An intelligent acquisition method and system for indoor actual measurement based on BIM-robot dog

Abstract

The present invention discloses an intelligent acquisition method and system for indoor actual measurement based on BIM-mechanical dog. The method includes: S1, generating a navigation map based on the BIM model; S2, generating an optimal scanning site, and planning the optimal scanning site according to the global map to obtain a shortest path site combination; S3, obtaining a planned path according to the shortest path site combination, and inputting the planned path into the mechanical dog so that the mechanical dog executes corresponding instructions. The intelligent acquisition method for indoor actual measurement based on BIM-mechanical dog of the present invention combines the BIM model information, enables the mechanical dog to avoid obstacles in real time, automatically plan the optimal path, and improve the ability to navigate in narrow indoor spaces, thereby improving the safety of the mechanical dog walking in the indoor environment, reducing a large amount of human intervention, and improving the scanning efficiency, achieving the purpose of intelligent acquisition of indoor actual measurement data.

Description

Technical Field

[0001] The present invention belongs to the field of building informatization, and particularly relates to an intelligent acquisition method and system for indoor actual measurement based on BIM-mechanical dog. Background Art

[0002] With the continuous development of artificial intelligence technology and scanning technology, three-dimensional laser scanning technology has emerged, aiming to solve various problems existing in traditional actual measurement methods and improve data acquisition methods. This technology uses a non-contact measurement method to obtain the geometric information of the object surface, scans with a laser beam, generates point cloud data, and performs processing and analysis to construct a high-precision three-dimensional model. Benefiting from the significant advantages of this technology such as non-contact, high precision, and high efficiency, three-dimensional laser scanning technology has been widely applied in multiple fields, such as tunnel, bridge, construction engineering, and road detection, providing stronger support for the development of related industries. Three-dimensional laser scanning acquisition mainly has three methods: handheld acquisition, backpack acquisition, and fixed-site scanning.

[0003] However, the traditional three-dimensional scanning acquisition method has the following deficiencies: There are mainly three methods for three-dimensional laser scanning acquisition: handheld acquisition, backpack acquisition, and fixed-site scanning. Handheld and backpack acquisition methods are smaller and lighter than traditional three-dimensional scanners and are very suitable for on-site scanning, but the accuracy of the collected point cloud data is relatively low and is not suitable for actual measurement. And the point cloud data suitable for actual measurement is usually collected by a three-dimensional laser scanner with higher accuracy, but most three-dimensional laser scanners are heavy and can only collect data in a fixed-site manner, which requires more effort and cooperation from construction workers, thus increasing the labor cost. The traditional three-dimensional laser scanning operation process is cumbersome, requires a large amount of human participation, is prone to ignoring the scanning of areas such as corners and dead ends, and there are also many uncertainties in the operation quality, making it difficult to quantitatively evaluate the scanning work. Summary of the Invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides an intelligent acquisition method and system for indoor actual measurement based on BIM-mechanical dog. The technical problems to be solved by the present invention are realized through the following technical solutions:

[0005] An intelligent acquisition method for indoor actual measurement based on BIM-mechanical dog includes:

[0006] S1. Generating a navigation map based on the BIM model;

[0007] S2. Generating an optimal scanning site and planning the optimal scanning site according to the global map to obtain a shortest path site combination;

[0008] S3. Obtain a planned path based on the shortest path site combination, and input the planned path into the robotic dog so that the robotic dog executes corresponding instructions.

[0009] In a specific embodiment, step S1 includes:

[0010] S11. Generate an initial point cloud model based on the BIM model;

[0011] S12. After intercepting the initial point cloud model at a preset height using a pass-through filter, perform radius filtering to remove noise points to obtain a filtered point cloud model;

[0012] S13. Calculate the occupancy probability at each grid in the filtered point cloud model, mark the grids with occupancy probability greater than the preset threshold as obstacle areas, and mark the grids with occupancy probability less than the preset threshold as passable areas to obtain a grid map;

[0013] S14. Use the initial position of the robotic dog as a reference point to register the grid map with the actual scene to obtain a navigation map.

[0014] In a specific embodiment, the preset height is the range between the ground surface height and the height of the robotic dog.

