Track robot multi-row target inspection method for irregular node steel structure

Abstract

The application discloses a track robot multi-row target inspection method for irregular node steel structures, which obtains three-dimensional coordinate values of multi-target inspection points according to a classification inspection principle, and calculates minimum distance points of the inspection points on a track and corresponding gimbal attitude values; the inspection points are divided into multiple rows according to a track direction, a snake-shaped inspection path is constructed, and the inspection points are divided into normal nodes and abnormal nodes according to the gimbal attitude values; robot moving time consumption and gimbal adjustment time consumption between the abnormal nodes and adjacent-row normal nodes are calculated, a cost function is constructed, and whether the abnormal nodes are included in the adjacent-row inspection path is judged; all the abnormal nodes not included are sorted according to track distances, an abnormal node inspection path is generated, all the inspection points of the snake-shaped inspection path and the abnormal node inspection path are sequentially connected in series, and a robot is driven to complete automatic inspection. The application realizes automatic arrangement and combination of multi-row similar target inspection through an algorithm, and improves inspection efficiency.

Description

Technical Field

[0001] This invention belongs to the field of track robot inspection and surveying technology, and in particular relates to a multi-row target inspection method for track robots for irregular node steel structures. Background Technology

[0002] The spatial steel structure inspection space is a three-dimensional space. The inspection objects are steel structure members, bolts and other node connections, purlin components, hangers and ceiling panels at different elevations and plane positions. The inspection objects are diverse, numerous, complex in relationship and highly repetitive. How to achieve automated inspection of multiple targets in this scenario is a current technical challenge.

[0003] Track-mounted inspection robots are relatively mature in industries such as power equipment rooms. However, the current inspection targets are small and relatively simple. Therefore, the current track-mounted inspection robots mostly rely on manual input for setting inspection routes and do not have inspection strategy planning schemes for tens of thousands of inspection targets. This hinders the automated inspection of track-mounted inspection robots in spatial steel structure scenarios.

[0004] In the early stages, a track-mounted inspection robot was developed for the inspection of spatial steel structures, and the automatic setting of multiple rows of inspection points at the same height was achieved. However, the inspection application scenarios are different, and the objectives of each inspection are different. Therefore, it is necessary to connect inspection points of different numbers, locations, and heights to generate reasonable inspection tasks each time. Faced with a large number of inspection points, manual methods alone cannot achieve the rationalization, efficiency, and precision of the inspection strategy.

[0005] The track layout algorithm proposed in the early research achieved a parallel arrangement of the track and inspection points, providing a fundamental guarantee for automated inspection. Although spatial steel structures are complex in both planar and height directions, most grids are relatively regular, meaning that most nodes are parallel in the X and Y directions and consistent or regularly varying in height. However, there are also multiple inspection points that are not parallel in plan and have different heights. For regular grids, using a serpentine path to plan the inspection route is the preferred solution, but how to incorporate irregular nodes into the serpentine path is a problem to be solved. The time consumption from the current inspection point to the next inspection point during the inspection process includes robot movement consumption and gimbal adjustment consumption. Based on the principle of minimizing consumption, a multi-row target inspection method for track-mounted robots with irregular node steel structures is proposed. Achieving the comprehensive optimization of the inspection strategy is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a method for multi-row target inspection of a space steel structure track robot facing irregular nodes, achieving the optimal arrangement and combination of multiple rows of targets in the space steel structure and meeting the requirements for automated inspection of space steel structures.

[0007] The specific technical solution of this invention is as follows:

[0008] A method for multi-row target inspection using a tracked robot for irregular node steel structures includes the following steps:

[0009] Step S1: Based on the three-dimensional space steel structure solid model, obtain the three-dimensional coordinate values ​​of multi-target inspection points according to the classification inspection principle, and calculate the minimum distance point of each inspection point on the track and the corresponding gimbal attitude value.

[0010] Step S2: Divide each inspection point into multiple rows along the track direction to construct a serpentine inspection path, and divide the inspection points into normal nodes and abnormal nodes according to the gimbal attitude value;

[0011] Step S3: Calculate the robot movement time between the abnormal node and the adjacent row of normal nodes. Adjustment time of gimbal Construct the cost function Determine whether abnormal nodes should be included in the adjacent inspection path;

[0012] when If an abnormal node is included in the adjacent row inspection path, it will be excluded from the adjacent row inspection path.

