Train carriage and steel seat frame combined scanning method based on multiple unmanned vehicles

Abstract

The invention provides a train carriage and steel seat frame combined scanning method and system based on multiple unmanned vehicles. The method comprises the steps that the section number of a train carriage is obtained and confirmed; delimiting a scanning range for the unmanned vehicle, and generating a scanning instruction; laser data are obtained through a scanning instruction; processing the laser data through a laser scanning system to obtain a scanning result message; judging whether the laser data contains complete data of the steel seat frame or not, and judging whether verification is passed or not; if so, updating the data of the steel seat frame in the warehouse; if not, the stacking position of the steel seat frame is blocked; judging whether the train carriage is completely scanned; if the result is yes, updating the train carriage data in the laser data; if not, the laser data are spliced, and whether the train carriage is completely scanned or not is judged again. According to the invention, the scanning data is spliced in an unmanned vehicle combined scanning mode, so that the efficiency of operation preparation is improved.

Description

Technical Field

[0001] The present invention belongs to the field of scanning technology, and specifically, relates to a joint scanning method of a train carriage and a steel seat frame based on multiple unmanned vehicles, and more specifically, to a joint scanning method and system of a train carriage and a steel seat frame based on multiple unmanned vehicles. Background Art

[0002] In train shipment logistics, trains are used as carriers to ship steel coils out of the factory. If the steel coils are placed directly in the carriage, they will roll and be damaged during the train transportation. Therefore, before placing the steel coils, a steel frame needs to be placed in the train carriage. The structure of the steel frame, such as Figure 8 shown.

[0003] The steel frame has protruding humps, and the steel coils are placed between the humps to effectively prevent the steel coils from rolling. Therefore, before loading the steel coils on the train, the steel frame needs to be hoisted into the train compartment.

[0004] In the unmanned crane operation, the crane completes each action by executing the instructions sent by the WMS system. The steel frame is lifted from one position and unloaded at another position. The instructions contain the XY coordinates of the lifting position and the unloading position to accurately locate the pick-up and placement positions of the steel frame. The crane grabs the steel frame by grabbing the four hump vertices in the center of the steel frame, such as Figure 6 As shown in the figure, it is necessary to scan the steel frame to obtain the center point of the steel frame. When unloading the steel frame into the carriage, it is necessary to scan the train carriage to obtain the accurate coordinates of the unloading into the carriage.

[0005] Traditionally, steel frames are usually placed on both sides of the warehouse for easy loading and unloading, such as Figure 7 Before lifting the steel frame, the system sends a movement command to the unmanned vehicle, so that the unmanned vehicle scans the entire steel frame range in the warehouse area to obtain accurate steel frame grabbing coordinates; and sends a movement command to the vehicle to scan the entire train, such as Figure 7 As shown, the coordinates of each train car are determined, and the unloading coordinates of the steel frame hoisted into each car are calculated. This means that before the steel frame is hoisted, it is necessary to scan the steel frame in the warehouse area first, and then scan the entire train, or the scanning range must cover the steel frame placement area and the train parking area. This solution causes the system to repeat operations and is inefficient; and if only one unmanned vehicle is used to scan the train car, the scanning time is long and the efficiency is low; if multiple unmanned vehicles are used for scanning, splicing is required. In addition, in order to ensure the accuracy of the steel frame information, it is generally necessary to manually count the steel frame. Manual counting often results in errors, which increases economic costs. Summary of the invention

[0006] In view of the defects in the prior art, the object of the present invention is to provide a joint scanning method of train carriages and steel seat frames based on multiple unmanned vehicles.

[0007] According to the present invention, a joint scanning method of a train carriage and a steel seat frame based on multiple unmanned vehicles is provided, comprising:

[0008] Step S1: Obtain and confirm the number of train carriages;

[0009] Step S2: Based on the number of train carriages, a scanning range is defined for the unmanned vehicle, and a scanning instruction is generated; through the scanning instruction, the unmanned vehicle is moved to the scanning end point, triggering the laser scanning system to scan the train carriages and steel frame, and obtaining laser data;

[0010] Step S3: Processing the laser data through the laser scanning system to obtain the steel frame scanning result and the train scanning result;

[0011] Step S4: Based on the scanning result of the steel frame, determine whether the laser data contains the complete data of the steel frame; if the result is yes, update the data of the steel frame in the warehouse; if the result is no, block the stacking position of the steel frame;

[0012] Based on the train scanning results, according to the unmanned vehicle scanning range divided by step S2 and the scanning direction of the unmanned vehicle, determine whether the train carriage has been completely scanned; if the result is yes, update the train carriage data in the laser data to obtain all the train carriage data; if the result is no, splice the laser data to obtain the complete train carriage data, and re-determine whether the train carriage has been completely scanned.

[0013] Preferably, the moving direction of the unmanned vehicle's large vehicle is the X direction, the moving direction of the unmanned vehicle's small vehicle is the Y direction, and the clamping direction of the unmanned vehicle is the Z direction;

[0014] The steel frame scanning result is a steel frame scanning result telegram; the train scanning result is a train scanning result telegram;

[0015] In the step S1, the number of train carriages is confirmed through the screen of the WMS system;

[0016] In step S2:

[0017] Step S2.1: Based on the number of train carriages, a scanning range is defined, and when the unmanned vehicle is detected to have arrived at the scanning starting point, a scanning instruction is sent to the unmanned vehicle to move to the scanning end point;

[0018] Step S2.2: receiving a scanning instruction, starting the unmanned vehicle to scan the scanning range defined in step S2.1, sending a train steel frame joint scanning telegram to the laser scanning system, triggering the laser scanning system to scan the train carriage and the steel frame, and obtaining laser data;

[0019] In step S3:

[0020] Step S3.1: Segment and integrate the laser data through the laser scanning system to obtain a single piece of data;

[0021] Step S3.2: judging whether the laser scanner has scanned the complete steel frame through the point cloud processing library of the laser scanning system, i.e. PCL; if the result is yes, calculating the spatial information of each stack of steel frames; if the result is no, continuing scanning;

[0022] The spatial information includes: the stacking position of the steel frame, the number of layers of the steel frame and the layer height of the steel frame;

[0023] Step S3.3: Calculate a single piece of laser data through the algorithm module of the laser scanning system and the point cloud database to generate a steel frame scanning result telegram and a train scanning result telegram;

[0024] Step S3.4: Send the steel frame scanning result telegram to the WMS system; send the train scanning result telegram to the WMS system;

[0025] The algorithm module is used to calculate laser data and generate scanning result electronic text.

