A Global Detection Trajectory Planning Method for Coordinate Measuring Machines Based on an Improved Ant Colony Algorithm

Abstract

This invention relates to a global detection trajectory planning method for coordinate measuring machines (CMMs) based on an improved ant colony algorithm. The method includes: constructing a distance-adjacency matrix; initializing relevant parameters of the improved ant colony algorithm and the initial iteration position of the ants; sequentially selecting the next node based on state transition probabilities until the ants have traversed all nodes and obtained a complete detection path; updating the pheromones between nodes based on the quality of the detection path; calculating the node fitness based on the detection path quality, and calculating the node selection probability based on the node fitness; selecting the initial node for the next ant colony iteration using a roulette wheel selection strategy based on the node selection probability; re-performing the ant colony iteration search for the detection path based on the updated pheromones and the initial node for the next iteration; and selecting the shortest detection path during the iteration process as the globally optimal detection trajectory. This invention improves the performance of global detection trajectory planning for CMMs.

Description

Technical Field

[0001] This invention relates to the field of digital inspection technology for coordinate measuring machines (CMMs), and in particular to a global inspection trajectory planning method for CMMs based on an improved ant colony algorithm. Background Technology

[0002] In coordinate measuring machine (CMM) measurement, the inspection trajectory refers to the complete path planned by the probe to measure all sampling points. When the structure of the part to be measured is complex and there are many inspection items, relying solely on the experience of the measurement personnel is insufficient to ensure the optimal probe measurement path. Therefore, in order to improve equipment utilization and work efficiency, effectively calculating and analyzing the inspection trajectory and planning the optimal CMM inspection trajectory scheme is a crucial step in CMM inspection planning.

[0003] The detection trajectory planning of coordinate measuring machines (CMMs) typically employs a hierarchical planning strategy, decomposing the planning task into two levels: local trajectory planning and global trajectory planning. Local trajectory planning focuses on finding the shortest measurement path for sampling points within the features to be measured. Since the risk of probe collisions is low and there's no need to change the probe angle when measuring within a single feature, performing local path planning first effectively simplifies the complexity of the detection path planning problem and reduces the probability of collisions. After local trajectory planning is completed, the global planning stage treats the geometric features to be measured as the planning object. Ignoring internal path details, it determines the optimal measurement order for all features to be measured, thereby planning the shortest measurement path at the level of the geometric features to be measured.

[0004] Traditional global detection trajectory planning methods first plan a preliminary trajectory based on the straight-line distance of features, and then perform unified collision avoidance on the trajectory to obtain the final detection trajectory. Due to the diversity of probe contact angles and the influence of complex geometric features of parts, the probability of collisions in global detection trajectories is relatively high. Therefore, the preliminary trajectory planned by this method needs to be frequently corrected and probe angle adjusted to avoid collisions, which offsets the efficiency improvement brought by path planning. Zhou Kaijun et al. proposed a global trajectory planning method based on feature grouping and genetic algorithm in their paper "Measurement Path Planning Method for Coordinate Measuring Machine Based on Genetic Algorithm [J]. China Mechanical Engineering, 2016, 27(12)". This method constructs a fitness function with factors such as the detection working plane, the number of probe angle changes and the total trajectory length as optimization objectives, performs feature grouping optimization through matrix crossover genetic algorithm and performs micro-sorting of detection trajectories within the group through sequence planning genetic algorithm, and realizes the planning of global detection trajectories. This method considers the influence of probe contact posture on detection trajectory and effectively optimizes the trajectory planning results through grouping preprocessing strategy. However, this method does not consider the impact of collision avoidance on the trajectory during the iterative optimization of the trajectory planning algorithm, resulting in the need for secondary obstacle avoidance correction of the trajectory results obtained by the algorithm, which limits the planning effect of the algorithm. Wang Gang proposed a detection path planning method based on obstacle avoidance path in his paper "Research and Implementation of Detection Path Planning System for Coordinate Measuring Machine [D]. Wuhan University of Technology, 2020". This method first determines the collision-free path between every two sampling points within the feature through obstacle avoidance rules, and then solves the local shortest path through a genetic algorithm. Then, it calculates the collision-free path between every two connecting endpoints using obstacle avoidance rules, and finally solves the obstacle avoidance time matrix of the connecting endpoints through a greedy algorithm, thereby determining the global detection trajectory of the feature. Experimental verification shows that this method effectively optimizes the algorithm planning effect, but the method solves the global trajectory through a greedy algorithm, which has certain limitations in multi-feature planning application scenarios. Summary of the Invention