[0013] Step S4: Sort all non-included abnormal nodes by track distance, generate abnormal node inspection paths, and sequentially connect all inspection points of the serpentine inspection path and the abnormal node inspection path to drive the robot to complete automatic inspection.

[0014] Preferably, in step S1, the classification inspection principle is that each inspection task uses one type of inspection point as the inspection object to formulate the inspection implementation strategy.

[0015] Preferably, in step S1, the minimum distance point is calculated based on the projection principle, which calculates the projection point of the inspection point on the track to determine the robot's movement distance on the track.

[0016] Preferably, in step S1, the gimbal attitude value includes at least one of the following: gimbal lifting height value, gimbal horizontal angle value, gimbal vertical angle value, and gimbal focal length value.

[0017] Preferably, in step S2, the serpentine inspection path is formed by connecting multiple rows of inspection points in sequence according to the path, creating a serpentine inspection path that alternates between left and right.

[0018] Preferably, in step S2, the abnormal node is the gimbal attitude value of the inspection point that is different from that of nodes in the same row or other rows, and the gimbal attitude needs to be adjusted separately for the abnormal node.

[0019] Preferably, in step S3, the robot movement time is... It is the time it takes for the robot to move from the current normal node to the next normal node;

[0020] Robot movement time , where v is the moving speed of the orbital robot.

[0021] Preferably, in step S3, the gimbal adjustment time is... It is the time taken for the gimbal to adjust from its current attitude to the attitude of the abnormal node. It is the maximum value among the time taken to adjust the gimbal's height, horizontal angle, vertical angle, and focal length.

[0022] Gimbal adjustment time =Max(Gimbal lifting time, horizontal angle adjustment time, vertical angle adjustment time, gimbal zoom time).

[0023] Preferably, the cost function is:

[0024] ;

[0025] Where K is the adjustment coefficient.

[0026] Preferably, the adjustment coefficient K is selected according to the following rules:

[0027] When the proportion of abnormal nodes included in the serpentine inspection path to the total number of abnormal nodes is ≤70%, K is the encouragement coefficient, and the adjustment coefficient K is a coefficient less than 1.

[0028] When the proportion of abnormal nodes included in the serpentine inspection path to the total number of abnormal nodes is less than 85%, the adjustment coefficient K=1.

[0029] When 85% or less is the proportion of the number of abnormal nodes included in the serpentine route to the total number of abnormal nodes, K is the penalty coefficient, and the adjustment coefficient K is a coefficient greater than 1.

[0030] The beneficial effects of this invention are:

[0031] 1. A method for multi-row target inspection of a spatial steel structure track robot facing irregular nodes is proposed, which solves the problem of automated inspection of complex three-dimensional spatial steel structures. The algorithm realizes the automatic arrangement and combination of inspection of multiple rows of similar targets, achieving low overall energy consumption and significantly improving inspection efficiency.

[0032] 2. The proposed algorithm solves the problem of manually entering inspection tasks, automates the process, and can adapt to different inspection task scenarios, avoiding inconsistencies caused by manual entry and significantly improving management efficiency.

[0033] 3. The proposed algorithm is generally applicable to steel structures with various spatial grids such as relatively regular space frames, trusses, and steel frames, and can be widely used in spatial steel structures in industries such as railway passenger stations, airports, rail transit, and stadiums.

[0034] 4. The proposed algorithm realizes the automated inspection needs of railway passenger station steel structures in different cycles and scenarios. The deployed inspection track robots can meet the needs of intelligent inspection of railway passenger station steel structures, replacing the original manual inspection, realizing scheduled and fixed-route inspection and real-time early warning, improving the operation and maintenance management level and decision-making efficiency of railway passenger stations, and ensuring the safe operation of railway passenger stations. Attached Figure Description

[0035] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that these drawings are designed for illustrative purposes only and are not intended to limit the scope of the present invention. Furthermore, unless specifically indicated, these drawings are intended only to conceptually illustrate the structural construction described herein and are not necessarily drawn to scale.

[0036] Figure 1 This is a flowchart of an application example in this invention.

