obstacle;

[0037] When there is an intersection point between the target path formed by the path access order corresponding to the target sub-region and the target obstacle, generate a target detour path, where the intersection point is the point on the target path that is closest to the target obstacle, and the distance between the intersection point and the center coordinates of the target obstacle is less than the radius;

[0038] Adjust the path access order corresponding to the target sub-region according to the target detour path.

[0039] In one embodiment, determining the movement speed of the concrete placing machine according to the construction path of the concrete placing machine and the amount of concrete poured within the coverage of the construction path includes:

[0040] Calculate the initial movement speed of the concrete placing machine, where the initial movement speed of the concrete placing machine is the value obtained by dividing the amount of concrete poured within the coverage of the construction path by the total length of the construction path;

[0041] Adjust the movement speed of the concrete placing machine at the turning point according to the curvature of the turning point of the construction path;

[0042] If the construction path passes through the target obstacle, adjust the movement speed of the concrete placing machine when encountering the target obstacle according to a preset speed adjustment formula;

[0043] Determine the movement speed of the concrete placing machine according to the initial movement speed, the movement speed of the concrete placing machine at the turning point, and the movement speed of the concrete placing machine when encountering the target obstacle.

[0044] In a second aspect, an embodiment of the present application provides a concrete placing machine path planning device, which has the function of implementing the concrete placing machine path planning method provided in the above first aspect. The function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, and the modules can be software and / or hardware. In one embodiment, the concrete placing machine path planning device includes:

[0045] An acquisition module: configured to obtain the boundary information of the target construction area and the position information of the target obstacle according to the construction map of the area to be concreted;

[0046] Partition module: used to divide the target construction area into multiple sub - areas according to the boundary information and the target obstacle position information;

[0047] Area sorting module: used to perform sub - area sorting on the multiple sub - areas to obtain an optimal sub - area sorting scheme;

[0048] Path sorting module: used to respectively determine the path access order corresponding to each sub - area;

[0049] Path adjustment module: taking one sub - area as the target sub - area, when there is a target obstacle in the target sub - area, adjusting the path access order corresponding to the target sub - area according to the target obstacle position information, and after adjusting the path access order corresponding to each target sub - area, updating the path access order corresponding to each sub - area to obtain the final paths within the multiple sub - areas;

[0050] Connection module: used to connect the final paths within the multiple sub - areas in sequence according to the optimal sub - area sorting scheme to determine the construction path of the concrete placing boom;

[0051] Determination rate module: determining the movement rate of the concrete placing boom according to the construction path of the concrete placing boom and the amount of concrete pouring within the coverage of the construction path;

[0052] Generation scheme module: generating the placing scheme of the concrete placing boom according to the construction path of the concrete placing boom and the movement rate of the concrete placing boom.

[0053] In one embodiment, the area sorting module is specifically used for:

[0054] Defining an objective function for sub - area sorting, where the minimum value of the objective function indicates the least number of path intersections;

[0055] Initializing the number of sub - area sorting schemes and the number of iterations;

[0056] Generating multiple initial sub - area sorting schemes according to the number of sub - area sorting schemes, and generating an initial random sub - area sorting for each initial sub - area sorting scheme;

[0057] Initializing a priority matrix, where the priority matrix includes the path priorities between any two sub - areas, and the path priority represents the priority of moving from one sub - area to another sub - area;

[0058] Performing heuristic search update on each initial sub - area sorting scheme according to the priority matrix and the number of iterations to obtain multiple updated sub - area sorting schemes;

[0059] After obtaining the multiple updated sub - area sorting schemes, determining the optimal sub - area sorting scheme.

[0060] In one embodiment, the area sorting module is further configured to:

[0061] Calculate the probability of moving from one sub-area to another in each sub-area sorting scheme according to the priority matrix;

[0062] Update each sub-area sorting scheme according to the probability and the number of iterations to obtain multiple updated sub-area sorting schemes.

[0063] In one embodiment, the path sorting module is specifically configured to:

[0064] Take one sub-area as the target sub-area in the sorting order of the optimal sub-area sorting scheme;

[0065] Determine the path access starting point of the target sub-area;

[0066] Determine the path access end point of the target sub-area according to the relative position of the next sub-area of the target sub-area;

[0067] Determine the optimal step length of the target sub-area;

[0068] Determine the path access order corresponding to the target sub-area according to the path access starting point, path access end point and optimal step length of the target sub-area;

[0069] After determining the path access order corresponding to each target sub-area, determine the path access order corresponding to each sub-area.

[0070] In one embodiment, the path sorting module is further specifically configured to:

[0071] If the target sub-area is the first area in the optimal sub-area sorting scheme, determine the vertex at the upper left corner of the target sub-area as the path access starting point of the target sub-area;

[0072] If the target sub-area is not the first area in the optimal sub-area sorting scheme, determine the point closest to the path access end point of the previous sub-area of the target sub-area in the target sub-area as the path access starting point of the target sub-area, where the previous sub-area of the target sub-area is the sub-area in the previous order of the target sub-area in the optimal sub-area sorting scheme.

[0073] In one embodiment, the path sorting module is further specifically configured to:

[0074] Obtain the central point coordinates of the next sub-region of the target sub-region, where the next sub-region of the target sub-region is the sub-region in the next order in the optimal sub-region sorting scheme;

[0075] Obtain the coordinates of the optional end points, where the optional end points are the three vertices in the target sub-region excluding the path access starting point;

[0076] Calculate the distances between the coordinates of the optional end points and the central point coordinates of the next sub-region respectively according to a preset function to obtain an end point estimation function;

[0077] Minimize the end point estimation function to solve the coordinates of the optimal end point, and determine the optimal end point as the path access end point of the target sub-region.

[0078] In one embodiment, the path sorting module is further specifically configured to:

[0079] Initialize the value range of the step size of the target sub-region, take a value for the step size within the range, and construct a value group of the step size;

[0080] Randomly select an initial step size from the value group;

[0081] Construct a fitness function according to the coverage rate of the path constructed by the step size and the distance between the path access end point of the target sub-region and the starting point of the next sub-region of the target sub-region;

[0082] Iteratively optimize the value of the initial step size until the value of the fitness function corresponding to the step size reaches a preset condition, and determine the optimal step size of the target sub-region.

[0083] In one embodiment, the path adjustment module is specifically configured to:

[0084] Obtain the central coordinate and radius of the target obstacle;

[0085] When there is an intersection point between the target path formed by connecting the path access orders corresponding to the target sub-region and the target obstacle, generate a target detour path, where the intersection point is the point on the target path closest to the target obstacle, and the distance between the intersection point and the central coordinate of the target obstacle is less than the radius;

[0086] Adjust the path access order corresponding to the target sub-region according to the target detour path.

[0087] In one embodiment, the determination rate module is specifically configured to:

[0088] Calculate the initial movement speed of the concrete distributor, where the initial movement speed of the concrete distributor is the value obtained by dividing the amount of concrete poured within the coverage of the construction path by the total length of the construction path;

[0089] Adjust the movement speed of the concrete distributor at the turning point according to the curvature of the turning point of the construction path;

[0090] If the construction path passes through the target obstacle, adjust the movement speed of the concrete distributor when encountering the target obstacle according to a preset speed adjustment formula;

[0091] Determine the movement speed of the concrete distributor based on the initial movement speed, the movement speed of the concrete distributor at the turning point, and the movement speed of the concrete distributor when encountering the target obstacle.

