Method and system for optimizing construction paths of multiple building robots

Abstract

The invention discloses a multi-building-robot construction path optimization method and system, and relates to the technical field of path optimization. The method comprises the following steps: receiving uploaded construction basic information; determining the total time of the task according to the arrival time, dividing the total time of the task into a plurality of time slices, and arranging the plurality of time slices according to a time sequence; searching and arranging the first time slice, and determining available robots and available task items; determining optimal pairing combination information; deleting the successfully paired task items in the task set, and deleting the task items of which the item delay cost is greater than or equal to the replacement cost in the task set; and the time slices are searched backwards step by step, the steps are repeated until the task set is empty or the time slices are searched, and the robot construction path is determined based on all the optimal pairing combination information. Dynamic matching of the robot and the task can be achieved, the transportation cost, the delay cost and the replacement cost are minimized, and the construction efficiency and the resource allocation precision are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of path optimization, in particular to a multi-construction robot construction path optimization method and system. BACKGROUND

[0002] Traditional path planning relies on manual scheduling, which cannot cope with complex scenarios of multiple robots and multiple tasks, is inefficient and prone to errors; and existing algorithms do not fully integrate time constraints (such as arrival time, maximum delay time) and cost factors (unit delay cost, replacement cost) of tasks, resulting in unreasonable path planning. Although some existing algorithms consider constraints, they have a large amount of calculation when dealing with multiple task pairings, making real-time optimization difficult and unsuitable for dynamic construction environments.

[0003] Existing technologies cannot accurately balance transportation costs, delay costs and replacement costs while meeting task time requirements, especially lacking efficient real-time adjustment mechanisms when tasks are dynamically updated. SUMMARY

[0004] To overcome the above problems or at least partially solve the above problems, embodiments of the present application provide a multi-construction robot construction path optimization method and system, which can realize dynamic matching of robots and tasks, minimize transportation costs, delay costs and replacement costs, and improve construction efficiency and resource allocation accuracy.

[0005] To solve the above technical problems, the technical solution adopted by the present application is as follows: In a first aspect, the present application provides a multi-construction robot construction path optimization method, comprising the following steps: receiving uploaded construction basic information; the construction basic information includes a robot set, a construction area set and a task set; each task item in the task set corresponds to an arrival time, a construction duration, a unit delay cost and a replacement cost; determining the total task time according to the arrival time, dividing the total task time into multiple time slices, and arranging the multiple time slices in chronological order; searching for the first arranged time slice, determining the available robots and the available task items; determining the pairing combination information of all available robots and all available task items, calculating the total transportation cost, the total delay cost and the total replacement cost of each pairing combination information, and determining the best pairing combination information; deleting the task items with successful pairing in the task set, and deleting the task items with project delay cost greater than replacement cost in the task set; searching the time slices backward step by step, repeating the above steps until the task set is empty or the time slice search is complete, and determining the robot construction path based on all the best pairing combination information.

[0006] The method can solve the problem that path planning in multi-robot construction depends on artificial experience and cannot effectively balance time constraints and cost control. The time constraints (arrival time, delay time) and cost constraints (transportation, delay, replacement cost) are quantitatively integrated to realize accurate scheduling. The complex task sequence is decomposed into phased sub-problems through time slice division to reduce the computational complexity and support real-time optimization. Based on the pairing strategy of permutation combination and cost weighting, the optimal solution is quickly converged in the multi-robot and multi-task scene. The method can realize dynamic matching of robots and tasks, minimize transportation cost, delay cost and replacement cost, and improve construction efficiency and resource allocation accuracy.

[0007] Based on the first aspect, further, the method for determining the total task time according to the arrival time, dividing the total task time into multiple time slices comprises the following steps: determine the task start time of the total task time according to the earliest arrival time of all task items; determine the longest delay duration of each task item based on the unit delay cost and the replacement cost, and determine the latest arrival time according to the longest delay duration; determine the task end time of the total task time according to the latest arrival time of all task items; obtain the total task time according to the task start time and the task end time, and divide the total task time into multiple time slices according to the slice interval.