[0015] In a specific embodiment, step S2 includes:

[0016] S21. Initialize the site combination, and determine the objective function according to the scanning coverage rate and the total distance of the sites from the door;

[0017] S22. Iteratively optimize the initialized site combination according to the genetic algorithm and the simulated annealing algorithm to obtain the optimal scanning sites;

[0018] S23. Determine the walking safety distance of the robotic dog according to the passable area, the obstacle area, and the inflation radius, and determine the shortest path between the sites through the A-Star algorithm;

[0019] S24. Determine the best arrangement order of the optimal scanning sites according to the genetic algorithm and the simulated annealing algorithm, and obtain the shortest path site combination according to the best arrangement order and the shortest path between the sites.

[0020] In a specific embodiment, determining the objective function according to the scanning coverage rate and the total distance of the sites from the door includes:

[0021] Normalize the total distance of the sites from the door to obtain the first weight index;

[0022] Obtain the second weight index according to the scanning coverage rate;

[0023] Adjust the first weight index and the second weight index to determine the objective function.

[0024] In a specific embodiment, step S22 includes:

[0025] S221. Determine the scanning coverage rate and the total distance of the stations from the door under the current combination according to the initialized station combination, and substitute the scanning coverage rate and the total distance of the stations from the door into the objective function to obtain the station combination score;

[0026] S222. Update the station combination using a genetic algorithm and calculate the score of the updated station combination;

[0027] S223. Compare the score of the updated station combination with the score of the station combination obtained previously. When the difference between the two does not meet the preset growth threshold, perform iterative update; when the difference between the two meets the preset growth threshold, determine whether the station combination meets the preset scanning coverage rate. If so, use this station combination as the optimal scanning station. If not, perform iterative update.

[0028] In a specific embodiment, step S24 includes:

[0029] S241. Calculate the length of the shortest path between stations determined by the A-Star algorithm and store it in a two-dimensional matrix;

[0030] S242. Randomly generate the station arrangement order and calculate the total distance between the stations in this arrangement order;

[0031] S243. Perform hybridization and mutation processing on the randomly generated station arrangement order using a genetic algorithm, and select the station combination with the shortest total path as the shortest path station combination.

[0032] In a specific embodiment, when the robotic dog reaches the position of the door and window opening, generate a transfer station and set an angle that conforms to the movement path of the transfer station, so that after the robotic dog reaches the transfer station, the expansion radius in the path is reduced; correspondingly, add the transfer station to the corresponding position in the shortest path stations.

[0033] In a specific embodiment, in response to receiving the position information of the robotic dog, when the position information does not coincide with the position of the first station and data collection has not started, control the robotic dog to move to the position of the first station; or when the position information is the position of the target point, control the robotic dog to perform data collection; or when the position information is the position of the last station and data collection is completed, control the robotic dog to move to the position of the first station.

[0034] The present invention also provides an intelligent indoor actual measurement and quantity collection system based on BIM-robotic dog, including:

[0035] A navigation map generation module for generating a navigation map based on a BIM model;

[0036] A scanning site planning module for generating an optimal scanning site and planning the optimal scanning site according to the global map to obtain a shortest path site combination;

[0037] A navigation control module for obtaining a planned path according to the shortest path site combination and inputting the planned path into the robotic dog so that the robotic dog executes corresponding instructions.

[0038] Advantages of the present invention:

[0039] 1. The indoor actual measurement and intelligent acquisition method based on BIM-robotic dog of the present invention uses BIM model data to generate a navigation map, which can reduce the time cost of generating the navigation map on the premise of ensuring the correctness and consistency of the data, and greatly reduce the time for re-collecting information by intercommunicating the BIM model data with the data of the robotic dog control platform, improving the efficiency of intelligent collection of indoor space.

[0040] 2. The indoor actual measurement and intelligent acquisition method based on BIM-robotic dog of the present invention combines BIM model information, enabling the robotic dog to avoid obstacles in real time, automatically plan the optimal path, and improve the navigation ability in narrow indoor spaces, thereby improving the safety of the robotic dog walking in the indoor environment, reducing a large amount of human intervention, and improving the scanning efficiency, achieving the purpose of intelligent collection of indoor actual measurement data.