[0005] Therefore, the technical problem to be solved by this invention is to overcome the defect of poor quality of global trajectory planning results in the prior art, and to provide a global detection trajectory planning method for coordinate measuring machines based on an improved ant colony algorithm, so as to improve the results of global trajectory planning. This method improves the ant colony algorithm from three aspects: multi-factor heuristic function, pheromone dynamic update mechanism, and roulette wheel initial point selection strategy, and applies it to the global optimal detection trajectory planning among the features to be tested, thereby improving the effect of global detection trajectory planning for coordinate measuring machines.

[0006] To address the aforementioned technical problems, this invention provides a global detection trajectory planning method for a coordinate measuring machine based on an improved ant colony algorithm, comprising the following steps: The workpiece's geometric features to be measured are obtained and used as nodes. A distance adjacency matrix is ​​constructed based on the nodes, and the distance adjacency matrix is ​​used to define the trajectory distance between each node. Initialize the relevant parameters of the improved ant colony algorithm and the initial iteration position of the ants, and evenly distribute the number of ants on each node as the starting node; The ant colony iterative search for the detection path includes: obtaining an improved heuristic value based on relevant parameters and the distance adjacency matrix; obtaining a state transition probability based on the improved heuristic value; each ant starting from the initial node and sequentially selecting the next node based on the state transition probability until the ant has traversed all nodes and obtained a complete detection path. Based on the quality of the detection path, update the pheromones between nodes; calculate the node fitness based on the quality of the detection path, and calculate the node selection probability based on the node fitness; based on the node selection probability, use a roulette wheel strategy to select the initial node for the next iteration of the ant colony. Based on the updated pheromone and the initial node of the ant colony for the next iteration, the ant colony iterative search and detection path process is repeated. In response to the current iteration count reaching the preset maximum iteration count, the shortest detection path length during the iteration process is selected as the globally optimal detection trajectory.

[0007] In one embodiment of the present invention, constructing a distance adjacency matrix based on the nodes includes: Based on the distance from the local trajectory endpoint of the current geometric feature to be tested to the local trajectory starting point of the next geometric feature to be tested, the trajectory distance between any two nodes is obtained. The trajectory distance between any two nodes is used as an element to generate a distance adjacency matrix.

[0008] In one embodiment of the present invention, the formula for calculating the state transition probability is: , in, Let be the state transition probability. For nodes i and j Pheromones between them For nodes i and j Improved heuristic values ​​between As a communication heuristic factor, As the expected heuristic factor, For the set of nodes that have not yet been visited, This represents the number of iterations.

[0009] In one embodiment of the present invention, the formula for calculating the improved heuristic value is as follows: , in, To improve the heuristic value; This is the weighting factor for the distance between nodes, with a value range of [0-4]. Represents a node i arrive j The straight-line distance between them; Represents a node i arrive j The increased distance between them due to collision avoidance; This is the weighting factor for the probe angle transformation, with a value range of [0-4]. Represents a node i arrive j The number of probe angle changes required; It is a fixed value representing the additional trajectory distance traveled due to the change in probe angle; This is a weighting factor for path smoothness, with a value range of [0-4]. Indicates at node i arrive j The number of inflection points on the path.

[0010] In one embodiment of the present invention, updating the inter-node pheromone based on the quality of the detection path includes: Update pheromones using the following formula: , in, For nodes i arrive j The increase in pheromones; ρ The pheromone dynamic evaporation coefficient is calculated using the following formula: , in, t This represents the current iteration number; After each iteration ρ The reduction value; This represents the initial maximum value of the pheromone evaporation coefficient.