[0092] This application iteratively optimizes to determine the optimal sub-region sorting and the path sorting corresponding to the sub-regions to determine the optimal final construction path of the concrete distributor, realizes the co-evolution of path planning, and greatly improves the path planning efficiency and execution flexibility of the concrete distributor in a complex environment. Brief Description of the Drawings

[0093] To more clearly illustrate the technical solutions in the embodiments of this application, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following drawings are only some embodiments of this application. For those skilled in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0094] Figure 1 is a schematic diagram of the scenario of the concrete distributor path planning device provided in the embodiment of this application;

[0095] Figure 2 is a schematic flowchart of an embodiment of the concrete distributor path planning method provided in the embodiment of this application;

[0096] Figure 3 is a schematic diagram of an embodiment scenario for dividing the initial sub-region in the embodiment of this application;

[0097] Figure 4 For the embodiment of this application to Figure 3 is a schematic diagram of an embodiment scenario for re-merging and dividing the divided initial sub-region;

[0098] Figure 5 is a schematic diagram of an embodiment scenario after completing the sub-region sorting using a preset algorithm in the embodiment of this application;

[0099] Figure 6 is a schematic diagram of an embodiment scenario of different path access order schemes that a target sub-region may have in the embodiment of this application;

[0100] Figure 7 This is a schematic diagram of an embodiment scenario for obtaining a construction path in an embodiment of the present application;

[0101] Figure 8 This is a schematic diagram of an embodiment scenario for generating a detour path in an embodiment of the present application;

[0102] Figure 9 This is a schematic structural diagram of a device for a concrete placing boom path planning method in an embodiment of the present application.

[0103] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. Detailed implementation manners

[0104] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0105] In the following description, specific embodiments of the present application will be described with reference to steps and symbols executed by one or more computers, unless otherwise specified. Therefore, these steps and operations will be mentioned several times as being executed by a computer. As used herein, computer execution includes operations of a computer processing unit that represents electronic signals in a structured form of data. This operation transforms the data or maintains it at a position in the computer's memory system, which can be reconfigured or otherwise changed in a manner well known to those skilled in the art to modify the operation of the computer. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the present application are described in the above text, which does not represent a limitation. Those skilled in the art will understand that the various steps and operations described below can also be implemented in hardware.

[0106] The term "module" or "unit" used herein can be regarded as a software object executed on the computing system. Different components, modules, engines, and services described herein can be regarded as implementation objects on the computing system. The devices and methods described herein are preferably implemented in software, but can also be implemented in hardware, all within the protection scope of the present application.

[0107] Those skilled in the art can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of this application means the presence of the stated features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The phrase "and / or" used herein includes all or any unit and all combinations of one or more of the associated listed items.

[0108] The embodiments of the present application provide a method, device and storage medium for path planning of a concrete placing boom.

[0109] Please refer to Figure 1 , Figure 1 FIG. is a schematic diagram of the scenario of the concrete placing boom path planning device provided by the embodiments of the present application. The concrete placing boom path planning device may include a concrete placing boom path planning system 100 and a user terminal 200. The concrete placing boom path planning system 100 is connected through a network, and a concrete placing boom path planning device is integrated in the concrete placing boom path planning system 100. In the embodiments of the present application, the concrete placing boom path planning system 100 may be a terminal device or a server, and the concrete placing boom path planning system 100 may send the construction path of the concrete placing boom to the user terminal 200.

[0110] In the embodiments of the present application, when the cloth spreading machine path planning system 100 is a server, the server can be an independent server or a server network or server cluster composed of servers. For example, the server described in the embodiments of the present application includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. Among them, the cloud server is composed of a large number of computers or network servers based on cloud computing. In the embodiments of the present application, communication between the server and the client can be achieved through any communication method, including but not limited to mobile communication based on the 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), or computer network communication based on the TCP / IP Protocol suite (TCP / IP) and the User Datagram Protocol (UDP).

[0111] It can be understood that when the cloth spreading machine path planning system 100 used in the embodiments of the present application is a terminal device, the terminal device can be a device that includes both receiving hardware and transmitting hardware, that is, a device with receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such terminal devices can include: cellular or other communication devices, which have a single-line display or a multi-line display or cellular or other communication devices without a multi-line display. Specifically, the cloth spreading machine path planning system 100 can be a desktop terminal or a mobile terminal, and the cloth spreading machine path planning system 100 can specifically be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0112] The terminal device involved in the embodiments of the present application can also be a device that provides voice and / or data connectivity to users, such as a handheld device with wireless connection capabilities, or other processing devices connected to a wireless modem. For example, a mobile phone (or "cellular" phone) and a computer with a mobile terminal. For instance, it can be a portable, pocket-sized, handheld, computer-integrated, or vehicle-mounted mobile device that exchanges voice and / or data with a wireless access network. For example, devices such as Personal Communication service (PCs) phones, cordless phones, Session Initiation Protocol (sIP) phones, Wireless Local Loop (WLL) stations, and Personal Digital Assistant (PDA).

[0113] Those skilled in the art can understand that Figure 1 the application environment shown is only one application scenario of the solution of the present application, and does not constitute a limitation on the application scenario of the solution of the present application. Other application environments may also include more or fewer computing devices than Figure 1 shown, or the network connection relationship of computing devices. For example Figure 1 only 1 computing device is shown in. It can be understood that the path planning device of the cloth spreading machine may also include one or more other computing devices, or / and one or more other computing devices network-connected to the path planning system 100 of the cloth spreading machine, and specific details are not limited here.

[0114] In addition, as Figure 1 shown, the path planning device of the cloth spreading machine may also include a memory 300 for storing data, such as storing obstacle position information, path access order, etc.

[0115] It should be noted that Figure 1 the scenario schematic diagram of the path planning device of the cloth spreading machine shown is only an example. The path planning device and scenario described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art know that with the evolution of the path planning device of the cloth spreading machine and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0116] The present application iteratively optimizes to determine the optimal sub-region sorting and the path sorting corresponding to the sub-regions to determine the optimal final construction path of the cloth spreading machine, realizes the co-evolution of path planning, and greatly improves the path planning efficiency and execution flexibility of the cloth spreading machine in a complex environment. The following is a detailed description in combination with specific embodiments.

[0117] In this embodiment, a description will be given from the perspective of the concrete placing boom path planning method, which can be specifically integrated into the concrete placing boom path planning system 100.

[0118] This application provides a concrete placing boom path planning method, which includes: obtaining the boundary information of the target construction area and the target obstacle position information according to the construction map of the area to be concreted; dividing the target construction area into multiple sub-areas according to the boundary information and the target obstacle position information; performing sub-area sorting on the multiple sub-areas to obtain an optimal sub-area sorting scheme; respectively determining the path access order corresponding to each sub-area; taking a sub-area as the target sub-area, when there is a target obstacle in the target sub-area, adjusting the path access order corresponding to the target sub-area according to the target obstacle position information, and after adjusting the path access order corresponding to each target sub-area, updating the path access order corresponding to each sub-area to obtain the final path within the multiple sub-areas; connecting the final paths within the multiple sub-areas in sequence according to the optimal sub-area sorting scheme to determine the construction path of the concrete placing boom; determining the movement speed of the concrete placing boom according to the construction path of the concrete placing boom and the amount of concrete pouring within the coverage of the construction path; generating the concrete placing scheme of the concrete placing boom according to the construction path of the concrete placing boom and the movement speed of the concrete placing boom.

[0119] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of an embodiment of the concrete placing boom path planning method in the embodiment of this application. The concrete placing boom path planning method includes the following steps 201 to 208:

[0120] 201. Obtain the boundary information of the target construction area and the target obstacle position information according to the construction map of the area to be concreted.

