Construction robot work path optimization method

Abstract

The application provides a construction robot operation path optimization method and belongs to the technical field of automatic scheduling and path optimization. The method comprises the following steps: step 1: based on a construction task, an optimal operation sequence is allocated to each robot; step 2: BIM data and site environment information are acquired, and the initial construction path of each robot is determined in combination with the optimal operation sequence of each robot; step 3: the initial construction paths of all robots are analyzed, and the construction path of each robot is determined based on the path analysis result; step 4: the position and speed of each robot are collected in real time during the construction process, the robot operation coverage, downtime and construction quality data are periodically counted, and then the path planning coefficient of each robot is determined; and step 5: the path planning parameter of each robot is iteratively optimized based on the path planning coefficient of each robot until the preset efficiency improvement target is met. The overall construction efficiency and intelligent level are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automation scheduling and path optimization, and particularly relates to a construction robot operation path optimization method. BACKGROUND

[0002] With the increasing requirements of the construction industry on construction efficiency, construction quality and intelligent degree, construction robots as an important tool for intelligent construction are gradually applied to actual construction projects. In order to further improve the operation efficiency and construction quality of construction robots, reasonable allocation of construction tasks and optimization of operation paths become technical problems to be solved.

[0003] In the prior art, there are some researches on construction robot operation path planning. For example, some methods allocate construction tasks statically and generate construction paths by using traditional path planning algorithms, and some schemes combine BIM model data to optimize the initial path. However, the above methods mostly have the following problems: the construction task allocation and path optimization process are disconnected and cannot be dynamically adjusted; the data collected in real time during the construction process cannot be combined for path optimization; there is no mechanism for iterative adjustment of path planning parameters based on actual operation performance, which limits the improvement of construction efficiency.

[0004] Therefore, the present application provides a construction robot operation path optimization method. SUMMARY

[0005] The present application provides a construction robot operation path optimization method, which dynamically determines the optimal construction path of the construction robot by combining construction task allocation, BIM data analysis and on-site environment perception, and iteratively optimizes based on path planning coefficients by collecting robot operation data in real time during the construction process. Compared with the prior art, the present application can effectively improve the robot operation coverage, shorten the downtime, reduce the construction energy consumption, and at the same time ensure the stability of the construction quality, significantly improve the overall construction efficiency and intelligent level, and has good engineering application prospect.

[0006] The present application provides a construction robot operation path optimization method, which includes:

[0007] Step 1: allocating an optimal operation sequence for each robot based on the construction task;

[0008] Step 2: obtaining BIM data and on-site environment information, and determining the initial construction path of each robot in combination with the optimal operation sequence of each robot;

[0009] Step 3: analyzing the initial construction paths of all robots and determining the construction path of each robot based on the path analysis results;

[0010] Step 4: Collect the position and speed of each robot in real time during the construction process, periodically count the robot operation coverage, downtime and construction quality data, and then determine the path planning coefficient of each robot;

[0011] Step 5: Iteratively optimize the path planning parameters of each robot based on the path planning coefficient of each robot until the preset efficiency improvement target is met.

[0012] Preferably, the optimal operation sequence is assigned to each robot based on the construction task, including:

[0013] Obtain the construction task based on the preset construction task database;

[0014] Obtain the attribute data of each robot based on the preset robot attribute table;

[0015] Construct a task-robot matching matrix based on the construction task and the attribute data of the robot, and then obtain the adaptation score of each task and each robot;

[0016] Sort all to-be-assigned construction tasks in descending order of adaptation score, select the to-be-assigned construction tasks before the preset ranking, and determine the to-be-assigned construction tasks before the preset ranking as the to-be-selected construction tasks;

[0017] Determine the score median based on the adaptation scores of all to-be-selected construction tasks;

[0018] Determine the dynamic adaptation score threshold based on the preset score and the score median;

[0019] Pre-assign the construction tasks with an adaptation score greater than the dynamic adaptation score threshold;

[0020] The remaining tasks after pre-assignment are matched again using a preset algorithm, and then the final assignment scheme is obtained, and the optimal operation sequence of each robot including the task sequence and the execution time node is generated.

[0021] Preferably, the remaining tasks after pre-assignment are matched again using a preset algorithm, including:

[0022] Determine the robot set and the task set based on the robot after pre-assignment and the remaining tasks after pre-assignment, and construct a plurality of robot-task pairs based on the robot set and the task set;

[0023] Determine the initial movement cost of each robot-task pair;

[0024] For each robot-task pair, adjust the initial movement cost according to its current power, on-site environment and historical power consumption;

[0025] construct a cost matrix based on the adjusted movement cost of each robot-task pair;

[0026] match each robot with the task with the minimum cost in the current cost matrix of the robot, if the task is not occupied, directly match, if the task is occupied by other robots, mark as potential conflict and perform conflict matching until the corresponding task is matched for the current robot;

[0027] When all robots have matched tasks, if a robot completes a task, match the remaining tasks in the order of the robot that completes the task until all remaining tasks are matched, then determine that the second matching is completed.

[0028] Preferably, the conflict matching includes:

[0029] If the task is occupied by other robots, determine the total cost change of reassigning the currently occupied task to the current robot, if the total cost change meets the preset requirement, execute the exchange;

[0030] If the total cost change does not meet the preset requirement, temporarily assign the current task to the current robot, and find a new task for the other robots that are occupied by the current task;

[0031] Record the total cost change corresponding to the replacement task during the process of finding a new task until the total cost change meets the preset requirement;

[0032] If a replacement task cannot be found, abandon the current task for the current robot, assign the task with the minimum cost in the current cost matrix to the current robot, if the task is not occupied, directly match, if the task is occupied by other robots, mark as potential conflict and perform conflict matching until the corresponding task is matched for the current robot, then end the matching.

[0033] Preferably, the BIM data and the site environment information are obtained, and the initial construction path of each robot is determined based on the optimal operation sequence of each robot, including:

[0034] Extract the three-dimensional coordinates and geometric data of the building components corresponding to the construction tasks from the preset BIM system;

[0035] Construct a point cloud map based on the site environment information;

[0036] Coordinate system calibration is performed on the BIM model and the point cloud map, and three-dimensional rasterization processing is performed on the work area in the point cloud map after coordinate system calibration;

[0037] Feature region marking is performed based on the regional features of the work area in the point cloud map after three-dimensional rasterization processing;

[0038] determine a passing cost corresponding to each feature region based on a preset database;

[0039] plan a path for each robot based on the passing cost corresponding to each feature region, a preset path planning algorithm of all robots and the optimal work sequence, and further determine an initial construction path for each robot.

[0040] Preferably, the initial construction paths of all robots are analyzed, and the construction path of each robot is determined based on the path analysis result, including:

[0041] detecting spatial conflict and resource conflict for the initial construction paths of all robots;

[0042] optimizing the construction path based on the detection result of spatial conflict and resource conflict to obtain the construction path of each robot.

