An optimal path planning method for multi-specification spot drilling tasks

Abstract

This invention belongs to the field of path planning technology and discloses an optimal path planning method for multi-specification point drilling tasks. The method includes teaching the locations of diamonds and drill holes, the properties of diamonds, and the properties of diamonds pasted to match each drill hole. A pick-and-place distance matrix is ​​generated based on the locations of diamonds and drill holes, and a diamond classification table is generated based on the properties of diamonds and the properties of diamonds pasted to match each drill hole. Based on the pick-and-place distance matrix and the diamond classification table, an optimal diamond-drill hole pairing scheme is solved using the Hungarian algorithm. Based on the optimal diamond-drill hole pairing scheme, an ant colony algorithm and a tabu search algorithm are used to determine the optimal diamond picking sequence, thereby obtaining the optimal path. This method eliminates the path access sequence constraints of the pick-and-place model, thereby simplifying the multi-specification point drilling problem into a generalized traveling salesman problem of a directed graph, significantly simplifying the computational complexity of path selection.

Description

Technical Field

[0001] The present invention belongs to the technical field of path planning, and in particular relates to an optimal path planning method for multi-specification spot drilling tasks. Background Art

[0002] As an advanced CNC machine, automatic spot drilling machines are widely used in precision manufacturing and assembly processes. Their primary function is to dispense adhesive onto the product being processed, suctioning and accurately attaching diamonds to the desired location on the decorative item. This process is crucial in the production of electronics, jewelry, and other high-end products. This equipment can increase the speed and efficiency of diamond application, enabling high-volume production.

[0003] Before mass production begins, it's often necessary to teach the diamonds, their drill hole locations, and their properties. Due to the diversity of diamond types, with varying sizes, shapes, colors, and other attributes, each diamond can only be placed within a limited set of locations. Placing all diamonds in their proper locations along an optimal path can save significant time and machinery costs, further improving production efficiency. This type of problem can be summarized as a traveling salesman problem with priority constraints. The standard traveling salesman problem involves a salesperson visiting a set of cities, each only once, ultimately returning to the starting city. The goal is to find the shortest path that minimizes the total distance traveled.

[0004] Traditional approaches to solving the common traveling salesman problem are not directly applicable to the point-drilling task of diamonds of various specifications. The constraints of this problem restrict the salesman's path choices. While an improved intelligent optimization algorithm that adds processing rules to address these constraints can search the complete path solution space, the processing of these constraints significantly increases the computational effort. Summary of the Invention

[0005] The purpose of the present invention is to provide an optimal path planning method for multi-specification spot drilling tasks, which can obtain a more optimal path in a relatively short time to solve the path planning problem of picking up and placing diamonds for a spot drilling rig. The spot drilling rig can significantly shorten the production cycle and improve the processing efficiency of the production line by following the planned diamond picking sequence.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] An optimal path planning method for a multi-specification spot drilling task, the optimal path planning method for a multi-specification spot drilling task comprising:

[0008] Teach the location of the diamond, the location of the drill hole, the properties of the diamond, and the properties of the diamond that matches each drill hole;

[0009] A pick-and-place distance matrix is ​​generated based on the locations of the diamonds and the drill holes, and a diamond classification table is generated based on the properties of the diamonds and the properties of the diamonds matched with each drill hole. The pick-and-place distance matrix records the distance between each diamond and each drill hole. The diamond classification table classifies diamonds with different properties into different categories and records the diamonds contained in each category and the matched drill holes.

[0010] Based on the pick-and-place point distance matrix and the diamond classification table, the problem of pairing diamonds and drill holes of the same type is formulated as an optimal assignment problem. The optimal diamond-drill hole pairing solution is obtained using the Hungarian algorithm.

[0011] Based on the optimal diamond drilling pairing scheme, the ant colony algorithm is used to generate the initial diamond picking sequence, and the initial diamond picking sequence is used as the input of the tabu search algorithm to obtain the optimal diamond picking sequence, that is, the optimal path.

[0012] Several optional methods are also provided below, but they are not intended to be additional limitations on the above-mentioned overall solution. They are merely further supplements or optimizations. Under the premise that there are no technical or logical contradictions, each optional method can be combined separately for the above-mentioned overall solution, or multiple optional methods can be combined.