[0037] Figure 2 This is a diagram showing the planar positional relationship of nodes and the inspection path in an application example of this invention. Detailed Implementation

[0038] First, it should be noted that the specific structure, features, and advantages of the present invention will be described in detail below by way of examples. However, all descriptions are for illustrative purposes only and should not be construed as limiting the present invention in any way. Furthermore, any single technical feature described or implied in the embodiments mentioned herein, or any single technical feature shown or implied in the accompanying drawings, can still be arbitrarily combined or deleted among these technical features to obtain more other embodiments of the present invention that may not be directly mentioned herein. Additionally, for the sake of simplifying the drawings, the same or similar technical features may be indicated only in one place in the same drawing.

[0039] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "setting," "connection," "fixing," "screw connection," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal connection of two components or the interaction between two components. Unless otherwise explicitly limited, those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0040] The following is in conjunction with the appendix Figure 1 -Appendix Figure 2 This invention will be described in detail.

[0041] Example 1:

[0042] A method for multi-row target inspection using a tracked robot for irregular node steel structures includes the following steps:

[0043] Step S1: Based on the three-dimensional space steel structure solid model, obtain the three-dimensional coordinate values ​​of multi-target inspection points according to the classification inspection principle, and calculate the minimum distance point of each inspection point on the track and the corresponding gimbal attitude value.

[0044] Step S2: Divide each inspection point into multiple rows along the track direction to construct a serpentine inspection path, and divide the inspection points into normal nodes and abnormal nodes according to the gimbal attitude value;

[0045] Step S3: Calculate the robot movement time between the abnormal node and the adjacent row of normal nodes. Adjustment time of gimbal Construct the cost function Determine whether abnormal nodes should be included in the adjacent inspection path;

[0046] when If an abnormal node is included in the adjacent row inspection path, it will be excluded from the adjacent row inspection path.

[0047] Step S4: Sort all non-included abnormal nodes by track distance, generate abnormal node inspection paths, and sequentially connect all inspection points of the serpentine inspection path and the abnormal node inspection path to drive the robot to complete automatic inspection.

[0048] Working principle:

[0049] The main objective of this invention is to maximize the efficiency of robot inspection by optimizing the path planning and node classification of inspection points.

[0050] First, a three-dimensional solid model of the spatial steel structure is established based on BIM model or 3D laser scanning. According to the principle of classified inspection, the coordinates of multiple target inspection points in space are determined, and the nearest position of each inspection point on the track and the attitude parameters required by the gimbal are calculated.

[0051] Here, multi-target inspection points refer to nodes of the same type that are relatively regular in planar position and height, but also have inconsistencies in planar position and height. For example, the upper chord nodes are all at the same height, have a regular planar layout, and are parallel to the track, but there are also one or more nodes with inconsistent or irregular heights.

[0052] The inspection points were then divided into multiple rows along the track's extension direction, forming a serpentine inspection path. Based on differences in gimbal attitude, the inspection points were categorized into normal and abnormal nodes. An abnormal node was defined as follows: for each row of inspection points, the median gimbal attitude value was identified as a reference standard. If the difference between the gimbal attitude value of an inspection point and this standard value exceeded 10%, it was marked as an abnormal node.

[0053] Then, by analyzing the robot's movement time and gimbal attitude adjustment time between abnormal nodes and normal nodes in adjacent rows, a cost function is constructed. This function serves as the basis for determining whether to include abnormal nodes in the inspection path of adjacent rows. Abnormal nodes that are not included are arranged according to their distance on the track, forming independent abnormal node inspection paths.

[0054] Here, "adjacent rows" refers to the row of normal nodes closest to the abnormal node, determined by the 3D coordinates of the abnormal and normal nodes. The distance standard is generally set according to project implementation needs and can be increased or decreased, typically within the range of 3-5 meters. If the abnormal node happens to be located between two rows and is equidistant from both, the row closest to the abnormal node is prioritized, or the scheme with the smaller gimbal attitude adjustment is prioritized. However, the probability of equidistant nodes is low in practice; this is only an example.

[0055] Finally, all inspection points in the serpentine inspection path and the abnormal node inspection path are sequentially connected to form a complete inspection sequence, driving the robot to complete the automatic inspection task in sequence.

[0056] This path planning and node classification method ensures that all preset inspection points are included in the complex three-dimensional steel structure space. At the same time, the combination of serpentine inspection path and abnormal node inspection path can reduce unnecessary back-and-forth between different posture points of the robot and repeated gimbal adjustments, reduce energy consumption, and significantly improve inspection efficiency.

