Construction robot operation path optimization method

Abstract

The invention provides a construction robot operation path optimization method, and belongs to the technical field of automatic scheduling and path optimization, and the method comprises the steps: 1, distributing an optimal operation sequence for each robot based on a construction task; 2, acquiring BIM data and field environment information, and determining an initial construction path of each robot in combination with the optimal operation sequence of each robot; 3, the initial construction paths of all the robots are analyzed, and the construction path of each robot is determined based on the path analysis result; 4, the position and speed of each robot are collected in real time in the construction process, the robot operation coverage rate, shutdown time and construction quality data are periodically counted, and then the path planning coefficient of each robot is determined; and 5, iteratively optimizing the path planning parameter of each robot based on the path planning coefficient of each robot until a preset efficiency improvement target is met. And the overall construction efficiency and the intelligent level are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automated scheduling and path optimization, and in particular to a method for optimizing the operation path of a construction robot. Background Art

[0002] As the construction industry continues to demand higher levels of efficiency, quality, and intelligence, construction robots, as a key tool for intelligent construction, are increasingly being used in actual construction projects. To further improve the efficiency and quality of construction robots, the rational allocation of construction tasks and the optimization of their operating paths have become pressing technical challenges.

[0003] Some existing research focuses on path planning for construction robots. For example, some approaches statically assign construction tasks and employ traditional path planning algorithms to generate construction paths, while others optimize initial paths by integrating BIM model data. However, most of these approaches suffer from the following issues: Task allocation is disconnected from the path optimization process, resulting in a lack of dynamic adjustment; path optimization fails to incorporate real-time data collected during construction; and there is a lack of a mechanism for iteratively adjusting path planning parameters based on actual work performance, resulting in limited improvements in construction efficiency.

[0004] Therefore, the present invention provides a method for optimizing the operation path of a construction robot. Summary of the Invention

[0005] This invention provides a method for optimizing the operation path of construction robots. By combining construction task allocation, BIM data analysis, and on-site environmental perception, this method dynamically determines the optimal construction path for construction robots. It then collects robot operation data in real time during the construction process and performs iterative optimization based on path planning coefficients. Compared to existing technologies, this method can effectively increase robot operation coverage, shorten downtime, and reduce construction energy consumption while ensuring the stability of construction quality. It significantly improves overall construction efficiency and intelligence, and has promising prospects for engineering applications.

[0006] The present invention provides a method for optimizing the operation path of a construction robot, comprising: Step 1: Assign the optimal work sequence to each robot based on the construction task; Step 2: Obtain BIM data and site environment information, and determine the initial construction path for each robot based on the optimal operation sequence for each robot; Step 3: Analyze the initial construction paths of all robots and determine the construction path of each robot based on the path analysis results; Step 4: During the construction process, the position and speed of each robot are collected in real time. The robot's operation coverage rate, downtime, and construction quality data are periodically collected to 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.

[0007] Preferably, an optimal operation sequence is assigned to each robot based on the construction task, including: Obtaining construction tasks based on a preset construction task database; The attribute data of each robot is obtained based on the preset robot attribute table; Based on the construction tasks and the robot's attribute data, a task-robot matching matrix is constructed to obtain the compatibility score between each task and each robot. Sort all construction tasks to be assigned by their fitness scores from high to low, filter out the construction tasks to be assigned before a preset ranking, and determine the construction tasks to be assigned before the preset ranking as the selected construction tasks; Determine the median score based on the fitness scores of all candidate construction tasks; Determine a dynamic fitness score threshold based on a preset score and a median score; Pre-allocate construction tasks whose fitness scores are greater than the dynamic fitness score threshold; The remaining tasks after pre-allocation are matched again using a preset algorithm to obtain the final allocation plan and generate the optimal operation sequence for each robot, including task sequence and execution time nodes.

[0008] Preferably, a preset algorithm is used to perform secondary matching on the remaining tasks after pre-allocation, including: Determine a robot set and a task set based on the pre-assigned robots and the remaining tasks after the pre-assignment, and construct a plurality of robot-task pairs based on the robot set and the task set; Determine the initial movement cost for each robot-task pair; For each robot-task pair, the initial movement cost is adjusted according to its current power as well as the on-site environment and historical power consumption; Construct a cost matrix based on the adjusted movement costs of each robot-task pair; Match each robot with the task with the lowest cost in its current cost matrix. If the task is not occupied, match it directly. If the task is occupied by another robot, mark it as a potential conflict and perform conflict matching until the current robot is matched with the corresponding task. When all robots have matched tasks, if a robot completes a task, the remaining tasks will be matched in the order of the robots that completed the task until all remaining tasks are matched. The secondary matching is then considered to be completed.

[0009] Preferably, performing conflict matching includes: If the task is already occupied by another robot, determine the total cost change of reallocating the currently occupied task to the current robot, and execute the exchange if the total cost change meets the preset requirements; If the total cost change does not meet the preset requirements, the current task will be temporarily assigned to the current robot, and new tasks will be found for other robots whose current tasks are occupied; In the process of searching for new tasks, the total cost change corresponding to the alternative tasks is recorded until the total cost change meets the preset requirements; If no alternative task can be found, the current robot will abandon the current task and be assigned the task with the lowest cost other than the current task in the current cost matrix. If the task is not occupied, it will be matched directly. If the task has been occupied by other robots, it will be marked as a potential conflict and conflict matching will be performed until the corresponding task is matched for the current robot.

[0010] Preferably, the BIM data and on-site environment information are obtained, and the initial construction path of each robot is determined in combination with the optimal operation sequence of each robot, including: Extract the 3D coordinates and geometric data of building components corresponding to the construction task from the preset BIM system; Construct an on-site point cloud map based on on-site environmental information; Calibrate the coordinate system of the preset BIM model and the on-site point cloud map, and perform three-dimensional rasterization on the work area in the on-site point cloud map after the coordinate system calibration; Marking feature areas based on regional features of the work area in the on-site point cloud map after three-dimensional rasterization processing; Determine the corresponding travel cost for each characteristic area based on a preset database; The path is planned based on the travel cost corresponding to each feature area, the preset path planning algorithm of all robots and the optimal operation sequence, and then the initial construction path of each robot is determined.

[0011] Preferably, analyzing the initial construction paths of all robots and determining the construction path of each robot based on the path analysis results includes: Perform spatial conflict and resource conflict detection on the initial construction paths of all robots; The construction path is optimized based on the spatial conflict and resource conflict detection results to obtain the construction path of each robot.