[0008] Based on the first aspect, further, the latest arrival time = arrival time + longest delay duration.

[0009] Based on the first aspect, further, the method for determining the available robots and the task items that can be performed comprises the following steps: determine the robots that do not perform tasks or can end the previous task within the time slice range as available robots; calculate the arrival time interval of all task items in the task set; determine whether the arrival time interval intersects with the time slice, and when there is an intersection, the corresponding task item is a task item that can be performed.

[0010] Based on the first aspect, further, the arrival time interval starts N minutes before the arrival time and ends at the latest arrival time.

[0011] Based on the first aspect, further, the method for calculating the total transportation cost, total delay cost and total replacement cost of each pairing combination information, and determining the best pairing combination information comprises the following steps: determine the current position of the robot, the earliest departure time and the target area, determine the transportation cost and the predicted arrival time, and obtain the total transportation cost; calculating the delay cost of the task item of the current execution according to the predicted arrival time; determining the task item using the alternative solution, calculating the total cost of the alternative; calculating the delay cost of the task item of the current execution and not using the alternative solution, obtaining the total delay cost; determining the best pairing combination information according to the total transportation cost, the total delay cost and the total cost of the alternative.

[0012] Based on the first aspect, further, the method of calculating the delay cost of the task item of the current execution and not using the alternative solution includes the following steps: calling the arrival time and the unit delay cost of the task item of the current execution and not using the alternative solution; determining the end time of the corresponding time slice according to the arrival time and the unit delay cost, and calculating the delay cost.

[0013] The second aspect of the present application provides a multi-robot construction path optimization system, which includes a construction information uploading module, a time slice division module, a time slice search module, a best pairing combination module, a task set updating module and a construction path determination module, wherein: The construction information uploading module is used to receive the uploaded construction basic information; the construction basic information includes a robot set, a construction area set and a task set; each task item in the task set corresponds to an arrival time, a construction duration, a unit delay cost and an alternative cost; The time slice division module is used to determine the total task time according to the arrival time, and divide the total task time into multiple time slices, which are arranged in time sequence; The time slice search module is used to search the first arranged time slice, and determine the available robots and the available task items; The best pairing combination module is used to determine the pairing combination information of all available robots and all available task items, calculate the total transportation cost, the total delay cost and the total cost of the alternative of each pairing combination information, and determine the best pairing combination information; The task set updating module is used to delete the paired task items in the task set, and delete the task items with the project delay cost greater than the alternative cost in the task set; The construction path determination module is used to search the time slices backward step by step, repeat the above steps until the task set is empty or the time slice search is completed, and determine the robot construction path based on all the best pairing combination information.

[0014] The system can solve the problems of path planning relying on manual experience and being unable to effectively balance time constraints and cost control in multi-robot construction by cooperating with a construction information uploading module, a time slice division module, a time slice search module, an optimal pairing combination module, a task set updating module, and a construction path determination module, etc., quantitatively integrates time constraints (arrival time, delay time) and cost constraints (transportation, delay, replacement cost), and realizes accurate scheduling; the complex task sequence is decomposed into phased sub-problems through time slice division, the calculation complexity is reduced, and real-time optimization is supported; based on the pairing strategy of permutation combination and cost weighting, the optimal solution is quickly converged in the multi-robot and multi-task scene. The method can realize dynamic matching of robots and tasks, minimize transportation cost, delay cost and replacement cost, and improve construction efficiency and resource allocation accuracy.