[0057] Furthermore, in the embodiments, it can also be considered that in step S1, the classification inspection principle is that each inspection task uses one type of inspection point as the inspection object to formulate an inspection implementation strategy.

[0058] In this embodiment, the inspection points include the upper chord nodes, lower chord nodes, purlin nodes, hanger connection nodes, roof floor slab, and ceiling slab of the spatial steel structure.

[0059] Furthermore, in the embodiments, it can also be considered that in step S1, the minimum distance point is calculated based on the projection principle, which calculates the projection point of the inspection point on the track line to determine the movement distance of the robot on the track.

[0060] Furthermore, in the embodiments, it can be considered that in step S1, the gimbal attitude value includes at least one of the following: gimbal lifting height value, gimbal horizontal angle value, gimbal vertical angle value, and gimbal focal length value.

[0061] In this embodiment, the gimbal attitude value is obtained through a gimbal attitude optimization algorithm, which includes: calculating the optimal shooting distance based on the spatial distance between the inspection point and the track, then calculating the required gimbal focal length, horizontal and vertical angles of the gimbal, and calculating the gimbal lifting height based on the height difference between the inspection point and the track.

[0062] Furthermore, in the embodiments, it can also be considered that in step S2, the inspection points are divided into multiple rows according to the direction parallel to the track, based on the three-dimensional coordinate values ​​of the inspection points and the track.

[0063] Furthermore, in the embodiments, it can also be considered that in step S2, the serpentine inspection path is to connect multiple rows of inspection points in sequence according to the path order to form a serpentine inspection path that alternates between left and right.

[0064] In this embodiment, multiple rows of inspection points are connected in sequence to form a serpentine inspection path. The serpentine inspection path alternates between left and right, for example, inspecting the left side first on the outward journey and the right side on the return journey, repeating this process multiple times to automate the inspection of multiple rows of inspection points. Alternatively, the right side can be inspected on the outward journey and the left side on the return journey, depending on the requirements of the project.

[0065] Furthermore, in the embodiments, it can also be considered that in step S2, the abnormal node is the gimbal attitude value of the inspection point is different from that of the nodes in the same row or other rows, and the gimbal attitude needs to be adjusted separately for the abnormal node.

[0066] In this embodiment, for each row of inspection points, the gimbal attitude value of all inspection points is first calculated, and then the median value of the gimbal attitude value of all inspection points is found as a reference standard.

[0067] If the number of inspection points in each row is odd, sort the gimbal attitude values ​​of each inspection point from smallest to largest, and use the gimbal attitude value in the middle as the median value. If the number is even, take the gimbal attitude values ​​of the two middle inspection points, and use the average of these two values ​​as the median value.

[0068] If at least one of the following gimbal attitude values ​​at a certain inspection point differs from the median value by more than 10%: gimbal elevation height, gimbal horizontal angle, gimbal vertical angle, and gimbal focal length, then it is marked as an abnormal node.

[0069] Furthermore, in the embodiments, it can also be considered that in step S3, the robot movement time is... It is the time it takes for the robot to move from the current normal node to the next normal node;

[0070] Robot movement time , where v is the moving speed of the orbital robot.

[0071] Furthermore, in the embodiments, it can also be considered that in step S3, the gimbal adjustment time is... It is the time taken for the gimbal to adjust from its current attitude to the attitude of the abnormal node. It is the maximum value among the time taken to adjust the gimbal's height, horizontal angle, vertical angle, and focal length.

[0072] Gimbal adjustment time =Max(Gimbal lifting time, horizontal angle adjustment time, vertical angle adjustment time, gimbal zoom time).

[0073] In this embodiment, the maximum value among the four values—time for adjusting the gimbal's height, horizontal angle, vertical angle, and focal length—is taken as the gimbal adjustment time. If there are two consecutive maximum values, then only one of them needs to be taken.

[0074] Furthermore, in the embodiments, the cost function can also be considered as:

[0075] ;

[0076] Where K is the adjustment coefficient.