[0121] Specifically, before obtaining the boundary information of the target construction area and the position information of the target obstacles based on the construction map of the area to be cloth-laid, the construction map of the area to be cloth-laid is obtained. Here, the area to be cloth-laid is the construction area of the cloth-laying machine, the construction area waiting to be cloth-laid. The boundary information of the construction area can be determined according to the construction map of the area to be cloth-laid. Specifically, the construction area can be a regular planar area, such as a rectangle, or an irregular figure. After obtaining the boundary information of the construction area, a corresponding planar figure can be drawn according to the area and boundary of the construction area for subsequent sub-region division of the construction area. Optionally, if there are obstacles in the construction area, the position information of the obstacles is obtained according to the construction map. Specifically, the position information of the obstacles can include the central coordinates of the obstacles on the plan view and the radius of the target obstacles. Optionally, when the target obstacle is an irregular figure, the radius of the target obstacle is the maximum radius of the target obstacle, that is, the distance between the point on the boundary of the target obstacle farthest from the central coordinates and the central coordinates.

[0122] 202. Divide the target construction area into multiple sub-regions according to the boundary information and the position information of the target obstacles.

[0123] Specifically, as Figure 3 shown, Figure 3 is a schematic diagram of an embodiment scenario for dividing the initial sub-regions in an embodiment of the present application. According to the boundary information and the position information of the target obstacles, a plan view of the target construction area can be drawn. After marking the positions of the obstacles on the plan view according to the proportion, the target construction area can be divided into multiple sub-regions, where it is ensured that there is no intersection between the boundary of each sub-region and the obstacles, that is, it is ensured that each target obstacle is within the regional boundary of each sub-region. Optionally, the basis for dividing the target sub-region into multiple sub-regions can be to facilitate the cloth-laying of the cloth-laying machine, to avoid generating duplicate paths in the subsequent path planning process, etc. Optionally, dividing the target sub-region into multiple sub-regions can be completed by a preset algorithm, such as a clustering algorithm, etc. Optionally, after obtaining the initial sub-region division result, the initially divided sub-regions can also be merged and divided. As Figure 4 shown, Figure 4 is for Figure 3Schematic diagram of an embodiment scenario for re - combining and dividing the initially divided sub - regions. After initially dividing the target construction area into multiple sub - regions, an optimization objective function can be initialized to iteratively optimize the partition, merge sub - regions, or re - divide sub - regions. Optionally, the basis for merging or re - dividing the initially divided sub - regions can be that the sub - regions are adjacent and connected. Optionally, the initially divided sub - regions can be re - divided and merged multiple times to obtain multiple merged solutions, and the optimization objective function can be used to evaluate the quality of the re - divided sub - regions until the optimal region - division scheme is selected, obtaining the final division plan and getting multiple sub - regions.

[0124] 203. Sort the multiple sub - regions to obtain an optimal sub - region sorting scheme.

[0125] Specifically, after completing the area division, multiple sub - regions are obtained 、 、 …… , and these sub - regions need to be sorted to ensure that all sub - regions can be connected in series in order to form a path with as few crossings as possible. To optimize the region sorting, the Ant Colony Optimization (ACO) algorithm can be used for optimization. The ant colony algorithm performs global search and optimization by simulating the pheromone paths left by ants when searching for food, and is particularly suitable for dealing with path - sorting problems. As Figure 5 shown, Figure 5 is a schematic diagram of an embodiment scenario after completing the sub - region sorting using a preset algorithm in an embodiment of this application. Optionally, the process of sorting the sub - regions using a preset algorithm includes: defining an objective function for sub - region sorting, where the minimum value of the objective function indicates the least number of path crossings; initializing the number of sub - region sorting schemes and the number of iterations; generating multiple initial sub - region sorting schemes according to the number of sub - region sorting schemes, and generating an initial random sub - region sorting for each initial sub - region sorting scheme; initializing a priority matrix, where the priority matrix includes the path priorities between any two sub - regions, and the path priority represents the priority of moving from one sub - region to another; performing heuristic search and update on each initial sub - region sorting scheme according to the priority matrix and the number of iterations to obtain multiple updated sub - region sorting schemes; and determining the optimal sub - region sorting scheme after obtaining the multiple updated sub - region sorting schemes.

[0126] 204. Determine the path access order corresponding to each sub - region respectively.

[0127] Specifically, after obtaining the sorting between each sub-region, the connection order between each sub-region can be determined. However, since there is still a fabric path between each sub-region, after determining the optimal sorting scheme between sub-regions, it is also necessary to determine the path planning within each sub-region respectively according to the order of the optimal sub-region sorting scheme. As Figure 6 shown, Figure 6 FIG. is a schematic diagram of an embodiment scenario of different path access order schemes that a target sub-region may have in an embodiment of the present application. That is, it is necessary to determine the optimal path access order corresponding to each sub-region, so as to determine the optimal path within each sub-region. After determining the path access order corresponding to each sub-region, the fabric path within each sub-region can be confirmed by connecting them in sequence according to the path scheme order. Optionally, the specific process of determining the path access order corresponding to each sub-region may include: taking a sub-region as the target sub-region according to the sorting order in the optimal sub-region sorting scheme; determining the path access starting point of the target sub-region; determining the path access ending point of the target sub-region according to the relative position of the next sub-region of the target sub-region; determining the optimal step length of the target sub-region; determining the path access order corresponding to the target sub-region according to the path access starting point, path access ending point and optimal step length of the target sub-region; after completing the determination of the path access order corresponding to each target sub-region, determining the path access order corresponding to each sub-region. Optionally, since the paths within each sub-region need to be connected according to the optimal sub-region sorting scheme to ensure that the entire path planning can obtain a complete and connectable path, there is a connection between the paths within two adjacent sub-regions in the optimal sub-region sorting scheme. Therefore, after obtaining the optimal sub-region sorting scheme, the path access order within each sub-region is determined respectively according to the sorting order.

[0128] 205. When a sub-region is the target sub-region and there is a target obstacle in the target sub-region, adjust the path access order corresponding to the target sub-region according to the target obstacle position information. After adjusting the path access order corresponding to each target sub-region, update the path access order corresponding to each sub-region to obtain the final path within multiple sub-regions.

[0129] Specifically, after determining the optimal path within each sub-region, it is also necessary to further adjust the optimal path. For example, when there is a target obstacle in the sub-region, ensure that the target obstacle in the sub-region can be avoided. This process involves refining and correcting the part of the path that passes through the obstacle to ensure that the path can bypass the obstacle while maintaining the overall smoothness and effectiveness of the path. Assume that there is an obstacle set within the sub-region , where each obstacle is represented by its center coordinates and radius as During the path adjustment process, all grid points in the path must be ensured to meet the following conditions to ensure that these points do not fall within the range of any obstacles:

[0130]

[0131] wherein, is the Euclidean distance between the grid point and the center of the obstacle . All points in the path should maintain a distance greater than its radius from the obstacle, thus avoiding the path passing through the obstacle.

[0132] 206. Connect the final paths in multiple sub-regions in sequence according to the optimal sub-region sorting scheme to determine the construction path of the cloth placing machine.

[0133] Specifically, after determining the optimal sub-region sorting scheme and the final path corresponding to each sub-region, the construction path of the cloth placing machine can be obtained by connecting the final paths of each sub-region according to the sub-region sorting scheme. As Figure 7 shown, Figure 7 is a schematic diagram of an embodiment scenario for obtaining the construction path in an embodiment of the present application. Specifically, for example: after determining the optimal sorting order of the sub-regions, it is necessary to effectively connect the paths of all sub-regions to form a complete construction path . Each sub-region has already planned its internal path, that is, the final path . Now it is necessary to connect the paths of these sub-regions in series to ensure a smooth transition during the coverage of the entire construction area by the cloth placing machine. For two adjacent sub-regions and , the cloth placing machine needs to move from the end point of the th sub-region to the starting point of the next sub-region. The coordinates of the end point and the starting point are and respectively. The construction process of the complete path can be summarized as: the cloth placing machine starts from the starting point of the first sub-region, covers the first sub-region along the path , and then enters the next sub-region along the connecting path , repeat this process until all sub - regions are covered and the paths are smoothly connected. Based on the determined order of sub - regions, the paths of each sub - region are reasonably connected to form a complete construction path. The connected path needs to ensure avoiding obstacles and maintaining a smooth transition, so as to achieve efficient and obstacle - free coverage of the concrete placer in the entire construction area.

