Method, device and system for planning supplementary spraying path of plane depainting point of bridge crane

Abstract

The invention discloses a bridge crane plane paint removal point spray repairing path planning method, device and system, and the method comprises the steps: obtaining the coordinates and attributes of a paint removal point, and building a multi-target TSP model containing obstacle avoidance and priority ranking; designing a genetic algorithm adaptive to the scene; and outputting the optimal path and guiding the operation of a supplementary spraying execution mechanism of the bridge crane. By adopting the technical scheme of the invention, the problems of repeated reciprocating, high energy consumption, missed spraying and the like in the traditional manual path planning are solved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent equipment maintenance technology, specifically relating to a method, device, and system for planning the path for repainting paint peeling points on the plane of a bridge crane. Background Technology

[0002] Bridge cranes are large lifting equipment commonly used in industrial production. Their main beams and end beams are exposed to complex working conditions for extended periods, making them prone to problems such as paint peeling and metal corrosion, requiring regular touch-up painting and maintenance. Traditional touch-up painting path planning relies on manual experience, which has the following drawbacks: 1. High path repetition rate: Manual planning easily leads to cranes moving back and forth, increasing invalid travel; 2. Ignoring obstacle constraints: This may result in the planning of a path that traverses the crane track and connecting structures, leading to collisions with the spraying mechanism; 3. Disordered priorities: The degree of corrosion at the paint peeling points was not considered, which may delay the repainting of severely peeling areas; 4. High energy consumption: Frequent start-stop and high-speed movement lead to excessive energy consumption of the crane.

[0003] In existing technologies, TSP and genetic algorithms have been used for path planning (such as AGV navigation), but they have not been adapted to the structural constraints and spraying characteristics of bridge cranes. Direct application of these algorithms can lead to low path feasibility and poor optimization results. Therefore, there is an urgent need for a dedicated path planning method that incorporates scenario constraints. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method, device, and system for planning the path for repainting paint peeling points on the plane of a bridge crane, which solves the problems of path repetition, obstacle collision, disordered priority, and excessive energy consumption.

[0005] To achieve the above objectives, the present invention provides the following solution: A method for planning the path for repainting paint peeling spots on the plane of a bridge crane, comprising: Obtain the coordinates and attribute parameters of all paint peeling points on the surface of the bridge crane to be repaired, including the paint peeling area and corrosion level. Construct a TSP path optimization model that includes obstacle avoidance and priority ranking; Based on the coordinates and attributes of the paint peeling points, the optimal path is obtained by solving the TSP path optimization model using an improved genetic algorithm. The optimal path is converted into instructions that the bridge crane control system can recognize, guiding the spraying actuator to operate in sequence.

[0006] As a preferred option, the TSP path optimization model aims to minimize the total repainting path, reduce crane energy consumption, and prioritize high-priority paint stripping points. The constraints include obstacle constraints, priority constraints, and operating radius constraints.

[0007] As a preferred option, the following constraints apply: Obstacles: The path must not cross the rails of the bridge crane or physical obstacles in the connecting structure; Priority constraints: The priority coefficient P is defined as α·S+β·L, and the paint removal points with higher P values ​​in the path are visited first; where α and β are weighting coefficients; Operating radius constraints: The maximum operating radius of the repainting actuator is R, and when the distance between two paint removal points is ≤R, they are merged into the same operating unit.

[0008] The present invention also provides a path planning device for repainting paint peeling spots on the plane of a bridge crane, comprising: The first processing module is used to obtain the coordinates and attribute parameters of all paint peeling points on the surface of the bridge crane to be repaired and sprayed. The attribute parameters include: paint peeling area and corrosion level. The second processing module is used to construct a TSP path optimization model that includes obstacle avoidance and priority ranking. The third processing module is used to solve the TSP path optimization model based on the coordinates and attributes of the paint peeling points, and obtain the optimal path. The fourth processing module is used to convert the optimal path into instructions that the bridge crane control system can recognize, guiding the spraying actuator to operate in sequence.

[0009] As a preferred option, the TSP path optimization model aims to minimize the total repainting path, reduce crane energy consumption, and prioritize high-priority paint stripping points. The constraints include obstacle constraints, priority constraints, and operating radius constraints.