[0026] Preferably, in step S2.1:

[0027] Step S2.1.1: Based on the train scanning range as the number of train carriages, the scanning range is divided into a plurality of unmanned vehicles; the number of the unmanned vehicles is more than 2;

[0028] Step S2.1.2: Determine the X-direction range of the unmanned vehicle obtained in step S2.1.1 through the WMS system;

[0029] Step S2.1.3: Based on the scanning range defined in step S2.1.1 and the existing information of the steel frame stored in the WMS system, combined with the center point of the steel frame placement position and the type of the placed steel frame, calculate the X-direction range and Y-direction range of each stack of steel frames;

[0030] Step S2.1.4: Generate a scanning instruction based on the scanning range, the X-direction range of the unmanned vehicle and the information of each stack of steel frames and send it to the unmanned vehicle;

[0031] In step S3.2:

[0032] Step S3.2.1: Integrate the point cloud data in the laser data to determine whether the laser scanner has scanned the complete steel frame; if the result is yes, execute step S3.2.2; if the result is no, continue scanning;

[0033] Step S3.2.2: Using the point cloud processing library, the scanned support point cloud of the steel frame is cut out to obtain the steel frame point cloud;

[0034] Step S3.2.3: Use the corresponding steel frame template to match the cropped steel frame point cloud and calculate the position and grasping points of the steel frame.

[0035] Step S3.2.4: Based on the posture and grasping points of the steel frame, the cropped steel frame point cloud is plane-fitted and clustered by a plane-fitting algorithm, thereby obtaining point cloud data of each layer of the steel frame, and according to the point cloud data of each layer of the steel frame, the number of layers of the steel frame and the height of each layer of the steel frame as spatial information are calculated;

[0036] In step S3.3:

[0037] Step S3.3.1: Identify the train carriages in the target area, calculate the frame position of the corresponding train carriages, obtain edge data, and determine whether the laser data is abnormal; if the result is yes, discard the abnormal laser data; if the result is no, execute step S3.3.2;

[0038] Step S3.3.2: using a clustering algorithm to identify laser data that meets the characteristics of train carriages, and calculating the node data of the head and tail of each train carriage through concave feature recognition, calculating the edge data of the train carriage skin, and combining the node data and the edge data to obtain the skin information;

[0039] The concave feature recognition is to identify the train carriage by judging whether the shape of the laser line meets the concave requirement; the concave requirement depends on the cross-sectional shape of the train;

[0040] Step S3.3.3: According to the car body information, the incomplete train carriages are assembled to obtain the total number of train sections; the single piece of laser data is calculated to generate and send out the steel frame scanning result telegram and the train scanning result telegram.

[0041] Preferably, in step S4:

[0042] Step S4.1: Based on the steel frame scanning result telegram, determine whether the laser data contains complete steel frame data; if the result is yes, update the data of the steel frame in the warehouse; if the result is no, block the stacking position of the steel frame;

[0043] Based on the train relative index number in the train scanning result message and the actual train carriage number within the unmanned vehicle scanning range, the actual train carriage number is sequentially corresponded to distinguish the laser data of each part of the train; the division of the parts is determined according to the number of unmanned vehicles;

[0044] Step S4.2: Based on the laser data of the front of the train or the laser data of the rear of the train, determine whether the laser scanner has scanned the entire train carriage; if the result is yes, execute step S4.3; if the result is no, update the point data of the border part of the train carriage obtained by scanning;

[0045] Step S4.3: Determine whether the train carriage size in the laser data conforms to the theoretical size corresponding to the train carriage model; if the result is yes, update the point data of the actual train carriage frame to obtain all the train carriage data; if the result is no, discard the point data of the train carriage frame;

[0046] Step S4.4: Based on the laser data of the middle part of the train, determine whether the laser scanner has scanned the complete train car; if the result is yes, update the point data of the actual train car border; if the result is no, splice the point data of the scanned train car border with the point data of the scanned train car border in step S4.2 to form the point data of the complete train car border, and re-execute step S4.2.

[0047] Preferably, in step S4.1:

[0048] Through the WMS system, the steel frame stack position information of the steel frame scanning result telegram is received, and the coordinate information of the four vertices of the steel frame stack position is obtained; based on the coordinate information, it is judged whether the laser data contains the complete steel frame data; if the result is yes, the data of the steel frame in the warehouse is further updated; if the result is no, the stack position of the steel frame is blocked;

[0049] In step S2, the steel seat frame is placed along the side of the train, and the laser scanner is used to scan the train carriage and the steel seat frame simultaneously.

[0050] According to the present invention, a railway carriage and steel frame joint scanning system based on multiple unmanned vehicles includes:

[0051] Module M1: Obtain and confirm the number of train carriages;

[0052] Module M2: Based on the number of train carriages, the scanning range is defined for the unmanned vehicle and a scanning instruction is generated. Through the scanning instruction, the unmanned vehicle is moved to the scanning end point, triggering the laser scanning system to scan the train carriages and steel frame to obtain laser data.

[0053] Module M3: Processing laser data through the laser scanning system to obtain the steel frame scanning results and train scanning results;

[0054] Module M4: Based on the scanning result of the steel frame, determine whether the laser data contains the complete data of the steel frame; if the result is yes, update the data of the steel frame in the warehouse; if the result is no, block the stacking position of the steel frame;

[0055] Based on the train scanning results, according to the unmanned vehicle scanning range divided by module M2 and the scanning direction of the unmanned vehicle, it is judged whether the train carriage has been scanned completely; if the result is yes, the train carriage data in the laser data is updated to obtain all the train carriage data; if the result is no, the laser data is spliced ​​to obtain the complete train carriage data, and it is re-judged whether the train carriage has been scanned completely.

[0056] Preferably, the moving direction of the unmanned vehicle's large vehicle is the X direction, the moving direction of the unmanned vehicle's small vehicle is the Y direction, and the clamping direction of the unmanned vehicle is the Z direction;

[0057] The steel frame scanning result is a steel frame scanning result telegram; the train scanning result is a train scanning result telegram;

[0058] In the module M1, the number of train carriages is confirmed through the screen of the WMS system;

[0059] In the module M2:

[0060] Module M2.1: Based on the number of train carriages, the scanning range is defined, and when the unmanned vehicle is detected to have arrived at the scanning starting point, a scanning instruction is sent to the unmanned vehicle to move to the scanning end point;

[0061] Module M2.2: Receives scanning instructions, starts the unmanned vehicle to scan the scanning range defined by module M2.1, sends the train steel frame joint scanning telegram to the laser scanning system, triggers the laser scanning system to scan the train carriage and steel frame, and obtains laser data;

[0062] In the module M3:

[0063] Module M3.1: Segment and integrate laser data through the laser scanning system to obtain single data;

[0064] Module M3.2: Use the point cloud processing library of the laser scanning system, i.e. PCL, to determine whether the laser scanner has scanned the complete steel frame; if the result is yes, calculate the spatial information of each stack of steel frames; if the result is no, continue scanning;

[0065] The spatial information includes: the stacking position of the steel frame, the number of layers of the steel frame and the layer height of the steel frame;

[0066] Module M3.3: Calculate the single data of laser data through the algorithm module and point cloud database of the laser scanning system to generate the steel frame scanning result telegram and the train scanning result telegram;

[0067] Module M3.4: Send steel frame scanning result telegram to WMS system; send train scanning result telegram to WMS system;

[0068] The algorithm module is used to calculate laser data and generate scanning result electronic text.