[0013] Preferably, the mathematical model of the optimal assignment problem is as follows:

[0014]

[0015] Where D is the path length, c ij represents the distance from diamond number i to hole number j, x ij is a binary variable. When diamond No. i does not drill into hole No. j, the value is 0. When diamond No. i drills into hole No. j, the value is 1. j x ij =1 means that only one diamond can be drilled in one hole, ∑ i x ij =1 means that a diamond can only be drilled into one hole.

[0016] Preferably, the method of obtaining the optimal diamond drilling pairing solution by using the Hungarian algorithm includes:

[0017] Extract the diamond drilling distance matrix of the required paired diamonds from the pick-and-place point distance matrix as the cost matrix;

[0018] For each row of the cost matrix, subtract the minimum value of the row so that at least one element in each row is zero;

[0019] For each column of the cost matrix, subtract the minimum value of the column so that at least one element in each column is zero;

[0020] Generate a preliminary diamond drilling pairing plan based on all zero elements in the current cost matrix;

[0021] Use the minimum number of straight lines to cover all zero elements in the cost matrix. If the number of lines covering the zero elements is equal to the required number of paired diamonds, then output the preliminary diamond drilling pairing plan as the optimal diamond drilling pairing plan; otherwise, adjust the cost matrix to generate a new zero element distribution, and re-pair and judge.

[0022] Preferably, generating a preliminary diamond drilling pairing plan based on all zero elements in the current cost matrix includes:

[0023] First, find the row with only one zero element, mark the zero element found, and delete the other zero elements in the column where the zero element is found;

[0024] Then find the column with only one zero element, mark the zero element found, and delete the other zero elements in the row where the zero element is found;

[0025] Pair the diamonds and drill holes corresponding to the same marked zero element to obtain a preliminary diamond-drill hole pairing solution.

[0026] Preferably, the adjusting cost matrix to generate a new zero element distribution includes:

[0027] Find the minimum value δ among the elements of the cost matrix not covered by the line;

[0028] For the elements of the cost matrix not covered by the line, subtract δ;

[0029] For the elements in the cost matrix that are crossed by two straight lines covering zero elements, add δ;

[0030] The other elements in the cost matrix remain unchanged and the adjustment is completed.

[0031] Preferably, the method of generating an initial diamond picking sequence using an ant colony algorithm comprises:

[0032] Initialize the ant colony algorithm;

[0033] Determine the starting position for each ant and randomly select the diamond drilling pair from the optimal diamond drilling pairing solution as the next position;

[0034] Each ant completes a round trip, calculates the path length obtained by each ant, records the current optimal solution, and takes the shorter of the current optimal solution and the historical optimal solution as the new historical optimal solution;

[0035] Update pheromones based on adaptive pheromone volatility factors and pheromone reward and punishment mechanisms;

[0036] Determine whether the number of iterations has reached the maximum number of iterations. If so, end the iteration and output the historical optimal solution as the initial diamond picking order. Otherwise, return to continue iteration.

[0037] The present invention provides an optimal path planning method for multi-specification spot drilling tasks. Based on the constraints of the pick-and-place model for spot drilling rigs, the path planning process is divided into two stages. First, the matching problem of similar diamond holes is addressed, filtering out invalid paths with long intervals. By selecting paths and the shortest pairing combination for each diamond hole, the constraints on diamond type are eliminated. While ensuring the quality of the solution space, the present invention optimizes the solution space through constraint elimination, significantly reducing path search costs while preserving the majority of excellent path solutions. The present invention uses a combined ant colony and tabu search algorithm for subsequent processing. Based on the ant path selection method of the ant colony algorithm, the present invention treats diamond hole pairs as a unit based on their pairing information and distance matrix, and probabilistically selects paths. This eliminates the path access order constraints of the pick-and-place model, thereby simplifying the multi-specification spot drilling problem into a generalized traveling salesman problem for a directed graph, significantly simplifying the computational complexity of path selection. Consequently, the present invention shortens the path length of the spot drilling rig's pick-and-place process, improving spot drilling efficiency, reducing equipment travel distances, lowering energy consumption, and saving costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flow chart of an optimal path planning method for multi-specification spot drilling tasks according to the present invention;

[0039] Figure 2 Flowchart of the Hungarian algorithm of the present invention;

[0040] Figure 3 Flowchart of the ant colony algorithm of the present invention;

[0041] Figure 4 is a flow chart of the taboo algorithm of the present invention;