[0010] As a preferred option, the following constraints apply: Obstacles: The path must not cross the rails of the bridge crane or physical obstacles in the connecting structure; Priority constraints: The priority coefficient P is defined as α·S+β·L, and the paint removal points with higher P values ​​in the path are visited first; where α and β are weighting coefficients; Operating radius constraints: The maximum operating radius of the repainting actuator is R, and when the distance between two paint removal points is ≤R, they are merged into the same operating unit.

[0011] The present invention also provides a path planning system for repairing paint peeling spots on the plane of a bridge crane, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a path planning method for repairing paint peeling spots on the plane of a bridge crane when executed by the processor.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Path efficiency: Compared with manual planning, the total path for touch-up spraying is shortened by 20%-30%, and the operation time is reduced by 15%-25%; 2. Safety: By using obstacle constraints, collisions between the spraying mechanism and the crane structure are avoided, reducing the failure rate to 0. 3. Energy consumption optimization: Crane energy consumption is reduced by 15%-25%, meeting energy conservation requirements; 4. Maintenance quality: High-priority paint peeling spots are treated first, reducing the corrosion spread rate of equipment by more than 40%. Attached Figure Description

[0013] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of the method for planning the repainting path for paint peeling points on the plane of a bridge crane according to an embodiment of the present invention; Figure 2 This is a schematic diagram showing the distribution of paint peeling points and obstacle areas on the main beam of a bridge crane. Figure 3 It is the evolution curve of an improved genetic algorithm; Figure 4 This is the path before optimization; Figure 5 This is the optimized path. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] Example 1 like Figure 1 As shown, this invention provides a path planning method for repainting paint peeling points on a bridge crane. By constructing a multi-constraint TSP model and improving the encoding, crossover, and mutation strategies of the genetic algorithm, path optimization adapted to specific scenarios is achieved. The specific steps are as follows: S1. Paint Scratching Point Information Collection: The coordinates (x, y) and attribute parameters of all paint scraping points on the surface (main beam, end beam) of the bridge crane to be repainted are obtained through machine vision inspection or manual annotation. The attribute parameters include the paint scraping area S and the corrosion level L, encoded using integers. S2. Establish a constrained TSP path optimization model: with the optimization objectives of "shortest total repainting path + lowest crane energy consumption + priority processing of high-priority paint peeling points", the constraints include: Obstacle constraints: The path must not cross physical obstacles such as the bridge crane's track and connecting structures. The obstacle area is represented by a polygon coordinate set Ω. Priority constraint: Define the priority coefficient P = α·S + β·L (α and β are weight coefficients), and the access order of the peeling points with higher P values ​​in the path is earlier; Operating radius constraint: The maximum operating radius of the touch-up spraying actuator is R. When the distance between two paint removal points is ≤ R, they are merged into the same operating unit. S3. Solving the model based on an improved genetic algorithm: S31. Coding: A hybrid "coordinate-unit" coding system is adopted, which maps the coordinates of paint peeling points to integer codes, and the work units are marked with set symbols; S32. Initialize the population: Randomly generate N initial paths, and filter out invalid paths (paths that intersect with Ω) through obstacle detection. S33. Fitness Function: Construct F=ω1·L t +ω2·E n +ω3·P o , where L t E is the total path length. n For crane energy consumption (positively correlated with the number of start-stop cycles), P o ω1, ω2, and ω3 are the priority violation values ​​(cumulative penalties for delayed access to high-P points), and their weights are ω1, ω2, and ω3. S34. Selection Operation: Use tournament selection to select parent individuals based on fitness values; S35. Crossing operation: Design a two-point crossing with obstacle detection. If the sub-path crosses the obstacle after the crossing, backtrack and adjust the crossing point. S36. Mutation operation: Using "neighborhood perturbation mutation", randomly select 1-2 points in the path and replan the local path within the surrounding safe zone (non-Ω); S37. Evolution Termination: When the rate of change of fitness value over K consecutive generations is ≤ ε, output the optimal path; S4. Path Execution: Converts the optimal path into instructions that the bridge crane control system can recognize, guiding the supplementary spraying actuator to operate in sequence.