[0134] 207. Determine the movement speed of the concrete placer according to the construction path of the concrete placer and the amount of concrete pouring within the coverage of the construction path.

[0135] Specifically, during the path planning process in the area, the speed of the concrete placer is not only physically limited but also directly related to the task requirements (such as the amount of concrete pouring) within the sub - region. To ensure the efficiency of path planning and execution, the speed of the concrete placer at each path point needs to meet a series of constraint conditions and be dynamically adjusted according to the task requirements.

[0136] Based on the cloth - laying task requirements, the average movement speed of the concrete placer can be calculated. Specifically, assume that the amount of concrete to be poured for each sub - region is , and the speed of the concrete placer should be adjusted according to the concrete demand. We can calculate the average speed of the concrete placer in this area by dividing the amount of concrete to be poured by the total length of the path , and the formula is as follows:

[0137]

[0138] Where: is the amount of concrete to be poured that the concrete placer needs to lay in the sub - region . is the total length of the path of the concrete placer in this sub - region.

[0139] This formula ensures that the speed is proportional to the task volume, enabling the speed to be adjusted accordingly with the change in the path length. If the amount of concrete to be poured is large, the speed of the concrete placer should be slow to ensure the precise execution of the task; while when the amount of concrete to be poured is small, the concrete placer can increase its speed to improve the construction efficiency.

[0140] Meanwhile, the speed of the concrete placer also needs to meet physical constraints. Assume that the maximum allowable speed of the concrete placer is , and its instantaneous speed needs to be kept below this upper limit at any time, and when the path has a large curvature or is close to an obstacle, the speed needs to be appropriately reduced. The specific speed constraint conditions are as follows:

[0141]

[0142] Optionally, when there is a target obstacle in the sub-region or when the cloth distributor encounters a path turn during the cloth distribution movement, the speed of the cloth distributor can be further adjusted to ensure that the cloth distributor successfully completes the cloth distribution.

[0143] 208. Generate a cloth distribution plan for the cloth distributor according to the construction path of the cloth distributor and the movement speed of the cloth distributor.

[0144] Specifically, after determining the globally optimal construction path of the cloth distributor and determining the movement speed of the cloth distributor in each sub-region, a corresponding cloth distribution plan for the cloth distributor can be generated. Optionally, if the cloth distributor is controlled by a cloth distributor control module during construction, the cloth distributor control module can obtain control instructions for the cloth distributor according to the parsed cloth distribution plan, so as to control the cloth distributor to perform cloth distribution construction according to the optimal construction path and the corresponding movement speed.

[0145] The embodiments of the present application iteratively optimize to determine the optimal sub-region sorting and the path sorting corresponding to the sub-regions to determine the optimal final construction path of the cloth distributor, realize the co-evolution of path planning, and greatly improve the path planning efficiency and execution flexibility of the cloth distributor in a complex environment.

[0146] In an implementation manner of the present application, performing sub-region sorting on multiple sub-regions to obtain an optimal sub-region sorting scheme includes:

[0147] Define an objective function for sub-region sorting, where the minimum value of the objective function indicates the least number of path crossings; initialize the number of sub-region sorting schemes and the number of iterations; generate multiple initial sub-region sorting schemes according to the number of sub-region sorting schemes, and generate an initial random sub-region sorting for each initial sub-region sorting scheme; initialize a priority matrix, where the priority matrix includes the path priority between any two sub-regions, and the path priority represents the priority of moving from one sub-region to another sub-region; perform heuristic search and update on each initial sub-region sorting scheme according to the priority matrix and the number of iterations to obtain multiple updated sub-region sorting schemes; after obtaining the multiple updated sub-region sorting schemes, determine the optimal sub-region sorting scheme.

[0148] Specifically, define an objective function for sub-region sorting, and obtain the optimal sub-region sorting scheme when the value of the objective function is the smallest during the search and update process. Optionally, the objective function can be used to represent the number of path crossings, for example: define the objective function , to reduce the number of path crossings. Assume that the connection line between regions and is , the objective function can be expressed as:

[0149]

[0150] Among them, represents the number of crossings between and . Path crossing increases the complexity of the path. Therefore, the goal is to minimize , that is, to reduce the number of crossings. Ideally, there should be no crossings.

[0151] Specifically, initialize the number m of sub-region sorting schemes and the number N of iterations. Correspondingly, when using the ant colony algorithm to complete the sub-region sorting, correspondingly, the number m of sub-region sorting schemes corresponds to the number of ants in the algorithm. According to the number of sub-region sorting schemes, generate multiple initial sub-region sorting schemes, and generate an initial random sub-region sorting for each initial sub-region sorting scheme, which corresponds to generating an initial random path sorting for each ant in the algorithm , and at the same time initialize the priority matrix between regions. Correspondingly, the priority corresponds to the pheromone concentration in the algorithm. The priority matrix corresponds to the pheromone concentration matrix in the algorithm, where represents the path priority from region to region . Optionally, in order to accelerate the search, heuristic information can also be defined, which represents the search heuristic information from region to . The heuristic information is usually related to the distance and connection between two regions. The calculation formula of the heuristic information can be defined as:

[0152]

[0153] where is the Euclidean distance between two regions.

[0154] Perform heuristic search and update on each initial sub-region sorting scheme according to the priority matrix and heuristic information to obtain multiple updated sub-region sorting schemes; after N rounds of iteration, after obtaining multiple updated sub-region sorting schemes, determine the optimal sub-region sorting scheme, where the objective function value of the optimal sub-region sorting scheme is the smallest. Optionally, at the end of each round of iteration, the current optimal sub-region sorting scheme can be recorded, and its corresponding objective function value can be recorded. During the iteration process, continuously update the optimal sub-region sorting scheme and update the objective function value corresponding to the optimal sorting scheme.

[0155] Optionally, to further improve the quality of the optimal sub-region sorting scheme, a local search mechanism can be introduced based on each path found by the ants. Large Neighborhood Search (LNS) can be used for local search, and the specific operations include:

[0156] Region exchange: Randomly select two regions from the path and , and exchange their positions in the sorting to generate a new solution .

[0157] Interval reverse: Randomly select a sub-interval from the current sorting , and reverse the order of the regions within this interval.

[0158] Local optimization: Fine-tune the local path by adjusting the positions of several regions in the path to reduce the number of crossings.

[0159] Through local search, the algorithm can further improve the solution found by the ants and avoid falling into local optima.

[0160] Pheromone update and selection mechanism

[0161] In each iteration, the new sorting scheme is accepted or rejected according to the following mechanism:

[0162]

[0163] where is the current temperature, is the acceptance probability when the new solution is not better than the old solution. The temperature decays exponentially after each iteration:

[0164]

[0165] where is the initial temperature, is the temperature decay coefficient, is the number of iterations. Through the pheromone update and selection mechanism, the algorithm gradually converges to the optimal sorting.

[0166] After iterations, the ant colony algorithm finally outputs the optimal sub-region sorting scheme , which minimizes the path crossings while ensuring the order optimization of each sub-region.

[0167] In the embodiments of this application, by introducing an algorithm and through iterative heuristic updates, the optimal sub-region sorting scheme is determined, which facilitates the subsequent path planning of the cloth spreader.