[0012] Preferably, the position, speed and task status data of each robot are collected in real time during the construction process, and the robot operation coverage, downtime and construction quality data are periodically counted to determine the path planning coefficient of each robot, including: The robot's position and speed are collected in real time using a preset positioning system and uploaded to the edge computing node based on preset time intervals. The path planning coefficient is determined based on the operation coverage, downtime, and construction quality of the cycle statistics and the data obtained from the edge computing nodes: ; in, 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: ; in, is the job coverage of the current robot i, is the preset target coverage, is the preset adjustment coefficient, is the downtime ratio of the current robot i, is the preset downtime threshold, is the penalty factor for the abnormal fluctuation of the speed and energy consumption of robot i, and the calculation formula is: ; in, is the current speed of robot i, The preset nominal speed of the robot, is the current energy consumption of robot i, is the current average energy consumption of all robots, are preset sensitivity coefficients, which are used to impose secondary penalties on abnormal fluctuations in speed and energy consumption. To compensate for the quality degradation caused by quality error, the calculation formula is: ; in, is the current construction quality of the current robot i, For the preset target quality, The maximum permissible quality deviation is preset.

[0013] Preferably, the path planning parameters of each robot are iteratively optimized based on the path planning coefficient of each robot until a preset efficiency improvement target is met, including: Define the optimization objective function with the robot's current position, remaining task sequence and environmental constraints as input; Based on the current path planning coefficients, 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; The optimized path planning parameters are sent to the robot controller to execute a new round of construction. During this process, the expected effect of the path planning parameter changes 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; When the comprehensive performance indicators of each robot are stable in the target range and the efficiency improvement rate converges in multiple consecutive optimization cycles, it is determined that the preset efficiency improvement target has been achieved, the iteration is terminated, and the current parameters are solidified as the final solution.

[0014] Compared with the prior art, the present invention has the following advantages: By combining construction task allocation, BIM data analysis, and on-site environmental perception, the optimal construction path for construction robots is dynamically determined. Robot operation data is collected in real time during the construction process, and iterative optimization is performed based on the path planning coefficients. Compared to existing technologies, this invention can effectively increase robot operation coverage, shorten downtime, and reduce construction energy consumption while ensuring the stability of construction quality. It significantly improves overall construction efficiency and intelligence, and has promising prospects for engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 It is a flowchart of a construction robot operation path optimization method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0018] Example 1

[0019] An embodiment of the present invention provides a method for optimizing an operation path of a construction robot, comprising: Step 1: Assign the optimal work sequence to each robot based on the construction task; Step 2: Obtain BIM data and site environment information, and determine the initial construction path for each robot based on the optimal operation sequence for each robot; Step 3: Analyze the initial construction paths of all robots and determine the construction path of each robot based on the path analysis results; Step 4: During the construction process, the position and speed of each robot are collected in real time. The robot's operation coverage rate, downtime, and construction quality data are periodically collected to 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.

[0020] In this embodiment, a construction task refers to the work content and objectives that a construction robot needs to complete during a specific construction phase. It includes specific work types, scopes, quality requirements, and time constraints, such as wall spraying, concrete pouring, or steel structure installation. Taking wall spraying as an example, a construction task may be defined as "Complete the fire retardant coating spraying 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 spraying area coordinates, paint dosage, and process parameters to guide the allocation of robot work sequences. The clear description of the construction task provides goal guidance for path planning, such as prioritizing emergency areas or coordinating the boundaries of the work areas of multiple robots.

[0021] In this embodiment, BIM data and on-site environmental 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. On-site environmental information is collected in real time through sensors, including dynamic parameters such as obstacle location, temperature and humidity, and light intensity. For example, in pipeline installation operations, the robot first retrieves static data such as the pipeline design path and embedded parts coordinates in the BIM. At the same time, through lidar scanning, it is found that the building materials temporarily stacked on site block the preset path. At this time, the dynamic obstacle coordinates are superimposed on the BIM model to generate an updated three-dimensional environmental map. This information fusion enables the robot to not only follow design specifications, but also avoid emergencies such as scaffolding movement and personnel activities in real time.

[0022] In this embodiment, the path planning coefficient is a multi-dimensional metric used to quantitatively assess a robot's operational efficiency. For example, a calculation of the coefficient for a bricklaying robot revealed that the current path enabled it to cover 85% of the target area (coverage rate) in an 8-hour operation. However, the robot experienced 45 minutes of downtime waiting for material replenishment (time utilization), and the brick joint error at the corner exceeded the standard by three times (construction quality). Based on this, the system generated a planning coefficient of 0.76 (out of a maximum score of 1.0), with the primary penalty being excessive downtime. This coefficient drives the algorithm to prioritize path connections at material replenishment points. By adding access to intermediate charging stations, the coefficient was increased to 0.89 for the next cycle. This dynamic feedback mechanism enables continuous improvement in path planning.

[0023] The beneficial effects of this technical solution include dynamically determining the optimal construction path for construction robots by combining construction task allocation, BIM data analysis, and on-site environmental perception. Furthermore, the robot's operational data is collected in real time during the construction process, and iterative optimization is performed based on the path planning coefficients. Compared to existing technologies, this invention can effectively increase robot operation coverage, shorten downtime, and reduce construction energy consumption while ensuring the stability of construction quality. This significantly improves overall construction efficiency and intelligence, and has promising prospects for engineering applications.

[0024] Example 2

[0025] An embodiment of the present invention provides a method for optimizing the operation path of a construction robot, which assigns an optimal operation sequence to each robot based on the construction task, including: Obtaining construction tasks based on a preset construction task database; The attribute data of each robot is obtained based on the preset robot attribute table; Based on the construction tasks and the robot's attribute data, a task-robot matching matrix is constructed to obtain the compatibility score between each task and each robot. Sort all construction tasks to be assigned by their fitness scores from high to low, filter out the construction tasks to be assigned before a preset ranking, and determine the construction tasks to be assigned before the preset ranking as the selected construction tasks; Determine the median score based on the fitness scores of all candidate construction tasks; Determine a dynamic fitness score threshold based on a preset score and a median score; Pre-allocate construction tasks whose fitness scores are greater than the dynamic fitness score threshold; The remaining tasks after pre-allocation are matched again using a preset algorithm to obtain the final allocation plan and generate the optimal operation sequence for each robot, including task sequence and execution time nodes.

[0026] In this embodiment, the preset construction task database includes task type, required equipment, estimated working hours and priority.