[0015] Based on the second aspect, further, the time slice division module includes a task start time unit, a latest arrival time unit, a task end time unit, and a task total time division unit, wherein: The task start time unit is configured to determine the task start time of the task total time according to the earliest arrival time of all task items; The latest arrival time unit is configured to determine the longest delay duration of each task item based on the unit delay cost and the replacement cost, and determine the latest arrival time according to the longest delay duration, the latest arrival time = the arrival time + the longest delay duration; The task end time unit is configured to determine the task end time of the task total time according to the latest arrival time of the last task item in all task items; The task total time division unit is configured to obtain the task total time according to the task start time and the task end time, and divide the task total time into multiple time slices according to the slice interval.

[0016] Based on the second aspect, further, the optimal pairing combination module includes a total transportation cost unit, a first delay cost unit, a total replacement cost unit, a second delay cost unit, and an optimal pairing combination unit, wherein: The total transportation cost unit is configured to determine the current position of the robot, the earliest departure time and the target area, determine the transportation cost and the predicted arrival time, and obtain the total transportation cost; The first delay cost unit is configured to calculate the delay cost of the task item executed this time according to the predicted arrival time; The total replacement cost unit is configured to determine the task item using the replacement scheme, and calculate the total replacement cost; The second delay cost unit is configured to calculate the delay cost of the task item not executed this time and not using the replacement scheme, and obtain the total delay cost; An optimal pairing combination unit is configured to determine optimal pairing combination information according to total transportation cost, total delay cost and total replacement cost.

[0017] The embodiments of the present application have at least the following advantages or beneficial effects: 1. Multi-dimensional constraint fusion: Quantitative integration of time constraints (arrival time, delay duration) and cost constraints (transportation, delay, replacement cost), realizing accurate scheduling.

[0018] 2. Dynamic time slice mechanism: Complex task sequences are decomposed into phased sub-problems through time slice division, reducing computational complexity and supporting real-time optimization.

[0019] 3. Replacement scheme decision: Automatically identify tasks with delay cost higher than replacement cost, intelligently select replacement schemes, and further optimize total cost.

[0020] 4. Efficient pairing algorithm: Based on permutation and combination and cost-weighted pairing strategy, it ensures fast convergence to the optimal solution in multi-robot, multi-task scenarios.

[0021] 5. Application value: Suitable for smart construction sites, through data-driven automated path planning, improving robot construction efficiency, reducing manual scheduling cost, especially suitable for large-scale, multi-task complex construction environment. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 A flow chart of a multi-construction robot construction path optimization method according to an embodiment of the present application; Figure 2 A flow chart of determining total task time in a multi-construction robot construction path optimization method according to an embodiment of the present application; Figure 3 A flow chart of determining available robots and available task items in a multi-construction robot construction path optimization method according to an embodiment of the present application; Figure 4 A flow chart of determining optimal pairing combination information in a multi-construction robot construction path optimization method according to an embodiment of the present application; Figure 5 A principle block diagram of a multi-construction robot construction path optimization system according to an embodiment of the present application.

[0024] Icon: 100, construction information uploading module; 200, time slice dividing module; 300, time slice searching module; 400, best matching combination module; 500, task set updating module; 600, construction path determining module. DETAILED DESCRIPTION

[0025] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the following will be a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings of 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. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0027] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, thus, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0028] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, thus, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0029] In the description of the embodiments of the present application, "a plurality of" represents at least 2. EMBODIMENT

[0030] As shown in the first aspect, the embodiments of the present application provide a multi-robot construction path optimization method, comprising the following steps: Figure 1 ​S100, receive uploaded construction basis information; the construction basis information includes a robot set, a construction area set and a task set; each task item in the task set corresponds to an arrival time, a construction duration, a unit delay cost and a replacement cost; S200, determine a total task time according to the arrival time, divide the total task time into a plurality of time slices, and arrange the plurality of time slices in time sequence; take the earliest task arrival time as a starting point, determine a latest arrival time in combination with a longest delay duration (calculated from the replacement cost and the unit delay cost), and divide the equal interval time slices (such as every 3 hours for one slice).