[0069] Preferably, in the module M2.1:

[0070] Module M2.1.1: Based on the train scanning range as the number of train carriages, the scanning range is divided into multiple unmanned vehicles; the number of the unmanned vehicles is more than 2;

[0071] Module M2.1.2: Use the WMS system to define the X-direction range of the unmanned vehicle obtained in module M2.1.1;

[0072] Module M2.1.3: Based on the scanning range defined by module M2.1.1 and the existing information of steel frames stored in the WMS system, combined with the center point of the steel frame placement position and the type of steel frame placed, calculate the X-direction range and Y-direction range of each stack of steel frames;

[0073] Module M2.1.4: Generate a scanning instruction based on the scanning range, the X-direction range of the unmanned vehicle and the information of each stack of steel frames and send it to the unmanned vehicle;

[0074] In the module M3.2:

[0075] Module M3.2.1: Integrate the point cloud data in the laser data to determine whether the laser scanner has scanned the complete steel frame; if the result is yes, execute module M3.2.2; if the result is no, continue scanning;

[0076] Module M3.2.2: Through the point cloud processing library, the scanned steel frame support point cloud is cut out to obtain the steel frame point cloud;

[0077] Module M3.2.3: Use the corresponding steel frame template to match the cropped steel frame point cloud and calculate the position and grasping points of the steel frame.

[0078] Module M3.2.4: Based on the posture and grasping points of the steel frame, the cropped steel frame point cloud is plane fitted and clustered through the plane fitting algorithm to obtain the point cloud data of each layer of the steel frame, and the number of layers of the steel frame and the height of each layer of the steel frame as spatial information are calculated based on the point cloud data of each layer of the steel frame;

[0079] In the module M3.3:

[0080] Module M3.3.1: Identify the train carriages in the target area, calculate the frame position of the corresponding train carriages, obtain edge data, and determine whether the laser data is abnormal; if the result is yes, throw out the abnormal laser data; if the result is no, execute module M3.3.2;

[0081] Module M3.3.2: Use clustering algorithm to identify laser data that meets the characteristics of train carriages, and calculate the node data of the head and tail of each train carriage through concave feature recognition, calculate the edge data of the train carriage skin, and combine the node data and the edge data to obtain the skin information;

[0082] The concave feature recognition is to identify the train carriage by judging whether the shape of the laser line meets the concave requirement; the concave requirement depends on the cross-sectional shape of the train;

[0083] Module M3.3.3: Based on the car body information, assemble the incomplete train carriages to obtain the total number of train sections; calculate the single data of the laser data, generate and send the steel frame scanning result telegram and the train scanning result telegram.

[0084] Preferably, in the module M4:

[0085] Module M4.1: Based on the steel frame scanning result telegram, determine whether the laser data contains the complete steel frame data; if the result is yes, update the data of the steel frame in the warehouse; if the result is no, block the stacking position of the steel frame;

[0086] Based on the train relative index number in the train scanning result message and the actual train carriage number within the unmanned vehicle scanning range, the actual train carriage number is sequentially corresponded to distinguish the laser data of each part of the train; the division of the parts is determined according to the number of unmanned vehicles;

[0087] Module M4.2: Based on the laser data of the front of the train or the laser data of the rear of the train, determine whether the laser scanner has scanned the entire train carriage; if the result is yes, execute module M4.3; if the result is no, update the point data of the border part of the train carriage obtained by scanning;

[0088] Module M4.3: Determine whether the train carriage size in the laser data conforms to the theoretical size corresponding to the train carriage model; if the result is yes, update the point data of the actual train carriage border to obtain all the train carriage data; if the result is no, discard the point data of the train carriage border;

[0089] Module M4.4: Based on the laser data of the middle part of the train, determine whether the laser scanner has scanned the complete train carriage; if the result is yes, update the point data of the actual train carriage border; if the result is no, splice the point data of the scanned train carriage border with the point data of the scanned train carriage border in module M4.2 to form the point data of the complete train carriage border, and re-execute module M4.2.

[0090] Preferably, in the module M4.1:

[0091] Through the WMS system, the steel frame stack position information of the steel frame scanning result telegram is received, and the coordinate information of the four vertices of the steel frame stack position is obtained; based on the coordinate information, it is judged whether the laser data contains the complete steel frame data; if the result is yes, the data of the steel frame in the warehouse is further updated; if the result is no, the stack position of the steel frame is blocked;

[0092] In the module M2, the steel seat frame is placed along the side of the train, allowing the laser scanner to scan the train car and the steel seat frame at the same time.

[0093] Compared with the prior art, the present invention has the following beneficial effects:

[0094] 1. The present invention adjusts the arrangement of the steel seat frames, and by moving the steel seat frames in the storage area to be placed next to the railway track, the system can scan the steel seat frame area next to the train while scanning the train.

[0095] 2. The present invention uses multiple unmanned vehicles to scan simultaneously, that is, the scanned data is spliced ​​by scanning together by multiple unmanned vehicles, thereby improving the efficiency of job preparation.

[0096] 3. The present invention provides key information such as the number of layers, layer height, center point, etc. of each stack of steel frames through scanning of the steel frames. That is to say, the information such as the number of layers, layer height, center point, etc. of each stack of steel frames can be obtained and compared with the information from manual inventory, thereby improving the accuracy of the steel frame information. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0098] Figure 1 A schematic diagram of the storage area layout provided by the present invention;

[0099] Figure 2 A schematic diagram of the positional relationship between the train carriage, the steel seat frame and the laser scanner provided by the present invention;

[0100] Figure 3 A schematic diagram of the reservoir area provided by the present invention;

[0101] Figure 4 A range diagram of each stack of steel frames provided by the present invention;

[0102] Figure 5 A schematic diagram of the system flow provided by the present invention;

[0103] Figure 6 This is a schematic diagram of the position of the crane grabbing the steel seat frame provided by the present invention; wherein 1, 2, 3, and 4 are four hump vertices;

[0104] Figure 7 A schematic diagram of the position of the steel frame and the railway track provided by the present invention;

[0105] Figure 8 This is a schematic structural diagram of the steel frame provided by the present invention. DETAILED DESCRIPTION

[0106] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0107] This solution consists of three parts: information system, unmanned vehicle, and laser scanning system. The information system, in this embodiment, takes the WMS system as an example;

[0108] The WMS system, i.e. warehouse management system, can monitor and manage the quantity and types of items in the warehouse in real time. Through efficient logistics operations and warehouse management, it can manage the inbound and outbound delivery of materials in the warehouse area to ensure the continuous operation of the warehouse.

[0109] The unmanned crane, i.e. the unmanned overhead crane, includes the unmanned crane PLC, various sensors and communication networks. The unmanned crane's trolley motion position information is used as the X-axis information, the unmanned crane's trolley motion position information is used as the Y-axis information, and the clamp height information is used as the Z-axis information; the X-axis information, Y-axis information and Z-axis information are uniformly collected by the unmanned crane's PLC, which receives the WMS system's lifting instructions, controls the unmanned crane to complete the unmanned crane action, and realizes the unmanned crane's automatic lifting function.

[0110] The laser scanning system includes: a laser scanner, a computer and system supporting software, which is used to obtain the position information and size information of the identified object. The laser scanner is specifically a three-dimensional laser scanner.