[0027] In this embodiment, the preset robot attribute table records parameters such as the working range, load capacity, and movement speed of each robot.

[0028] In this embodiment, the secondary matching stage is based on the goal of minimizing the total moving distance of all robots. It adopts the improved Hungarian algorithm, adds penalty terms, and adds hard constraints to tasks with process dependencies: subsequent tasks must be performed by the same robot or meet the process handover standards.

[0029] 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 the robot attribute table and dynamically adjusting the fitness threshold, the construction tasks are reasonably screened and pre-allocated, thereby 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.

[0030] Example 3: The embodiment of the present invention provides a method for optimizing the operation path of a construction robot, which uses a preset algorithm to perform secondary matching on the remaining tasks after pre-allocation, including: Determine a robot set and a task set based on the pre-assigned robots and the remaining tasks after the pre-assignment, and construct a plurality of robot-task pairs based on the robot set and the task set; Determine the initial movement cost for each robot-task pair; For each robot-task pair, the initial movement cost is adjusted according to its current power as well as the on-site environment and historical power consumption; Construct a cost matrix based on the adjusted movement costs of each robot-task pair; Match each robot with the task with the lowest cost in its current cost matrix. If the task is not occupied, match it directly. If the task is occupied by another robot, mark it as a potential conflict and perform conflict matching until the current robot is matched with the corresponding task. Once all robots have matched their tasks, the secondary matching is considered complete.

[0031] In this embodiment, a robot set and a task set are determined: each robot includes: current position, remaining power, task execution capability, etc., and each task in the task set includes: target position, urgency, estimated time, etc.

[0032] In this embodiment, the initial movement cost Using path planning algorithms (such as or Dijkstra) computing robot To the task The shortest path distance is calculated, which takes into account the impact of static obstacles. For grid maps, the distance can be normalized to the scale of the map (e.g., each grid represents 1 meter), including: Power penalty , according to the robot's current remaining power and task estimated power consumption , dynamically adjust the cost, the formula for the power penalty term is:

[0033] in, is the maximum power of the robot, 1 is the penalty coefficient, , usually when the battery is low 1. A larger value means that when the battery is low, the cost of robot movement is higher, which prevents robots with low battery from performing long-distance tasks; Environmental penalties It is the impact of dynamic environmental factors (such as congested areas and temporary obstacles) on the path. The formula for the environmental penalty term is:

[0034] The dynamic obstacle density is calculated through real-time SLAM or sensor detection. If the path passes through a high-congestion area, the cost will increase.

[0035] Historical power consumption learning items By recording the actual power consumption of historical tasks , correct the estimated power consumption error of the task, and the formula for the historical power consumption learning item is:

[0036] in, It's a task Estimated power consumption, is the number of executions of the historical task. When the power consumption of the historical task is higher than the estimate, k is the kth execution, and the cost will be increased. is the preset historical impact coefficient, .

[0037] Comprehensive corrected movement cost, final corrected movement cost is the sum of all the above cost items:

[0038] This cost takes into account the base distance, power penalty, environmental impact, and learning corrections for historical power consumption; In this embodiment, the cost matrix is constructed as follows: Initialize the matrix: Matrix dimension: m×n (m robots, n tasks); Initial value: all =∞ (indicates no match); calculate the corrected cost pair by pair for each pair (ri, tj): call the path planning algorithm to calculate ; Get the robot's power and environment data in real time; Load the task's power consumption records from the historical database; Calculate according to the above formula ; Triggering condition of matrix dynamic update strategy: robot power change Δ ≥10%; environment map update (such as adding new obstacles); task queue changes (adding / canceling tasks); partial update: only recalculate the affected rows / columns (such as only updating the corresponding rows of robots with low battery).

[0039] The beneficial effects of this technical solution include: By applying a secondary matching optimization strategy to the remaining tasks after pre-assignment, taking into account the robot's current battery level, the on-site environment, and historical energy consumption, dynamically adjusting movement costs and constructing an accurate cost matrix, the system achieves efficient matching between robots and tasks. By introducing a mechanism for identifying and resolving potential conflicts, the system ensures the continuity and rationality of the matching process, effectively reducing overall movement costs, improving operational efficiency, and enhancing the intelligence of task allocation.

[0040] Example 4: An embodiment of the present invention provides a method for optimizing the operation path of a construction robot, performing conflict matching, including: If the task is already occupied by another robot, determine the total cost change of reallocating the currently occupied task to the current robot, and execute the exchange if the total cost change meets the preset requirements; If the total cost change does not meet the preset requirements, the current task will be temporarily assigned to the current robot, and new tasks will be found for other robots whose current tasks are occupied; In the process of searching for new tasks, the total cost change corresponding to the alternative tasks is recorded until the total cost change meets the preset requirements; If no alternative task can be found, the current robot will abandon the current task and be assigned the task with the lowest cost other than the current task in the current cost matrix. If the task is not occupied, it will be matched directly. If the task has been occupied by other robots, it will be marked as a potential conflict and conflict matching will be performed until the corresponding task is matched for the current robot.

[0041] In this embodiment, the alternative task is found based on the cost matrix. The purpose is to find a path from the idle robot to the idle task with alternating non-matching edges and matching edges, and reduce the total movement cost by exchanging the matching relationship on the path. Assume that there is a robot Want to perform tasks , but the task Already been robot The process of occupation and replacement task search is as follows: Path rule: Find a path that meets the following conditions: , goal: find the final path to the idle task The path of the robot that is not currently assigned is selected. Start. Find unmatched edges: select the edge with the smallest cost in the cost matrix , check the task Is it occupied: If Not occupied, match directly , the search for alternative tasks is complete. If quilt If occupied, try: temporarily let (non-matching edges), and let Find new matches (recursively perform DFS). During path expansion, record the replacement tasks , which reduces the total cost; in, Adjust Match: If Find new tasks , then adjust the matching to , the augmentation path is successful. If no suitable alternative path can be found, backtrack and continue searching The next optional task , Layer limit: To avoid too deep recursion, you can set a maximum search depth (such as 3 layers). If it exceeds, the path will be abandoned. Example: Initial situation: Assume the following cost matrix:

[0042] Current Match: , , total cost = 3 + 6 = 9, for a better solution: try to make match (lower cost), triggering augmenting path search: Want to match ,but quilt Occupancy, recursive check Can I switch to other tasks? You can try to match (Original match is ), the total cost after the exchange = 2 + 5 = 7, which is obviously lower, the new matching: , , total cost = 2 + 5 = 7, a better solution is obtained. The algorithm termination condition ensures that the final matching is the global optimal or local optimal, and the total cost cannot be further reduced by augmenting the path; 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 current robot is matched with the corresponding task, and the conflict matching in the matching is terminated. The following processes are included: 1. If the task has been occupied by other robots, the total cost change of reallocating the currently occupied task to the current robot is determined. If the total cost change meets the preset requirements, the exchange is performed; 2. If the total cost change does not meet the preset requirements, the current task is temporarily assigned to the current robot, and new tasks are sought for other robots whose current tasks are occupied; 3. In the process of searching for new tasks, the total cost change corresponding to the alternative task is recorded until the total cost change meets the preset requirements; 4. If no alternative task can be found, the current robot abandons the current task, and the task with the smallest cost other than the current task in the current cost matrix 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 above matching processes 1-4 are executed until the current robot is matched with the corresponding task, and the matching is terminated.