[0031] S300, search the first arranged time slice, and determine the available robots and the available task items; S400, determine the pairing combination information of all the available robots and all the available task items, calculate the total transportation cost, the total delay cost and the total replacement cost of each pairing combination information, and determine the best pairing combination information; S500, delete the successfully paired task items in the task set, and delete the task items with the project delay cost greater than the replacement cost in the task set; S600, search the time slices backward step by step, repeat the above steps until the task set is empty or the time slice search is completed, determine the robot construction path based on all the best pairing combination information, delete the paired tasks and the tasks with the delay cost greater than the replacement cost (use the replacement scheme by default), and repeat the matching by advancing the time slices backward until the tasks are completed or the time slices are ended, and generate the robot construction path.

[0032] The method can solve the problem that the path planning in the construction of multiple building robots depends on artificial experience and cannot effectively balance the time constraint and the cost control, quantitatively integrates the time constraint (arrival time and delay duration) and the cost constraint (transportation, delay and replacement cost), and realizes accurate scheduling; the complex task sequence is decomposed into stage sub-problems through time slice division, the calculation complexity is reduced, and real-time optimization is supported; based on the pairing strategy of permutation combination and cost weighting, the optimal solution is quickly converged in the multi-robot and multi-task scene. The method can realize dynamic matching of robots and tasks, minimize the transportation cost, the delay cost and the replacement cost, and improve the construction efficiency and the resource allocation accuracy.

[0033] The application is suitable for intelligent building sites, and can improve the robot construction efficiency and reduce the artificial scheduling cost through data-driven automatic path planning, and is especially suitable for large-scale, multi-task and complex construction environment.

[0034] Based on the first aspect, further, Figure 2 The method of determining the total task time according to the arrival time and dividing the total task time into a plurality of time slices includes the following steps: S201, determining a task start time of the total task time according to the earliest arrival time of all task items; S202, determining a longest delay duration of each task item based on a unit delay cost and a substitution cost, and determining a latest arrival time according to the longest delay duration; the latest arrival time = the arrival time + the longest delay duration; S203, determining a task end time of the total task time according to the latest arrival time of all task items; S204, obtaining the total task time according to the task start time and the task end time, and dividing the total task time into multiple time slices according to a time slice interval.

[0035] In some embodiments of the present application, the earliest task arrival time is taken as a starting point, the latest arrival time is determined in combination with the longest delay duration of the task (calculated from the substitution cost and the unit delay cost), and the equal-interval time slices (such as every 3 hours for one slice) are divided.

[0036] Based on the first aspect, further as shown in Figure 3 The method for determining the above-mentioned available robots and the task items that can be performed includes the following steps: S301, determining the robots that do not perform tasks or can end the previous task within the time slice range as the available robots; S302, calculating the arrival time interval of all task items in the task set; the arrival time interval starts from N minutes before the arrival time and ends at the latest arrival time; S303, determining whether the arrival time interval intersects with the time slice, and when there is an intersection, the corresponding task item is the task item that can be performed.

[0037] In some embodiments of the present application, the available robots and the task screening: in each time slice, the robots that are idle or complete the previous task, and the tasks that have an intersection between the arrival time interval and the time slice are screened.

[0038] Based on the first aspect, further as shown in Figure 4 The method for calculating the total transportation cost, the total delay cost and the total substitution cost of each pairing combination information and determining the optimal pairing combination information includes the following steps: S401, determining the current position of the robot, the earliest departure time and the target area, determining the transportation cost and the predicted arrival time, and obtaining the total transportation cost; S402, calculating the delay cost of the task item executed this time according to the predicted arrival time; S403, determining the task item using the substitution scheme, and calculating the total substitution cost; S404, calculate the delay cost of the task item not executed and not replaced this time, and obtain the total delay cost; S405, determine the best pairing combination information according to the total transportation cost, the total delay cost and the total replacement cost.