[0168] In an implementation of the present application, heuristic search update is performed on each initial sub-region sorting scheme according to the priority matrix and the number of iterations, and multiple updated sub-region sorting schemes are obtained, including:

[0169] Calculate the probability of moving from one sub-region to another in each sub-region sorting scheme according to the priority matrix; update each sub-region sorting scheme according to the probability and the number of iterations to obtain multiple updated sub-region sorting schemes.

[0170] Specifically, the probability of each ant moving region can also be calculated according to the priority matrix and heuristic information, and heuristic search update is performed on each initial sub-region sorting scheme according to the number of iterations to obtain multiple updated sub-region sorting schemes. The specific process may include: for each sub-region sorting scheme, that is, for each ant, construct a path according to the priority matrix and heuristic information. When the ant chooses to move from the current region to the next region the probability

[0171]

[0172] is determined by the following formula: and are the weight parameters of the path priority and heuristic information, is the set of regions not yet visited by the ant. Further, during the path construction process, each ant selects a path from one region to another according to the above probability, and gradually constructs the sorting scheme of the entire region . Each ant will traverse all regions, and finally form a complete region sorting path, that is, generate an updated sub-region sorting scheme. According to the number of iterations, each round of iteration is completed. During each iteration, the sub-region sorting scheme of each ant is continuously updated. At the same time, after each round of iteration, the ant will update the path priority according to the quality of the path. Optionally, part of the pheromone can also be evaporated to avoid over-strengthening of the early path. The formula is as follows:

[0173]

[0174] where is the evaporation coefficient of the path priority, . Then, based on the path found by each ant

[0175]

[0176] where is a constant is the objective function value of the path found by the ant, that is, the number of path crossings. The priority of the path with fewer path crossings increases more, thus guiding more ants to choose high-quality paths. After each round of iteration is completed, at the end of a new round of iteration, each ant will perform a new round of iteration with the updated priority matrix, update its sub-region sorting scheme, and complete the heuristic search update. Optionally, after multiple rounds of iteration, the ants gradually construct an increasingly optimal sorting scheme. After each round of iteration ends, record the current best sorting and its corresponding objective function value . Iteration After several rounds, determine that the final sub-region sorting scheme is the optimal sub-region sorting scheme.

[0177] The embodiment of the present application completes the iteration and updates the priority matrix to ensure that the optimal sub-region sorting scheme is globally optimal.

[0178] In an implementation manner of the present application, the path access order corresponding to each sub-region is determined respectively, including: taking a sub-region as the target sub-region according to the sorting order in the optimal sub-region sorting scheme; determining the path access starting point of the target sub-region; determining the path access ending point of the target sub-region according to the relative position of the next sub-region of the target sub-region; determining the optimal step length of the target sub-region; determining the path access order corresponding to the target sub-region according to the path access starting point, path access ending point and optimal step length of the target sub-region; after determining the path access order corresponding to each target sub-region, determine the path access order corresponding to each sub-region.

[0179] Specifically, within each sub-region the path planning is based on a grid map, and the construction area is divided into a series of regular grids. Each grid cell is marked as passable or impassable , and the concrete placing boom needs to be at Plan paths in the grid to ensure covering the entire area and avoiding obstacles. According to the order in the optimal sub-region sorting scheme, solve the path access order corresponding to each sub-region respectively, so as to obtain the paths inside each sub-region. Optionally, take the first sub-region in the optimal sub-region sorting scheme as the target sub-region, determine the starting point of the path access of the target sub-region as the vertex in the upper left corner, determine the ending point of the path access of the first sub-region according to the position of the second sub-region, determine the optimal step length of the target sub-region, and thus, starting from the starting point of the path access, with the ending point of the path access as the midpoint and the optimal step length as the step length, make a round-trip movement within the target sub-region until the path covers the entire sub-region and connects each grid within the sub-region, so as to obtain the path access order of the target sub-region. Correspondingly, then take the second sub-region in the optimal sub-region sorting scheme as the target sub-region, and repeat the procedure for determining the path access order until the path access order of each sub-region is determined.

[0180] In the embodiment of the present application, through the path planning inside each sub-region, the optimality of the path inside each sub-region is determined, which is convenient for subsequent generation of construction paths.

[0181] In an implementation manner of the present application, determining the starting point of the path access of the target sub-region includes:

[0182] If the target sub-region is the first region in the optimal sub-region sorting scheme, then determine the vertex in the upper left corner of the target sub-region as the starting point of the path access of the target sub-region; if the target sub-region is not the first region in the optimal sub-region sorting scheme, then determine the point in the target sub-region that is closest to the ending point of the path access of the previous sub-region of the target sub-region as the starting point of the path access of the target sub-region, where the previous sub-region of the target sub-region is the sub-region in the previous order of the target sub-region in the optimal sub-region sorting scheme.

[0183] Specifically, first select a starting point for each sub-region. If the current sub-region is not the first sub-region , then the starting point is determined based on the ending point of the previous sub-region , and select the passable grid cell closest to the ending point as the starting point of the current region to ensure the continuity of the movement of the cloth laying machine. If the current sub-region is the first region , by default, select the grid cell in the upper left corner as the starting point, which is convenient for the cloth laying machine to start construction from the sub-region boundary.

[0184] When determining the starting point After that, the cloth placing machine will perform path planning within the current sub-region. For each rectangular sub-region, there are three different path planning modes, and these three modes correspond to three different end-point selections respectively. Assume the current region is rectangular, and its four vertices are , excluding the starting point , there are still three vertices that can be used as end points . The cloth placing machine will select one of these three remaining vertices as the path end point according to the specific construction requirements and the estimation of the relative position of the next region.

[0185] The embodiments of the present application determine the starting points of the paths inside different sub-regions to ensure the successful connection of the paths between each sub-region.

[0186] In an implementation manner of the present application, according to the relative position of the next sub-region of the target sub-region, determining the path access end point of the target sub-region includes:

[0187] Obtain the central point coordinates of the next sub-region of the target sub-region, where the next sub-region of the target sub-region is the next sub-region in the optimal sub-region sorting scheme; obtain the coordinates of the optional end points, where the optional end points are the three vertices in the target sub-region excluding the path access starting point; calculate the distances between the coordinates of the optional end points and the central point coordinates of the next sub-region respectively according to a preset function to obtain an end point estimation function; minimize the end point estimation function to solve the coordinates of the optimal end point, and determine the optimal end point as the path access end point of the target sub-region.

[0188] Specifically, the next sub-region is represented by the boundary . To select the optimal end point of the current region , we introduce the central point of the next region, and its coordinates are:

[0189]

[0190] At this time, the end point selection goal of the current sub-region of the cloth placing machine is to find the end point with the optimal relative position to the next region . To select the most suitable end point, a relative position estimation function is used, which calculates the distance between each potential end point (i.e., ) and the central point of the next region. The specific formula is:

[0191]

[0192] where, and are respectively the potential end points Coordinates. The goal of the cloth laying machine is to minimize this relative position estimation function to find the optimal end point. Specifically, by minimizing the relative position offset function , the optimal end point is obtained :

[0193] ;

[0194] Thus, it is determined that the optimal end point is the path access end point of the target sub-region.

[0195] In the embodiment of the present application, the optimal path access end point of each sub-region is calculated through the relative position estimation function, which facilitates the subsequent determination of the optimal path within each sub-region.

[0196] In one implementation manner of the present application, determining the optimal step length of the target sub-region includes:

[0197] Initializing the value range of the step length of the target sub-region, taking values of the step length within the range to construct a value group of the step length; randomly selecting an initial step length from the value group; constructing a fitness function according to the coverage rate of the path constructed by the step length and the distance between the path access end point of the target sub-region and the starting point of the next sub-region of the target sub-region; iteratively optimizing the value of the initial step length until the value of the fitness function corresponding to the step length reaches a preset condition, and determining the optimal step length of the target sub-region.