[0043] In this embodiment, the successful termination condition is that all robots have successfully matched tasks: matching number = , that is, all rows or columns of the cost matrix are covered, failure termination condition: no augmenting path can be found, indicating that the local optimum has been reached: in the cost matrix, there is no unmatched robot Able to find the augmenting path to the idle task, mathematical expression: for any robot , all possible tasks Either it is occupied or adjusting the match cannot reduce the total movement cost. Post-termination processing: output the final match and its total movement cost, and re-run after dynamic adjustment: If the power or environment changes cause the cost matrix to be updated, the algorithm needs to be re-executed to optimize the match.

[0044] In this embodiment, the total cost change meeting the preset requirement means that the total cost change is less than 0.

[0045] The beneficial effects of this technical solution include: by introducing a total cost change assessment mechanism into the task conflict matching process, it can flexibly adjust task allocation when task resources are occupied. Through various strategies such as swapping, temporary allocation, and potential conflict resolution, it dynamically optimizes robot operation paths, significantly improving overall operational efficiency and resource utilization. This method effectively avoids operational stagnation or resource waste caused by task conflicts, ensuring the rationality of task allocation and optimal path planning for robot swarms in complex operating environments.

[0046] Example 5: The embodiment of the present invention provides a method for optimizing the operation path of a construction robot, which obtains BIM data and on-site environment information, and determines the initial construction path of each robot in combination with the optimal operation sequence of each robot, including: Extract the 3D coordinates and geometric data of building components corresponding to the construction task from the preset BIM system; Construct an on-site point cloud map based on on-site environmental information; Calibrate the coordinate system of the preset BIM model and the on-site point cloud map, and perform three-dimensional rasterization on the work area in the on-site point cloud map after the coordinate system calibration; Marking feature areas based on regional features of the work area in the on-site point cloud map after three-dimensional rasterization processing; Determine the corresponding travel cost for each characteristic area based on a preset database; The path is planned based on the travel cost corresponding to each feature area, the preset path planning algorithm of all robots and the optimal operation sequence, and then the initial construction path of each robot is determined.

[0047] In this embodiment, constructing the on-site point cloud map based on the on-site environment information is to construct the on-site point cloud map through laser SLAM.

[0048] In this embodiment, the three-dimensional rasterization process is based on a preset grid resolution process, and the grid resolution is set to 5 cm×5 cm×5 cm.

[0049] In this embodiment, the regional features of the working area in the on-site point cloud map are fixed structural components and newly added obstacles (such as stacked materials and vehicles); In this embodiment, the area marking includes: permanent obstacle areas (such as load-bearing columns) and temporary obstacle areas (such as material storage areas), and the judgment rules and implementation methods are as follows: (1) Judgment of permanent obstacle areas: Data pre-labeling: Extract the coordinate range of structural components (such as columns and walls) from the BIM model or CAD drawings and mark them as permanent obstacle areas; preset them as impassable areas in the navigation system (set to infinite cost in the cost map); Verification mechanism: Align the laser radar scanning point cloud with the BIM model to confirm whether the static obstacle position matches (error < 5cm); if the deviation exceeds the threshold, trigger manual review (to avoid construction errors causing path planning failure), (2) Determination of temporary obstacle areas: Dynamic detection: Sensor input: LiDAR / camera detects new obstacles (such as stacked materials, vehicles) in real time; Change detection algorithm: Compare the current frame point cloud with the static map to extract new occluded areas (such as using OctoMap for dynamic updates); Spatiotemporal attribute labeling: Add a timestamp and expected existence time to temporary obstacles (such as "Steel stacking area: expected to remain for 8 hours"). If it does not disappear after the timeout, an alarm is generated to prompt manual confirmation (to avoid sensor misdetection or unupdated plan changes); Flexible path planning: Temporary obstacle areas are set as high-cost areas (not infinite) in the cost map. The robot can bypass them but is not completely blocked; through Dynamic replanning algorithms adjust paths in real time.

[0050] In this embodiment, the passage cost corresponding to each area is determined based on a preset database: permanent obstacle area: infinite cost, robot operation area: basic cost × 1.2, manual construction area: basic cost × 1.5.

[0051] In this embodiment, during the initial construction path planning phase of the construction robot's operation path optimization, the construction area must first be divided into several characteristic areas based on the BIM model, and actual environmental information is obtained through on-site scanning, thereby assigning a travel cost centered on time and energy consumption to each characteristic area (such as the work surface, material storage area, passage, etc.). The travel costs in high-density work areas, narrow passages, or uphill and downhill areas are higher. Combined with the robot's optimal operation sequence (i.e., the shortest duration operation chain generated by task decomposition and scheduling algorithms), a hierarchical path planning strategy is adopted: the first layer is based on The algorithm generates a preliminary coarse-grained path for each robot, prioritizing the characteristic areas with the lowest travel cost to ensure no task overlap. The second layer verifies the feasibility of multi-robot collaboration through space-time corridor technology. If a space-time conflict is detected (two or more robots occupying the same space at the same time), a conflict resolution mechanism is activated, dynamically adjusting the path's time window or spatial trajectory. Specific methods include time delay (postponing low-priority tasks), spatial rerouting (circumventing high-cost areas), or task reallocation (adjusting the work sequence). The optimized path must also pass dynamic constraint checks, including turning radius adaptation to the mechanical structure and speed / acceleration limits. It is ultimately smoothed into a continuous trajectory that meets the robot's motion capabilities (for example, using Bezier curves to eliminate sudden corner changes), and a sequence of discrete path points with timestamps is output as the initial construction path.

[0052] In this embodiment, the preset path planning algorithm is combined with the improved algorithm.