[0039] In some embodiments of the present application, the determination of the best pairing combination specifically includes: pairing combination evaluation: for all pairing combinations of robots and tasks, calculate three costs: total transportation cost: calculated based on robot location, task area and path distance; total delay cost: delay cost of not completing the task on time (including tasks not executed and not replaced); total replacement cost: additional cost of selecting a replacement (such as replacing a robot). Best combination determination: select the pairing combination with the lowest total cost by cost weighting calculation (cost base = α1 x transportation cost + α2 x delay cost + α3 x replacement cost).

[0040] Based on the first aspect, further, the method of calculating the delay cost of the task item not executed and not replaced this time includes the following steps: retrieve the arrival time and unit delay cost of the task item not executed and not replaced this time; determine the termination time of the corresponding time slice according to the arrival time and the unit delay cost, and calculate the delay cost.

[0041] As shown in Figure 5 the second aspect, the present application provides a multi-construction robot construction path optimization system, which includes a construction information uploading module 100, a time slice division module 200, a time slice search module 300, a best pairing combination module 400, a task set updating module 500 and a construction path determination module 600, wherein: The construction information uploading module 100 is used to receive uploaded construction basic information; the construction basic information includes a robot set, a construction area set and a task set; each task item in the task set corresponds to an arrival time, a construction duration, a unit delay cost and a replacement cost; The time slice division module 200 is used to divide the total task time into multiple time slices according to the arrival time, and the multiple time slices are arranged in time sequence; The time slice search module 300 is used to search the first arranged time slice and determine the available robots and the available task items; The best pairing combination module 400 is used to determine the pairing combination information of all available robots and all available task items, calculate the total transportation cost, the total delay cost and the total replacement cost of each pairing combination information, and determine the best pairing combination information; The task set updating module 500 is configured to delete the task items with successful pairing from the task set and delete the task items with the project delay cost greater than the replacement cost from the task set. The construction path determining module 600 is configured to search the time slices backward step by step, repeat the above steps until the task set is empty or the time slice search is completed, and determine the robot construction path based on all the optimal pairing combination information.

[0042] The system can solve the problem that the path planning in the construction of multiple robots depends on manual experience and cannot effectively balance the time constraint and the cost control by cooperation of the construction information uploading module 100, the time slice dividing module 200, the time slice searching module 300, the optimal pairing combination module 400, the task set updating module 500 and the construction path determining module 600, quantitatively integrates the time constraint (arrival time, delay time) and the cost constraint (transportation cost, delay cost, replacement cost) to realize accurate scheduling, divides the complex task sequence into stage sub-problems by time slice division to reduce the calculation complexity and support real-time optimization, and ensures fast convergence to the optimal solution in the multi-robot and multi-task scene based on the pairing strategy of permutation combination and cost weighting. The method can realize dynamic matching of robots and tasks, minimize the transportation cost, the delay cost and the replacement cost, and improve the construction efficiency and the resource allocation accuracy.

[0043] According to the second aspect, the time slice dividing module 200 further includes a task start time unit, a latest arrival time unit, a task end time unit and a task total time dividing unit, wherein: The task start time unit is configured to determine the task start time of the task total time according to the earliest arrival time of all the task items. The latest arrival time unit is configured to determine the longest delay duration of each task item based on the unit delay cost and the replacement cost, and determine the latest arrival time according to the longest delay duration, the latest arrival time = the arrival time + the longest delay duration. The task end time unit is configured to determine the task end time of the task total time according to the latest arrival time of the last task item among all the task items. The task total time dividing unit is configured to obtain the task total time according to the task start time and the task end time, and divide the task total time into multiple time slices according to the slice interval.