[0041] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0042] Figure 1 is a schematic flowchart of an indoor actual measurement and intelligent acquisition method based on BIM-robotic dog provided by an embodiment of the present invention;

[0043] Figure 2 is a schematic flowchart of step S2 in an indoor actual measurement and intelligent acquisition method based on BIM-robotic dog provided by an embodiment of the present invention;

[0044] Figure 3 is a schematic flowchart of step S2 in an indoor actual measurement and intelligent acquisition method based on BIM-robotic dog provided by an embodiment of the present invention;

[0045] Figure 4 is a schematic flowchart of step S22 in an indoor actual measurement and intelligent acquisition method based on BIM-robotic dog provided by an embodiment of the present invention;

[0046] Figure 5It is a schematic flowchart of step S24 in an indoor actual measurement and intelligent acquisition method based on BIM-mechanical dog provided by an embodiment of the present invention;

[0047] Figure 6 It is a block diagram of an indoor actual measurement and intelligent acquisition system module based on BIM-mechanical dog provided by an embodiment of the present invention. Embodiment

[0048] The following further describes the present invention in detail with specific embodiments, but the embodiments of the present invention are not limited thereto. Embodiment

[0049] Please refer to Figure 1 , Figure 1 It is a schematic flowchart of an indoor actual measurement and intelligent acquisition method based on BIM-mechanical dog provided by an embodiment of the present invention, including:

[0050] S1. Generate a navigation map based on the BIM model;

[0051] The data extracted from the BIM model usually needs to be processed and transformed additionally before it can be used for site planning. Although there are many BIM software plugins in the prior art that can extract information from the BIM model, the data extracted by these extraction tools does not meet the requirements of site planning. For example, the data extracted by these plugins may contain too much detailed information, or there are problems such as incorrect hierarchical classification and topological relationships, making it difficult to directly use this data for site planning. Therefore, it is necessary to perform secondary processing and transformation on the extracted BIM data to transform it into a three-dimensional mathematical model suitable for site planning. This process includes removing redundant information, reconstructing the shape and position of basic components, and restoring the topological relationships between components. In this way, a three-dimensional model with the same size as the real building and having the characteristics and topological relationships of basic components can be generated for use in site planning.

[0052] In this embodiment, a new 3D mathematical model is reconstructed through BIM information. First, the platform needs to generate vertical planes to be scanned, including the elevations of walls, columns, and beams and the planes of beams. To create the vertical planes to be scanned, the platform needs to perform the operation of dividing planes for spatial nodes. This process requires reading the elevations and the positions of door and window openings in the BIM model and automatically creating geometric figures to obtain a series of vertical planes to be scanned with door and window opening information. After completing the creation of the vertical planes to be scanned, the platform needs to process the areas to be scanned parallel to the floor, mainly including the bottom surfaces of beams and the ceiling areas. To represent the ceiling area, segmented processing is required and multiple functions are used to represent it. The generation of the bottom surface area of the beam is based on the positioning lines and widths of the beams. To improve the accuracy of the scanning scheme, the platform needs to delete the overlapping areas between the bottom surfaces of beams and the ceiling to ensure the accuracy of the generated planes. Finally, the platform needs to perform meshing on the planes to be scanned and divide them along the XY axis at a set distance (set to 200 mm in this embodiment). The purpose of meshing is to evenly divide the areas to be scanned into multiple small areas to check the visibility of each area, so as to determine whether the areas near the grid points are visible. When setting the distance, the model accuracy and computer computing power need to be considered, because too small a distance will improve the model accuracy but cause too large a computational load, and vice versa. Since the BIM component information extracted cannot directly generate the areas to be scanned, the platform needs to perform a series of complex processing operations to ensure that the new model has the same dimensions as the real building and has basic component features and topological relationships for use in site planning.

[0053] After meshing the areas to be scanned, the range of the potential scanning area needs to be defined. In this embodiment, an expression within the same space is used to calculate the range of this area. By extracting the set of X coordinates in the same space, the range of the potential scanning area needs to maintain a certain safety distance from obstacles such as walls and columns. This is because the scanner is installed on a robotic dog, and the safety distance of the robotic dog is its maximum radius to ensure the safety of the robotic dog when walking. Once the expression of the potential scanning area is obtained, it needs to be meshed to generate a better site. This can make the heuristic algorithm more efficient and tidy and quickly converge to the optimal scanning scheme. In addition, to further improve the quality of the scanning scheme, the potential scanning area can also be segmented to allow the robot to scan at different positions and angles. The segmented scanning areas should have no obstacles blocking each other, which can be achieved by checking the visibility between the areas. Finally, factors such as computer computing power and model accuracy need to be considered to select an appropriate grid size.

[0054] In a specific embodiment, please refer to Figure 2 , step S1 includes:

[0055] S11. Generate an initial point cloud model according to the BIM model;

[0056] S12. After intercepting the initial point cloud model of a preset height using a straight-through filter, perform radius filtering to remove noise points to obtain a filtered point cloud model;

[0057] S13. Calculate the occupancy probability at each grid in the filtered point cloud model, mark the grids with an occupancy probability greater than the preset threshold as obstacle areas, and mark the grids with an occupancy probability less than the preset threshold as passable areas to obtain a grid map;

[0058] S14. Use the initial position of the robotic dog as a reference point to register the grid map with the actual scene to obtain a navigation map.