[0111] Place the steel frame along the railway track. Figure 1As shown. The placement position of each stack of steel frames is relatively fixed, or the gap between two adjacent stacks of steel frames is greater than 30 mm; and the value of the gap is determined based on the traveling scanning speed and scanning frequency of the vehicle;

[0112] A laser scanner is installed on the unmanned vehicle above the train carriage.

[0113] The relative positions of the steel frame, train carriages, and laser scanners, e.g. Figure 2 The laser scanner’s scanning field of view should be able to cover the train carriage and steel frame without any obstruction.

[0114] This solution moves the steel frame to the side of the railway track, scans the train, and scans the steel frame next to the train, avoiding repeated scanning and improving the system operation efficiency.

[0115] Step S1: The operator determines the total number of train sections to be scanned on the WMS system screen;

[0116] Alternatively, the number of train carriages is obtained through an MES system or other systems.

[0117] Step S2: After receiving the train arrival information, the WMS system defines a scanning range for each unmanned vehicle according to the distribution of the steel frame warehouse area, and generates a scanning instruction;

[0118] First, send a moving instruction to the unmanned vehicle. After the unmanned vehicle reaches the scanning starting point, it starts the scanning process.

[0119] As the unmanned vehicle moves toward the scanning end point, it triggers the laser scanning system to scan the train and steel frame.

[0120] Step S3: After the scanning is completed, the laser scanning system processes the data; after the data processing is completed, the steel frame scanning module sends the steel frame stack position information telegram of each stack, that is, the steel frame scanning result telegram to the WMS system, and the train scanning module sends the coordinate information scanning result of each train carriage to the WMS system, that is, the train scanning result telegram is sent to the WMS system;

[0121] Alternatively, during the scanning process, the processed scanning results are synchronously returned to the WMS system.

[0122] Step S4: After the WMS system receives the steel frame stack information telegram, that is, the steel frame scanning result telegram, it verifies the number of layers scanned by the laser and the number of layers in the system, and blocks the stack if they are not equal; in other words, it determines whether the laser data contains the complete data of the steel frame; if the result is yes, the data of the steel frame in the warehouse is updated; if the result is no, the stack of the steel frame is blocked; the degree of skewness of the steel frame is verified, and the stack is blocked if the skewness exceeds a certain degree. Specifically, the verification is performed according to the following method: for the XYZ values ​​of the four vertices of the hump obtained by scanning, the degree of skewness of the steel frame is verified through the Y difference of different vertices Y1 and Y2. If the steel frame is not skewed, the Y difference is theoretically 0, and the skewness exceeds n, the stack is blocked.

[0123] Too much skewness of the steel frame will result in the inability to accurately grasp the four vertices when the crane grabs the steel frame. The n value is based on the design size of the clamp in the embodiment. After repeated tests, it is found that n is 0 to 150 mm. Beyond this range, the success rate of grabbing the steel frame decreases;

[0124] Verify the layer height and inclination by using the XYZ values ​​of each layer scanned by laser scanning;

[0125] For stacks that fail the inspection, the stacks are blocked; no lifting is carried out at the blocked stacks;

[0126] For the data that has passed the verification, the corresponding steel frame data in the warehouse area will be updated.

[0127] Based on the train relative index number in the train scanning result telegram and the actual train car number within the unmanned vehicle scanning range, as well as the unmanned vehicle scanning range divided by step S2 and the unmanned vehicle scanning direction, it is determined whether the train car has been completely scanned; if the result is yes, the train car data in the laser data is updated to obtain all the train car data; if the result is no, the laser data is spliced ​​to obtain the complete train car data, and it is re-determined whether the train car has been completely scanned.

[0128] In other words, based on the train scanning result message, according to the initial scanning range of each unmanned vehicle, the relative carriage number of each carriage in the laser data is converted into the corresponding actual carriage number, and the data of each carriage in the message is judged:

[0129] For a complete carriage scan, the point data of the carriage is obtained, that is, the coordinate data of the four vertices of the carriage;

[0130] For incompletely scanned carriages, the point data of the train carriage border portion obtained by scanning is updated as laser data, that is, the coordinate data of the two vertices of the junction of the two unmanned vehicles scanning the half of the carriage each scanned, constitute the point data of 4 points of a complete carriage.

[0131] After obtaining the coordinates of the four vertices of the carriage, the length of the train carriage is checked. According to the different models of train carriages, the train carriages whose scanned length values ​​differ from the theoretical length values ​​of the train carriage models by more than 200mm to 300mm will not be updated, and the data that pass the check will be updated to the data of the corresponding train carriages of the train. And so on.

[0132] That is to say, in step S4:

[0133] Step S4.1: Based on the steel frame scanning result telegram, determine whether the laser data contains complete steel frame data; if the result is yes, update the data of the steel frame in the warehouse; if the result is no, block the stacking position of the steel frame;

[0134] Based on the train relative index number in the train scanning result message and the actual train carriage number within the unmanned vehicle scanning range, the actual train carriage number is corresponded in sequence, so as to distinguish the laser data of each part of the train; specifically, according to the number of three unmanned vehicles, the laser data of the front of the train, the laser data of the rear of the train and the laser data of the middle of the train are distinguished;

[0135] Step S4.2: Based on the laser data of the front of the train or the laser data of the rear of the train, determine whether the laser scanner has scanned the entire train carriage; if yes, execute step S4.3; if not, update the point data of the scanned train carriage border; the point data is laser data.

[0136] Step S4.3: Determine whether the train carriage size in the laser data conforms to the theoretical size corresponding to the train carriage model; if yes, update the point data of the actual train carriage frame to obtain all the train carriage data; if no, discard the point data of the train carriage frame;

[0137] Step S4.4: Based on the laser data of the middle part of the train, determine whether the laser scanner has scanned the complete train car; if so, update the point data of the actual train car border; if not, splice the point data of the scanned train car border with the point data of the scanned train car border in step S4.2 to form the point data of the complete train car border, and re-execute step S4.2.

[0138] In step S2:

[0139] Step S2.1: Divide the scanning range.

[0140] Step S2.2: Generate a scanning instruction, and generate a scanning instruction as a movement instruction for the unmanned vehicle participating in the scanning.

[0141] Step S2.3: a scanning instruction is sent to the unmanned vehicle; the number of the unmanned vehicles is more than 2.

[0142] After the WMS system detects that the unmanned vehicle has arrived at the scanning starting point, it sends an instruction to the unmanned vehicle to move to the scanning end point. After the unmanned vehicle is started, it sends a train steel seat frame joint scanning telegram to the laser scanning system according to the range determined in step S2.1.

[0143] In step S2.1:

[0144] Step S2.1.1: Based on the train scanning range as the number of train carriages, the scanning range is divided into a plurality of unmanned vehicles; the number of the unmanned vehicles is more than 2;

[0145] Step S2.1.2: Determine the X-direction range of the unmanned vehicle obtained in step S2.1.1 through the WMS system;

[0146] Step S2.1.3: Based on the scanning range defined in step S2.1.1 and the existing information of the steel frame stored in the WMS system, combined with the center point of the steel frame placement position and the size of the placed steel frame, calculate the X-direction range and Y-direction range of each stack of steel frames;

[0147] Step S2.1.4: Generate a scanning instruction based on the scanning range, the X-direction range of the unmanned vehicle and the information of each stack of steel frames and send it to the unmanned vehicle;

[0148] In other words, in step S2.1:

[0149] Step S2.1.1: Determine the train range. Since the laser system of each unmanned vehicle scans independently, the scanned train carriage data are the number and data of train carriages within the scanning range of the unmanned vehicle. Therefore, the WMS system is required to process the laser data of each unmanned vehicle to form complete train carriage data in the warehouse area.