[0044] According to the second aspect, the optimal pairing combination module 400 further includes a transportation total cost unit, a first delay cost unit, a replacement total cost unit, a second delay cost unit and an optimal pairing combination unit, wherein: The transportation total cost unit is configured to determine the current position of the robot, the earliest departure time and the target area, determine the transportation cost and the predicted arrival time, and obtain the transportation total cost. a first delay cost unit configured to calculate a delay cost of a task item in the current execution according to the predicted arrival time; a substitute total cost unit configured to determine a task item using a substitute solution and calculate a substitute total cost; a second delay cost unit configured to calculate a delay cost of a task item not executed in the current execution and not using the substitute solution, to obtain a total delay cost; an optimal pairing combination unit configured to determine optimal pairing combination information according to the total transportation cost, the total delay cost and the substitute total cost.

[0045] According to the second aspect, the second delay cost unit further includes: an information retrieval subunit configured to retrieve an arrival time of a task item not executed in the current execution and not using the substitute solution and a unit delay cost; a delay cost calculation subunit configured to calculate a delay cost according to the arrival time and a termination time of a corresponding time slice.

[0046] As shown in Figure 5 the third aspect, the embodiments of the present application provide an electronic device, which includes a memory for storing one or more programs; a processor. When the one or more programs are executed by the processor, the method of any one of the above first aspect is implemented.

[0047] Further includes a communication interface, the memory, the processor and the communication interface are directly or indirectly electrically connected with each other, to realize the transmission or interaction of data. For example, these elements can be electrically connected with each other through one or more communication buses or signal lines. The memory can be used to store software programs and modules, and the processor can execute various functional applications and data processing by executing the software programs and modules stored in the memory. The communication interface can be used for signaling or data communication with other node devices.

[0048] In the embodiments of the present application, the memory can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read only memory (PROM), an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM) and the like.

[0049] A processor can be an integrated circuit chip with signal processing capabilities. This processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0050] In the embodiments provided in this application, it should be understood that the disclosed methods, systems, and approaches can also be implemented in other ways. The method and system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods, systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0051] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0052] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the methods described in any of the first aspects above. If the functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0053] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0054] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for optimizing construction paths of multiple construction robots, characterized in that, Includes the following steps: Receive uploaded basic construction information; the basic construction information includes robot set, construction area set, and task set; each task item in the task set corresponds to arrival time, construction duration, unit delay cost, and substitution cost; The total task time is determined based on the arrival time, and the total task time is divided into multiple time slices, which are arranged in chronological order. Search the top-ranked time slice to determine which robots can be used and which tasks can be performed. Determine the pairing information for all available robots and all available tasks, calculate the total transportation cost, total delay cost, and total substitution cost for each pairing information, and determine the optimal pairing information; Tasks that are successfully matched are removed from the task set; tasks whose delay cost is greater than or equal to the replacement cost are removed from the task set. The search proceeds sequentially through time slices, repeating the above steps until the task set is empty or the time slice search is complete. Based on all the best pairing information, the robot's construction path is determined.

2. The method for optimizing construction paths of multiple construction robots according to claim 1, characterized in that, The method for determining the total task time based on the arrival time and dividing the total task time into multiple time slices includes the following steps: The task start time is determined by the earliest arrival time of all task items; The maximum delay time for each task item is determined based on the unit delay cost and the alternative cost, and the latest arrival time is determined based on the maximum delay time. The task end time is determined by the latest arrival time of the last task in all tasks. The total task time is obtained based on the task start time and task end time, and then divided into multiple time slices according to the slice interval.

3. The method for optimizing construction paths of multiple construction robots according to claim 2, characterized in that, The latest arrival time = arrival time + longest delay time.

4. The method for optimizing construction paths of multiple construction robots according to claim 2, characterized in that, The method for determining which robots can be used and which tasks can be performed includes the following steps: Robots that do not perform tasks or can terminate the previous task within the time slice are identified as usable robots. Calculate the arrival time intervals for all tasks in the task set; Determine whether the arrival time interval intersects with the time slice. If so, the corresponding task item is a workable task item.