[0011] In one embodiment of the present invention, updating the inter-node pheromones based on the quality of the detection path further includes: Add the optimal path pheromone increment to the historical optimal detection path. The worst historical detection path is deducted for the pheromone reduction amount. And limit pheromone concentration to [ , Within the range; and These represent the maximum and minimum pheromone concentrations, respectively.

[0012] In one embodiment of the present invention, calculating the node fitness based on the quality of the detection path includes: The fitness of a node is calculated using the following formula: , in, For node fitness; For nodes i The trajectory length starting from δ is the attenuation rate. t This represents the number of iterations.

[0013] In one embodiment of the present invention, calculating the node selection probability based on the node fitness includes: The node selection probability is calculated using the following formula: ,

[0014] in, n The total number of nodes. i This is the number of the starting node for the iteration. For node fitness.

[0015] In one embodiment of the present invention, the initial node for the next iteration of the ant colony is selected based on the node selection probability using a roulette wheel strategy, including: Map the node selection probability to Range, generate random numbers The initial node for the next iteration is selected using the following formula: , in, i This is the number of the starting node for the iteration. k For the selection probability range in the roulette wheel betting strategy, those that meet the conditions k This is the final determined starting node for the next iteration. i , The probability of the starting node being selected is obtained based on the node's fitness.

[0016] The technical solution of the present invention has the following advantages compared with the prior art: The global detection trajectory planning method for coordinate measuring machines based on the improved ant colony algorithm described in this invention, compared with the traditional ant colony algorithm technology, comprehensively considers factors such as collision avoidance cost, number of angle transformations and path smoothness in the improved heuristic values ​​between nodes, and reconstructs the heuristic function of the ant colony algorithm, which enhances the adaptability of the algorithm to complex workpiece features and improves the global optimization capability of the algorithm. It has the advantages of high trajectory planning quality and adaptability to complex workpieces.

[0017] This invention improves the global search capability and convergence efficiency of the algorithm by dynamically adjusting the volatile coefficient, implementing a pheromone reward and punishment mechanism, and limiting the pheromone concentration in the pheromone dynamic update mechanism.

[0018] This invention selects the initial node for the next iteration based on a roulette wheel strategy. By optimizing the selection of the initial node through probability, this probability-based random selection mechanism not only ensures the positive feedback effect of high-quality starting points being selected frequently, but also maintains the diversity of the search through randomness, thus accelerating the convergence speed of the ant colony towards high-quality starting points. Attached Figure Description

[0019] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0020] Figure 1 This is a flowchart of the improved ant colony algorithm of the present invention.

[0021] Figure 2 Define a graph for detecting distances between elements.

[0022] Figure 3 The CAD model of the workpiece to be tested.

[0023] Figure 4 This is the trajectory planning result of the traditional ant colony algorithm.

[0024] Figure 5 The result is the trajectory planning result of Wang Gang's algorithm.

[0025] Figure 6 This is the algorithm planning result of this invention. Detailed Implementation

[0026] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0027] Example 1 Reference Figure 1 As shown, the global detection trajectory planning method for a coordinate measuring machine based on an improved ant colony algorithm includes the following steps: Step S1: Obtain the geometric features of the workpiece to be measured and use them as nodes. Construct a distance adjacency matrix based on the nodes. The distance adjacency matrix is ​​used to define the trajectory distance between each node.

[0028] Figure 3The geometric feature information of the workpiece to be measured is shown in Table 1. Based on the distance from the local trajectory endpoint of the current geometric feature to be measured to the local trajectory starting point of the next geometric feature to be measured, the trajectory distance between any two nodes is obtained. The trajectory distance between any two nodes is used as an element to generate a distance adjacency matrix.

[0029] It should be noted that when planning the global trajectory using the improved ant colony algorithm, the geometric features to be tested are treated as nodes in the algorithm. Since geometric features are not points without area, the distances between nodes need to be defined. The distance from feature 1 to feature 2 is defined as: the length of the trajectory from the endpoint of the local trajectory within feature 1 to the starting point of the local feature within feature 2, such as... Figure 2 In , , and Based on the above definition, the distances between the measured features in the workpiece are calculated, and the distance adjacency matrix of the nodes in the ant colony algorithm is constructed.