[0053] The beneficial effects of this technical solution include: by combining BIM data with on-site environmental information, using coordinate system calibration and 3D rasterization, a precise construction site environmental model is constructed, enabling the identification of work area features and assessment of access costs. By comprehensively considering access costs and the optimal robot operation sequence, a pre-set path planning algorithm is employed to rationally determine the initial construction path for each robot, effectively improving the rationality and efficiency of construction paths and reducing path conflicts and energy costs.

[0054] Example 6: An embodiment of the present invention provides a method for optimizing the operation path of a construction robot, which analyzes the initial construction paths of all robots and determines the construction path of each robot based on the path analysis results, including: Perform spatial conflict and resource conflict detection on the initial construction paths of all robots; The construction path is optimized based on the spatial conflict and resource conflict detection results to obtain the construction path of each robot.

[0055] In this embodiment, spatial conflict detection: checks whether the paths of different robots occupy the same space (such as intersections, narrow passages) at the same time.

[0056] In this embodiment, resource conflict detection: detecting the competition among multiple robots for the same equipment (such as a lifting platform) or task area.

[0057] In this embodiment, construction paths are optimized based on the results of spatial and resource conflict detection to determine a construction path for each robot. Path intersections are analyzed using a conflict graph model from graph theory to identify potential spatiotemporal conflicts between robots and dynamic obstacles (such as workers and equipment). Path timing or spatial distribution is then adjusted based on a time window protocol (such as Spatio-Temporal Planning (STP)). Secondly, the logical feasibility of the paths is verified by combining construction process dependencies (e.g., "water and electricity installation can only proceed after the wall is dry"). Task sequences are then reallocated using mixed integer linear programming (MILP) to eliminate deadlocks. For example, if the painting path of robot A overlaps with the transportation path of robot B in spatiotemporal time, the high-priority task (spraying) is prioritized for continuous execution, while robot B adopts a detour or delayed start strategy. Finally, hierarchical path weights are generated based on an energy consumption model (taking into account ground friction and slope angle) and task urgency. For example, paths for urgent tasks (such as concrete pouring) are allowed to sacrifice 10% energy consumption in exchange for a 15% time reduction. The optimized path must meet the following requirements: ① No hard conflicts (100% safety); ② Total construction time reduced by ≥8% compared to the initial plan; and ③ Energy consumption fluctuations per robot must not exceed ±20% of the mean. Path data is stored in a 3D spatiotemporal trajectory format (XYZ + timestamp) and synchronized to the cloud-based BIM model for real-time monitoring.

[0058] The beneficial effect of this technical solution is that by performing spatial and resource conflict detection on all robots' initial construction paths, potential conflicts can be promptly identified. Based on these detection results, construction paths are optimized, and the robots' work sequence and path planning are rationally adjusted. This method effectively improves the efficiency of collaborative work during the robot construction process, reduces the risk of delays and resource waste caused by path conflicts, and ensures the orderly and efficient progress of construction tasks. Overall,

[0059] Example 7: The embodiment of the present invention provides a method for optimizing the operation path of a construction robot. During the construction process, the position, speed, and task status data of each robot are collected in real time. The robot operation coverage rate, downtime, and construction quality data are periodically counted to determine the path planning coefficient of each robot. The method includes: The robot's position and speed are collected in real time using a preset positioning system and uploaded to the edge computing node based on preset time intervals. The path planning coefficient is determined based on the operation coverage, downtime, and construction quality of the cycle statistics and the data obtained from the edge computing nodes:

[0060] in, 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:

[0061] in, is the job coverage of the current robot i, is the preset target coverage, is the preset adjustment coefficient, is the downtime ratio of the current robot i, is the preset downtime threshold, is the penalty factor for the abnormal fluctuation of the speed and energy consumption of robot i, and the calculation formula is:

[0062] in, is the current speed of robot i, The preset nominal speed of the robot, is the current energy consumption of robot i, is the current average energy consumption of all robots, are preset sensitivity coefficients, which are used to impose secondary penalties on abnormal fluctuations in speed and energy consumption. To compensate for the quality degradation caused by quality error, the calculation formula is:

[0063] in, is the current construction quality of the current robot i, For the preset target quality, The maximum permissible quality deviation is preset.

[0064] In this embodiment, the operation coverage rate, downtime and construction quality are periodically counted: Position 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 rate = completed area / planned area × 100%; downtime = total downtime / running time × 100%; construction quality = 1-(number of defect points / number of inspection points).

[0065] In this embodiment, coverage is determined as: Completed Area / Planned Area × 100%. Coverage quantifies the robot's completion of the construction area, ensuring maximum construction efficiency. The following steps are followed: Area Division: Based on the BIM model or on-site mapping, the construction site is divided into several grid cells (e.g., 0.5m x 0.5m) or blocks, each of which is uniquely identified. Completed Area Statistics: The robot's onboard sensors (e.g., LiDAR, visual SLAM) record its trajectory and operating status in real time, marking completed grid cells. If the robot performs a specific task (e.g., spraying or masonry) and achieves a predetermined accuracy (e.g., spraying thickness meets the required standards, masonry blocks are installed in place), the unit is considered completed. Planned Construction Area Definition: Based on the construction task list, the total area to be covered for each operation phase (including the main operation area and auxiliary paths) is clearly defined, excluding temporary restricted areas (e.g., uncleared material areas) or inaccessible areas (e.g., structural obstacles). Coverage Calculation: The percentage of completed qualified units relative to the total planned units is calculated on a cycle basis (e.g., hourly). If the target is ≥95%, the coverage rate is met; if it is below a threshold (such as 90%), path optimization is triggered (such as adjusting the robot arm posture to reduce omissions); In this embodiment, downtime is determined. Downtime reflects the robot's availability and directly impacts construction progress. Accurate statistics require distinguishing between active downtime (charging, maintenance) and passive downtime (failure, blockage). Specific steps include: state classification: defining the robot's operating state (e.g., "operating," "charging," "failure pending repair," "path blocked") and recording state transition timestamps in real time through the control system; effective operating time statistics: excluding planned downtime (e.g., daily charging periods), accumulating unplanned downtime (e.g., battery depletion, robot arm jamming, network outages); time normalization: calculating the ratio of total unplanned downtime to total operating time within a statistical period (e.g., an 8-hour shift). If this ratio exceeds a threshold (5%), the cause is analyzed (e.g., insufficient battery life or excessive collision frequency) and the path planning strategy is adjusted (e.g., adding charging stations or reducing movement speed); dynamic threshold correction: dynamically adjusting the downtime threshold based on historical data (e.g., battery efficiency changes caused by seasonal temperature fluctuations) to avoid misjudging performance degradation due to environmental fluctuations. Work coverage rate = completed area / planned area × 100%. Construction quality determination: Construction quality directly impacts project acceptance, requiring a quantitative assessment of the consistency of work precision. Taking spray thickness as an example, the coefficient determination process is as follows: Quality indicator definition: Define the standard range for key parameters (e.g., a design spray thickness of 2.0mm with an allowable deviation of ±0.3mm). Real-time data collection: Obtain actual work parameters through embedded sensors (e.g., ultrasonic thickness gauges) or periodic spot checks (e.g., manual sampling at five points every 10 minutes), and record deviations from target values. Variance calculation: Calculate the standard deviation (σ) of thickness data from all sampling points within the same work area. If σ ≤ 0.3mm, quality is considered stable; if σ > 0.3mm, process fluctuations (e.g., nozzle clogged or uneven travel speed) are identified. Root cause analysis: Combine path data (e.g., decreased overlap when the robot turns) and equipment status (e.g., pump pressure fluctuations) to identify the causes of quality defects. For example, excessive path curvature can lead to uneven spraying, requiring smoothing the trajectory or reducing travel speed.