[0077] In this embodiment, a constructed cost function is used to determine whether abnormal nodes should be included in the adjacent inspection path, avoiding an increase in overall inspection time due to individual abnormal nodes. Specifically, the gimbal travels back and forth once in the serpentine inspection path, hence a fixed value of 2 is set. K is an incentive or penalty coefficient, the value of which is determined by the proportion of abnormal nodes to the total number of abnormal nodes. The coefficient K can be adjusted through learning and optimization using actual data from different projects to achieve dynamic optimality.

[0078] Furthermore, in the embodiments, the rule for determining the value of the adjustment coefficient K can be as follows:

[0079] When the proportion of abnormal nodes included in the serpentine inspection path to the total number of abnormal nodes is ≤70%, K is the encouragement coefficient, and the adjustment coefficient K is a coefficient less than 1.

[0080] When the proportion of abnormal nodes included in the serpentine inspection path to the total number of abnormal nodes is less than 85%, the adjustment coefficient K=1.

[0081] When 85% or less is the proportion of the number of abnormal nodes included in the serpentine route to the total number of abnormal nodes, K is the penalty coefficient, and the adjustment coefficient K is a coefficient greater than 1.

[0082] In summary, this invention combines a serpentine inspection path with an abnormal node inspection path, which significantly reduces unnecessary back-and-forth travel between different posture points and repeated gimbal adjustments, thereby significantly improving inspection efficiency.

[0083] Application Example 1:

[0084] The following uses Example 1 as an example to further illustrate the application of the present invention.

[0085] like Figure 1 As shown, the multi-row target inspection method for a track robot oriented towards irregular node steel structures of the present invention includes the following steps:

[0086] S1: Based on the established three-dimensional spatial steel structure entity model, according to the classification inspection principle, obtain the three-dimensional coordinate values ​​of multi-target inspection points, calculate the minimum distance point of the inspection point on the track and the corresponding gimbal attitude value.

[0087] Nine upper chord nodes were selected, and their three-dimensional coordinate values ​​were obtained. The minimum distance between the upper chord nodes and the track, the gimbal lifting height, the gimbal horizontal angle, the gimbal vertical angle, and the gimbal focal length were calculated, as shown in Table 1 below.

[0088] Table 1 Basic Information of the Upper Chord Node

[0089] Upper chord node Node encoding 3D coordinates (mm) Track distance (mm) Gimbal lifting height (mm) Horizontal angle of the gimbal (degrees) Vertical angle of the gimbal (degrees) Focal length (mm) 1 9,A,SQ,3,0 1620.50, 2100.20, 2900.80 2100.2 33 270 110.6 29.60 2 9,A,SQ,3,1 1450.30, 4950.60, 2650.10 4950.6 79 270 90 24.80 3 9,A,SQ,3,2 1580.90，8150.40，3020.70 8150.4 0 270 114.76 29.77 4 9,B,SQ,3,0 2420.10，1900.50，3050.30 1900.5 0 270 107.41 43.37 5 9,B,SQ,3,1 2650.80，5100.20，2950.90 5100.2 33 270 103.97 46.71 6 9,B,SQ,3,2 2480.40，7950.60，3120.50 7950.6 0 270 108.48 44.72 7 9,C,SQ,3,0 2820.10，2800.50，3050.30 2800.5 0 270 105.06 49.94 8 9,C,SQ,3,1 3450.70，6420.30，2980.60 6420.3 33 270 101.29 60.17 9 9,C,SQ,3,2 4350.20，7700.80，3080.40 7700.8 0 270 100.28 75.6

[0090] The upper chord nodes 1-9 are randomly selected local areas from the actual project. It can be seen that their planar positions and heights are inconsistent; the positional relationships are shown in [the diagram]. Figure 2 As shown.

[0091] S2: Divide the inspection points into multiple rows along the track direction to construct a serpentine inspection path. Classify the inspection points into normal nodes and abnormal nodes based on the gimbal attitude value.

[0092] Based on the three-dimensional coordinates of the inspection points and the track, the inspection points can be divided into two rows according to the direction parallel to the track. The first row includes upper chord node 1, upper chord node 2, and upper chord node 3; the second row includes upper chord node 4, upper chord node 5, upper chord node 6, and upper chord node 7.

[0093] Calculating the median values ​​for gimbal attitude adjustment at nodes 4-7 in the second row reveals a significant change in focal length. The median focal length is (46.71 + 44.72) / 2 = 45.715mm.