[0030] In Table 1, the probe angle “A×B×” represents the probe’s rotation angle around axis A (X-axis) and the probe’s rotation angle around axis B (Y-axis).

[0031] Table 1: Geometric feature information of the workpiece to be measured

[0032] Step S2: Initialize the relevant parameters of the improved ant colony algorithm and the initial iteration position of the ants.

[0033] Step S201: The specific settings of the relevant parameters for improving the ant colony algorithm are shown in Tables 2 and 3.

[0034] Table 2: Relevant parameters of the improved ant colony algorithm

[0035] Table 3: Relevant parameters of the improved ant colony algorithm (II)

[0036] Among the relevant parameters are the number of nodes. n ant count m Communication heuristic factor α Expected heuristic factors β pheromone constant Q Weighting factor for distance between nodes Weighting factor for probe angle transformation Weighting factors for path smoothness The additional distance traveled due to changes in probe angle The reduction value of the pheromone evaporation coefficient after each iteration The initial maximum value of the pheromone volatile coefficient , Optimal path pheromone increment Worst path pheromone deduction Attenuation rate δ Maximum number of iterations Pheromones extreme values and And other related parameters.

[0037] Step S202: Distribute 44 ants evenly across 22 nodes as the starting nodes for algorithm iteration. Proceed to step S3 to begin algorithm iteration.

[0038] Step S3: Ant colony iterative search for detection paths. Based on the algorithm parameters initialized in Step S2 and the distance-adjacency matrix of the geometric features to be tested, an improved ant colony algorithm is used to iteratively search for global detection paths. The specific process is as follows: Each ant starts from the initial node and selects the next node sequentially based on the state transition probability, until the ant has traversed all nodes and constructed a complete detection path. The formula for calculating the state transition probability is: , in, Let be the state transition probability. For nodes i and j Pheromones between them For nodes i and j Improved heuristic values ​​between As a communication heuristic factor, As the expected heuristic factor, This set of unvisited nodes in the problem space can be used as the target set for the next move. This represents the number of iterations.

[0039] Specifically, the improved heuristic values ​​between nodes Taking into account distance, probe angle change, and path smoothness, the calculation formula is as follows: , in, To improve the heuristic value; This is the weighting factor for the distance between nodes, with a value range of [0-4]. Represents a node i arrive j The straight-line distance between them; Represents a node i arrive j The increased distance between them due to collision avoidance; This is the weighting factor for the probe angle transformation, with a value range of [0-4]. Represents a node i arrive j The number of probe angle changes required; It is a fixed value representing the additional trajectory distance traveled due to the change in probe angle; This is a weighting factor for path smoothness, with a value range of [0-4]. Indicates at node i arrive j The number of inflection points on the path.

[0040] After all ants have traversed all nodes and constructed a complete detection path, determine whether the current iteration count has reached the preset maximum iteration count. If the maximum iteration count has been reached, proceed to step S5; otherwise, proceed to step S4 to update pheromones and select the initial node for the next iteration.

[0041] Step S4: Update the pheromone based on the path quality and select the initial node for the next iteration based on the roulette wheel betting strategy.

[0042] Based on the detection path generated in step S3, the pheromone matrix is ​​updated according to the path quality, and the initial node for the next iteration is selected using a roulette wheel betting strategy. The specific steps are as follows: Step S401: Update global pheromones. Update inter-node pheromones based on path quality, using the following formula: , in, For nodes i To the node j The pheromone increment is the sum of pheromones left by ants that traversed this path during this iteration. Pheromone evaporation coefficient. ρ Dynamic adjustment, the calculation formula is: , in, t This represents the current iteration number. After each iteration ρ The reduction value, The initial maximum value of the pheromone evaporation coefficient; when the initial maximum value decreases to... It will not decrease further after that. This represents the minimum value of the pheromone evaporation coefficient.

[0043] In addition, a reward and penalty mechanism based on path quality is set when updating pheromones, and the optimal path pheromone increment is added to the historical best detection path. The worst historical detection path is deducted for the pheromone reduction amount. And limit pheromone concentration to [ , Within the range; and These represent the maximum and minimum pheromone concentrations, respectively. By limiting the extreme values ​​of pheromone concentration, we can prevent excessive or insufficient pheromone levels between nodes.