[0059] Traditional robots equipped with scanners need to use the navigation map of the current floor when cruising indoors at fixed points. Usually, to generate a robot navigation map, a lidar is required to manually scan the current floor plane, and then the scanned information is converted into a navigation map. However, this embodiment adopts a new map generation method to automatically create a map suitable for the navigation and positioning of the robotic dog through the information provided by BIM. To make the generated navigation map more concise and representative, a straight-through filter is used to intercept the point cloud model within a certain height range. The selected height range is that the bottom height is the ground surface height and the top height is the height of the robotic dog, because the obstacles within this height range will directly affect the movement of the robot. The point cloud model intercepted by the straight-through filter needs to be further denoised using the radius filtering method. Before using the radius filtering, the radius length needs to be set, and points exceeding this length are recognized as noise points and automatically deleted. For the point cloud model obtained after radius filtering, in specific implementation, a node can be created on the ROS platform and the filtered indoor space point cloud information can be published externally in the form of a topic, and then the relevant information is received at another port to generate a navigation map. By adopting the above method, the information extracted from BIM can be automatically converted into a data format that meets the requirements of the scanning site plan, and the indoor site information and topological relationship of BIM can be automatically generated into the data format required for the ROS navigation map, thereby realizing the multiple utilization of BIM information in multiple scenarios. Since this system does not require manual scanning, it can greatly improve the efficiency and accuracy of generating the navigation map.

[0060] In addition, before receiving the point cloud information, when generating the navigation map, it is necessary to consider how to optimize the accuracy and efficiency of raster map generation. First, for the point cloud data generated by the BIM model, the Bayesian filtering algorithm is used to calculate the occupancy probability at each raster, and then the obstacles and passable areas are determined. Then, the system performs binarization processing on the rasters according to the threshold of the occupancy probability, marking the rasters with an occupancy probability greater than the threshold as obstacles and the rasters with an occupancy probability less than the threshold as passable areas. By using the probability raster map, we can more accurately depict the indoor environment and avoid information loss when only using the raster occupancy rate to represent the map.

[0061] After determining the raster map, in this embodiment, the BIM model is converted into a navigation map that can be used by the robotic dog. Preferably, in order to improve the accuracy of the map, this embodiment can use the initial position of the robotic dog as a reference point, and by calculating the translation and rotation matrix, the generated navigation map is aligned with the actual building space, so as to achieve accurate registration of the real environment, BIM model, and ROS navigation map. Through this method, the indoor environment can be described more accurately, and the positioning accuracy and navigation efficiency of the robotic dog can be improved.

[0062] The indoor actual measurement and intelligent acquisition method based on BIM-robotic dog of the present invention uses the BIM model data to generate a navigation map, which can reduce the time cost of generating the navigation map on the premise of ensuring the correctness and consistency of the data, and greatly reduce the time for re-collecting information by intercommunicating the BIM model data with the data of the robotic dog control platform, improving the efficiency of intelligent collection of indoor spaces.

[0063] S2. Generate the optimal scanning sites, and plan the shortest path site combination according to the global map;

[0064] Scanning site planning is a problem of seeking the optimal solution, which requires constructing a mathematical model and continuously iterating and selecting within the permitted range of the mathematical model until an optimal solution is finally obtained. To automate the process of site planning, heuristic algorithms are widely used. Heuristic algorithms are a class of algorithms constructed based on intuition or experience. They give feasible solutions to the combinatorial optimization problems to be solved under acceptable computational time and space costs. These algorithms can be applied to site planning to help optimize the process of site planning, thereby improving work efficiency and accuracy.

[0065] In a specific embodiment, please refer to Figure 3 , the step S2 includes:

[0066] S21. Initialize the site combination, and determine the objective function according to the scanning coverage rate and the total distance of the sites from the door;

[0067] S22. Iteratively optimize the initialized site combination according to the genetic algorithm and the simulated annealing algorithm to obtain the optimal scanning sites;

[0068] In a specific embodiment, please refer to Figure 4 , the step S22 includes:

[0069] S221. Determine the scanning coverage rate and the total distance of the sites from the door under the current combination according to the initialized site combination, and substitute the scanning coverage rate and the total distance of the sites from the door into the objective function to obtain the site combination score;

[0070] In a specific embodiment, determining the objective function according to the scanning coverage rate and the total distance of the sites from the door includes:

[0071] Normalize the total distance of the sites from the door to obtain the first weight index;

[0072] Obtain the second weight index according to the scanning coverage rate;

[0073] Adjust the first weight index and the second weight index to determine the objective function.