[0042] Figure 5 Schematic diagram of the 2-opt exchange operation of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0045] The current multi-specification point drilling task can be formulated as a traveling salesman problem with precedence constraints. The standard traveling salesman problem involves a salesperson who must visit a set of cities, each of which can only be visited once, ultimately returning to the starting city. The goal is to find the shortest path that minimizes the total travel distance. The traveling salesman problem, which conforms to the pick-and-place model for point drilling rigs, has the following two constraints:

[0046] 1) After the drill bit reaches the diamond position and completes the picking action, it must reach the diamond placement position in the next step; similarly, after the drill bit places the diamond, it must reach the next diamond placement position in the next step.

[0047] 2) During the picking and placing process, the properties of the diamonds must correspond, and the situation of drilling one type of diamond point to the placement position of another type of diamond cannot occur.

[0048] The mathematical model of the multi-specification point drilling task can be summarized as follows:

[0049]

[0050] Where L represents the length of the diamond placement path, D ab represents the moving distance from node a to node b, x ab is a binary decision variable, indicating whether node a is connected to node b (a is not connected when it is 0, and it is connected when it is 1), and m represents the number of diamonds or drill holes.

[0051] The special constraints of this problem can be expressed as follows:

[0052]

[0053] y kh ·δ kh =y kh (5)

[0054]

[0055] In the formula, constraints (1) and (2) are path connectivity constraints, ensuring that each point can only be visited once. Constraint (3) is the pick-and-place order constraint. For each diamond k, is the pickup point, Is the placement point, must be forced to pick up the point At the placement point Constraint (4) is a subpath elimination constraint to prevent loop generation, where u p 、u k represents the access order of node p and node k in the complete path (value range 1 to n), n represents the total number of nodes in the path, that is, the sum of the number of diamonds and holes, V is the node set, x pk is a binary decision variable, indicating whether node p is connected to node k (a value of 0 indicates no connection, a value of 1 indicates connection). Constraint (5) Attribute matching constraint, diamond k can only be placed in a hole h that meets the same attributes. kh is a binary variable indicating whether diamond k is placed in hole h, δ kh is a parameter. If the properties of diamond k and hole h are the same, then δ kh =1, otherwise δ kh = 0. This constraint ensures that diamond k cannot be placed in hole h when the attributes do not match.

[0056] In order to solve the problem that traditional methods for ordinary traveling salesman problems cannot be directly applied to the point drilling task of diamonds of various specifications, this embodiment first eliminates some constraints, removes some low-quality path solutions, optimizes the solution space of the path, and transforms the problem into a generalized traveling salesman problem of a two-way graph. In this way, the search for the optimal path is completed while preserving a large number of high-quality solutions, thus reducing the search time. Figure 1 As shown, for solving the mathematical model of the multi-specification spot drilling task, this embodiment provides an optimal path planning method for the multi-specification spot drilling task, including the following steps:

[0057] Step 1: Teach the diamond location, drill hole location, diamond attributes, and the attributes of the diamond to be pasted into each drill hole. Diamond attributes include size, color, and shape. However, the teaching process only labels one type of diamond with one attribute. This characteristic of a diamond type allows it to be universally pasted into a specific drill hole.

[0058] For ease of description, this embodiment numbers each diamond and drill hole. This numbering sequence consists of 2q items (e.g., 1-10), where q represents the number of diamonds. The first q items (e.g., 1-5) are diamond pickup points, and the last q items (e.g., 6-10) are diamond placement points. Due to the diversity of diamond types, including sizes, shapes, and colors, each diamond can only be placed within a limited set of placement locations.

[0059] Step 2: Obtain a pick-up and placement point distance matrix based on the locations of the diamonds and the drill holes, and obtain a diamond classification table based on the properties of the diamonds and the properties of the diamonds matched and pasted to each drill hole.

[0060] The pick-and-place distance matrix records the distance between each diamond and each drill hole. This embodiment uses Euclidean distance as the measurement. The pick-and-place distance matrix serves as the basis for the distance between cities in the Traveling Salesman Problem (TSP). This matrix omits invalid distances between diamond cities and between drill hole cities.

[0061] The diamond classification table classifies diamonds with different properties into different categories, and records the diamonds contained in each category and the matching drill holes. This embodiment provides a diamond classification table as shown in Table 1.