[0043] Preferably, the position, speed and task state data of each robot are collected in real time during the construction process, and the robot work coverage, downtime and construction quality data are periodically counted, and further the path planning coefficient of each robot is determined, including:

[0044] the position and speed of the robot are collected in real time by using a preset positioning system, and are uploaded to an edge computing node based on a preset time interval;

[0045] the path planning coefficient is determined based on the periodically counted work coverage, downtime and construction quality and the data obtained from the edge computing node:

[0046] ;

[0047] wherein, is the path planning coefficient of the current robot i, is the efficiency factor corresponding to the current robot i, and the calculation formula is:

[0048] ;

[0049] wherein, is the work coverage of the current robot i, is a preset target coverage, is a preset adjustment coefficient, is the downtime proportion of the current robot i, is a preset downtime threshold, is a penalty factor for punishing abnormal fluctuations of the speed and energy consumption of the robot i, and the calculation formula is:

[0050] ;

[0051] wherein, current speed of the current robot i, preset nominal speed of the robot, current energy consumption of the current robot i, current average energy consumption of all robots, preset sensitivity coefficient for secondary punishment of abnormal fluctuations in speed and energy consumption, is a quality degradation compensation for compensating the impact of mass error, and the calculation formula is:

[0052] ;

[0053] wherein, current construction quality of the current robot i, preset target quality, preset maximum allowable quality deviation.

[0054] Preferably, the path planning parameters of each robot are iteratively optimized based on the path planning coefficients of each robot until the preset efficiency improvement target is met, including:

[0055] The current position of the robot, the remaining task sequence and the environmental constraints are input to define an optimization objective function;

[0056] Based on the current path planning coefficient, a preset optimization method is used to search for a better solution in the parameter space of the current parameter path planning, and the path planning parameters are optimized based on the optimal solution;

[0057] The optimized path planning parameters are sent to the robot controller to perform a new round of construction. In this process, the expected effect after changing the path planning parameters is simulated through the preset digital twin platform, and compared with the actual construction data. If the deviation exceeds the threshold, the parameter readjustment mechanism is triggered;

[0058] When the comprehensive performance indicators of each robot in the continuous multiple optimization cycles are stable in the target interval and the efficiency improvement amplitude converges, it is determined that the preset efficiency improvement target is reached, and the iteration is terminated and the current parameters are solidified as the final scheme.

[0059] Compared with the prior art, the beneficial effects of the present application are as follows:

[0060] By combining construction task allocation, BIM data analysis and on-site environment perception, the optimal construction path of the construction robot is dynamically determined, and the robot operation data is collected in real time during the construction process. Iterative optimization is performed based on the path planning coefficient. Compared with the prior art, the present application can effectively improve the robot operation coverage, shorten the downtime, reduce the construction energy consumption, and at the same time ensure the stability of the construction quality, significantly improve the overall construction efficiency and intelligent level, and has good engineering application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0062] Figure 1 is a flowchart of the construction robot operation path optimization method provided by the embodiments of the present application. DETAILED DESCRIPTION

[0063] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely in the following with reference to the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort belong to the protection scope of the present application.

[0064] Embodiment 1

[0065] The embodiments of the present application provide a construction robot operation path optimization method, which comprises:

[0066] Step 1: assigning an optimal operation sequence to each robot based on a construction task;

[0067] Step 2: obtaining BIM data and site environment information, and determining an initial construction path of each robot in combination with the optimal operation sequence of each robot;

[0068] Step 3: analyzing the initial construction paths of all robots, and determining a construction path of each robot based on the path analysis result;

[0069] Step 4: collecting the position and speed of each robot in real time during the construction process, periodically counting robot operation coverage, downtime and construction quality data, and then determining a path planning coefficient of each robot;

[0070] Step 5: iteratively optimizing the path planning parameters of each robot based on the path planning coefficient of each robot until a preset efficiency improvement target is met.

[0071] In this embodiment, the construction task refers to the work content and target that the construction robot needs to complete in a specific construction phase. It includes specific work types, scope, quality requirements, and time constraints, such as wall painting, concrete pouring, or steel structure installation, etc. Taking wall painting as an example, the construction task may be defined as "complete the fireproof coating painting of all partition walls on the B2 floor within 3 days, with a coating thickness of 2mm and a uniform surface without bubbles". The task will be broken down into specific requirements such as painting area coordinates, paint quantity, process parameters, etc., to guide the allocation of robot work sequences. The clear description of the construction task provides a goal-oriented path planning, such as prioritizing emergency areas or coordinating the work area boundaries of multiple robots.

[0072] In this embodiment, BIM data and site environment information are obtained. BIM data refers to the three-dimensional spatial data and engineering attribute data stored in the building information model, such as wall structure, pipeline layout, component size, etc. Site environment information is collected in real time through sensors, including obstacle position, temperature and humidity, light intensity, etc. For example, in a pipe installation operation, the robot first retrieves the pipe design path and embedded part coordinates from the BIM, and at the same time, discovers through laser radar scanning that the temporary pile of building materials blocks the preset path. At this time, the dynamic obstacle coordinates are superimposed with the BIM model to generate an updated three-dimensional environment map. This information fusion enables the robot to follow the design specifications while avoiding sudden situations such as scaffold movement and personnel activity.

[0073] In this embodiment, the path planning coefficient is a multi-dimensional index for quantitatively evaluating the efficiency of the robot's work. For example, the coefficient calculation of a certain bricklaying robot shows that the current path covers 85% of the target area (coverage rate) in 8 hours of operation, but causes 45 minutes of downtime (time utilization rate) due to waiting for material replenishment, and the brick joint error at the corner exceeds the standard 3 times (construction quality). The system generates a planning coefficient of 0.76 (full score 1.0) based on this, with the main deduction being the excessive downtime. This coefficient drives the algorithm to prioritize optimizing the path connection of material supply points, and by adding a mid-way charging station access node, the coefficient of the next cycle is improved to 0.89. This dynamic feedback mechanism enables continuous improvement of path planning.

[0074] The beneficial effects of the above technical solutions are: by combining construction task allocation, BIM data analysis, and site environment perception, the optimal construction path of the construction robot is dynamically determined, and the robot operation data is collected in real time during the construction process, and iterative optimization is performed based on the path planning coefficient. Compared with the prior art, the present application can effectively improve the robot operation coverage rate, shorten the downtime, reduce the construction energy consumption, and at the same time ensure the stability of the construction quality, significantly improve the overall construction efficiency and intelligent level, and has good engineering application prospect.

[0075] Embodiment 2

[0076] The embodiment of the present application provides a construction robot operation path optimization method, optimizes an operation sequence of each robot based on a construction task, and comprises the following steps.

[0077] Obtaining a construction task based on a preset construction task database;

[0078] Obtaining attribute data of each robot based on a preset robot attribute table;

[0079] Constructing a task-robot matching matrix based on the construction task and the attribute data of the robot, and then obtaining an adaptation degree score of each task and each robot;

[0080] Sorting all to-be-allocated construction tasks in descending order of the adaptation degree score, screening out to-be-allocated construction tasks before a preset ranking, and determining the to-be-allocated construction tasks before the preset ranking as to-be-selected construction tasks;

[0081] Determining a score median based on the adaptation degree scores of all to-be-selected construction tasks;

[0082] Determining a dynamic adaptation degree score threshold based on a preset score and the score median;

[0083] Pre-allocating a construction task with an adaptation degree score greater than the dynamic adaptation degree score threshold;

[0084] Using a preset algorithm to perform secondary matching on the remaining tasks after pre-allocation, and then obtaining a final allocation scheme, and generating an optimal operation sequence of each robot, which comprises a task sequence and an execution time node.