[0094] Upper chord node 8: Focal length (60.17-45.715) / 45.715=31.6%>10%, therefore upper chord node 8 is an abnormal node;

[0095] Upper chord node 9: Focal length (75.6-45.715) / 45.715=65.4%>10%, therefore upper chord node 9 is also an abnormal node.

[0096] These two rows of upper chord nodes can be connected in sequence to form a serpentine inspection route. The first row is inspected first, and the second row is inspected on the return trip, achieving the goal of minimizing inspection time. That is, upper chord node 1 → upper chord node 2 → upper chord node 3 → upper chord node 6 → upper chord node 5 → upper chord node 7 → upper chord node 4.

[0097] S3: Calculate the robot movement time and gimbal adjustment time between the abnormal node and the adjacent row of normal nodes, and construct the cost function. Introduce an incentive or penalty mechanism to determine whether an abnormal node should be included in the adjacent inspection route. Abnormal nodes should be included in the adjacent inspection route. Otherwise, they should not be included.

[0098] By examining the three-dimensional coordinates of the abnormal and normal nodes, it can be seen that the adjacent rows of abnormal nodes 8 and 9 are both the second row.

[0099] Given that v is the moving speed of the orbital robot = 400 mm / s;

[0100] The gimbal lifting rate is The horizontal turning rate of the gimbal is The vertical turning speed of the gimbal is The gimbal zoom rate is

[0101] (1) Calculate abnormal node 8

[0102] Abnormal node 8 is located between normal nodes 5 and 6. Calculate... :

[0103] Robot movement time =7.13s

[0104] Gimbal adjustment time:

[0105] The time taken for the gimbal to rise and fall is 0.033 * 30 = 0.99 seconds.

[0106] The time taken for the gimbal to level is 0 * 0.08 = 0 seconds.

[0107] Vertical tilting time = (108.48 - 101.29) * 0.1 = 0.72s;

[0108] The gimbal zoom time is (60.17 - 44.72) * 0.06 = 0.93 seconds.

[0109] Gimbal adjustment time =Max(gimbal lifting time, horizontal angle adjustment time, vertical angle adjustment time, gimbal zoom time)=0.99s.

[0110] Calculate the adjustment factor K:

[0111] The proportion of anomalous nodes included in the serpentine route to the total number of anomalous nodes is 7 / 9 = 77.8%, therefore K = 1.

[0112] calculate

[0113] Therefore, abnormal node 8 should be included in the adjacent row (row 2), and the inspection route (8 points) should be updated, i.e., Upper chord node 1 → Upper chord node 2 → Upper chord node 3 → Upper chord node 6 → Upper chord node 8 → Upper chord node 5 → Upper chord node 7 → Upper chord node 4, see Figure 2 As shown.

[0114] (2) Calculate abnormal node 9

[0115] Abnormal node 9 is also located between normal nodes 5 and 6. Calculation :

[0116] Robot movement time =7.13s

[0117] Gimbal adjustment time:

[0118] The time taken for the gimbal to rise and fall is 0.033 * 30 = 0.99 seconds.

[0119] The time taken for the gimbal to level is 0 * 0.08 = 0 seconds.

[0120] Vertical tilting time = (108.48 - 100.28) * 0.1 = 0.82s;

[0121] The gimbal zoom time is (75.6 - 44.72) * 0.06 = 1.85 seconds.

[0122] Gimbal adjustment time =Max(gimbal lifting time, horizontal angle adjustment time, vertical angle adjustment time, gimbal zoom time)=1.85s.

[0123] Calculate the adjustment factor K:

[0124] The proportion of abnormal nodes included in the serpentine route to the total number of abnormal nodes is 8 / 9 = 88.9% > 85%;

[0125] Based on previous learning and optimization across multiple projects, K=2.25;

[0126]

[0127] Therefore, abnormal node 9 should not be included in the adjacent row (row 2), see Figure 2 The red ball node is shown.

[0128] S4: Sort all non-included abnormal nodes according to their track distance to generate an abnormal node inspection route. Then, sequentially connect all inspection points of the serpentine route and the abnormal node inspection route to drive the track robot to complete the automatic inspection.