[0198] Optionally, within the current target sub-region , the path planning of the cloth laying machine involves the selection of the step length . The step length directly determines the coverage efficiency of the cloth laying machine within the region. To find the optimal step length, a series of candidate paths are first generated according to different step lengths. Assume that the range of the step length is , and the system takes multiple discrete step length values within this range, and each step length generates a corresponding coverage path. These paths use a reciprocating motion mode, starting from the starting point and moving along a row to the boundary, then returning in the reverse direction, and repeating this process until the entire region is completely covered.

[0199] Optionally, to find the optimal step length , a simulated annealing algorithm can be introduced to optimize the step length selection. The simulated annealing algorithm explores the step length space step by step and uses the fitness function for evaluation to find the step length value that maximizes the quality of the path planning. The specific process is as follows:

[0200] 1. Initial step length selection: Randomly select an initial step length from the step length set , and calculate its fitness function to evaluate the effectiveness of the path.

[0201] 2. Fitness Function Evaluation: The goal of the fitness function is to measure the performance of the path in the following aspects: Maximizing Coverage: Let the total area of the sub-region be , and the area covered by the concrete placing boom be . Then, a part of the fitness function is the coverage rate:

[0202]

[0203] Smoothness of Connection between Regions: The end point of the path planning should maintain an appropriate distance from the estimated starting point of the next region to ensure a smooth transition. A part of the fitness function is used to evaluate the impact of the step size selection on the transition between regions, and the goal is to minimize the distance from the end point of the path to the starting point of the next region:

[0204]

[0205] where

[0206] 3. Comprehensive Fitness Function: The overall fitness function can be expressed as:

[0207]

[0208] where , are the importance weights of the coverage rate and the connection between regions respectively.

[0209] 4. Step Size Update and Annealing Process: Neighborhood Step Size Selection: In each iteration, starting from the current step size , a neighboring step size is randomly selected, which can be obtained by making a small random perturbation to the current step size. The acceptance criterion of simulated annealing is as follows:

[0210]

[0211] where is the current temperature, and the temperature gradually decreases as the number of iterations increases.

[0212] 5. Temperature Decrease: As the number of iterations increases, the temperature gradually decreases, and the temperature decrease can be achieved in the following way:

[0213]

[0214] where is the initial temperature, is the temperature decay coefficient, is the current iteration number. At high temperatures, the system is more likely to accept a worse solution. As the temperature decreases, the system gradually only accepts better solutions, thus finding the global optimal solution.

[0215] 6. Step size determination: After multiple rounds of annealing iteration, the optimal step size is finally obtained , and the corresponding path has a high coverage rate and can be smoothly connected to the next area.

[0216] The embodiment of the present application introduces a simulated annealing algorithm to iteratively optimize the optimal step size in each sub-region, which is convenient for constructing the optimal path within the sub-region subsequently.

[0217] In an implementation manner of the present application, the position information of the target obstacle includes the center coordinates and the radius. When there is a target obstacle in the target sub-region, adjusting the path access order corresponding to the target sub-region according to the position information of the target obstacle includes:

[0218] Obtain the center coordinates and the radius of the target obstacle; when there is an intersection point between the target path formed by the path access order corresponding to the target sub-region and the target obstacle, generate a target detour path, where the intersection point is the point on the target path that is closest to the target obstacle, and the distance between the intersection point and the center coordinates of the target obstacle is less than the radius; adjust the path access order corresponding to the target sub-region according to the target detour path.

[0219] Specifically, as Figure 8 shown, Figure 8 is a schematic diagram of an embodiment scenario for generating a detour path in the embodiment of the present application. When it is detected that the path in the target sub-region intersects with a certain target obstacle in the target sub-region, the path can be locally adjusted so that the path can bypass the obstacle and continue to move forward. The specific adjustment process can be carried out through the following steps:

[0220] 1. Detection of the intersection point: First, determine the intersection point between the path in the target sub-region and the obstacle . This point is the point on the path that is closest to the center of the obstacle, and its distance is less than the radius of the obstacle .

[0221] 2. Local path reconstruction: Once an intersection point is detected, local path reconstruction can be carried out in the vicinity of the intersection point. The goal of the reconstructed path is to bypass along the edge of the obstacle so that the new path can bypass the obstacle while maintaining the shape of the original path as much as possible. Optionally, the path can be adjusted through the following steps: Determine the path bypass direction, and based on the path direction and the relative position of the obstacle, select the clockwise or counterclockwise direction to bypass the obstacle. Generate a bypass segment. During the bypass process, each new point of the path needs to maintain a distance from the center of the obstacle as , where is a small safety distance to ensure that the path does not cling to the edge of the obstacle. The bypass formula can be expressed as: ;

[0222] 3. Return to the obstacle-free area: After the bypass path is generated, reconnect the bypass path after bypassing the obstacle with the obstacle-free area of the original path, adjust the access order of the new path, and regenerate the optimal path in the target sub-region according to the updated path access order.

[0223] In the embodiments of the present application, by generating a bypass path and re-updating the optimal path in the target sub-region, it is ensured that the concrete placing process of the placing machine will not be affected by obstacles.

[0224] In an implementation manner of the present application, the movement speed of the placing machine is determined according to the construction path of the placing machine and the amount of concrete pouring within the coverage of the construction path, including:

[0225] Calculate the initial movement speed of the placing machine, where the initial movement speed of the placing machine is the value obtained by dividing the amount of concrete pouring within the coverage of the construction path by the total length of the construction path; Adjust the movement speed of the placing machine at the turning point according to the curvature at the turning point of the construction path; If the construction path passes through the target obstacle, adjust the movement speed of the placing machine when encountering the target obstacle according to a preset speed adjustment formula; Determine the movement speed of the placing machine according to the initial movement speed, the movement speed of the placing machine at the turning point, and the movement speed of the placing machine when encountering the target obstacle.

[0226] Specifically, when the placing machine is at a path turning point or encounters an obstacle, the speed needs to be further adjusted. The specific adjustment method depends on the local curvature of the path and the distance from the obstacle . When the path curvature is large (i.e., the turning radius is small), the placing machine needs to decelerate to maintain accurate path following. The speed calculation formula at this time is:

[0227]

[0228] Where:

[0229] is the coefficient for adjusting the speed reduction, is the point where the path curvature is located.

[0230] In addition, when the concrete placing boom approaches an obstacle, its speed needs to decrease as the distance to the obstacle decreases. Assume the distance to the obstacle is , then the speed constraint is further modified to:

[0231]

[0232] where, is the parameter for adjusting the influence of the obstacle.

[0233] Within the sub-region, the speed of the concrete placing boom is dynamically adjusted according to the preset amount of concrete to be placed, the path curvature, and the distance to the obstacle. After obtaining the corresponding adjustment rate, a specific speed adjustment plan can be generated based on the initial movement rate and the adjusted movement rate. The speed adjustment plan is synthesized into the placing plan. When the control module of the concrete placing boom parses the placing plan, it can obtain the specific rate adjustment instruction, so as to control the concrete placing boom during the task execution to ensure that the speed meets the requirements of concrete placing and at the same time ensure safety and accuracy.

[0234] The embodiment of the present application ensures that the concrete placing boom can maintain the task efficiency while ensuring the feasibility of path planning through speed adjustment, and avoids execution errors caused by excessive speed.

[0235] To facilitate better implementation of the concrete placing boom path planning method provided by the embodiment of the present application, the embodiment of the present application also provides a device based on the above-mentioned concrete placing boom path planning method. The meanings of the terms are the same as those in the above-mentioned concrete placing boom path planning method, and the specific implementation details can refer to the description in the embodiment of the concrete placing boom path planning method.

[0236] The device of the concrete placing boom path planning method in the embodiment of the present application has the function of implementing the corresponding concrete placing boom path planning method in the above-mentioned embodiment. The function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, and the modules can be software and / or hardware.