[0085] In the embodiment, the preset construction task database comprises a task type, required equipment, estimated working hours and priority.

[0086] In the embodiment, the preset robot attribute table records parameters such as a working range, load capacity and motion speed of each robot.

[0087] In the embodiment, the secondary matching stage is based on the target of minimizing the total moving distance of all robots, uses an improved Hungarian algorithm, increases a penalty term, adds a hard constraint condition to a task with process dependence, and the hard constraint condition is that a subsequent task must be executed by the same robot or meet a process handover standard.

[0088] The beneficial effects of the above technical solution are as follows: By constructing a task-robot matching matrix based on the construction task database and robot attribute table and dynamically adjusting the adaptation threshold, construction tasks are reasonably screened and pre-allocated, achieving efficient matching between construction tasks and robot capabilities. By introducing a dynamic adaptation mechanism and a secondary matching strategy, the rationality and flexibility of task allocation are effectively improved, resource waste and task conflicts are avoided, the robot's operation sequence and execution nodes are optimized, and the overall construction efficiency and task completion quality are significantly improved.

[0089] Example 3:

[0090] This invention provides a method for optimizing the work path of a construction robot, which performs secondary matching on the remaining tasks after pre-allocation using a preset algorithm, including:

[0091] Based on the pre-assigned robots and the remaining pre-assigned tasks, determine the robot set and the task set, and construct several robot-task pairs based on the robot set and the task set;

[0092] Determine the initial movement cost for each robot-task pair;

[0093] For each robot-task pair, the initial movement cost is adjusted based on its current battery level, the on-site environment, and historical power consumption.

[0094] Construct a cost matrix based on the adjusted movement cost for each robot-task pair;

[0095] For each robot, match the task with the lowest cost in its current cost matrix. If the task is not occupied, match it directly. If the task has been occupied by another robot, mark it as a potential conflict and perform conflict matching until a corresponding task is matched for the current robot.

[0096] Once all robots have been matched with a task, the second matching process is considered complete.

[0097] In this embodiment, a robot set and a task set are determined: each robot includes: current location, remaining battery power, task execution capability, etc., and each task in the task set includes: target location, urgency level, estimated time, etc.

[0098] In this embodiment, the initial movement cost Use path planning algorithms (such as...) Or Dijkstra's computational robot To the mission The shortest path distance, this cost takes into account the effect of static obstacles. For grid maps, the distance can be normalized to the map's size scale (e.g., each grid represents 1 meter), including:

[0099] Battery Penalty Based on the robot's current remaining battery power and estimated power consumption of the task The formula for dynamically adjusting the cost and the power penalty is:

[0100]

[0101] in, This is the robot's maximum battery capacity. 1 is the penalty coefficient. Typically when the battery is low A higher value (1) indicates that the lower the battery level, the higher the cost of moving the robot, thus preventing the robot with low battery from performing long-distance tasks.

[0102] Environmental penalties This refers to the impact of dynamic environmental factors (such as congested areas and temporary obstacles) on the route. The formula for the environmental penalty term is:

[0103]

[0104] The dynamic obstacle density is calculated using real-time SLAM or sensor detection. The cost increases if the path passes through highly congested areas.

[0105] Historical power consumption learning items By recording the actual power consumption of historical tasks To correct the error in the estimated power consumption of the task, the formula for the historical power consumption learning term is:

[0106]

[0107] in, It is a task The estimated power consumption This represents the number of times a historical task has been executed. When the power consumption of a historical task exceeds the estimate, k represents the k-th execution, and the cost will be increased. The historical influence coefficient is the preset value. .

[0108] The final adjusted movement cost is calculated by combining the adjusted movement costs. It is the sum of all the above cost terms:

[0109]

[0110] This cost takes into account the base distance, power penalty, environmental impact, and learning corrections based on historical power consumption;

[0111] In this embodiment, the step of constructing the cost matrix is ​​as follows: Initialize the matrix: Matrix dimension: m×n (m robots, n tasks); Initial values: all =∞ (indicates no match); calculate the correction cost for each pair (ri, tj) : call the path planning algorithm to calculate ; real-time acquisition of robot power and environmental data; load the power consumption records of the task from the historical database; calculate according to the foregoing formula ; matrix dynamic update strategy trigger condition: robot power change Δ ≥10%; environmental map update (such as adding new obstacles); task queue change (adding / canceling tasks); local update: only recalculate the affected row / column (such as only update the row corresponding to the robot with low power).

[0112] The beneficial effects of the above technical solutions are: by using the secondary matching optimization strategy for the remaining tasks after pre-allocation, fully considering the current power of the robot, the on-site environment and the historical energy consumption, dynamically adjusting the moving cost, constructing an accurate cost matrix, and realizing efficient matching of the robot and the task. By introducing the potential conflict identification and processing mechanism, the continuity and rationality of the matching process are guaranteed, the overall moving cost is effectively reduced, and the intelligent level of the operation efficiency and task allocation is improved.

[0113] Embodiment 4:

[0114] The embodiment of the application provides a building robot operation path optimization method, and conflict matching, which comprises the following steps:

[0115] If the task is occupied by other robots, determine the total cost change of reassigning the currently occupied task to the current robot, and if the total cost change meets the preset requirement, execute the exchange;

[0116] If the total cost change does not meet the preset requirement, temporarily assign the current task to the current robot, and find a new task for the other robot whose task is occupied by the current robot;

[0117] Record the total cost change corresponding to the replacement task in the process of finding the replacement task until the total cost change meets the preset requirement;

[0118] If the replacement task cannot be found, the current robot gives up the current task, and the current robot is assigned a task with the minimum cost in the current cost matrix except the current task. If the task is not occupied, directly match, if the task is occupied by other robots, mark as potential conflict and perform conflict matching, until the corresponding task is matched to the current robot, then end the matching.