[0129] Inspection route: Upper chord node 1 → Upper chord node 2 → Upper chord node 3 → Upper chord node 6 → Upper chord node 8 → Upper chord node 5 → Upper chord node 7 → Upper chord node 4.

[0130] Upper chord node 9 is inspected separately as an abnormal node, driving the track robot to complete the automatic inspection in sequence.

[0131] The above embodiments and application examples have provided a detailed description of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the patent coverage of the present invention.

Claims

1. A method for multi-row target inspection using a tracked robot for irregular node steel structures, characterized in that, Includes the following steps: Step S1: Based on the three-dimensional space steel structure solid model, obtain the three-dimensional coordinate values ​​of multi-target inspection points according to the classification inspection principle, and calculate the minimum distance point of each inspection point on the track and the corresponding gimbal attitude value. Step S2: Divide each inspection point into multiple rows along the track direction to construct a serpentine inspection path, and divide the inspection points into normal nodes and abnormal nodes according to the gimbal attitude value; Step S3: Calculate the robot movement time between the abnormal node and the adjacent row of normal nodes. Time spent adjusting the gimbal Construct the cost function Determine whether abnormal nodes should be included in the adjacent inspection path; when If an abnormal node is included in the adjacent row inspection path, it will be excluded from the adjacent row inspection path. Step S4: Sort all non-included abnormal nodes by track distance, generate abnormal node inspection paths, and sequentially connect all inspection points of the serpentine inspection path and the abnormal node inspection path to drive the robot to complete automatic inspection.

2. The method for multi-row target inspection of a track robot for irregular node steel structures according to claim 1, characterized in that: In step S1, the principle of classified inspection is to formulate an inspection implementation strategy for each inspection task by taking one type of inspection point as the inspection object.

3. The method for multi-row target inspection of a track robot for irregular node steel structures according to claim 1, characterized in that: In step S1, the minimum distance point is calculated based on the projection principle, which determines the distance the robot moves on the track by projecting the inspection point onto the track.

4. The method for multi-row target inspection of a track robot for irregular node steel structures according to claim 1, characterized in that: In step S1, the gimbal attitude value includes at least one of the following: gimbal lifting height value, gimbal horizontal angle value, gimbal vertical angle value, and gimbal focal length value.

5. The method for multi-row target inspection of a track robot for irregular node steel structures according to claim 1, characterized in that: In step S2, the serpentine inspection path is formed by connecting multiple rows of inspection points in sequence according to the path, creating a serpentine inspection path that alternates between left and right.

6. The method for multi-row target inspection of a track robot for irregular node steel structures according to claim 1, characterized in that: In step S2, the abnormal node is the gimbal attitude value of the inspection point that is different from that of the nodes in the same row or other rows, and the gimbal attitude needs to be adjusted separately for the abnormal node.

7. The method for multi-row target inspection of a track robot for irregular node steel structures according to claim 1, characterized in that: In step S3, the robot's movement time is... It is the time it takes for the robot to move from the current normal node to the next normal node; Robot movement time , where v is the moving speed of the orbital robot.

8. The method for multi-row target inspection of a track robot for irregular node steel structures according to claim 1, characterized in that: In step S3, the gimbal adjustment takes time. It is the time taken for the gimbal to adjust from its current attitude to the attitude of the abnormal node. It is the maximum value among the time taken to adjust the gimbal's height, horizontal angle, vertical angle, and focal length. Gimbal adjustment time =Max(Gimbal lifting time, horizontal angle adjustment time, vertical angle adjustment time, gimbal zoom time).

9. The method for multi-row target inspection of a track robot for irregular node steel structures according to claim 1, characterized in that: The cost function is: ； Where K is the adjustment coefficient.

10. The method for multi-row target inspection of a track robot for irregular node steel structures according to claim 9, characterized in that: The rule for determining the value of the adjustment coefficient K is as follows: When the proportion of abnormal nodes included in the serpentine inspection path to the total number of abnormal nodes is ≤70%, K is the encouragement coefficient, and the adjustment coefficient K is a coefficient less than 1. When the proportion of abnormal nodes included in the serpentine inspection path to the total number of abnormal nodes is less than 85%, the adjustment coefficient K=1. When 85% or less is the proportion of the number of abnormal nodes included in the serpentine route to the total number of abnormal nodes, K is the penalty coefficient, and the adjustment coefficient K is a coefficient greater than 1.

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