[0150] WMS system splices train data;

[0151] Specifically, the splicing is performed based on the data of the head or tail of the train carriage.

[0152] If the train in the depot is too long to be completely scanned by two unmanned vehicles, then three unmanned vehicles are needed; therefore, based on the scanning range of the train, the three unmanned vehicles are the unmanned vehicle for scanning the front of the train, the unmanned vehicle for scanning the middle of the train, and the unmanned vehicle for scanning the rear of the train.

[0153] like Figure 3As shown in the figure, the starting position of the train track is in area D and the ending position is beyond the transfer area, while the position of each pile of steel frames in the storage area is fixed in areas A, B, C and D and a small area in the transfer area and is placed along the edge of the train track. Therefore, according to the distribution of train tracks and steel frames, the basic rule of scanning one or two areas with an unmanned vehicle can not only scan the complete train data, but also enable each pile of steel frames in the storage area to be completely scanned.

[0154] According to the above rules, the WMS system automatically divides the X-direction range of each unmanned vehicle scanning the train. The operator can also determine the range of the train carriage to be scanned on the screen of the WMS system.

[0155] Step S2.1.2: Determine the range of each stack of steel frames within the scanning range of this unmanned vehicle.

[0156] like Figure 4 As shown, after determining the scanning range of each unmanned vehicle, since the position of each stack of steel frames is relatively fixed, the steel frames within the scanning area of ​​the unmanned vehicle are determined according to the scanning range of the unmanned vehicle determined in step S2.1.1;

[0157] Step S2.1.3: Based on the center point of the steel frame placement position and the size of the placed steel frame, calculate the X-direction range of the unmanned vehicle trolley and the Y-direction range of the unmanned vehicle trolley for each stack of steel frames, and then compose the information of each stack of steel frames based on the steel frame model and number of layers stored in the WMS system.

[0158] The range of the steel frame is mathematically expressed as:

[0159] xMin=xb÷2-m

[0160] xMax=x+b÷2+n

[0161] yMin=ya÷2-p

[0162] yMax=y+a÷2+q

[0163] Among them, xMin represents the minimum value of the steel frame range in the X direction; xMax represents the maximum value of the steel frame range in the X direction; yMin represents the minimum value of the steel frame range in the Y direction; yMax represents the maximum value of the steel frame range in the Y direction; b represents the length of the steel frame in the X direction; a represents the length of the steel frame in the Y direction; m, n, p and q are all non-fixed values ​​and can be modified according to the actual situation on site.

[0164] In step S3:

[0165] Step S3.1: The laser scanning system collects the speed data and laser data of the designated unmanned vehicle in the X and Y directions in the corresponding area according to the different areas of the steel seat frame of the on-site train, and divides and integrates the laser data to obtain a single piece of data;

[0166] For example, an unmanned vehicle is equipped with a laser above the train track and steel frame area to scan the entire target area. The laser scanning range includes the train scanning area and a row of steel frame areas near the track.

[0167] After collecting the laser data, the laser scanning system divides and re-integrates the laser data according to the corresponding relationship between the laser and the area, providing corresponding laser processing data for different calculation programs.

[0168] Step S3.2: Calculate the stacking position, number of layers, and height of each layer of each stack of steel frames as spatial information based on the point cloud data obtained as laser data, and send the calculation result to the WMS system;

[0169] In step S3.2:

[0170] Step S3.2.1: During the scanning process, the point cloud data acquired by the laser is integrated in real time, and it is checked whether the steel frame is scanned completely;

[0171] That is to say, it is determined whether the laser scanner has scanned the complete steel frame; if yes, the complete steel frame has been scanned, and the grab points and layer heights of the steel frame as spatial information are calculated in parallel; that is to say, PCL, i.e. Point Cloud Library, is used to identify the scanned steel support and calculate its stacking position, number of layers, and height of each layer as spatial information; if no, the complete steel frame has not been scanned, and the following telegram is continued to be received and the scanning is continued;

[0172] Step S3.2.2: According to the approximate range given by the WMS system, use PCL to crop the scanned steel bracket point cloud to crop the steel frame point cloud;

[0173] Step S3.2.3: Use the corresponding steel frame template to align the cropped steel frame point cloud, calculate the accurate position and posture of the steel frame, and obtain its grasping point.

[0174] Step S3.2.4: Based on the posture and grasping points of the steel frame, a plane fitting algorithm is used to perform plane fitting on the cropped point cloud, and the results after plane fitting are clustered to obtain the point cloud of each layer. Then, based on the point cloud data of each layer, the number of layers and the height of each layer are calculated; when the vehicle's X-direction speed is detected to be 0, the program considers that the vehicle's scan is completed.

[0175] Step S3.3: The laser scanning system on each automated guided vehicle provides single laser data, i.e., a single piece of data, to the algorithm module of the laser scanning system. Then, through the calculation of the algorithm module of the laser scanning system, the steel seat frame scanning result message and the train scanning result message are generated. Among them, the steel seat frame scanning result message is obtained through the point cloud database, and the train scanning result message is obtained through the algorithm module of the laser scanning system.

[0176] Then, return the calculation result to the WMS system, that is, send the steel seat frame scanning result message to the WMS system; send the train scanning result message to the WMS system.

[0177] In the step S3.3:

[0178] Step S3.3.1: After obtaining all the complete laser data, identify the train carriages in the target area and calculate the border positions of the corresponding train carriages.

[0179] If there are abnormalities in the laser data, such as the position jump of the automated guided vehicle, etc., check the abnormal data and throw an exception to the host computer.

[0180] Step S3.3.2: If there are no abnormalities in the laser data, use the clustering algorithm to identify the train carriages, identify the laser data that conforms to the characteristics of the train carriages, and through the concave feature recognition, calculate the nodes at the head and tail of each train carriage, calculate the edge data of the train carriage skin, and obtain the skin information.

[0181] Specifically, in the step 3.3.2, for the concave feature recognition, the train carriage is an uncovered regular box-shaped object. If viewed from the side cross-section perspective, the train cross-section is in the shape of "凵". That is, if this shape appears, it can be considered as a train carriage, and those that are not this shape will not be a carriage. Therefore, by identifying whether the shape of the laser line in the current area meets the concave requirements, it can be determined as part of a train carriage. After judging whether the scanning object is part of a train carriage through the concave feature recognition, since each train carriage must be continuous, the method of splicing the concave parts together continuously and considering the non-concave part as the end of the carriage is used to cluster and distinguish each train carriage.