[0119] In this embodiment, the replacement task is found based on the cost matrix, the purpose is to find a path from the idle robot to the idle task, and the path alternates between non-matching edges and matching edges, and the total moving cost is reduced by exchanging the matching relationship on the path, assuming that there are robots want to perform tasks But the task Already been robot The process of occupying and replacing tasks is as follows: Path rules: Find paths that meet the following conditions: Objective: Find the final path to the idle task. The path, and the matching relationships on the exchange path can reduce the total cost, DFS-style alternative task search process: Select starting point: from the currently unassigned robot. Begin. Find unmatched edges: Select the edge with the minimum cost from the cost matrix. Inspection task Is it occupied? If Not occupied, match directly The alternative task search is complete. If... quilt If occupied, try: temporarily relinquishing (Non-matching edge), and let Find new matches (recursively perform DFS). During path expansion, record alternative tasks. This reduces the total cost;

[0120] in, Adjust matching: If Can new tasks be found Then adjust the matching to The augmentation path was successful. If no suitable alternative path can be found, backtrack and continue the search. The next optional task Layer limit: To avoid excessive recursion, a maximum search depth can be set (e.g., 3 layers). If the depth is exceeded, the path is abandoned. Example: Initial case: Assume the following cost matrix:

[0121]

[0122] Current match: , The total cost is 3 + 6 = 9. To find a better solution: try making... match (Lower cost), triggers augmenting path search: Want to match ,but quilt Occupied, recursive check Can I switch to another task? You can try matching (Original match was) The total cost after the swap = 2 + 5 = 7, which is obviously lower. New match: , , total cost = 2 + 5 = 7, a better solution is obtained, and the termination condition of the algorithm ensures that the final matching is globally optimal or locally optimal, and the total cost cannot be further reduced by the augmented path;

[0123] In this embodiment, if the task has been occupied by other robots, it is marked as a potential conflict and conflict matching is performed until the corresponding task is matched for the current robot. The conflict matching in the matching process includes the following steps: 1. If the task has been occupied by other robots, determine the total cost change of reassigning the currently occupied task to the current robot, and if the total cost change meets the preset requirement, perform the exchange; 2. If the total cost change does not meet the preset requirement, temporarily assign the current task to the current robot, and find a new task for the other robots that are currently occupied by the current task; 3. Record the total cost change corresponding to the replacement task during the process of finding a new task until the total cost change meets the preset requirement; 4. If a replacement task cannot be found, the current robot gives up the current task, and the task with the minimum cost in the current cost matrix except the current task is assigned to the current robot. If the task is not occupied, it is directly matched, if the task has been occupied by other robots, it is marked as a potential conflict, and the matching process of 1-4 above is performed, until the corresponding task is matched for the current robot, and the matching process is ended.

[0124] In this embodiment, the successful termination condition is that all robots have successfully matched tasks: the matching number = the number of rows or columns of the cost matrix, and the failure termination condition is that the augmented path cannot be found, indicating that the local optimum has been reached: in the cost matrix, there is no robot that can find an augmented path to the idle task , the row or column of the cost matrix is completely covered, the failure termination condition: unable to find an augmented path, indicating that the local optimum has been reached: in the cost matrix, there is no robot that can find an augmented path to the idle task , mathematical expression: for any robot , all possible tasks are either occupied or adjusting the matching cannot reduce the total moving cost, and the processing after termination: output the final matching and the total moving cost, and re-run after dynamic adjustment: if the power and environmental changes cause the cost matrix to update, the algorithm needs to be re-executed to optimize the matching.

[0125] In this embodiment, the total cost change meeting the preset requirement means that the total cost change is less than 0.

[0126] The beneficial effects of the above technical solutions are: by introducing the total cost change evaluation mechanism in the task conflict matching process, the task allocation can be flexibly adjusted when the task resources are occupied, and by using various strategies such as exchange, temporary allocation and potential conflict processing, the robot operation path is dynamically optimized, and the overall operation efficiency and resource utilization are significantly improved. The method can effectively avoid the operation stagnation or resource waste caused by task conflict, and ensure the rationality of task allocation and the optimality of path planning of the robot group in a complex operation environment.

[0127] Embodiment 5:

[0128] The embodiment of the application provides a building robot operation path optimization method, BIM data and site environment information are acquired, and an initial construction path of each robot is determined according to an optimal operation sequence of each robot, and the method comprises the following steps:

[0129] Three-dimensional coordinates and geometric data of building components corresponding to construction tasks are extracted from a preset BIM system;

[0130] A site point cloud map is constructed based on site environment information;

[0131] The preset BIM model and the site point cloud map are calibrated in a coordinate system, and a three-dimensional gridding process is performed on an operation area in the site point cloud map after the coordinate system calibration;

[0132] Feature region marking is performed based on region features of the operation area in the site point cloud map after the three-dimensional gridding process;

[0133] The corresponding passing cost of each feature region is determined based on a preset database;

[0134] The path of each feature region is planned based on the corresponding passing cost of each feature region, a preset path planning algorithm of all robots and the optimal operation sequence, and then the initial construction path of each robot is determined.

[0135] In the embodiment, the site point cloud map is constructed based on the site environment information by laser SLAM.

[0136] In the embodiment, the three-dimensional gridding process is a preset gridding resolution process, and the gridding resolution is set to 5cm*5cm*5cm.

[0137] In the embodiment, the region features of the operation area in the site point cloud map are fixed structural components and newly added obstacles (such as stacked materials and vehicles);

[0138] In this embodiment, the area marking includes: permanent obstacle area (such as load-bearing column) and temporary obstacle area (such as material stacking area), the determination rule and the implementation method are as follows, (1) determination of permanent obstacle area: data pre-marking: extract the coordinate range of structural members (such as column, wall) from BIM model or CAD drawing, and mark as permanent obstacle area; preset as non-passable area in the navigation system (set as infinite cost in the cost map), verification mechanism: through the alignment of laser radar point cloud and BIM model, confirm whether the position of static obstacle matches (error <5cm); if the deviation exceeds the threshold, trigger manual review (avoid construction error leading to path planning failure), (2) determination of temporary obstacle area: dynamic detection: sensor input: laser radar / camera real-time detection of new obstacles (such as stacked materials, vehicles); change detection algorithm: compare the current frame point cloud with the static map, extract the new occluded area (such as using OctoMap dynamic update), time and space attribute marking: add timestamp and expected existence time length to the temporary obstacle (such as "steel stacking area: expected to exist for 8 hours"), if it exceeds the time limit and does not disappear, generate an alarm prompt for manual confirmation (avoid sensor mis-detection or plan change not updated), flexible path planning: temporary obstacle area is set as high-cost area (not infinite) in the cost map, the robot can bypass but not completely block; through dynamic re-planning algorithm to adjust the path in real time.

[0139] In this embodiment, the pre-set database is used to determine the passing cost corresponding to each area: permanent obstacle area: infinite cost, robot working area: basic cost x 1.2, manual construction area: basic cost x 1.5.

[0140] In this embodiment, in the initial construction path planning stage of the construction robot working path optimization, first, the construction area is divided into several feature areas based on the BIM model, and the actual environment information is obtained through on-site scanning, so as to assign a passing cost with time and energy as the core to each feature area (such as working surface, material stacking area, passageway, etc.), and the passing cost of high-density working area, narrow passageway or up-and-down slope area is higher. Combined with the optimal working sequence of the robot (i.e. the shortest construction period working chain generated by task decomposition and scheduling algorithm), a hierarchical path planning strategy is adopted: the first layer is based on the global path planning of the robot, and the second layer is based on the local path planning of the robot. The algorithm generates a preliminary coarse-grained path for each robot, prioritizing the feature area with the lowest passage cost to ensure no task intersection; the second layer verifies the multi-machine coordination feasibility through the space-time corridor technique, and if a path space-time conflict (two or more robots occupying the same space at the same time) is detected, the conflict resolution mechanism is started, and the time window or spatial trajectory of the path is dynamically adjusted, including time delay (low-priority task moving backward), spatial rerouting (detouring high-cost areas), or task redistribution (adjusting the work sequence). The optimized path also needs to pass the dynamic constraint check, including the turning radius adaptation to the mechanical structure and the speed / acceleration limit, and finally the smooth processing to a continuous trajectory that meets the robot's motion ability (such as eliminating abrupt corners through Bezier curves), and outputting a discrete path point sequence with timestamps as the initial construction path.