[0074] S222. Update the site combination using the genetic algorithm and calculate the site combination score after the update;

[0075] Compare the site combination score after the update with the site combination score obtained previously. When the difference between the two does not meet the preset growth threshold, perform iterative update; when the difference between the two meets the preset growth threshold, determine whether the site combination meets the preset scanning coverage rate. If so, use this site combination as the optimal scanning site; if not, perform iterative update.

[0076] In the scanning site planning, generally, using a heuristic algorithm can generate an optimal scanning scheme. By adjusting the objective function, the present application can complete the scanning of all indoor spaces with the least number of sites used. When determining the objective function, two limiting factors need to be considered: the scanning coverage rate and the total distance of the sites from the door. To meet these limitations, through continuous search and selection, find the optimal solution in the feasible region, that is, the least number of sites to complete the scanning of all indoor spaces, and the shortest distance between the sites.

[0077] The scanning coverage rate is an important indicator to measure the scanning quality. To ensure that the scanning scheme meets the requirement of complete scanning of the area to be scanned, a certain proportion of coverage rate needs to be satisfied. In addition, this embodiment also considers the scanning requirements of the actual scanner, which usually requires adding a station between two different spaces in the building interior to increase the overlap rate of the point clouds in different spaces, so as to achieve point cloud registration. Therefore, when automatically generating the station plan, the position near the door also needs to be considered when determining the station distribution, because the total distance of such a station layout is relatively short, meeting the requirement of the shortest path in the optimal scheme.

[0078] Subsequently, the Pareto optimal solution theory can be used to linearly combine the two limiting factors to obtain the optimal solution. For integrating these two limiting factors, the total station distance needs to be normalized. The coverage rate value is usually less than 1, while the total distance value of the station from the door is often much larger than the coverage rate value. If only the coverage rate and the total distance of the station from the door are added or subtracted to the objective function, the coverage rate will not effectively affect the objective function. For this reason, in this embodiment, the total distance of the station from the door is normalized to reflect the distance degree between the station and the door. Of course, it can also be achieved by dividing the total distance of the station from the door by the total distance of all the doors in the building interior, making the generated ratio less than 1 and close to the coverage rate value. Therefore, these two limiting factors can be integrated into the objective function. By adjusting the weights of these two factors, the objective function can be optimized to meet the actual needs. If a higher scanning coverage rate is required in the actual project, the weight of the coverage rate can be increased or the weight of the total distance of the station from the door can be decreased to generate a scanning scheme that better meets the actual needs.

[0079] After the objective function is determined, this embodiment uses a method combining genetic algorithm and simulated annealing algorithm to optimize the site planning. The genetic algorithm searches for the global optimal solution through operations such as gene crossover and mutation, while the simulated annealing algorithm avoids falling into the local optimal solution by means of random jumps. Combining the two algorithms can improve the search efficiency and accuracy while ensuring the global search ability. Finally, through multiple iterations and gradual optimization of the objective function, an optimal scanning site combination plan with the least number of sites, the highest coverage rate, and the shortest total distance from the sites to the doors can be obtained. The automatic generation of site planning adopts the genetic algorithm to optimize the planning of scanning sites. The algorithm includes steps such as initialization, individual evaluation, population selection, individual coding, population hybridization, and individual mutation. Before the algorithm runs, the mathematical model generated from BIM information needs to be input into the algorithm framework. During the process of the algorithm generating the scanning plan, the system needs to perform visibility checks on the area to be scanned, and the results obtained from the checks can be used as the input value of the scanning coverage rate. The visibility check uses the ray method, that is, beams are emitted in all directions from the starting point, and whether the area is visible is determined by checking whether the rays intersect with obstacles. Before the visibility check, the research also needs to determine the height of the scanner. The visibility check of the area to be scanned obtains the number of grid points that can be covered by the scanner by checking all grid points in the area to be scanned, so as to calculate the scanning coverage rate of the indoor space of the building no longer.