[0062] Table 1 Diamond classification table

[0063] Type 1 Type 2 …… …… …… Type N Diamond Collection 1、2、4 …… …… …… …… …… Drilling Collection 6、5、3 …… …… …… …… ……

[0064] The diamond classification table shown in Table 1 can be understood as classifying diamonds into N categories. For example, category 1 includes diamonds numbered 1, 2, and 4 and drill holes numbered 6, 5, and 3. Any of diamonds 1, 2, and 4 can be assigned to any of drill holes 6, 5, and 3. Diamonds with the same attributes can be placed in multiple identical drill holes.

[0065] Step 3: Based on the pick-and-place point distance matrix and the diamond classification table, the pairing problem of diamonds and drill holes of the same type is constructed as an optimal assignment problem, and the optimal diamond-drill hole pairing solution is obtained by solving it using the Hungarian algorithm.

[0066] This embodiment abstracts the problem of pairing similar diamonds and drill holes into a standard optimal assignment problem. After finding a path and the shortest pairing path solution, the paired diamond-drill hole pairs are treated the same as other independent diamond-drill hole pairs, acting as fixed continuous pick-and-place nodes in path sorting. Specifically, the fixed continuous pick-and-place nodes are treated as cities in a generalized traveling salesman problem, and the problem is treated as a generalized traveling salesman problem for a directed graph. The following mathematical model is used to construct the standard optimal assignment problem:

[0067]

[0068] Where D represents the path length, and the objective function is to find the pairing solution that minimizes the sum of the total pairing paths among the same type of diamonds and holes. ij represents the distance from diamond number i to hole number j, x ij is a binary variable. When diamond No. i does not drill into hole No. j, the value is 0. When diamond No. i drills into hole No. j, the value is 1. j x ij =1 means that only one diamond can be drilled in one hole, ∑ i xij =1 means that a diamond can only be drilled into one hole.

[0069] like Figure 2 As shown, the Hungarian algorithm adopted in this embodiment has the following specific steps:

[0070] (1) Extract the diamond-drilling distance matrix of the required paired diamonds from the pick-and-place distance matrix as the cost matrix. The rows / columns in the cost matrix are diamonds, the columns / rows are drilling holes, and the elements are the diamond-drilling distances.

[0071] (2) Row reduction: For each row of the cost matrix, subtract the minimum value of the row so that at least one element in each row is zero.

[0072] (3) Column reduction: Based on row reduction, for each column of the cost matrix, the minimum value of the column is subtracted so that at least one element in each column is zero.

[0073] (4) Trial pairing: Find all zero elements in the cost matrix, perform preliminary pairing of diamonds and drill holes, and generate a preliminary diamond-drill hole pairing scheme, as follows:

[0074] First, search for a row with only one zero element, mark the zero element found, and delete the other zero elements in the column where the zero element is found. If no zero element is found, perform a column search; then search for a column with only one zero element, mark the zero element found, and delete the other zero elements in the row where the zero element is found. If no zero element is found, perform a preliminary pairing; pair the diamonds and drill holes corresponding to the same marked zero element to obtain a preliminary diamond-drill hole pairing solution.

[0075] (5) Check the zero coverage and use the minimum number of straight lines (row lines or column lines) to cover all zero elements in the cost matrix. If the number of lines covering the zero elements is equal to the required number of paired diamonds, jump to step (7); otherwise, go to step (6) to adjust the matrix.

[0076] (6) Matrix adjustment: In the case of insufficient zero coverage, a new zero element distribution is generated by adjusting the cost matrix and returning to step (4). The matrix adjustment process is as follows: find the minimum value δ among the elements in the cost matrix that are not covered by the lines; for the elements in the cost matrix that are not covered by the lines, subtract δ; for the elements in the cost matrix that are crossed by two lines that cover zero elements, add δ; the other elements in the cost matrix remain unchanged, and the adjustment is completed.

[0077] (7) Output a preliminary diamond drill hole pairing solution as the optimal diamond drill hole pairing solution. The paired diamond drill hole pair is denoted as pair(i)=(i, j). Where i represents the diamond drill hole pair numbered i, and this number is consistent with the diamond number in this pairing. j represents the drill hole number in this pairing. Where i and j are determined by the position of the zero-marked element in the matrix. In one embodiment, i is the row coordinate of the zero-marked element, and j is the column coordinate of the zero-marked element.