[0141] In this embodiment, the preset path planning algorithm is improved algorithm.

[0142] The beneficial effects of the above technical solutions are: by combining BIM data and site environment information, based on coordinate system calibration and three-dimensional rasterization processing, the construction site environment model is accurately constructed, and then the work area feature marking and passage cost evaluation are realized. By considering the passage cost and the optimal work sequence of the robot, the preset path planning algorithm is used to reasonably determine the initial construction path of each robot, effectively improving the rationality of the construction path and the work efficiency, and reducing the path conflict and energy cost.

[0143] Embodiment 6:

[0144] The embodiment of the application provides an architectural robot work path optimization method, analyzes the initial construction paths of all robots, determines the construction path of each robot based on the path analysis result, including:

[0145] Detecting the space conflict and resource conflict of the initial construction paths of all robots.

[0146] Based on the space conflict and resource conflict detection results, the construction path is optimized to obtain the construction path of each robot.

[0147] In this embodiment, the space conflict detection: checks whether the paths of different robots occupy the same space (such as intersection points, narrow passages) at the same time.

[0148] In this embodiment, the resource conflict detection: detects the competition of multiple robots for the same equipment (such as lifting platforms) or task area.

[0149] In this embodiment, the construction path of each robot is optimized based on the results of spatial conflict and resource conflict detection: the path intersection is analyzed using the conflict graph model in graph theory, the potential robot-robot collision, robot-dynamic obstacle (such as worker, equipment) space-time conflict is identified, and the path timing or spatial distribution is adjusted based on the time window protocol (such as STP, Spatio-Temporal Planning); secondly, the path logic feasibility is verified combined with the construction process dependency relationship (such as "wall drying can proceed with water and electricity installation"), the task sequence is redistributed through mixed integer linear programming (MILP) to eliminate deadlock, for example: if the spraying path of robot A overlaps with the transportation path of robot B in space-time, the high-priority task (spraying) is preferentially ensured to be continuously executed, and robot B adopts a detour or delayed start strategy; finally, based on the energy consumption model (considering ground friction, climbing angle) and task urgency, a hierarchical path weight is generated, for example, the path of an urgent task (such as concrete pouring) allows sacrificing 10% of energy consumption to shorten 15% of time. The optimized path needs to meet: ① no hard conflict (100% safety); ② the total construction time is reduced by ≥8% compared with the initial scheme; ③ the single robot energy consumption fluctuation does not exceed the mean value ± 20%. The path data is stored in the format of three-dimensional space-time trajectory (XYZ+timestamp), and is synchronized to the cloud BIM model for real-time monitoring.

[0150] The beneficial effects of the above technical solution are: by detecting the spatial conflict and resource conflict of all robot initial construction paths, potential operation conflict problems can be identified in time, and construction path optimization is implemented based on the detection results to reasonably adjust the robot operation sequence and path planning. This method effectively improves the collaborative operation efficiency in the robot construction process, reduces the operation delay and resource waste risk caused by path conflict, and ensures the orderly and efficient progress of the construction task.

[0151] Embodiment 7:

[0152] The embodiment of the application provides a building robot operation path optimization method, which collects the position, speed and task state data of each robot in real time during the construction process, periodically calculates the robot operation coverage, downtime and construction quality data, and then determines the path planning coefficient of each robot, including:

[0153] The position and speed of the robot are collected in real time by using a preset positioning system, and are uploaded to an edge computing node based on a preset time interval;

[0154] The path planning coefficient is determined based on the periodically calculated operation coverage, downtime and construction quality, and the data obtained from the edge computing node:

[0155]

[0156] in, Let be the path planning coefficient for robot i. The efficiency factor corresponding to the current robot i is calculated using the following formula:

[0157]

[0158] in, The current operational coverage of robot i. For the preset target coverage, The preset adjustment coefficient, This represents the percentage of downtime for robot i. The preset downtime threshold, The penalty factor, which penalizes abnormal fluctuations in the speed and energy consumption of robot i, is calculated using the following formula:

[0159]

[0160] in, Let i be the current speed of robot i. The preset nominal speed of the robot, Let i be the current energy consumption of robot i. The current average energy consumption of all robots. The preset sensitivity coefficients are used to apply secondary penalties for abnormal fluctuations in speed and energy consumption. The formula for calculating quality degradation compensation, used to compensate for the impact of quality errors, is as follows:

[0161]

[0162] in, For the current construction quality of robot i, For the preset target quality, This is the preset maximum permissible quality deviation.

[0163] In this embodiment, the following statistics are collected periodically: operation coverage, downtime, and construction quality: Location data: Beidou + UWB hybrid positioning, update frequency 10Hz; Speed ​​data: encoder + IMU data fusion; Task status: operation progress photo comparison, once per minute; Performance indicators are calculated in real time: Operation coverage = Completed area / Planned area × 100%; Downtime = Total downtime / Running time × 100%; Construction quality = 1 - (Number of defect points / Number of inspection points).

[0164] In this embodiment, the determination of the job coverage (Coverage), Coverage = Completed Area / Planned Area x 100%, is used to quantify the completion degree of the construction area by the robot, ensuring the maximum efficiency of the construction. The determination process follows the following steps: Area division: Based on the BIM model or field surveying, the construction site is divided into several grid units (such as 0.5m x 0.5m) or area blocks, and each unit is given a unique identification. Completed area statistics: Through the sensors (such as laser radar, visual SLAM) carried by the robot, the trajectory and working state are recorded in real time, and the grid units that have been constructed are marked. If the robot performs specific work (such as spraying, masonry) and reaches the preset precision (such as qualified spraying thickness, block installation in place), the unit is considered completed. Plan construction area definition: According to the construction task list, the total area to be covered in each work stage (including the main work area and auxiliary path) is determined, and the temporary forbidden area (such as the area where the material is not cleaned) or the unreachable area (such as the structural obstacle) is excluded. Coverage calculation: In units of cycles (such as every hour), the percentage of the number of completed qualified units to the total number of planned units is calculated. If the target is ≥95%, the coverage is up to standard; if it is lower than the threshold (such as 90%), the path optimization (such as adjusting the mechanical arm posture to reduce omission) is triggered.