[0080] The scanning coverage rate obtained through the visibility check and the total distance from the sites to the doors are the criteria for the genetic algorithm to evaluate the plan. The higher the score, the more it meets the restrictive conditions and the easier it is to be retained in the next iteration during the selection process. The system will compare the difference between the scores of the current population and the optimal individual of the previous population. If it is greater than the threshold, it means that it is still approaching the global optimal solution, otherwise, continue the iteration. When the objective function increases, the optimal site combination plan will be output.

[0081] S23. Determine the safe walking distance of the robotic dog according to the passable area, obstacle area, and inflation radius, and determine the shortest path between sites through the A-Star algorithm;

[0082] S24. Determine the best arrangement order of the optimal scanning sites according to the genetic algorithm and the simulated annealing algorithm, and obtain the shortest path site combination according to the best arrangement order and the shortest path between sites.

[0083] In a specific embodiment, please refer to Figure 5 , the step S24 includes:

[0084] S241. Calculate the lengths of the shortest paths between sites determined by the A-Star algorithm and store them in a two-dimensional matrix;

[0085] S242. Randomly generate the arrangement order of sites and calculate the total distance between sites in this arrangement order;

[0086] S243. Hybridize and mutate the randomly generated station arrangement order through a genetic algorithm, and select the station combination with the shortest total path as the shortest path station combination.

[0087] After generating the optimal scanning stations, this embodiment uses the generated optimal scanning stations to generate a global map. The global map adopts a 0-1 format, where 0 represents the passable area indoors of the building, and 1 represents obstacles and the expansion radius. Before generating the global map, this embodiment first determines the safety distance when the robotic dog walks. The A* algorithm is used to obtain the shortest path between stations, and at the same time, the best arrangement order of the optimal scanning stations is obtained through a genetic algorithm + simulated annealing algorithm. The length of the generated path is automatically calculated and stored in a two-dimensional matrix for use in generating the subsequent optimal scanning scheme. This framework uses a genetic algorithm and a simulated annealing algorithm for global and local planning to obtain the best scanning scheme.

[0088] In a specific embodiment, when the robotic dog reaches the position of the door and window opening, a transfer station is generated and an angle conforming to the movement path of the transfer station is set, so that the expansion radius in the path is reduced after the robotic dog reaches the transfer station; correspondingly, the transfer station is added to the corresponding position in the shortest path stations.

[0089] Before the robotic dog starts to execute the scanning scheme, due to the fact that the preset expansion radius in the traditional path planning algorithm is too large (for the safety of the robotic dog, the expansion radius is generally preset to 1.5 times the maximum radius of the robotic dog), it cannot pass through the limited and narrow space indoors. Therefore, a new transfer point needs to be set in front of the door to pass through the limited area indoors by dynamically adjusting the parameters of the path planning. This embodiment inputs the scanning stations into the model constructed based on the BIM model information. After completing the station position information and map information, a method that can automatically generate transfer stations and the station orientation based on the door frame orientation is used. The purpose is to reduce the drawback of the robotic dog rotating too much during movement in the narrow indoor passage area by adding transfer stations and setting the direction angles conforming to the movement path. After the robotic dog reaches the transfer point, the expansion radius of the path planning in SLAM is dynamically reduced, improving the ability to pass through the limited indoor space. Because in the limited indoor space, if the robotic dog does not generate a transfer point and the preset angle of the transfer point in advance at a sufficient position, the robotic dog will not be able to turn when entering the narrow indoor space or will cause an accident of the robotic dog colliding with the wall due to excessive turning. The newly added scanning transfer points and azimuth angles will be automatically added to the original scanning scheme according to the station order of the scanning scheme.

[0090] S3. Obtain a planned path according to the shortest path station combination, and input the planned path into the robotic dog so that the robotic dog executes the corresponding instructions.

[0091] Specifically, in response to receiving the position information of the robotic dog, when the position information does not coincide with the position of the first station and data collection has not started, the robotic dog is controlled to move to the position of the first station; or when the position information is the position of the target point, the robotic dog is controlled to perform data collection; or when the position information is the position of the last station and data collection is completed, the robotic dog is controlled to move to the position of the first station.