[0078] Step 4: Based on the optimal diamond drilling pairing scheme, use the ant colony algorithm to generate the initial diamond picking sequence, and use the initial diamond picking sequence as the input of the tabu search algorithm to obtain the optimal diamond picking sequence, that is, the optimal path.

[0079] like Figure 3 As shown in Figure 2, the specific steps of the ant colony algorithm are as follows:

[0080] Step 4-1. Initialize the ant colony algorithm: including the number of ants, pheromone importance factor, heuristic importance factor, total pheromone release amount, reward coefficient, penalty coefficient, maximum number of iterations for the ant colony algorithm, and maximum number of iterations for the taboo algorithm. These parameters are determined based on experience and multiple experiments.

[0081] At the same time, the pheromone initial matrix is ​​initialized according to the distance between the drilling position of the diamond drilling pair and the diamond position of the diamond drilling pair as follows:

[0082]

[0083] Where, τ ii′ represents the pheromone concentration of the path from the drilling position of diamond drilling pair No. i to the diamond position of diamond drilling pair No. i′, K is a constant, d ii′ It represents the distance from the drilling position of the diamond drilling pair No. i to the diamond position of the diamond drilling pair No. i′.

[0084] Step 4-2: Determine the starting position for each ant and randomly select the diamond drilling pair in the optimal diamond drilling pairing solution as the next position.

[0085] The starting point and ending point in the TSP problem are fixed at the robot's origin. Therefore, the ant is placed at the origin and first visits a diamond position according to probability. Then, based on probability, the next diamond-drilling pair is randomly selected for visit. The selection probabilities are as follows:

[0086]

[0087] Where, The k in represents the kth ant, The picking, placing, and picking process from diamond position i to its paired drilling position j, and then from paired drilling position j to the next diamond position i′ is a single process for probabilistic selection. ii′ , (t) represents the pheromone concentration from the drilling position of diamond drilling pair i to the diamond position of diamond drilling pair i′ in the tth iteration, η ii′ (t) is the heuristic function, which represents the expected degree of the ant's movement from the drilling position of diamond pair i to the diamond position of diamond pair i' in the tth iteration. α is the pheromone importance factor, and β is the heuristic importance factor. k represents the placement points accessible to the kth ant, i.e., the set of idle holes where the ant can place the diamond it currently picks up. k represents the pickup points accessible to the kth ant, i.e., the set of diamonds that the ant has not yet drilled, τ hn (t) represents the pheromone concentration from the placement point h to the pickup point n in the tth iteration, η hn (t) represents the expected degree from the placement point h to the pickup point n in the tth iteration. ii′ The expression of (t) is as follows:

[0088]

[0089] Where σ(t) is the dynamic factor of the heuristic function in the tth iteration, ρ is the pheromone volatility factor, and t max is the maximum number of iterations, and the dynamic factor decreases as the number of iterations increases. hn (t) is calculated in the same way. At the beginning of the iteration, the ant colony is guided by the heuristic function, while in the later stages of the iteration, the influence of the heuristic function is weakened and the influence of the pheromone is increased.

[0090] In step 4-3, each ant completes a round trip. The path lengths are calculated for each ant, and the current optimal solution and the average length are recorded. The current optimal solution is compared with the previous optimal solution, and the shorter one is retained as the new historical optimal solution. During the first execution, the current optimal solution is recorded as the historical optimal solution.

[0091] In one tour, each ant makes choices based on diamond drill hole pairs, that is, it moves from one diamond drill hole pair to the next diamond drill hole pair, moves from the diamond to the paired drill hole within the diamond drill hole pair, and moves from the previous paired drill hole position to the next paired diamond position between adjacent diamond drill hole pairs, and so on, until all diamonds are posted and finally return to the starting point.

[0092] Step 4-4: Update the pheromone according to the adaptive pheromone volatility factor and the pheromone reward and punishment mechanism.

[0093] Among them, the pheromone volatility factors are as follows:

[0094]

[0095] Where λ is the setting parameter, t is the number of iterations of the ant colony algorithm search, and the upper and lower limits of the pheromone volatility factor are set to (0.1, 0.7) to keep its value within a certain range.