[0165] In this embodiment, the determination of downtime, which reflects the availability of the robot, directly affects the construction progress, and its accurate statistics need to distinguish between active downtime (charging, maintenance) and passive downtime (failure, blockage). The specific steps include: state classification: define the robot operating state (such as "working", "charging", "fault to repair", "path blockage"), and record the state switching timestamp in real time through the control system; effective working time statistics: after excluding planned downtime (such as daily charging period), accumulate unplanned downtime (such as unexpected battery depletion, mechanical arm jamming, network interruption caused by stagnation); time normalization: calculate the proportion of total unplanned downtime in total running time within the statistical period (such as 8-hour shift). If the proportion exceeds the threshold (5%), the cause (such as insufficient battery endurance or high collision frequency) needs to be analyzed and the path planning strategy needs to be adjusted (such as increasing charging sites or reducing moving speed); dynamic threshold correction: dynamically adjust the downtime threshold according to historical data (such as seasonal temperature difference leading to battery efficiency change) to avoid misjudgment of performance degradation due to environmental fluctuations. Job coverage rate = completed area / planned area × 100%, determination of construction quality: construction quality directly affects project acceptance, and the consistency of job accuracy needs to be quantitatively evaluated. Taking the spraying thickness as an example, the coefficient determination process is as follows: quality index definition: clearly define the standard range of key parameters (such as spraying thickness design value 2.0mm, allowable deviation ±0.3mm), real-time data acquisition: through embedded sensors (such as ultrasonic thickness gauge) or periodic sampling (such as manual sampling of 5 points every 10 minutes), obtain the actual construction parameters and record the deviation from the target value. Variance calculation: calculate the standard deviation (σ) of thickness data for all sampling points in the same working area. If σ≤0.3mm, it is considered that the quality is stable; if σ>0.3mm, it is considered that there is process fluctuation (such as nozzle blockage or uneven moving speed). Root cause analysis: combined with path data (such as spraying overlap rate decreases when robot turns) and equipment status (such as pump pressure fluctuation), locate the cause of quality defects. For example, excessive path curvature may cause uneven spraying, which requires smoothing the trajectory or reducing the travel speed.

[0166] In this embodiment, the preset target coverage range is obtained by comprehensively considering factors such as the workload, robot operation capabilities, and construction cycle in actual engineering scenarios such as construction. For example, if the construction site size is fixed and the robot performance is stable, a reasonable coverage ratio range is derived based on the robot's operational coverage in similar past projects, combined with the project's timeline and other requirements. Common values ​​are between 80% and 95%, meaning that the robot is expected to complete the work in most areas within a specified time. However, considering various actual interference factors (such as equipment failure and complex site conditions), it is not set to 100%. Theoretical calculations are performed based on parameters such as the construction area, the robot's single-operation coverage area, and the operation time. For example, if the total construction area is... The robot's coverage area per unit time is The total planned operation time is The ideal coverage area can then be calculated as follows: The theoretical target coverage rate is Based on the theoretical value, further adjustments were made considering factors such as practical feasibility and on-site complexity to ultimately determine a reasonable value. The value can be selected, and the range of values ​​is: , usually set up in The value is set between these values ​​based on the coverage requirements of the specific construction task. Operations with high coverage requirements (such as ground cleaning and lawn mowing) are usually set to a higher value, close to 1; if there are inaccessible areas or the coverage requirements are moderate, the value can be slightly lower, but generally not lower than 0.8.

[0167] In this embodiment, the preset adjustment coefficient range is obtained through sensitivity analysis: by changing the coefficient value, the influence on the efficiency factor and the final path planning coefficient is observed. In multiple simulation experiments or actual tests, if the preset adjustment coefficient is too small, such as 0.01, it is found that the efficiency factor changes drastically when the job coverage deviates slightly from the target coverage, resulting in path planning being too sensitive and unstable; if the value is too large, such as 1, the effect of coverage deviation on the efficiency factor is weak and cannot effectively reflect the impact of coverage difference on robot efficiency. After extensive testing and comparison of the system's stability and the rationality of its response to coverage changes under different values, the default value of 0.1 was determined, and it was found that it should generally be a decimal greater than 0, and the value range is usually within a small numerical range. The default value of 0.1 is used to ensure exponential decay when the coverage deviates.

[0168] In this embodiment, the preset speed abnormal fluctuation sensitivity coefficient takes a value range of a real number greater than 0. The determination method is as follows: it is determined according to the influence degree of the robot speed on the work effect and the overall system. If the fluctuation of the robot speed has a greater influence on the work quality, efficiency or other aspects, a larger preset speed abnormal fluctuation sensitivity coefficient value needs to be selected, so that the penalty term can be quickly increased when the speed deviates from the standard speed, to prompt the robot to run near the standard speed. For example, in some high-precision machining tasks, the instability of the robot speed may cause the machining quality to decline, and the preset speed abnormal fluctuation sensitivity coefficient will take a larger value. Conversely, if the speed fluctuation has a smaller influence on the work, the preset speed abnormal fluctuation sensitivity coefficient can take a smaller value. The specific value usually needs to be determined through experiments and analysis of the actual work situation, to achieve the purpose of effectively controlling the speed fluctuation without being too harsh on the normal speed adjustment of the robot.

[0169] In this embodiment, the preset energy consumption abnormal fluctuation sensitivity coefficient takes a value range of a real number greater than 0, and the determination method is similar to that of the preset speed abnormal fluctuation sensitivity coefficient. The preset energy consumption abnormal fluctuation sensitivity coefficient is determined according to the influence of energy consumption fluctuation on the system. If the abnormal fluctuation of energy consumption has an important influence on the energy management, cost control and other aspects of the entire work system, the value of the preset energy consumption abnormal fluctuation sensitivity coefficient needs to be increased, so that the penalty term can be significantly increased when the energy consumption deviates from the average energy consumption, thereby prompting the robot to maintain the stability of energy consumption as much as possible. For example, in some mobile robot work scenarios where energy is limited, stable control of energy consumption is crucial, and the preset energy consumption abnormal fluctuation sensitivity coefficient will take a larger value. In some scenarios that are not sensitive to energy consumption, the preset energy consumption abnormal fluctuation sensitivity coefficient can be relatively small. The specific value is also determined through monitoring and analysis of the energy consumption in actual work, combined with the system's ability to withstand energy consumption fluctuation.

[0170] In this embodiment, The value range is greater than 0, and it is determined according to the quality requirement. The determination method is as follows: It is determined according to the quality standard and the allowable error range of the specific construction task. For some high-precision tasks such as aerospace part machining, the quality deviation requirement is very strict, and a smaller value close to 0 can be taken. For some tasks with relatively low quality requirements, such as ordinary building wall plastering, a larger value can be taken. It needs to consider the nature of the task, the requirements of subsequent use and cost, etc. Generally, it is determined by statistical analysis of the quality data of similar tasks in the past, combined with the special requirements of the current task and customer expectations. The value of ) is used to ensure that the robot's operation is not overly constrained while ensuring quality, and at the same time, the path planning coefficient can be effectively adjusted through the quality compensation value.

[0171] In this embodiment, the preset downtime threshold ranges from (0, 1), with a common value around (0.1-0.2) (i.e., (10%-20%)). This value is determined based on the robot's tasks and actual operating conditions. Generally, considering that the robot may stop for various reasons (such as charging, fault repair, waiting for materials, etc.), an acceptable upper limit for downtime needs to be set. For tasks with high requirements for continuous operation, downtime can severely impact production efficiency; therefore, the downtime threshold is set relatively low, perhaps around 0.1. For non-continuous operations or tasks less sensitive to downtime, the downtime threshold can be appropriately higher, perhaps around 0.2.