[0092] After forming a new scanning plan, in this embodiment, it is first necessary to locate the initial position of the robotic dog on the global map converted from the BIM model and automatically register the map with the actual environment. If the current position of the robotic dog does not coincide with the position of the first station, the robotic dog will automatically move to the first station through the navigation module of the system. The navigation module will automatically perform path planning and real-time dynamic obstacle avoidance based on the input global map and target points. Among them, the global path planning is responsible for planning the path of the robotic dog in the indoor global environment, and the local path planning is responsible for optimizing the trajectory of the walking path of the robotic dog. The path planning module will continuously issue instructions regarding the movement of the robotic dog, such as the speed magnitude and steering angle, and the robotic dog will receive the relevant instructions and move according to the topic information. During the movement of the robotic dog by the system, the position of the robotic dog in the positioning module is continuously read and it is judged whether the current position has moved to the target point position. After the robotic dog reaches the target point position, it will collect the surrounding environment information. After the collection work is completed, the system will automatically read the next scanning station of the scanning plan and set this scanning station as the target point. After the robotic dog completes the scanning work of the last scanning station, it will automatically navigate to the initial position.

[0093] The indoor actual measurement and intelligent collection method based on BIM-robotic dog of the present invention combines BIM model information, enabling the robotic dog to avoid obstacles in real time, automatically plan the optimal path, and improve the navigation ability in narrow indoor spaces. Thereby, it improves the safety of the robotic dog walking in the indoor environment, reduces a large amount of human intervention, and improves the scanning efficiency, achieving the purpose of intelligent collection of indoor actual measurement data.

[0094] Please refer to Figure 6 , Figure 6 which is a block diagram of an indoor actual measurement and intelligent collection system based on BIM-robotic dog provided by an embodiment of the present invention, including:

[0095] A navigation map generation module 1, used to generate a navigation map based on the BIM model;

[0096] A scanning station planning module 2, used to generate the optimal scanning stations and plan the shortest path station combination for the optimal scanning stations according to the global map;

[0097] The navigation control module 3 is configured to obtain a planned path based on the shortest path site combination and input the planned path into the robotic dog so that the robotic dog executes corresponding instructions.

[0098] In the present invention, unless otherwise clearly defined and limited, the terms "mounted", "connected", "connected", "fixed" and other terms should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral body; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0099] In the present invention, unless otherwise clearly defined and limited, the first feature being "on" or "under" the second feature may include the direct contact between the first and second features, or may include the situation where the first and second features are not in direct contact but in contact through other features therebetween. Moreover, the first feature being "above", "over" and "on top of" the second feature includes that the first feature is directly above and obliquely above the second feature, or merely means that the horizontal height of the first feature is higher than that of the second feature. The first feature being "under", "below" and "beneath" the second feature includes that the first feature is directly below and obliquely below the second feature, or merely means that the horizontal height of the first feature is lower than that of the second feature.

[0100] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification.

[0101] Although the present application has been described in conjunction with various embodiments herein, however, in the process of implementing the claimed present application, those skilled in the art can understand and achieve other variations of the disclosed embodiments by viewing the accompanying drawings, the disclosure content, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "one" does not exclude a plurality of situations. A single processor or other unit can implement several functions recited in the claims. Certain measures are recited in mutually different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0102] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, an apparatus (device), or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects, which are collectively referred to herein as "modules" or "systems" for the sake of simplicity. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program code. The computer program is stored / distributed in a suitable medium, provided together with other hardware or as part of the hardware, and can also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0103] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatuses (devices), and computer program products of the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0104] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0106] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. An intelligent acquisition method for indoor actual measurement based on BIM-robotic dog, characterized in that, Including: S1. Generate a navigation map based on the BIM model; S2. Generate an optimal scanning site, and plan the optimal scanning site according to the global map to obtain a shortest-path site combination; S3. Obtain a planned path according to the shortest-path site combination, and input the planned path into the robotic dog so that the robotic dog executes corresponding instructions; The step S2 includes: S21. Initialize the site combination, and determine the objective function according to the scanning coverage rate and the total distance of the site from the door; S22. Iteratively optimize the initialized site combination according to the genetic algorithm and the simulated annealing algorithm to obtain the optimal scanning site; S23. Determine the safe walking distance of the robotic dog according to the passable area, the obstacle area and the inflation radius, and determine the shortest path between sites through the A-Star algorithm; S24. Determine the best arrangement order of the optimal scanning sites according to the genetic algorithm and the simulated annealing algorithm, and obtain the shortest-path site combination according to the best arrangement order and the shortest path between sites; The step S22 includes: S221. Determine the scanning coverage rate and the total distance of the site from the door under the current combination according to the initialized site combination, and substitute the scanning coverage rate and the total distance of the site from the door into the objective function to obtain the site combination score; S222. Update the site combination using the genetic algorithm and calculate the site combination score after the update; S223. Compare the site combination score after the update with the site combination score obtained previously. When the difference between the two does not meet the preset growth threshold, perform iterative update; when the difference between the two meets the preset growth threshold, determine whether the site combination meets the preset scanning coverage rate. If so, use this site combination as the optimal scanning site. If not, perform iterative update.