[0066] In this embodiment, the preset target coverage value range is obtained by comprehensively considering factors such as the amount of construction tasks, the robot's operating capabilities, and the construction period in actual engineering scenarios such as building construction. For example, if the scale of the construction site is fixed and the robot's performance is stable, a reasonable coverage ratio range is obtained based on the degree of operation coverage that the robot can achieve in similar projects in the past, combined with the requirements of the current project's construction period. Common values are between 80% and 95%, which means that the robot is expected to complete the operation of most areas as much as possible within the specified time, but considering the various interference factors that actually exist (such as equipment failure, complex site conditions, etc.), it will not be set to 100%. Theoretical calculation: Theoretical calculation is based on parameters such as the construction area, the robot's single operation coverage area, and the operation time. For example, the total area of the facility is , the robot covers an area per unit time of The total planned operation time is , then the ideal coverage area can be calculated as , the theoretical target coverage is Based on the theoretical value, further appropriate corrections are made in combination with factors such as actual operation feasibility and site complexity to finally determine the reasonable Value, the value range is , located in The value is set between 0.000 and 0.100, depending on the coverage requirements of the specific construction task. Operations with high coverage requirements (such as floor cleaning and lawn mowing) typically have 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 less than 0.8.

[0067] In this embodiment, the preset adjustment coefficient value range is obtained in the following manner: Sensitivity analysis: by changing the coefficient value, observe its influence on the efficiency factor and the final path planning coefficient. In multiple simulation experiments or actual tests, if the preset adjustment coefficient value is too small, such as 0.01, it will be found that when the operation coverage slightly deviates from the target coverage, the efficiency factor changes dramatically, resulting in the path planning being too sensitive and unstable; if the value is too large, such as 1, the coverage deviation has a weak effect on the efficiency factor, and cannot effectively reflect the impact of the coverage difference on the robot efficiency. After a large number of tests comparing the stability of the system under different values, the rationality of the response to coverage changes, etc., the default value of 0.1 is determined, and it is concluded that it should generally be a decimal greater than 0, and the value range is usually in a smaller numerical interval. The default value is 0.1, which is used to ensure exponential decay when the coverage deviates; In this embodiment, the preset speed abnormal fluctuation sensitivity coefficient has a value range of: a real number greater than 0. Determination method: It is determined based on the degree of influence of the robot speed on the work effect and the overall system. If the fluctuation of the robot speed has a greater impact on the work quality, efficiency or other aspects, it is necessary to select a larger preset speed abnormal fluctuation sensitivity coefficient value so that when the speed deviates from the standard speed, the penalty term can increase rapidly to encourage the robot to keep running near the standard speed. For example, in some processing tasks with high precision requirements, the instability of the robot speed may cause the processing quality to decline. At this time, the preset speed abnormal fluctuation sensitivity coefficient will take a larger value. On the contrary, if the speed fluctuation has little effect on the operation, 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 working conditions, so as to achieve the purpose of effectively controlling speed fluctuations without being too harsh on the normal speed adjustment of the robot; In this embodiment, the preset energy consumption abnormal fluctuation sensitivity coefficient has a value range of real numbers greater than 0 and is determined in a similar manner to the preset speed abnormal fluctuation sensitivity coefficient. The preset energy consumption abnormal fluctuation sensitivity coefficient is determined based on the impact of energy consumption fluctuations on the system. If abnormal energy consumption fluctuations have a significant impact on the energy management, cost control, and other aspects of the entire operating system, it is necessary to increase the value of the preset energy consumption abnormal fluctuation sensitivity coefficient so that when energy consumption deviates from the average energy consumption, the penalty term can be significantly increased, thereby encouraging the robot to maintain energy consumption stability as much as possible. For example, in some mobile robot operation scenarios with limited energy, stable energy consumption control is crucial, and the preset energy consumption abnormal fluctuation sensitivity coefficient will take a larger value. In some scenarios that are less sensitive to energy consumption, the preset energy consumption abnormal fluctuation sensitivity coefficient can be relatively small. Its specific value is also determined by monitoring and analyzing the energy consumption situation in actual operation, combined with the system's ability to withstand energy consumption fluctuations.

[0068] In this embodiment, (The maximum allowable quality deviation at the current stage), value range: greater than 0, determined according to quality requirements, determination method: It is determined based on the quality standards and allowable error range of the specific construction task. For some tasks with high precision requirements, such as aerospace parts processing, the quality deviation requirements are very strict. It will take a smaller value, possibly close to 0. For some tasks with relatively low quality requirements, such as ordinary building wall plastering, It can take a larger value. It needs to comprehensively consider factors such as the nature of the task, subsequent use requirements and cost. Generally speaking, it will be reasonably determined by statistical analysis of the quality data of previous similar tasks, combined with the special requirements of the current task and customer expectations. ) value to ensure that the robot's operation will not be overly constrained while ensuring quality, and the path planning coefficient can be effectively adjusted through the mass compensation value.