[0182] Step S3.3.3: Based on the calculated head and tail nodes of each train section and the car body information, the complete train car and the incomplete train car are distinguished, and according to the different train car states, the XYZ information including the four edges of the complete car body and the half train car information of the incomplete car body are provided; the steel frame scanning result telegram and the train scanning result telegram are generated. In other words, according to the car body information, the incomplete train cars are spliced ​​to obtain the total number of train sections; the steel frame scanning result telegram and the train scanning result telegram are sent.

[0183] Specifically, the laser scanning system does not perform the train splicing function. For incompletely scanned train carriages, it only calculates the carriage border data within the scanning range and forms a train scanning result telegram, which is spliced ​​by the WMS system.

[0184] In step S4, i.e., during the conversion into the actual carriage:

[0185] Step S4.1: Based on the steel frame scanning result telegram, determine whether the laser data contains complete steel frame data; if the result is yes, update the data of the steel frame in the warehouse; if the result is no, block the stacking position of the steel frame;

[0186] Based on the train relative index number in the train scanning result message and the actual train carriage number within the unmanned vehicle scanning range, the train carriage numbers are sequentially matched to the actual train carriage numbers, thereby distinguishing the laser data of each part of the train; the division of the parts is determined according to the number of the unmanned vehicle;

[0187] Specifically, the unmanned vehicles of the scanned train are divided according to the total number of train sections and the number of unmanned vehicles determined to be scanned;

[0188] Taking three unmanned vehicles as an example, there are unmanned vehicles that scan the front area of ​​the train, unmanned vehicles that scan the middle area of ​​the train, and unmanned vehicles that scan the rear area of ​​the train.

[0189] It can be seen from step S2.2.1 that the conversion of train car data needs to start from the head of the train car or the tail of the train car, so if the laser data of the train car scanning the head or the tail of the train car is received first, the conversion is performed directly; if the laser data of the train car scanning the middle of the train car is received first, the laser data of this area is temporarily stored, and the conversion is performed after the laser data of the head or the tail of the train car is received.

[0190] Obtain laser data from the front of the train or the unmanned vehicle at the rear of the train;

[0191] Specifically, the laser data of the front of the train starts with the train car head number N input from the screen, and according to the scanning direction of the train, specifically, front → rear, rear → front, and the number of scanned cars M, the relative index number i of the laser data is converted into the actual train car number in sequence: N+i-1, i.e. front → rear; N+Mi, i.e. rear → front.

[0192] The laser data at the rear of the train starts with the rear number N of the train car input on the screen. According to the scanning direction of the train, i.e., front → rear, rear → front, and the number of scanned cars M, the relative index number i of the laser data is converted into the actual train car number: N-M+i, i.e., front → rear; N-i+1, i.e., rear → front.

[0193] In step S2, the steel seat frame is placed along the side of the train, and the laser scanner is used to scan the train carriage and the steel seat frame at the same time. Specifically, the steel seat frame is placed along the side of the train, and one scanner can scan the train area and the steel seat frame area at the same time. Therefore, a scanner is installed on the vehicle, and the steel seat frame information and the train information can be collected in one scan.

[0194] Step S4.2: Based on the laser data of the front of the train or the laser data of the rear of the train, determine whether the laser scanner has scanned the entire train car; if the result is yes, execute step S4.3; if the result is no, update the point data of the scanned train car border part; the point data is laser data.

[0195] Step S4.3: For the scanned complete train carriage, the theoretical size corresponding to the train carriage model and the train carriage size scanned by the laser are verified, and if the size exceeds a certain range, the data of this train carriage is discarded;

[0196] Specifically, an alarm is triggered in the WMS system to notify the operator to scan the carriage separately; if the carriage passes the verification, the data of four points of the actual train carriage border is updated; for the incompletely scanned train carriage, the data of two points of the scanned half border is updated.

[0197] Step S4.4: Based on the laser data of the middle part of the train, determine whether the laser scanner has scanned the complete train carriage; if yes, update the point data of the actual train carriage frame as the laser data; that is, obtain the laser data of the unmanned vehicle in the middle part of the scanned train; if the laser data scanned by the unmanned vehicle in the middle part is incomplete, splice the incomplete half of the train carriage data with the half of the frame data of step S4.2 to form four point data of a complete train carriage frame, and re-execute step S4.2;

[0198] If the verification fails, the data is discarded;

[0199] If the verification is successful, the train car is updated.

[0200] Step S4.4: Similarly, the data of all train carriages are calculated.

[0201] The present invention also provides a joint scanning system for train carriages and steel seat frames based on multiple unmanned vehicles. The joint scanning system for train carriages and steel seat frames based on multiple unmanned vehicles can be realized by executing the process steps of the joint scanning method for train carriages and steel seat frames based on multiple unmanned vehicles, that is, technical personnel in this field can understand the joint scanning method for train carriages and steel seat frames based on multiple unmanned vehicles as a preferred implementation of the joint scanning system for train carriages and steel seat frames based on multiple unmanned vehicles.

[0202] According to the present invention, a railway carriage and steel seat frame joint scanning system based on multiple unmanned vehicles includes: a WMS system, an unmanned vehicle, a laser scanning system and an identification object;

[0203] The WMS system is used to monitor and manage the warehouse; the unmanned vehicle is used for automatic lifting; the laser scanning system is used to obtain the location information and size information of the identified object;

[0204] The steel frame to be identified is placed between the railway track and the warehouse; the railway carriage to be identified is placed on the railway track;

[0205] The unmanned vehicle is provided with a laser scanner of a laser scanning system, which is located above the train carriage;

[0206] The scanning range of the laser scanner covers the train carriage and the steel seat frame;

[0207] According to the present invention, a railway carriage and steel frame joint scanning system based on multiple unmanned vehicles includes:

[0208] Module M1: Obtain and confirm the number of train carriages;

[0209] Module M2: Based on the number of train carriages, the scanning range is defined for the unmanned vehicle and a scanning instruction is generated. Through the scanning instruction, the unmanned vehicle is moved to the scanning end point, triggering the laser scanning system to scan the train carriages and steel frame to obtain laser data.

[0210] Module M3: Process laser data through the laser scanning system, obtain scanning results, and send out train data;

[0211] Module M4: Based on the scanning results, determine whether the laser data contains the complete data of the steel frame; if the result is yes, update the data of the steel frame in the warehouse; if the result is no, block the stacking position of the steel frame;

[0212] Based on the train data, according to the unmanned vehicle scanning range divided by module M2 and the scanning direction of the unmanned vehicle, it is judged whether the train carriage has been scanned completely; if the result is yes, the train carriage data in the laser data is updated to obtain all the train carriage data; if the result is no, the laser data is spliced ​​to obtain the complete train carriage data, and it is re-judged whether the train carriage has been scanned completely.