[0096] The pheromone reward and penalty mechanism involves the ant colony generating a current global optimal solution after each path search and comparing it with the previous global optimal solution. If the previous optimal solution is better, the pheromone concentration on that path is increased, and a prize operator is introduced to increase the attractiveness of that path to the next generation of ants. The formula for updating pheromones is as follows:

[0097] τ ii′ (t+1)=(1-ρ)τ ii′ (t)+Δτ ii′ +Δτ ex

[0098]

[0099] Δτ ex =prize / min_Length prize

[0100] Where, τ ii′ (t+1) represents the pheromone concentration from the drilling position of diamond drilling pair i to the diamond position of diamond drilling pair i′ in the t+1th iteration, Δτ ii′ is the pheromone left by the previous generation of ants along the path, Δτ ex is the reward and punishment factor, min_Length prize is the current global optimal solution, prize is the prize operator, n is the number of ants, It represents the pheromone concentration of the kth ant from the drilling position of diamond drilling pair i to the diamond position of diamond drilling pair i′.

[0101] In addition to the prize operator, this embodiment also introduces a punish operator. When the number of iterations exceeds θ and the shortest path is still not updated, the path is considered a dead end of the local optimal solution. In this case, the punish operator is used to weaken the pheromone of the path. ec The formula is as follows:

[0102] Δτ ex =-punish / min_Length punish

[0103] Where, punish is the penalty coefficient, min_Length punish is the current optimal solution for a dead end.

[0104] Step 4-5: Determine whether the number of iterations has been reached. If so, end the iteration and output the historical optimal solution as the initial diamond picking order. Otherwise, return to step 4-2 to continue the iteration.

[0105] like Figure 4 As shown, the specific steps of the tabu algorithm are as follows:

[0106] Step 4-6: Initialize the tabu algorithm parameters and rules. The initialization parameters include the maximum number of iterations.

[0107] Step 4-7: Check whether the current solution meets the termination principle. If so, output the optimal solution. Otherwise, go to step 4-8. The termination principle is that the current number of iterations is greater than or equal to the maximum number of iterations.

[0108] Step 4-8: Use the 2-opt method to swap the two positions of the current solution, generate a swap solution, and determine the candidate solution. Figure 5 As shown, the exchange principle of the 2-opt method is as follows: randomly select two non-adjacent edges (for example, the edge from sequence number 2 to sequence number 3, and the edge from sequence number 48 to sequence number 48); disconnect the two edges to form two separate paths; reverse the order of one of the paths (for example, reverse the order of the path from sequence numbers 3, 4, 47, 48 to the path order from sequence numbers 48, 47, 4, 3), and then reconnect the two new edges (for example, add the edge from sequence number 2 to sequence number 48, and the edge from sequence number 3 to sequence number 49) to form a new path.

[0109] Step 4-9: Check whether the current solution satisfies the contempt principle. If so, proceed to step 4-10; otherwise, proceed to step 4-11. The contempt principle is that the path length of the current solution is less than the historical optimal solution.

[0110] Step 4-10: Take the optimal solution that meets the contempt criterion as the new current solution, and replace the taboo object that first enters the taboo table with the taboo object corresponding to the optimal solution, update the optimal state, and enter step 4-7.

[0111] Step 4-11: Determine the taboo attributes of the objects corresponding to the candidate solutions. Select the best solution for the object not in the taboo table as the current solution and replace the first taboo object in the taboo table with it, proceeding to step 4-7. Finally, an ideal picking path is obtained that meets the multi-specification diamond pick-and-place constraint model of the point drill.

[0112] This example uses a hybrid ant colony-tabu search algorithm to optimize the traveling salesman problem by removing some constraints. This not only improves the ant colony algorithm's adaptability to the remaining constraints but also incorporates the tabu search algorithm to enhance the algorithm's global search capabilities, achieving excellent results in solving this problem. This example uses a hybrid ant colony-tabu search algorithm, adding pheromones to the ant colony algorithm's initialization phase; introducing a dynamic heuristic function factor; introducing an adaptive volatility factor and a pheromone reward and penalty mechanism; using the ant colony algorithm's solution as the initial solution for the tabu algorithm; and using the 2-opt algorithm for neighborhood search. These steps improve the algorithm's global search capabilities and accelerate its convergence to varying degrees.

[0113] Currently, most companies use a sequential placement method, which involves placing diamonds in designated locations in the order they were taught or placed, without implementing a corresponding optimization algorithm to optimize the path. This wastes time and money. However, this invention optimizes the point drilling path, significantly reducing both time and machine costs.