[0237] Please refer to Figure 9 , Figure 9Schematic diagram of the structure of the cloth distributor path planning method and device provided by the embodiments of the present application. The cloth distributor path planning method and device can be applied to a computing device in a scenario where cloth distributor path planning is required. Specifically, the cloth distributor path planning method and device 900 may include an acquisition module 901, a division module 902, a region sorting module 903, a path sorting module 904, a path adjustment module 905, a connection module 906, a speed determination module 907, and a plan generation module 908, as follows:

[0238] Acquisition module: configured to obtain the boundary information of the target construction area and the position information of the target obstacles according to the construction map of the area to be cloth-distributed;

[0239] Division module: configured to divide the target construction area into multiple sub-areas according to the boundary information and the position information of the target obstacles;

[0240] Region sorting module: configured to perform sub-region sorting on the multiple sub-areas to obtain an optimal sub-region sorting scheme;

[0241] Path sorting module: configured to respectively determine the path access order corresponding to each sub-area;

[0242] Path adjustment module: taking a sub-area as the target sub-area, when there are target obstacles in the target sub-area, adjusting the path access order corresponding to the target sub-area according to the position information of the target obstacles, and after adjusting the path access order corresponding to each target sub-area, updating the path access order corresponding to each sub-area to obtain the final path within the multiple sub-areas;

[0243] Connection module: configured to connect the final paths within the multiple sub-areas in sequence according to the optimal sub-region sorting scheme to determine the construction path of the cloth distributor;

[0244] Speed determination module: determining the movement speed of the cloth distributor according to the construction path of the cloth distributor and the amount of concrete pouring within the coverage of the construction path;

[0245] Plan generation module: generating a cloth distribution plan for the cloth distributor according to the construction path of the cloth distributor and the movement speed of the cloth distributor.

[0246] In one embodiment, the region sorting module is specifically configured to:

[0247] Define the objective function for sub-region sorting, where the minimum value of the objective function indicates the least number of path crossings;

[0248] Initialize the number of sub-region sorting schemes and the number of iterations;

[0249] Generate a plurality of initial sub-region sorting schemes according to the number of the sub-region sorting schemes, and generate an initial random sub-region sorting for each initial sub-region sorting scheme;

[0250] Initialize a priority matrix, where the priority matrix includes the path priorities between any two sub-regions, and the path priority represents the priority of moving from one sub-region to another sub-region;

[0251] Perform heuristic search update on each initial sub-region sorting scheme according to the priority matrix and the number of iterations to obtain a plurality of updated sub-region sorting schemes;

[0252] After obtaining the plurality of updated sub-region sorting schemes, determine an optimal sub-region sorting scheme.

[0253] In one embodiment, the region sorting module is further configured to:

[0254] Calculate the probability of moving from one sub-region to another sub-region in each sub-region sorting scheme according to the priority matrix;

[0255] Update each sub-region sorting scheme according to the probability and the number of iterations to obtain a plurality of updated sub-region sorting schemes.

[0256] In one embodiment, the path sorting module is specifically configured to:

[0257] Take one sub-region as a target sub-region in the sorting order of the optimal sub-region sorting scheme;

[0258] Determine the path access starting point of the target sub-region;

[0259] Determine the path access ending point of the target sub-region according to the relative position of the next sub-region of the target sub-region;

[0260] Determine the optimal step length of the target sub-region;

[0261] Determine the path access order corresponding to the target sub-region according to the path access starting point, the path access ending point and the optimal step length of the target sub-region;

[0262] After determining the path access order corresponding to each target sub-region, determine the path access order corresponding to each sub-region.

[0263] In one embodiment, the path sorting module is specifically further configured to:

[0264] If the target sub-region is the first region in the optimal sub-region sorting scheme, determine the vertex at the upper left corner of the target sub-region as the path access starting point of the target sub-region;

[0265] If the target sub-region is not the first region in the optimal sub-region sorting scheme, then determine the path access start point of the target sub-region as the point in the target sub-region that is closest to the path access end point of the previous sub-region of the target sub-region, where the previous sub-region of the target sub-region is the sub-region in the previous order of the target sub-region in the optimal sub-region sorting scheme.

[0266] In one embodiment, the path sorting module is further specifically configured to:

[0267] Obtain the central point coordinates of the next sub-region of the target sub-region, where the next sub-region of the target sub-region is the sub-region in the next order of the target sub-region in the optimal sub-region sorting scheme;

[0268] Obtain the coordinates of the optional end points, where the optional end points are the three vertices in the target sub-region excluding the path access start point;

[0269] Calculate the distances between the coordinates of the optional end points and the central point coordinates of the next sub-region respectively according to a preset function to obtain an end point estimation function;

[0270] Minimize the end point estimation function to solve the coordinates of the optimal end point, and determine the optimal end point as the path access end point of the target sub-region.

[0271] In one embodiment, the path sorting module is further specifically configured to:

[0272] Initialize the value range of the step size of the target sub-region, take values for the step size within the range, and construct a value set of the step size;

[0273] Randomly select an initial step size from the value set;

[0274] Construct a fitness function according to the coverage rate of the path constructed by the step size and the distance between the path access end point of the target sub-region and the start point of the next sub-region of the target sub-region;

[0275] Iteratively optimize the value of the initial step size until the value of the fitness function corresponding to the step size reaches a preset condition, and determine the optimal step size of the target sub-region.

[0276] In one embodiment, the path adjustment module is specifically configured to:

[0277] Obtain the central coordinates and radius of the target obstacle;

[0278] When there is an intersection point between the target path formed by connecting the path access orders corresponding to the target sub-regions and the target obstacle, a target detour path is generated, where the intersection point is the point on the target path that is closest to the target obstacle, and the distance between the intersection point and the center coordinates of the target obstacle is less than the radius;

[0279] Adjust the path access order corresponding to the target sub-region according to the target detour path.

[0280] In one embodiment, the determining rate module is specifically configured to:

[0281] Calculate the initial movement rate of the paver, where the initial movement rate of the paver is the value obtained by dividing the concrete pouring volume within the coverage of the construction path by the total length of the construction path;

[0282] Adjust the movement rate of the paver at the turning point according to the curvature of the turning point of the construction path;

[0283] If the construction path passes through the target obstacle, adjust the movement rate of the paver when encountering the target obstacle according to a preset speed adjustment formula;

[0284] Determine the movement rate of the paver according to the initial movement rate, the movement rate of the paver at the turning point, and the movement rate of the paver when encountering the target obstacle.

[0285] In the embodiments of this application, the ant colony optimization (ACO) algorithm is used to simulate the foraging behavior of ants, and the pheromone mechanism is used to optimize the sorting of the global path. The access order of each sub-region is dynamically generated by the ant colony algorithm. By leveraging the characteristic that ant colonies cooperate to select the optimal path in a complex environment, it gradually converges to the global optimal path, reducing path intersections and redundancies. The ant colony algorithm plays the role of a "selection mechanism" in the evolutionary algorithm in this process. Through the iterative update of the pheromone concentration, the global optimality of the paths between regions is achieved. Next, the simulated annealing (SA) algorithm plays a core role in local path planning. This algorithm dynamically adjusts the path step size and local adjustments by simulating the temperature decay mechanism of the simulated annealing process to avoid getting trapped in local optima. The temperature decay process of simulated annealing is similar to the "mutation and elimination mechanism" in the evolutionary algorithm, gradually reducing the selection of inferior paths and finally approaching the global optimal solution. This part strengthens the evolutionary search and local optimization in path planning, ensuring smooth transition and full coverage of the concrete placing boom in irregular construction areas. In addition, the construction area is discretized through a grid map model. The concrete placing boom dynamically plans the path based on the feasible area, combines real-time obstacle avoidance strategies, and adjusts the path selection in real time according to environmental changes. This dynamic obstacle avoidance and path adjustment process effectively reflects the adaptive characteristics of the evolutionary algorithm, and the concrete placing boom can achieve an evolutionary correction of the global path through the optimization of local paths.