[0044] It should be noted that during the iterative process of the ant colony algorithm, when a historically optimal path is discovered, pheromone rewards are applied to each edge of that path, thereby increasing the pheromone increment of the historically optimal detection path. This increases the probability that subsequent ants will choose this path, creating a positive incentive. Conversely, for the worst historical path, the pheromone deduction amount is reduced by subtracting the pheromone amount from the worst historical detection path. By imposing penalties, the negative impact on subsequent searches can be reduced, thereby improving the pheromone update process.

[0045] Simultaneously, maximum and minimum pheromone concentration values ​​are set for each path. When the concentration exceeds... When this happens, the path is truncated to prevent excessive selection by ants due to pheromone oversaturation; if it is lower than If so, it will be elevated to avoid the path being completely abandoned due to pheromone scarcity.

[0046] Step S402: Select the initial node for the next iteration of the ant colony. Calculate the node fitness based on path quality: , in, For node fitness, For nodes i The path length starting from the origin. δ The attenuation rate, t Let be the number of iterations. The node selection probability is: , Map the node selection probability to Range, generate random numbers The initial node for the next iteration is selected using the following formula: , in, i This is the number of the starting node for the iteration. k For the selection probability range in the roulette wheel betting strategy, those that meet the conditions k This is the final determined starting node for the next iteration. i , The probability of the starting node being selected is obtained based on the node's fitness.

[0047] After completing the pheromone update and initial node selection, return to step S3.

[0048] Step S5: Output the algorithm planning results and select the shortest detection path length during the iteration process as the globally optimal detection trajectory.

[0049] To verify the effectiveness of the global planning algorithm proposed in this invention in improving the overall trajectory quality, the improved ant colony planning algorithm in this paper is compared with the traditional ant colony planning algorithm and the global detection trajectory planning algorithm proposed by Wang Gang in his paper "Research and Implementation of Detection Path Planning System for Coordinate Measuring Machine [D]. Wuhan University of Technology, 2020." Three sets of comparative experiments were designed. All three sets of test experiments... Figure 3 The workpiece to be tested is shown to undergo global detection trajectory planning, while maintaining the same collision avoidance method, sampling strategy, and local trajectory. Three sets of comparative experiments were conducted using the three planning methods described above (traditional ant colony algorithm, Wang Gang algorithm, and the algorithm of this invention, respectively), and the trajectory planning results are shown below. Figure 4 , Figure 5 and Figure 6 As shown in Table 4, the detailed information of the detection trajectory planning results is as follows.

[0050] Table 4: Detailed information on the detection trajectory planning results

[0051] The test results show that, due to the traditional ant colony planning algorithm's "plan first, then correct" strategy, the trajectory obtained by the algorithm needs to undergo secondary correction through probe angle transformation and collision avoidance, resulting in low quality of the planned trajectory. Wang Gang's planning algorithm plans a global trajectory based on solving the feature obstacle avoidance time matrix, and the quality of the planned trajectory is significantly improved compared to the traditional ant colony planning algorithm. However, greedy algorithms, lacking a global perspective, have relatively limited ability to obtain the global optimal solution when dealing with trajectory planning problems with a large number of features. This invention enhances the algorithm's ability to obtain the global optimal solution in three-coordinate detection trajectory planning problems by improving the heuristic function, information update method, and ant colony iteration initial point determination method of the traditional ant colony algorithm. The results in Table 4 show that, in this comparative test, the trajectory length planned by the algorithm of this invention is reduced by 23% compared to the traditional ant colony algorithm and by 7% compared to Wang Gang's planning algorithm, verifying the effectiveness of the global planning algorithm proposed in this invention.