[0069] In this embodiment, the preset downtime threshold has a value range of (0, 1), and a common value may be around (0.1-0.2) (i.e. (10%-20%)). The determination method is: this value is determined based on the robot's operating tasks and actual operating conditions. Generally speaking, considering that the robot may stop for various reasons (such as charging, fault repair, waiting for materials, etc.), it is necessary to set an acceptable upper limit for downtime. For some tasks with high continuous operation requirements, downtime will seriously affect production efficiency, so the downtime threshold will be set lower, perhaps around 0.1, and for some non-continuous operations or tasks that are relatively less sensitive to downtime, the downtime threshold can be appropriately higher, perhaps around 0.2; In this embodiment, the preset target quality value range is determined as follows: The preset target quality value range is determined based on factors such as specific construction task requirements, industry standards, and customer expectations. Different work tasks have different quality measurement standards. For example, in construction, there are clear industry specifications and quality acceptance standards for wall flatness, concrete pouring density, etc. These standards will serve as an important basis for determining the target quality. If the quality measurement standard is a 0-100 scoring system, then the preset target quality may be set between 80-95 based on the specific task, indicating that the quality of the work completed by the robot is expected to reach a higher level.

[0070] In this embodiment, the preset range of values of the nominal speed of the robot is determined as follows: The preset range of values of the nominal speed of the robot is determined based on the design performance of the robot and the actual operation requirements. When designing a robot, a speed range that can ensure its stable and efficient operation will be determined based on factors such as its mechanical structure, power system, and control system. The limitations of the operating scenario will also affect its value. If the robot needs to operate in an environment with a large number of people or a narrow space, the nominal speed will be relatively low to ensure safety; in some open, unmanned industrial environments, the nominal speed can be appropriately increased. The value range is determined according to the performance of the robot. It is generally a fixed value. The common value 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.

[0071] In this embodiment, the calculation logic features: nonlinear response: when the coverage is insufficient or the shutdown exceeds the limit, the efficiency factor is greatly reduced, and the nonlinear weighted response is adopted; active jitter suppression: frequent path switching is avoided through the secondary penalty of energy consumption and speed, reducing the impact of small 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 calculated The range is [0, 1] and directly maps to the path adjustment priority. Through the above formula, PPC evaluates the priority of the robot's current path and comprehensively considers the path adjustment strategy based on factors such as efficiency, penalty, and quality.

[0072] The beneficial effects of this technical solution include dynamically determining each robot's path planning coefficient by collecting real-time data on robot position, speed, and task status during the construction process, combined with periodic statistical data on work coverage, downtime, and construction quality. By constructing an optimization model that comprehensively considers work efficiency, resource utilization, speed and energy stability, and quality compensation, adaptive adjustment and optimization of the construction path are achieved. This method effectively improves the construction robot's operational continuity and path rationality, while reducing efficiency losses and quality risks caused by operational anomalies.

[0073] Example 8: An embodiment of the present invention provides a method for optimizing the operation path of a construction robot, which iteratively optimizes the path planning parameters of each robot based on the path planning coefficient of each robot until a preset efficiency improvement target is met, including: Define the optimization objective function with the robot's current position, remaining task sequence and environmental constraints as input; Based on the current path planning coefficients, 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; The optimized path planning parameters are sent to the robot controller to execute a new round of construction. During this process, the expected effect of the path planning parameter changes 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; When the comprehensive performance indicators of each robot are stable in the target range and the efficiency improvement rate converges in multiple consecutive optimization cycles, it is determined that the preset efficiency improvement target has been achieved, the iteration is terminated, and the current parameters are solidified as the final solution.

[0074] In this embodiment, environmental constraints include obstacle distribution and charging station locations; In this embodiment, the optimization objective function is defined to comprehensively consider the trade-offs between construction efficiency (maximizing coverage), operational stability (minimizing downtime), and construction quality (e.g., ensuring that the spray thickness variance meets the required standards). This objective function typically takes the form of a weighted summation, where the weight coefficients of each KPI are dynamically adjusted based on the actual construction priority. For example, during a rush phase, the weight of coverage can be appropriately increased, while construction quality constraints can be tightened in quality-sensitive areas.

[0075] In this embodiment, a better solution is searched. For example, if the statistics of the current cycle show that the construction quality of a robot has declined due to frequent turning (thickness variance > 0.3mm), the quality is improved by reducing the path curvature or increasing the dwell time in the overlapping spraying area (adjusting the speed-precision balance parameter); if the downtime of another robot exceeds the standard (> 5%) due to insufficient battery life, its path is optimized to the nearest charging station and the frequency of high-power consumption actions (such as climbing) is shortened. At the same time, its operation sequence is dynamically updated to avoid task interruption.

[0076] In this embodiment, comprehensive performance indicators include coverage ≥95%, downtime <4%, and quality variance ≤0.28 mm.

[0077] In this embodiment, the preset efficiency improvement target is determined to have been achieved when the comprehensive performance indicators of each robot remain stable within the target range and the efficiency improvement converges over multiple consecutive optimization cycles. By monitoring the performance stability and optimization benefit decay over multiple cycles, the optimal balance of path parameters is determined. For example, under the initial parameters of a wall-painting robot, the coverage rate was 82% and the downtime rate was 15%. After three iterative optimizations (parameter adjustments: the spray path overlap ratio was reduced from 20% to 12%, and the corner deceleration threshold was increased from 0.5 m / s to 0.8 m / s), the coverage rate increased to 92%. However, the fourth optimization attempt only improved the coverage rate to 93%, and the fifth optimization attempt even degraded the edge spraying quality due to excessive speed (coverage rate regressed to 91%). At this point, the system detects: 1) the coverage rate has fluctuated between 90% and 93% over the past three cycles (entering a stable range); 2) the efficiency gain has gradually decayed from 10% to 0.5% (convergence); and 3) the digital twin simulation indicates that further parameter adjustments will only yield a marginal benefit of ≤0.3%. The system determines that the preset goal of "coverage ≥ 90% and increase < 1% for three consecutive times" has been achieved, terminates optimization, and solidifies the current parameters. Furthermore, before solidification, the twin simulation data is compared with the actual construction records. If the actual thickness deviation of a spray corner exceeds 5% of the simulation value (the trigger threshold), the previous version parameters are 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 cause the fluctuation of indicators such as the welding gun movement speed and angle adjustment frequency to be less than 2% for five consecutive cycles, and the welding time per unit length is stabilized at 4.3±0.1 minutes, the process stability requirements are determined to be met and the iteration is terminated.