[0172] In this embodiment, the preset target quality range is determined as follows: The preset target quality range is determined based on specific construction task requirements, industry standards, and customer expectations. Different tasks have different quality measurement standards. For example, in building construction, there are clear industry specifications and quality acceptance standards for wall flatness and concrete pouring density. These standards serve as important bases for determining the target quality. If the quality measurement standard is a 0-100 rating system, then the preset target quality may be set between 80 and 95 depending on the specific task, indicating that the robot is expected to achieve a high level of work quality.

[0173] In this embodiment, the preset nominal speed range of the robot is determined as follows: The preset nominal speed range of the robot is determined based on the robot's design performance and actual operational requirements. During the robot's design, a speed range that ensures stable and efficient operation is determined based on factors such as its mechanical structure, power system, and control system. The limitations of the operational environment also affect this value. If the robot needs to operate in a densely populated or confined space, the nominal speed will be relatively low to ensure safety; while in some open, unmanned industrial environments, the nominal speed can be appropriately increased. The value range is determined based on the robot's performance and is generally a fixed value. A common range may be between (0.5-5) m / s, but the specific value will vary depending on the type of robot and the application scenario.

[0174] In this embodiment, the calculation logic features: nonlinear response: when the coverage is insufficient or the downtime is out of limit, the efficiency factor is greatly reduced, and the nonlinear weighted response; active damping of jitter: avoid frequent path switching through the quadratic penalty of energy consumption and speed, and reduce the impact of minor fluctuations; quality veto: if the quality degradation compensation , the path planning coefficient is forced to zero, and the current path execution is stopped; dynamic normalization: the final calculation range is between 0 and 1, which is directly mapped to the path adjustment priority. Through the above formula, the PPC evaluates the priority of the current path of the robot, and considers the adjustment strategy of the path according to the efficiency, penalty, quality and other factors.

[0175] The beneficial effects of the above technical solutions are: by collecting the position, speed and task state data of the robot in real time during the construction process, combining the periodically statistical operation coverage, downtime and construction quality information, the path planning coefficient of each robot is dynamically determined. By constructing an optimization model that comprehensively considers operation efficiency, resource utilization, speed and energy consumption stability and quality compensation, the self-adaptive adjustment and optimization of the construction path are realized. This method can effectively improve the operation continuity and path rationality of the construction robot, and reduce the efficiency loss and quality risk caused by operation abnormalities.

[0176] Embodiment 8:

[0177] The embodiment of the application provides a construction robot operation path optimization method, iteratively optimizes the path planning parameters of each robot based on the path planning coefficient of each robot until the preset efficiency improvement target is met, including:

[0178] Taking the current position of the robot, the remaining task sequence and the environmental constraint as input, an optimization objective function is defined;

[0179] Based on the current path planning coefficient, a preset optimization method is used to search for a better solution in the parameter space of the current parameter path planning, and the path planning parameters are optimized based on the optimal solution;

[0180] The optimized path planning parameters are sent to the robot controller to execute a new round of construction, in the process, the expected effect after changing the path planning parameters is simulated through the preset digital twin platform, and compared with the actual construction data, if the deviation exceeds the threshold, the parameter readjustment mechanism is triggered;

[0181] When the comprehensive performance indicators of each robot in the continuous multiple optimization periods are stable in the target interval and the efficiency improvement amplitude converges, it is determined that the preset efficiency improvement target is reached, and the iteration is terminated and the current parameters are fixed as the final scheme.

[0182] In this embodiment, the environmental constraints are obstacle distribution, charging station position; ​

[0183] In this embodiment, the optimization objective function is defined to comprehensively consider the trade-off between construction efficiency (maximize coverage rate), operational stability (minimize downtime) and construction quality (e.g. spray thickness variance meets the standard). The objective function is usually in the form of weighted summation, where the weight coefficients of each KPI are dynamically adjusted according to the actual construction priority, for example, the coverage rate weight can be appropriately increased in the rush stage, while the construction quality constraint is strengthened in the quality sensitive area.

[0184] In this embodiment, better solutions are searched, for example: if the current period statistics show that a robot causes construction quality to decline (thickness variance > 0.3 mm) due to frequent turning, the quality is improved by reducing the path curvature or increasing the residence time in the spray overlap area (adjusting the speed-precision balance parameter); if another robot causes downtime to exceed the standard (> 5%) due to insufficient battery endurance, its path is optimized to the nearest charging station and the frequency of high-power actions (such as climbing) is reduced, and its work sequence is dynamically updated to avoid task interruption.

[0185] In this embodiment, the comprehensive performance index is, for example, coverage rate ≥ 95%, downtime < 4%, and quality variance ≤ 0.28 mm.

[0186] In this embodiment, when the comprehensive performance indicators of each robot in multiple consecutive optimization cycles are stable in the target interval and the efficiency improvement amplitude converges, it is determined that the preset efficiency improvement target is reached: by monitoring the performance stability and optimization yield decay trend in multiple cycles, it is determined whether the path parameters have reached the optimal balance state. For example, the initial parameters of a certain wall spraying robot have an operation coverage of 82% and a downtime rate of 15%. After three iterations of optimization, the coverage is improved to 92% (parameter adjustment: spraying path overlap rate is reduced from 20% to 12%, and corner deceleration threshold is increased from 0.5 m / s to 0.8 m / s), but the fourth optimization only improves to 93%, and the fifth optimization actually causes the edge spraying quality to decrease due to excessive speed (the coverage is rolled back to 91%). At this time, the system detects that: 1) the coverage fluctuates between 90%-93% in the last three cycles (into the stable interval); 2) the efficiency increase amplitude decays from 10% to 0.5% (converges); 3) the digital twin simulation shows that further parameter adjustment can only bring a marginal yield of ≤0.3%. The system determines that the target of "coverage ≥90% and increase amplitude ≤1% for three consecutive times" has been reached, and terminates the optimization and solidifies the current parameters. At the same time, before solidification, the simulation data and actual construction records are compared: if the actual thickness deviation of a certain spraying corner exceeds the simulation value by 5% (trigger threshold), the previous version of the parameter is automatically rolled back and the speed parameter range is locked, ensuring that the final solution has both theoretical optimality and engineering feasibility. This mechanism is also applicable to steel structure welding, when the welding path parameters make the welding gun moving speed, angle adjustment frequency, and other indicators fluctuate by less than 2% for five consecutive cycles, and the welding time per unit length is stable at 4.3±0.1 minutes, it is determined that the process stability requirement is met, and the iteration is terminated.

[0187] The beneficial effects of the above technical solutions are: by iteratively optimizing the path planning parameters of the construction robot based on the path planning coefficients, dynamically adjusting the optimization objective function in combination with the current position, remaining task sequence, and environmental constraints, and predicting the parameter change effect using the digital twin platform, and comparing the actual construction data in real time, the adaptive optimization and correction of the path planning parameters are realized. By setting the efficiency improvement target and monitoring the convergence of the comprehensive performance indicators, the efficiency of the robot operation and the intelligent optimization level of the construction path are effectively improved, and the real-time performance, stability, and construction quality control capability of the system are enhanced.