2. The indoor actual measurement and intelligent acquisition method based on BIM-robot dog according to claim 1, wherein The step S1 includes: S11. Generate an initial point cloud model according to the BIM model; S12. After intercepting the initial point cloud model at a preset height using a pass-through filter, perform radius filtering to remove noise to obtain a filtered point cloud model; S13. Calculate the occupancy probability at each grid in the filtered point cloud model, mark the grids with an occupancy probability greater than the preset threshold as the obstacle area, and mark the grids with an occupancy probability less than the preset threshold as the passable area to obtain a grid map; S14. Use the initial position of the robotic dog as a reference point to register the grid map with the actual scene to obtain a navigation map.

3. The indoor actual measurement and intelligent acquisition method based on BIM-robot dog according to claim 2, characterized in that, The preset height is the range between the ground height and the height of the robotic dog.

4. The indoor actual measurement and intelligent acquisition method based on BIM-robot dog according to claim 1, characterized in that Determining the objective function according to the scanning coverage rate and the total distance of the site from the door includes: Normalize the total distance of the site from the door to obtain the first weight index; Obtain the second weight index according to the scanning coverage rate; Adjust the first weight index and the second weight index to determine the objective function.

5. The indoor actual measurement and intelligent acquisition method based on BIM-robot dog according to claim 1, characterized in that The step S24 includes: S241. Calculate the length of the shortest path between sites determined by the A-Star algorithm and store it in a two-dimensional matrix; S242. Randomly generate the site arrangement order and calculate the total distance between the sites in this arrangement order; S243. Use a genetic algorithm to perform hybridization and mutation processing on the randomly generated site arrangement order, and select the site combination with the shortest total path as the shortest path site combination.

6. The indoor actual measurement and intelligent acquisition method based on BIM-mechanical dog according to any one of claims 1-5, characterized in that, When the robotic dog reaches the position of the door and window opening, a transfer station is generated and an angle that conforms to the movement path of the transfer station is set, so that after the robotic dog reaches the transfer station, the expansion radius in the path is reduced; correspondingly, the transfer station is added to the corresponding position in the shortest path sites.

7. The intelligent acquisition method for indoor actual measurement based on BIM-robotic dog according to any one of claims 1-5, in response to receiving the position information of the robotic dog, when the position information does not coincide with the position of the first site and the acquisition has not started, control the robotic dog to move to the position of the first site; or when the position information is the position of the target point, control the robotic dog to perform information acquisition; or when the position information is the position of the last site and the information acquisition is completed, control the robotic dog to move to the position of the first site.

8. An indoor actual measurement and intelligent acquisition system based on BIM-robot dog, characterized in that, Including: A navigation map generation module for generating a navigation map based on the BIM model; A scanning site planning module for generating an optimal scanning site and planning the optimal scanning site according to the global map to obtain a shortest path site combination; A navigation control module for obtaining a planned path according to the shortest path site combination and inputting the planned path into the robotic dog so that the robotic dog executes the corresponding instructions; Generating an optimal scanning site and planning the optimal scanning site according to the global map to obtain a shortest path site combination, including: Initializing the site combination and determining the objective function according to the scanning coverage rate and the total distance of the site from the door; Performing iterative optimization on the initialized site combination according to the genetic algorithm and the simulated annealing algorithm to obtain the optimal scanning site; Determining the safe walking distance of the robotic dog according to the passable area, the obstacle area and the expansion radius, and determining the shortest path between sites through the A-Star algorithm; Determining the best arrangement order of the optimal scanning sites according to the genetic algorithm and the simulated annealing algorithm, and obtaining the shortest path site combination according to the best arrangement order and the shortest path between sites; Performing iterative optimization on the initialized site combination according to the genetic algorithm and the simulated annealing algorithm to obtain the optimal scanning site, including: Determining the scanning coverage rate and the total distance of the site from the door under the current combination according to the initialized site combination, substituting the scanning coverage rate and the total distance of the site from the door into the objective function to obtain the site combination score; Using the genetic algorithm to update the site combination and calculating the site combination score after the update; Comparing the site combination score after the update with the site combination score obtained previously. When the difference between the two does not meet the preset growth threshold, perform iterative update; when the difference between the two meets the preset growth threshold, determine whether the site combination meets the preset scanning coverage rate. If so, use this site combination as the optimal scanning site. If not, perform iterative update.

Images