[0018] Furthermore, the obstacle region Ω in step S2 is obtained by extracting the structural features of the bridge crane plane through 3D modeling to generate the polygonal boundary coordinates of the obstacle region. The crane energy consumption E in step S33... n The calculation method is as follows: ,in For the number of start-stop cycles, K is the moving speed between adjacent paint peeling points, and k1 and k2 are energy consumption coefficients.

[0019] The two-point crossover with obstacle detection described in step S35 is as follows: For parent individuals A and B, a crossover interval [a, b] is randomly selected, and genes within the interval are exchanged; the ray tracing method is used to detect whether the sub-path intersects with the obstacle region Ω. If they intersect, the crossover interval is narrowed to [a, b-1] or [a+1, b], and the detection is repeated until the path is safe. After the crossover, duplicate paint patch numbers are found within the same individual, while unique numbers are retained. Conflicting numbers are eliminated using a partial mapping method.

[0020] The mutation strategy randomly selects two points and swaps their positions. To improve the local search capability of the genetic algorithm, multiple consecutive evolutionary reversal operations are introduced after selection, crossover, and mutation. Here, "evolution" refers to the unidirectionality of the reversal operator, meaning that only those with improved fitness after reversal are accepted; otherwise, the reversal is invalid.

[0021] Furthermore, in step S3, population initialization is generally based on experience, with a value between 50 and 100. The selection operation involves selecting individuals from the old population to the new population with a certain probability; the higher the fitness value of an individual, the greater the probability of being selected. The parameters of the genetic algorithm are set as follows: population size N = 50-100, crossover probability Pc = 0.7-0.9, mutation probability Pm = 0.01-0.05, number of generations K = 50-200, and convergence threshold ε = 0.001.

[0022] Taking the main beam plane of a certain type of bridge crane as an example, a high-definition camera is used to capture surface images, and an edge detection algorithm is used to identify paint peeling points. The coordinates (unit: m) and attributes of 14 paint peeling points are output: Examples of coordinates: (16.47, 96.10), (16.47, 94.44), ..., (22.09, 92.55), etc. Figure 2 ; Paint peeling area S: 0.01-0.1m², corrosion level L: 1-5 (level 5 is the most severe). Obstacle region Ω: Tracks on both sides of the main beam (polygon coordinate set [(14, 100]-(26, 100)-(26, 99.8)-(14, 99.8)).

[0023] Priority coefficients: α=0.6, β=0.4, P=0.6S+0.4L, operating radius R=0.5m, and paint removal points with a spacing ≤0.5m are merged into operating units (a total of 14 units are merged); weight coefficients: ω1=0.5 (path length), ω2=0.3 (energy consumption), ω3=0.2 (priority).

[0024] Encoding: Map 14 job units to integer codes from 1 to 14, e.g., unit (16.47, 96.10) corresponds to code "1"; Population size N=80, Pc=0.8, Pm=0.03, K=100, ε=0.001; Fitness calculation: F=0.5L t +0.3E n +0.2P o ,in Crossover operation: Randomly select the crossover interval [5,15], swap them, and then detect obstacles using the ray casting method. If the obstacle crosses the track, adjust it to [6,15]. Mutation operation: Randomly select the code "8" and replan the path with adjacent points within a 0.5m safe zone around it. The optimal fitness value of the genetic algorithm is as follows: Figure 3 .

[0025] Before optimization (manual planning 11->7->2->12->1->10->13->9->5->4->3->14->6->8->11), such as Figure 4 The total path length is 57.3223, energy consumption is 120kWh, and high-priority points lag by an average of 3 positions. After optimization (in this invention, 5->6->12->7->13->11->9->10->1->8->2->3->14->4->5), as follows: Figure 5 The total path length is 30.0991m (shortened by 47%), energy consumption is 66kWh (reduced by 45%), and high-priority points are all accessed in the first 5 times, with no risk of collision.

[0026] Example 2 The present invention also provides a path planning device for repainting paint peeling spots on the plane of a bridge crane, comprising: The first processing module is used to obtain the coordinates and attribute parameters of all paint peeling points on the surface of the bridge crane to be repaired and sprayed. The attribute parameters include: paint peeling area and corrosion level. The second processing module is used to construct a TSP path optimization model that includes obstacle avoidance and priority ranking. The third processing module is used to solve the TSP path optimization model based on the coordinates and attributes of the paint peeling points, and obtain the optimal path. The fourth processing module is used to convert the optimal path into instructions that the bridge crane control system can recognize, guiding the spraying actuator to operate in sequence.