[0286] In summary, through the combination of the global search of the ant colony algorithm and the local optimization of simulated annealing, the co-evolution of path planning is achieved. By leveraging the idea of the evolutionary algorithm, this algorithm greatly improves the path planning efficiency and execution flexibility of the concrete placing boom in complex environments, achieving optimal performance in both global and local path planning.

[0287] The technical solutions provided by the embodiments of this application have been introduced in detail above. Specific examples are used in the embodiments of this application to elaborate on the principles and implementation manners of the embodiments of this application. The description of the above embodiments is only used to help understand the method and its core idea of the embodiments of this application; at the same time, for those of ordinary skill in the art, based on the idea of the embodiments of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the embodiments of this application.

Claims

1. A path planning method for a concrete placing boom, characterized in that, The method for path planning of the concrete placing boom includes: Obtaining the boundary information of the target construction area and the position information of the target obstacles according to the construction map of the area to be concreted; Dividing the target construction area into multiple sub-areas according to the boundary information and the position information of the target obstacles, and the target obstacles are located within the area boundaries of the multiple sub-areas; Performing sub-area sorting on the multiple sub-areas to obtain an optimal sub-area sorting scheme, where the optimal sub-area sorting scheme is a scheme in which all sub-areas are connected in series in order and the number of cross paths is the least; Respectively determining the path access order corresponding to each sub-area; Taking a sub-area as the target sub-area, when there are target obstacles in the target sub-area, adjusting the path access order corresponding to the target sub-area according to the position information of the target obstacles, and after adjusting the path access order corresponding to each target sub-area, updating the path access order corresponding to each sub-area to obtain the final path within the multiple sub-areas; Connecting the final paths within the multiple sub-areas in order according to the optimal sub-area sorting scheme to determine the construction path of the concrete placing boom; Determining the movement speed of the concrete placing boom according to the construction path of the concrete placing boom and the amount of concrete pouring within the coverage of the construction path; Generating a concrete placing scheme for the concrete placing boom according to the construction path of the concrete placing boom and the movement speed of the concrete placing boom.

2. The cloth machine path planning method according to claim 1, characterized in that, The performing sub-area sorting on the multiple sub-areas to obtain an optimal sub-area sorting scheme includes: Defining an objective function for sub-area sorting, where when the value of the objective function is the smallest, it indicates that the number of path crossings is the least; Initializing the number of sub-area sorting schemes and the number of iterations; Generating multiple initial sub-area sorting schemes according to the number of sub-area sorting schemes, and generating an initial random sub-area sorting for each initial sub-area sorting scheme; Initializing a priority matrix, where the priority matrix includes the path priorities between any two sub-areas, and the path priority represents the priority of moving from one sub-area to another sub-area; Performing heuristic search update on each initial sub-area sorting scheme according to the priority matrix and the number of iterations to obtain multiple updated sub-area sorting schemes; After obtaining the multiple updated sub-area sorting schemes, determining the optimal sub-area sorting scheme.

3. The cloth spreading machine path planning method according to claim 2, characterized in that, The performing heuristic search update on each initial sub-area sorting scheme according to the priority matrix and the number of iterations to obtain multiple updated sub-area sorting schemes includes: Calculating the probability of moving from one sub-area to another sub-area in each sub-area sorting scheme according to the priority matrix; Updating each sub-area sorting scheme according to the probability and the number of iterations to obtain multiple updated sub-area sorting schemes.

4. The cloth spreading machine path planning method according to claim 1, characterized in that The respectively determining the path access order corresponding to each sub-area includes: Taking a sub-area as the target sub-area in the sorting order in the optimal sub-area sorting scheme; Determining the path access starting point of the target sub-area; Determining the path access ending point of the target sub-area according to the relative position of the next sub-area of the target sub-area; Determining the optimal step size of the target sub-area; Determine the path access order corresponding to the target sub-region according to the path access start point, path access end point, and optimal step size of the target sub-region; After determining the path access order corresponding to each target sub-region, determine the path access order corresponding to each sub-region.

5. The cloth spreading machine path planning method according to claim 4, characterized in that, The determination of the path access start point of the target sub-region includes: If the target sub-region is the first region in the optimal sub-region sorting scheme, determine the vertex at the upper left corner of the target sub-region as the path access start point of the target sub-region; If the target sub-region is not the first region in the optimal sub-region sorting scheme, determine the point in the target sub-region that is closest to the path access end point of the previous sub-region of the target sub-region as the path access start point of the target sub-region, where the previous sub-region of the target sub-region is the sub-region in the previous order of the target sub-region in the optimal sub-region sorting scheme.

6. The cloth spreading machine path planning method according to claim 4, characterized in that The determination of the path access end point of the target sub-region according to the relative position of the next sub-region of the target sub-region includes: Obtain the central point coordinates of the next sub-region of the target sub-region, where the next sub-region of the target sub-region is the sub-region in the next order of the target sub-region in the optimal sub-region sorting scheme; Obtain the coordinates of the optional end points, where the optional end points are the three vertices of the target sub-region excluding the path access start point; Calculate the distances between the coordinates of the optional end points and the central point coordinates of the next sub-region respectively according to a preset function to obtain an end point estimation function; Minimize the end point estimation function to solve the coordinates of the optimal end point, and determine the optimal end point as the path access end point of the target sub-region.

7. The cloth spreading machine path planning method according to claim 4, wherein, The determination of the optimal step size of the target sub-region includes: Initialize the value range of the step size of the target sub-region, take values for the step size within this range, and construct a value group of the step size; Randomly select an initial step size from the value group; Construct a fitness function according to the coverage rate of the path constructed by the step size and the distance between the path access end point of the target sub-region and the start point of the next sub-region of the target sub-region; Iteratively optimize the value of the initial step size until the value of the fitness function corresponding to the step size reaches a preset condition, and determine the optimal step size of the target sub-region.

8. The cloth spreading machine path planning method according to claim 1, characterized in that, The position information of the target obstacle includes the central coordinates and the radius. When there is a target obstacle in the target sub-region, adjusting the path access order corresponding to the target sub-region according to the target obstacle position information includes: Obtain the central coordinates and the radius of the target obstacle; When there is an intersection point between the target path formed by the path access order corresponding to the target sub-region and the target obstacle, generate a target detour path, where the intersection point is the point on the target path that is closest to the target obstacle, and the distance between the intersection point and the central coordinates of the target obstacle is less than the radius; Adjust the path access order corresponding to the target sub-region according to the target detour path.

9. The cloth spreading machine path planning method according to claim 1, characterized in that, Determining the movement speed of the concrete distributor according to the construction path of the concrete distributor and the amount of concrete poured within the coverage of the construction path includes: Calculating the initial movement speed of the concrete distributor, where the initial movement speed of the concrete distributor is the value obtained by dividing the amount of concrete poured within the coverage of the construction path by the total length of the construction path; Adjusting the movement speed of the concrete distributor at the turning of the construction path according to the curvature at the turning of the construction path; If the construction path passes through the target obstacle, adjusting the movement speed of the concrete distributor when encountering the target obstacle according to a preset speed adjustment formula; Determining the movement speed of the concrete distributor according to the initial movement speed, the movement speed of the concrete distributor at the turning, and the movement speed of the concrete distributor when encountering the target obstacle.

10. A path planning analysis device for a concrete placing boom, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. Wherein, when the processor executes the computer program, it implements the concrete distributor path planning method according to any one of claims 1 to 9.

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