[0213] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0214] In the description of the present application, it should be understood that the terms "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0215] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A joint scanning method of train carriages and steel seat frames based on multiple unmanned vehicles, characterized in that: include: Step S1: Obtain and confirm the number of train carriages; Step S2: Based on the number of train carriages, a scanning range is defined for the unmanned vehicle, and a scanning instruction is generated; through the scanning instruction, the unmanned vehicle is moved to the scanning end point, triggering the laser scanning system to scan the train carriages and steel frame, and obtaining laser data; Step S3: Processing laser data through a laser scanning system to obtain a steel frame scanning result and a train scanning result; Step S4: Based on the steel frame scanning result, determine whether the laser data contains complete steel frame data; if the result is yes, update the data of the steel frame in the warehouse; If the answer is no, the stacking position of the steel frame is blocked; Based on the train scanning results, according to the unmanned vehicle scanning range divided by step S2 and the scanning direction of the unmanned vehicle, determine whether the train carriage has been completely scanned; if the result is yes, update the train carriage data in the laser data to obtain all the train carriage data; if the result is no, splice the laser data to obtain the complete train carriage data, and re-determine whether the train carriage has been completely scanned.

2. The method for joint scanning of train carriages and steel seat frames based on multiple unmanned vehicles according to claim 1 is characterized in that: The moving direction of the unmanned vehicle is taken as the X direction, the moving direction of the unmanned vehicle is taken as the Y direction, and the clamp direction of the unmanned vehicle is taken as the Z direction; The steel frame scanning result is a steel frame scanning result telegram; the train scanning result is a train scanning result telegram; In the step S1, the number of train carriages is confirmed through the screen of the WMS system; In step S2: Step S2.1: Based on the number of train carriages, a scanning range is defined, and when the unmanned vehicle is detected to have arrived at the scanning starting point, a scanning instruction is sent to the unmanned vehicle to move to the scanning end point; Step S2.2: receiving a scanning instruction, starting the unmanned vehicle to scan the scanning range defined in step S2.1, sending a train steel frame joint scanning telegram to the laser scanning system, triggering the laser scanning system to scan the train carriage and the steel frame, and obtaining laser data; In step S3: Step S3.1: Segment and integrate the laser data through the laser scanning system to obtain a single piece of data; Step S3.2: judging whether the laser scanner has scanned the complete steel frame by using the point cloud processing library of the laser scanning system, i.e., PCL; If the answer is yes, then calculate the spatial information of each stack of steel frames; If the result is no, continue scanning; The spatial information includes: the stacking position of the steel frame, the number of layers of the steel frame and the layer height of the steel frame; Step S3.3: Calculate a single piece of laser data through the algorithm module of the laser scanning system and the point cloud database to generate a steel frame scanning result telegram and a train scanning result telegram; Step S3.4: Send the steel frame scanning result telegram to the WMS system; send the train scanning result telegram to the WMS system; The algorithm module is used to calculate laser data and generate scanning result electronic text.

3. The method for joint scanning of train carriages and steel seat frames based on multiple unmanned vehicles according to claim 2 is characterized in that: In step S2.1: Step S2.1.1: Based on the train scanning range as the number of train carriages, the scanning range is divided into a plurality of unmanned vehicles; the number of the unmanned vehicles is more than 2; Step S2.1.2: Determine the X-direction range of the unmanned vehicle obtained in step S2.1.1 through the WMS system; Step S2.1.3: Based on the scanning range defined in step S2.1.1 and the existing information of the steel frame stored in the WMS system, combined with the center point of the steel frame placement position and the type of the placed steel frame, calculate the X-direction range and Y-direction range of each stack of steel frames; Step S2.1.4: Generate a scanning instruction based on the scanning range, the X-direction range of the unmanned vehicle and the information of each stack of steel frames and send it to the unmanned vehicle; In step S3.2: Step S3.2.1: Integrate the point cloud data in the laser data to determine whether the laser scanner has scanned the complete steel frame; if the result is yes, execute step S3.2.2; if the result is no, continue scanning; Step S3.2.2: Using the point cloud processing library, the scanned support point cloud of the steel frame is cut out to obtain the steel frame point cloud; Step S3.2.3: Use the corresponding steel frame template to match the cropped steel frame point cloud and calculate the position and grasping points of the steel frame. Step S3.2.4: Based on the posture and grasping points of the steel frame, the cropped steel frame point cloud is plane-fitted and clustered by a plane-fitting algorithm, thereby obtaining point cloud data of each layer of the steel frame, and according to the point cloud data of each layer of the steel frame, the number of layers of the steel frame and the height of each layer of the steel frame as spatial information are calculated; In step S3.3: Step S3.3.1: Identify the train carriages in the target area, calculate the frame position of the corresponding train carriages, obtain edge data, and determine whether the laser data is abnormal; if the result is yes, discard the abnormal laser data; if the result is no, execute step S3.3.2; Step S3.3.2: using a clustering algorithm to identify laser data that meets the characteristics of train carriages, and calculating the node data of the head and tail of each train carriage through concave feature recognition, calculating the edge data of the train carriage skin, and combining the node data and the edge data to obtain the skin information; The concave feature recognition is to identify the train carriage by judging whether the shape of the laser line meets the concave requirement; the concave requirement depends on the cross-sectional shape of the train; Step S3.3.3: According to the car body information, the incomplete train carriages are assembled to obtain the total number of train sections; the single piece of laser data is calculated to generate and send out the steel frame scanning result telegram and the train scanning result telegram.

4. The method for joint scanning of train carriages and steel seat frames based on multiple unmanned vehicles according to claim 3 is characterized in that: In step S4: Step S4.1: Based on the steel frame scanning result telegram, determine whether the laser data contains complete steel frame data; if the result is yes, update the data of the steel frame in the warehouse; if the result is no, block the stacking position of the steel frame; Based on the train relative index number in the train scanning result message and the actual train carriage number within the unmanned vehicle scanning range, the actual train carriage number is sequentially corresponded to distinguish the laser data of each part of the train; the division of the parts is determined according to the number of unmanned vehicles; Step S4.2: Based on the laser data of the front of the train or the laser data of the rear of the train, determine whether the laser scanner has scanned the entire train carriage; if the result is yes, execute step S4.3; if the result is no, update the point data of the border part of the train carriage obtained by scanning; Step S4.3: Determine whether the train carriage size in the laser data conforms to the theoretical size corresponding to the train carriage model; if the result is yes, update the point data of the actual train carriage frame to obtain all the train carriage data; if the result is no, discard the point data of the train carriage frame; Step S4.4: Based on the laser data of the middle part of the train, determine whether the laser scanner has scanned the complete train car; if the result is yes, update the point data of the actual train car border; if the result is no, splice the point data of the scanned train car border with the point data of the scanned train car border in step S4.2 to form the point data of the complete train car border, and re-execute step S4.

2.

5. The method for joint scanning of train carriages and steel seat frames based on multiple unmanned vehicles according to claim 4 is characterized in that: In step S4.1: Through the WMS system, the steel frame stack position information of the steel frame scanning result telegram is received, and the coordinate information of the four vertices of the steel frame stack position is obtained; based on the coordinate information, it is judged whether the laser data contains the complete steel frame data; if the result is yes, the data of the steel frame in the warehouse is further updated; if the result is no, the stack position of the steel frame is blocked; In step S2, the steel seat frame is placed along the side of the train, and the laser scanner is used to scan the train carriage and the steel seat frame simultaneously.