[0027] As one embodiment of the present invention, the TSP path optimization model takes the shortest total repainting path, the lowest crane energy consumption, and the priority processing of high-priority paint stripping points as optimization objectives. The constraints include: obstacle constraints, priority constraints, and operating radius constraints.

[0028] As one embodiment of the present invention, the obstacle constraint is as follows: the path must not cross the track of the bridge crane or the physical obstacles of the connecting structure; the priority constraint is defined as the priority coefficient P = α・S + β・L, and the access order of the paint removal point with the higher P value in the path is earlier; where α and β are weight coefficients; the working radius constraint is as follows: the maximum working radius of the repainting actuator is R, and when the distance between two paint removal points is ≤R, they are merged into the same working unit.

[0029] Example 3 The present invention also provides a path planning system for repairing paint peeling spots on the plane of a bridge crane, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a path planning method for repairing paint peeling spots on the plane of a bridge crane when executed by the processor.

[0030] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for planning the path for repainting paint peeling spots on the plane of a bridge crane, characterized in that, include: Obtain the coordinates and attribute parameters of all paint peeling points on the surface of the bridge crane to be repaired, including the paint peeling area and corrosion level. Construct a TSP path optimization model that includes obstacle avoidance and priority ranking; Based on the coordinates and attributes of the paint peeling points, the optimal path is obtained by solving the TSP path optimization model using an improved genetic algorithm. The optimal path is converted into instructions that the bridge crane control system can recognize, guiding the spraying actuator to operate in sequence.

2. The method for planning the path for repainting paint peeling points on the plane of a bridge crane as described in claim 1, characterized in that, The TSP path optimization model aims to minimize the total repainting path, reduce crane energy consumption, and prioritize high-priority paint stripping points. The constraints include obstacle constraints, priority constraints, and operating radius constraints.

3. The method for planning the path for repainting paint peeling points on the plane of a bridge crane as described in claim 2, characterized in that, Obstacle constraints: The path must not cross the bridge crane's tracks or connecting structural physical obstacles; Priority constraint: Define the priority coefficient P = α·S + β·L, and the access order of the peeling points with higher P values ​​in the path is earlier; where α and β are weight coefficients; Operating radius constraint: The maximum operating radius of the touch-up spraying actuator is R. When the distance between two paint removal points is ≤ R, they are merged into the same operating unit.

4. A path planning device for repainting paint peeling spots on the plane of a bridge crane, characterized in that, include: The first processing module is used to obtain the coordinates and attribute parameters of all paint peeling points on the surface of the bridge crane to be repaired and sprayed. The attribute parameters include: paint peeling area and corrosion level. The second processing module is used to construct a TSP path optimization model that includes obstacle avoidance and priority ranking. The third processing module is used to solve the TSP path optimization model based on the coordinates and attributes of the paint peeling points, and obtain the optimal path. The fourth processing module is used to convert the optimal path into instructions that the bridge crane control system can recognize, guiding the spraying actuator to operate in sequence.

5. The bridge crane planar paint peeling point repair spraying path planning device as described in claim 4, characterized in that, The TSP path optimization model aims to minimize the total repainting path, reduce crane energy consumption, and prioritize high-priority paint stripping points. The constraints include obstacle constraints, priority constraints, and operating radius constraints.

6. The bridge crane planar paint peeling point repair spraying path planning device as described in claim 5, characterized in that, Obstacle constraints: The path must not cross the bridge crane's tracks or connecting structural physical obstacles; Priority constraint: Define the priority coefficient P = α·S + β·L, and the access order of the peeling points with higher P values ​​in the path is earlier; where α and β are weight coefficients; Operating radius constraint: The maximum operating radius of the touch-up spraying actuator is R. When the distance between two paint removal points is ≤ R, they are merged into the same operating unit.

7. A path planning system for repairing paint peeling spots on the plane of a bridge crane, characterized in that, include: The system includes a memory and a processor, wherein the memory stores a computer program executed by the processor, the computer program performing the bridge crane planar paint peeling point repair spraying path planning method as described in any one of claims 1-3 when executed by the processor.

Images