5. The method for optimizing construction paths of multiple construction robots according to claim 4, characterized in that, The arrival time interval starts N minutes before the arrival time and ends at the latest arrival time.

6. The method for optimizing construction paths of multiple construction robots according to claim 1, characterized in that, The method for calculating the total transportation cost, total delay cost, and total substitution cost for each pairing combination to determine the optimal pairing combination includes the following steps: Determine the robot's current location, earliest departure time, and target area; determine the transportation cost and predicted arrival time; and obtain the total transportation cost. Calculate the delay cost of the task project to be executed based on the predicted arrival time; Identify the tasks for which alternative solutions are used and calculate the total cost of the alternatives. Calculate the delay cost of the tasks that were not executed and for which no alternative solutions were used, and obtain the total delay cost. The optimal pairing information is determined based on the total transportation cost, total delay cost, and total substitution cost.

7. The method for optimizing construction paths of multiple construction robots according to claim 6, characterized in that, The method for calculating the delay cost of tasks that were not executed and for which no alternative solutions were used includes the following steps: Retrieve the arrival time and unit delay cost of the tasks that were not executed and for which no alternative solutions were used in this instance; The termination time of the corresponding time slice is determined based on the arrival time and the unit delay cost, and the delay cost is calculated.

8. A construction path optimization system for multiple construction robots, characterized in that, It includes a construction information upload module, a time slice division module, a time slice search module, an optimal matching module, a task set update module, and a construction path determination module, among which: The construction information uploading module is used to receive uploaded basic construction information; the basic construction information includes a robot set, a construction area set, and a task set; each task item in the task set corresponds to an arrival time, construction duration, unit delay cost, and substitution cost; The time slice division module is used to determine the total task time based on the arrival time, divide the total task time into multiple time slices, and arrange the multiple time slices in chronological order. The time slice search module is used to search the top-ranked time slices to determine which robots can be used and which tasks can be performed. The optimal pairing module is used to determine the pairing information of all available robots and all available tasks, calculate the total transportation cost, total delay cost and total substitution cost of each pairing information, and determine the optimal pairing information. The task set update module is used to delete successfully matched task items from the task set, and to delete task items whose delay cost is greater than or equal to the replacement cost from the task set. The construction path determination module is used to progressively search for time slices, repeating the above steps until the task set is empty or the time slice search is completed, and then determines the robot's construction path based on all the best pairing information.

9. A multi-construction robot construction path optimization system according to claim 8, characterized in that, The time slice division module includes a task start time unit, a latest arrival time unit, a task end time unit, and a total task time division unit, wherein: The task start time unit is used to determine the start time of the total task time based on the earliest arrival time of all task items. The latest arrival time unit is used to determine the maximum delay time for each task item based on the unit delay cost and the alternative cost. The latest arrival time is determined based on the maximum delay time. Latest arrival time = arrival time + maximum delay time. The task end time unit is used to determine the task end time based on the latest arrival time of the last task item in all task items; The total task time division unit is used to obtain the total task time based on the task start time and task end time, and to divide the total task time into multiple time slices according to the slice interval.

10. A multi-construction robot construction path optimization system according to claim 8, characterized in that, The optimal pairing and combination module includes a total transportation cost unit, a first delay cost unit, a substitute total cost unit, a second delay cost unit, and an optimal pairing and combination unit, wherein: The total transportation cost unit is used to determine the robot's current location, earliest departure time, and target area, to determine the transportation cost and predict the arrival time, and thus obtain the total transportation cost. The first delay cost unit is used to calculate the delay cost of the task item to be executed this time based on the predicted arrival time. The alternative total cost unit is used to identify task items that use alternative solutions and calculate the alternative total cost. The second delay cost unit is used to calculate the delay cost of tasks that were not executed and for which no alternatives were used, to obtain the total delay cost. The optimal pairing unit is used to determine the optimal pairing information based on the total transportation cost, total delay cost, and total substitution cost.

Images