6. A joint scanning system for train carriages and steel frames based on multiple unmanned vehicles, characterized in that: include: Module M1: Obtain and confirm the number of train carriages; Module M2: Based on the number of train carriages, the scanning range is defined for the unmanned vehicle and a scanning instruction is generated. Through the scanning instruction, the unmanned vehicle is moved to the scanning end point, triggering the laser scanning system to scan the train carriages and steel frame to obtain laser data. Module M3: Processing laser data through the laser scanning system to obtain the steel frame scanning results and train scanning results; Module M4: Based on the scanning result of the steel frame, determine whether the laser data contains the complete data of the steel frame; if the result is yes, update the data of the steel frame in the warehouse; if the result is no, block the stacking position of the steel frame; Based on the train scanning results, according to the unmanned vehicle scanning range divided by module M2 and the scanning direction of the unmanned vehicle, it is judged whether the train carriage has been scanned completely; if the result is yes, the train carriage data in the laser data is updated to obtain all the train carriage data; if the result is no, the laser data is spliced ​​to obtain the complete train carriage data, and it is re-judged whether the train carriage has been scanned completely.

7. The railway carriage and steel frame joint scanning system based on multiple unmanned vehicles according to claim 6 is characterized in that: The moving direction of the unmanned vehicle is taken as the X direction, the moving direction of the unmanned vehicle is taken as the Y direction, and the clamp direction of the unmanned vehicle is taken as the Z direction; The steel frame scanning result is a steel frame scanning result telegram; the train scanning result is a train scanning result telegram; In the module M1, the number of train carriages is confirmed through the screen of the WMS system; In the module M2: Module M2.1: Based on the number of train carriages, the scanning range is defined, and when the unmanned vehicle is detected to have arrived at the scanning starting point, a scanning instruction is sent to the unmanned vehicle to move to the scanning end point; Module M2.2: Receives scanning instructions, starts the unmanned vehicle to scan the scanning range defined by module M2.1, sends the train steel frame joint scanning telegram to the laser scanning system, triggers the laser scanning system to scan the train carriage and steel frame, and obtains laser data; In the module M3: Module M3.1: Segment and integrate laser data through the laser scanning system to obtain single data; Module M3.2: Use the point cloud processing library of the laser scanning system, i.e. PCL, to determine whether the laser scanner has scanned the complete steel frame; if the result is yes, calculate the spatial information of each stack of steel frames; If the result is no, continue scanning; The spatial information includes: the stacking position of the steel frame, the number of layers of the steel frame and the layer height of the steel frame; Module M3.3: Calculate the single data of laser data through the algorithm module and point cloud database of the laser scanning system to generate the steel frame scanning result telegram and the train scanning result telegram; Module M3.4: Send steel frame scanning result telegram to WMS system; send train scanning result telegram to WMS system; The algorithm module is used to calculate laser data and generate scanning result electronic text.

8. The railway carriage and steel frame joint scanning system based on multiple unmanned vehicles according to claim 7 is characterized in that: In the module M2.1: Module M2.1.1: Based on the train scanning range as the number of train carriages, the scanning range is divided into multiple unmanned vehicles; the number of the unmanned vehicles is more than 2; Module M2.1.2: Use the WMS system to define the X-direction range of the unmanned vehicle obtained in module M2.1.1; Module M2.1.3: Based on the scanning range defined by module M2.1.1 and the existing information of steel frames stored in the WMS system, combined with the center point of the steel frame placement position and the type of steel frame placed, calculate the X-direction range and Y-direction range of each stack of steel frames; Module M2.1.4: Generate a scanning instruction based on the scanning range, the X-direction range of the unmanned vehicle and the information of each stack of steel frames and send it to the unmanned vehicle; In the module M3.2: Module M3.2.1: Integrate the point cloud data in the laser data to determine whether the laser scanner has scanned the complete steel frame; if the result is yes, execute module M3.2.2; if the result is no, continue scanning; Module M3.2.2: Through the point cloud processing library, the scanned steel frame support point cloud is cut out to obtain the steel frame point cloud; Module M3.2.3: Use the corresponding steel frame template to match the cropped steel frame point cloud and calculate the position and grasping points of the steel frame. Module M3.2.4: Based on the posture and grasping points of the steel frame, the cropped steel frame point cloud is plane fitted and clustered through the plane fitting algorithm to obtain the point cloud data of each layer of the steel frame, and the number of layers of the steel frame and the height of each layer of the steel frame as spatial information are calculated based on the point cloud data of each layer of the steel frame; In the module M3.3: Module M3.3.1: Identify the train carriages in the target area, calculate the frame position of the corresponding train carriages, obtain edge data, and determine whether the laser data is abnormal; if the result is yes, throw out the abnormal laser data; if the result is no, execute module M3.3.2; Module M3.3.2: Use clustering algorithm to identify laser data that meets the characteristics of train carriages, and calculate the node data of the head and tail of each train carriage through concave feature recognition, calculate the edge data of the train carriage skin, and combine the node data and the edge data to obtain the skin information; The concave feature recognition is to identify the train carriage by judging whether the shape of the laser line meets the concave requirement; the concave requirement depends on the cross-sectional shape of the train; Module M3.3.3: Based on the car body information, assemble the incomplete train carriages to obtain the total number of train sections; calculate the single data of the laser data, generate and send the steel frame scanning result telegram and the train scanning result telegram.

9. The railway carriage and steel frame joint scanning system based on multiple unmanned vehicles according to claim 8 is characterized in that: In the module M4: Module M4.1: Based on the steel frame scanning result telegram, determine whether the laser data contains the complete steel frame data; if the result is yes, update the data of the steel frame in the warehouse; if the result is no, block the stacking position of the steel frame; Based on the train relative index number in the train scanning result message and the actual train carriage number within the unmanned vehicle scanning range, the actual train carriage number is sequentially corresponded to distinguish the laser data of each part of the train; the division of the parts is determined according to the number of unmanned vehicles; Module M4.2: Based on the laser data of the front of the train or the laser data of the rear of the train, determine whether the laser scanner has scanned the entire train carriage; if the result is yes, execute module M4.3; if the result is no, update the point data of the border part of the train carriage obtained by scanning; Module M4.3: Determine whether the train carriage size in the laser data conforms to the theoretical size corresponding to the train carriage model; if the result is yes, update the point data of the actual train carriage border to obtain all the train carriage data; if the result is no, discard the point data of the train carriage border; Module M4.4: Based on the laser data of the middle part of the train, determine whether the laser scanner has scanned the complete train carriage; if the result is yes, update the point data of the actual train carriage border; if the result is no, splice the point data of the scanned train carriage border with the point data of the scanned train carriage border in module M4.2 to form the point data of the complete train carriage border, and re-execute module M4.

2.

10. The railway carriage and steel frame joint scanning system based on multiple unmanned vehicles according to claim 9 is characterized in that: In the module M4.1: Through the WMS system, the steel frame stack position information of the steel frame scanning result telegram is received, and the coordinate information of the four vertices of the steel frame stack position is obtained; based on the coordinate information, it is judged whether the laser data contains the complete steel frame data; if the result is yes, the data of the steel frame in the warehouse is further updated; if the result is no, the stack position of the steel frame is blocked; In the module M2, the steel seat frame is placed along the side of the train, allowing the laser scanner to scan the train car and the steel seat frame at the same time.

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