[0052] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0054] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0055] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0056] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A global detection trajectory planning method for a coordinate measuring machine based on an improved ant colony algorithm, characterized in that, Includes the following steps: The workpiece's geometric features to be measured are obtained and used as nodes. A distance adjacency matrix is ​​constructed based on the nodes, and the distance adjacency matrix is ​​used to define the trajectory distance between each node. Initialize the relevant parameters of the improved ant colony algorithm and the initial iteration position of the ants, and evenly distribute the number of ants on each node as the starting node; The ant colony iterative search for the detection path includes: obtaining an improved heuristic value based on relevant parameters and the distance adjacency matrix; obtaining a state transition probability based on the improved heuristic value; each ant starting from the starting node and sequentially selecting the next node based on the state transition probability until the ant has traversed all nodes and obtained a complete detection path. Based on the quality of the detection path, update the pheromones between nodes; calculate the node fitness based on the quality of the detection path, and calculate the node selection probability based on the node fitness; based on the node selection probability, use a roulette wheel strategy to select the initial node for the next iteration of the ant colony. Based on the updated pheromone and the initial node of the ant colony for the next iteration, the ant colony iterative search and detection path process is repeated. In response to the current iteration count reaching the preset maximum iteration count, the shortest detection path length during the iteration process is selected as the globally optimal detection trajectory; The formula for calculating the improved heuristic value is as follows: ； in, To improve the heuristic value; This is the weighting factor for the distance between nodes, with a value range of [0-4]. This represents the straight-line distance between nodes i and j; This represents the increased distance between nodes i and j due to collision avoidance. This is the weighting factor for the probe angle transformation, with a value range of [0-4]. This represents the number of probe angle changes required to move from node i to j; It is a fixed value representing the additional trajectory distance traveled due to the change in probe angle; This is a weighting factor for path smoothness, with a value range of [0-4]. This represents the number of inflection points on the path from node i to j; Based on the quality of the detection path, update the pheromones between nodes, including: Update pheromones using the following formula: ； in, ρ is the pheromone increment from node i to j; ρ is the pheromone dynamic volatility coefficient, calculated using the following formula: ； Where t is the current iteration number; This represents the reduction value of ρ after each iteration; This represents the initial maximum value of the pheromone evaporation coefficient; Calculate node fitness based on the quality of the detection path, including: The fitness of a node is calculated using the following formula: ； in, For node fitness; Let denoted as the trajectory length starting from node i, δ as the decay rate, and t as the number of iterations.

2. The global detection trajectory planning method for a coordinate measuring machine based on an improved ant colony algorithm according to claim 1, characterized in that, Constructing a distance adjacency matrix based on the nodes includes: Based on the distance from the local trajectory endpoint of the current geometric feature to be tested to the local trajectory starting point of the next geometric feature to be tested, the trajectory distance between any two nodes is obtained. The trajectory distance between any two nodes is used as an element to generate a distance adjacency matrix.

3. The global detection trajectory planning method for a coordinate measuring machine based on an improved ant colony algorithm according to claim 1, characterized in that, The formula for calculating the state transition probability is: ； in, Let be the state transition probability. The pheromone value between nodes i and j For the improved heuristic value between nodes i and j, As a communication heuristic factor, As the expected heuristic factor, For the set of nodes that have not yet been visited, This represents the number of iterations.

4. The global detection trajectory planning method for a coordinate measuring machine based on an improved ant colony algorithm according to claim 1, characterized in that, Based on the quality of the detection path, updating the pheromones between nodes also includes: Add the optimal path pheromone increment to the historical optimal detection path. The worst historical detection path is deducted for the pheromone reduction amount. And limit pheromone concentration to [ , Within the range; and These represent the maximum and minimum pheromone concentrations, respectively.

5. The global detection trajectory planning method for a coordinate measuring machine based on an improved ant colony algorithm according to claim 1, characterized in that, Calculating the node selection probability based on the node fitness includes: The node selection probability is calculated using the following formula: ； Where n is the total number of nodes, and i is the number of the starting node for the iteration. For node fitness.

6. The global detection trajectory planning method for a coordinate measuring machine based on an improved ant colony algorithm according to claim 1, characterized in that, Based on the node selection probability, the initial node for the next iteration of the ant colony is selected using a roulette wheel selection strategy, including: Map the node selection probability to Range, generate random numbers The initial node for the next iteration is selected using the following formula: ； Where i is the number of the starting node of the iteration, k is the selection probability interval in the roulette wheel selection strategy, and k that meets the conditions is the final determined starting node i for the next iteration. The probability of the starting node being selected is obtained based on the node's fitness.

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