[0078] The beneficial effects of this technical solution include iteratively optimizing the construction robot's path planning parameters based on the path planning coefficients, dynamically adjusting the optimization objective function based on the current position, remaining task sequence, and environmental constraints, and using the digital twin platform to predict the effects of parameter changes and compare them with actual construction data in real time, enabling adaptive optimization and correction of path planning parameters. By setting efficiency improvement targets and monitoring the convergence of comprehensive performance indicators, the robot's operating efficiency and the intelligent optimization level of the construction path are effectively improved, enhancing the system's real-time performance, stability, and construction quality control capabilities.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A construction robot operation path optimization method, characterized in that: include: Step 1: Assign the optimal work sequence to each robot based on the construction task; Step 2: Obtain BIM data and site environment information, and determine the initial construction path for each robot based on the optimal operation sequence for each robot; Step 3: Analyze the initial construction paths of all robots and determine the construction path of each robot based on the path analysis results; Step 4: During the construction process, the position and speed of each robot are collected in real time. The robot's operation coverage rate, downtime, and construction quality data are periodically collected to 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 operation path optimization method according to claim 1, characterized in that: Assign each robot the optimal work sequence based on the construction task, including: Obtaining construction tasks based on a preset construction task database; The attribute data of each robot is obtained based on the preset robot attribute table; Based on the construction tasks and the robot's attribute data, a task-robot matching matrix is constructed to obtain the compatibility score between each task and each robot. Sort all construction tasks to be assigned by their suitability scores from high to low, filter out the construction tasks to be assigned before a preset ranking, and determine the construction tasks to be assigned before the preset ranking as the selected construction tasks; Determine the median score based on the fitness scores of all candidate construction tasks; Determine a dynamic fitness score threshold based on a preset score and a median score; Pre-allocate construction tasks whose fitness scores are greater than the dynamic fitness score threshold; The remaining tasks after pre-allocation are matched again using a preset algorithm to obtain the final allocation plan and generate the optimal operation sequence for each robot, including task sequence and execution time nodes.

3. The construction robot operation path optimization method according to claim 2, characterized in that: The remaining tasks after pre-allocation are matched again using a preset algorithm, including: Determine a robot set and a task set based on the pre-assigned robots and the remaining tasks after the pre-assignment, and construct a plurality of robot-task pairs based on the robot set and the task set; Determine the initial movement cost for each robot-task pair; For each robot-task pair, the initial movement cost is adjusted according to its current power as well as the on-site environment and historical power consumption; Construct a cost matrix based on the adjusted movement costs of each robot-task pair; Match each robot with the task with the lowest cost in its current cost matrix. If the task is not occupied, match it directly. If the task is occupied by another robot, mark it as a potential conflict and perform conflict matching until the current robot is matched with the corresponding task. When all robots have matched tasks, if a robot completes a task, the remaining tasks will be matched in the order of the robots that completed the task until all remaining tasks are matched. The secondary matching is then considered to be completed.

4. The construction robot operation path optimization method according to claim 3, characterized in that: Perform conflict matching, including: If the task is already occupied by another robot, determine the total cost change of reallocating the currently occupied task to the current robot, and execute the exchange if the total cost change meets the preset requirements; If the total cost change does not meet the preset requirements, the current task will be temporarily assigned to the current robot, and new tasks will be found for other robots whose current tasks are occupied; In the process of searching for new tasks, the total cost change corresponding to the alternative tasks is recorded until the total cost change meets the preset requirements; If no alternative task can be found, the current robot will abandon the current task and be assigned the task with the lowest cost other than the current task in the current cost matrix. If the task is not occupied, it will be matched directly. If the task has been occupied by other robots, it will be marked as a potential conflict and conflict matching will be performed until the corresponding task is matched for the current robot.

5. The construction robot operation path optimization method according to claim 1, characterized in that: Obtain BIM data and on-site environment information, and combine the optimal operation sequence of each robot to determine the initial construction path for each robot, including: Extract the 3D coordinates and geometric data of building components corresponding to the construction task from the preset BIM system; Construct an on-site point cloud map based on on-site environmental information; Calibrate the coordinate system of the preset BIM model and the on-site point cloud map, and perform three-dimensional rasterization on the work area in the on-site point cloud map after the coordinate system calibration; Marking feature areas based on regional features of the work area in the on-site point cloud map after three-dimensional rasterization processing; Determine the corresponding travel cost for each characteristic area based on a preset database; The path is planned based on the travel cost corresponding to each feature area, the preset path planning algorithm of all robots and the optimal operation sequence, and then the initial construction path of each robot is determined.

6. The construction robot operation path optimization method according to claim 1, characterized in that: Analyze the initial construction paths of all robots and determine the construction path of each robot based on the path analysis results, including: Perform spatial conflict and resource conflict detection on the initial construction paths of all robots; The construction path is optimized based on the spatial conflict and resource conflict detection results to obtain the construction path of each robot.

7. The construction robot operation path optimization method according to claim 1, characterized in that: During the construction process, the position, speed, and task status data of each robot are collected in real time. The robot's operation coverage, downtime, and construction quality data are periodically counted to determine the path planning coefficient of each robot, including: The robot's position and speed are collected in real time using a preset positioning system and uploaded to the edge computing node based on preset time intervals. The path planning coefficient is determined based on the operation coverage, downtime, and construction quality of the cycle statistics and the data obtained from the edge computing nodes: ； in, 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: ； in, is the job coverage of the current robot i, is the preset target coverage, is the preset adjustment coefficient, is the downtime ratio of the current robot i, is the preset downtime threshold, is the penalty factor for the abnormal fluctuation of the speed and energy consumption of robot i, and the calculation formula is: ； in, is the current speed of robot i, The preset nominal speed of the robot, is the current energy consumption of robot i, is the current average energy consumption of all robots, are preset sensitivity coefficients, which are used to impose secondary penalties on abnormal fluctuations in speed and energy consumption. To compensate for the quality degradation caused by quality error, the calculation formula is: ； in, is the current construction quality of the current robot i, For the preset target quality, The maximum permissible quality deviation is preset.

8. The construction robot operation path optimization method according to claim 1, characterized in that: Iteratively optimize each robot's path planning parameters based on its path planning coefficients until the preset efficiency improvement goals are met, including: Define the optimization objective function with the robot's current position, remaining task sequence and environmental constraints as input; Based on the current path planning coefficients, 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; The optimized path planning parameters are sent to the robot controller to execute a new round of construction. During this process, the expected effect of the path planning parameter changes 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; When the comprehensive performance indicators of each robot are stable in the target range and the efficiency improvement rate converges in multiple consecutive optimization cycles, it is determined that the preset efficiency improvement target has been achieved, the iteration is terminated, and the current parameters are solidified as the final solution.

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