[0188] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent substitutions for part of the technical features; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing a construction robot work path, characterized by, Comprise: Step 1: assign an optimal work sequence to each robot based on the construction task, comprising: pre-assigning the construction task, and using a preset algorithm to perform secondary matching on the remaining tasks after pre-assignment; Wherein, using a preset algorithm to perform secondary matching on the remaining tasks after pre-assignment, comprising: Determine a robot set and a task set based on the robots after pre-assignment and the remaining tasks after pre-assignment, and construct a plurality of robot-task pairs based on the robot set and the task set; Determine the initial movement cost of each robot-task pair; For each robot-task pair, adjust the initial movement cost according to its current power and the on-site environment and historical power consumption; Construct a cost matrix based on the adjusted movement cost of each robot-task pair; Match the task with the minimum cost in the current cost matrix for each robot, if the task is not occupied, directly match, if the task is occupied by other robots, mark as potential conflict, and perform conflict matching until the corresponding task is matched for the current robot; When all robots have matched tasks, if a robot completes a task, match the remaining tasks in the order of the robot that completes the task until all remaining tasks are matched, then determine that the secondary matching is complete; Wherein, performing conflict matching, comprising: If the task is occupied by other robots, determine the total cost change of reassigning the currently occupied task to the current robot, if the total cost change meets the preset requirement, execute the exchange; If the total cost change does not meet the preset requirement, temporarily assign the current task to the current robot, and find a new task for the other robots that are occupied by the current task; Record the total cost change corresponding to the replacement task during the process of finding a new task until the total cost change meets the preset requirement; If a replacement task cannot be found, abandon the current task for the current robot, assign the task with the minimum cost in the current cost matrix to the current robot except the current task, if the task is not occupied, directly match, if the task is occupied by other robots, mark as potential conflict and perform conflict matching until the corresponding task is matched for the current robot, then end the matching; Step 2: obtain BIM data and on-site environment information, and determine the initial construction path of each robot in combination with the optimal work sequence of each robot; Step 3: analyze the initial construction path of all robots, and determine the construction path of each robot based on the path analysis result; Step 4: collect the position and speed of each robot in real time during construction, periodically count the robot work coverage, downtime and construction quality data, and then determine the path planning coefficient of each robot; Step 5: iteratively optimize the path planning parameters of each robot based on the path planning coefficient of each robot until the preset efficiency improvement target is met.

2. The construction robot work path optimization method according to claim 1, characterized by, Assign an optimal work sequence to each robot based on the construction task, comprising: Obtain the construction task based on the preset construction task database; Determine the attribute data of each robot based on the preset robot attribute table; Construct a task-robot matching matrix based on the construction tasks and attribute data of the robots, and then obtain the adaptation score of each task and each robot; Sort all the to-be-assigned construction tasks in descending order of the adaptation score, select the to-be-assigned construction tasks before the preset ranking, and determine the to-be-assigned construction tasks before the preset ranking as the to-be-selected construction tasks; Determine the score median based on the adaptation scores of all the to-be-selected construction tasks; Determine the dynamic adaptation score threshold based on the preset score and the score median; Pre-assign the construction tasks with adaptation scores greater than the dynamic adaptation score threshold; Perform secondary matching on the remaining tasks after pre-assignment using a preset algorithm, and then obtain the final assignment scheme to generate the optimal work sequence of each robot containing the task sequence and the execution time node.

3. The construction robot work path optimization method according to claim 1, characterized by, Obtain BIM data and site environment information, and determine the initial construction path of each robot based on the optimal work sequence of each robot, including: Extract the three-dimensional coordinates and geometric data of the construction task corresponding building components from the preset BIM system; Construct a point cloud map based on the site environment information; Coordinate system calibration is performed on the preset BIM model and the point cloud map, and three-dimensional rasterization processing is performed on the work area in the point cloud map after coordinate system calibration; Feature region marking is performed based on the region features of the work area in the point cloud map after three-dimensional rasterization processing; Determine the passage cost corresponding to each feature region based on the preset database; Plan the path based on the passage cost corresponding to each feature region, the optimal work sequence of all robots, and the preset path planning algorithm, and then determine the initial construction path of each robot.

4. The construction robot work path optimization method according to claim 1, characterized by, Analyze the initial construction path of all robots, and determine the construction path of each robot based on the path analysis result, including: Perform spatial conflict and resource conflict detection on the initial construction path of all robots; Based on the spatial conflict and resource conflict detection results, the construction path optimization is performed to obtain the construction path of each robot.

5. The construction robot work path optimization method according to claim 1, characterized by, In the construction process, the position, speed and task state data of each robot are collected in real time, and the robot work coverage, downtime and construction quality data are periodically counted to determine the path planning coefficient of each robot, including: The position and speed of the robot are collected in real time using a preset positioning system, and are uploaded to the edge computing node based on a preset time interval; Determine the path planning coefficient based on the periodically counted work coverage, downtime and construction quality, and the data obtained from the edge computing node: ； wherein, is a path planning coefficient of the current robot i, is an efficiency factor corresponding to the current robot i, and the calculation formula is: ； wherein, is the job coverage rate of the current robot i, is the preset target coverage rate, is the preset adjustment coefficient, is the downtime ratio of the current robot i, is the preset downtime threshold, is the penalty factor for penalizing abnormal fluctuations in speed and energy consumption of the robot i, and the calculation formula is: ； wherein, is the current speed of the current robot i, is the nominal speed of the robot, is the current energy consumption of the current robot i, is the current average energy consumption of all robots, is the preset sensitivity coefficient, respectively used for the secondary punishment of abnormal fluctuations in speed and energy consumption, is the quality degradation compensation for compensating the influence caused by the quality error, and the calculation formula is: ； wherein, is a current construction quality of the current robot i, is a preset target quality, is a preset maximum allowable quality deviation.

6. The construction robot work path optimization method according to claim 1, characterized by, Iteratively optimize the path planning parameters of each robot based on the path planning coefficient of each robot until the preset efficiency improvement target is met, including: Define the optimization objective function with the current position of the robot, the remaining task sequence and the environmental constraints as inputs; Based on the current path planning coefficient, use a preset optimization method to search for a better solution in the parameter space of the current parameter path planning, and optimize the path planning parameters based on the optimal solution; The optimized path planning parameters are sent to the robot controller to perform a new round of construction. In this process, the expected effect of the changed path planning parameters is simulated through the preset digital twin platform, and compared with the actual construction data. If the deviation exceeds the threshold, the parameter re-adjustment mechanism is triggered. When the comprehensive performance indicators of each robot in the continuous multiple optimization cycles are stable in the target interval and the efficiency improvement amplitude converges, it is determined that the preset efficiency improvement target is reached, the iteration is terminated, and the current parameters are solidified as the final scheme.

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