[0114] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The above-described embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person skilled in the art would be able to make numerous modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. An optimal path planning method for multi-specification spot drilling tasks, characterized in that: The optimal path planning method for multi-specification spot drilling tasks includes: Teach the location of the diamond, the location of the drill hole, the properties of the diamond, and the properties of the diamond that matches each drill hole; A pick-and-place distance matrix is ​​generated based on the locations of the diamonds and the drill holes, and a diamond classification table is generated based on the properties of the diamonds and the properties of the diamonds matched with each drill hole. The pick-and-place distance matrix records the distance between each diamond and each drill hole. The diamond classification table classifies diamonds with different properties into different categories and records the diamonds contained in each category and the matched drill holes. Based on the pick-and-place point distance matrix and the diamond classification table, the problem of pairing diamonds and drill holes of the same type is formulated as an optimal assignment problem. The optimal diamond-drill hole pairing solution is obtained using the Hungarian algorithm. Based on the optimal diamond drilling pairing scheme, the ant colony algorithm is used to generate the initial diamond picking sequence, and the initial diamond picking sequence is used as the input of the tabu search algorithm to obtain the optimal diamond picking sequence, that is, the optimal path.

2. The optimal path planning method for multi-specification spot drilling tasks according to claim 1, characterized in that: The mathematical model of the optimal assignment problem is as follows: Where D represents the path length, c ij represents the distance from diamond number i to hole number j, x ij is a binary variable. When diamond No. i does not drill into hole No. j, the value is 0. When diamond No. i drills into hole No. j, the value is 1. j x ij =1 means that only one diamond can be drilled in one hole, ∑ i x ij =1 means that a diamond can only be drilled into one hole.

3. The optimal path planning method for multi-specification spot drilling tasks according to claim 1, characterized in that: The optimal diamond drilling pairing solution obtained by the Hungarian algorithm includes: Extract the diamond drilling distance matrix of the required paired diamonds from the pick-and-place point distance matrix as the cost matrix; For each row of the cost matrix, subtract the minimum value of the row so that at least one element in each row is zero; For each column of the cost matrix, subtract the minimum value of the column so that at least one element in each column is zero; Generate a preliminary diamond drilling pairing plan based on all zero elements in the current cost matrix; Use the minimum number of straight lines to cover all zero elements in the cost matrix. If the number of lines covering the zero elements is equal to the required number of paired diamonds, then output the preliminary diamond drilling pairing plan as the optimal diamond drilling pairing plan; otherwise, adjust the cost matrix to generate a new zero element distribution, and re-pair and judge.

4. The optimal path planning method for multi-specification spot drilling tasks according to claim 3, characterized in that: The method generates a preliminary diamond drilling pairing plan based on all zero elements in the current cost matrix, including: First, find the row with only one zero element, mark the zero element found, and delete the other zero elements in the column where the zero element is found; Then find the column with only one zero element, mark the zero element found, and delete the other zero elements in the row where the zero element is found; Pair the diamonds and drill holes corresponding to the same marked zero element to obtain a preliminary diamond-drill hole pairing solution.

5. The optimal path planning method for multi-specification spot drilling tasks according to claim 3, characterized in that: The adjustment cost matrix generates a new zero element distribution, including: Find the minimum value δ among the elements of the cost matrix not covered by the line; For the elements of the cost matrix not covered by the line, subtract δ; For the elements in the cost matrix that are crossed by two straight lines covering zero elements, add δ; The other elements in the cost matrix remain unchanged and the adjustment is completed.

6. The optimal path planning method for multi-specification spot drilling tasks according to claim 1, characterized in that: The method of using the ant colony algorithm to generate an initial diamond picking sequence includes: Initialize the ant colony algorithm; Determine the starting position for each ant and randomly select the diamond drilling pair from the optimal diamond drilling pairing solution as the next position; Each ant completes a round trip, calculates the path length obtained by each ant, records the current optimal solution, and takes the shorter of the current optimal solution and the historical optimal solution as the new historical optimal solution; Update pheromones based on adaptive pheromone volatility factors and pheromone reward and punishment mechanisms; Determine whether the number of iterations has reached the maximum number of iterations. If so, end the iteration and output the historical optimal solution as the initial diamond picking order. Otherwise, return to continue iteration.

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