Optimal path planning method for multi-specification spot drilling task

Abstract

The invention belongs to the technical field of path planning, and discloses an optimal path planning method for a multi-specification spot drilling task, which comprises the following steps of: teaching the positions of diamonds and drilling holes, the attributes of the diamonds and the attributes of the diamonds matched and adhered to each drilling hole; obtaining a pick-and-place point distance matrix according to the positions of the diamonds and the drilled holes, and obtaining a diamond classification table according to the attributes of the diamonds and the attributes of the diamonds matched and pasted to each drilled hole; based on the pick-and-place point distance matrix and the diamond classification table, solving through a Hungary algorithm to obtain an optimal diamond drilling pairing scheme; and based on the optimal diamond drilling pairing scheme, obtaining an optimal diamond picking sequence by using an ant colony algorithm and a tabu search algorithm, namely obtaining an optimal path. According to the method, the path access sequence constraint of the pick-and-place model is eliminated, so that the multi-specification diamond spot problem is simplified into the generalized traveling salesman problem of the directed graph, and the calculated amount of path selection is greatly simplified.

Description

Technical Field

[0001] The 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 equipment, the automatic spot drilling machine is widely used in precision manufacturing and assembly processes. Its main function is to dispense glue on the product to be processed, suck up the diamond and accurately attach it to the designated position of the decoration. This process is crucial in the production of electronic products, jewelry and other high-end products. This equipment can increase the processing speed of diamond sticking, improve processing efficiency, and achieve mass production.

[0003] Before mass production, it is usually necessary to teach the diamonds, drilling positions and properties. Due to the diversity of diamond types, different diamonds have different sizes, shapes, colors and other properties, and each diamond can only be placed in a limited set of placement positions. Placing all diamonds in the corresponding positions according to the optimal path can save a lot of time and machinery costs, and further improve production efficiency. This type of problem can be summarized as a traveling salesman problem with priority constraints. The ordinary traveling salesman problem refers to a salesman who needs to visit a set of cities, and each city can only be visited once, and finally returns to the starting city. The goal is to find a shortest path to minimize the total travel distance.

[0004] In the multi-specification diamond point drilling task, the traditional method for solving the ordinary traveling salesman problem cannot be directly applied to this problem. The constraints of this problem limit the range of the traveling salesman's path selection. Although the improved intelligent optimization algorithm with added processing rules for the constraints can search in the complete path solution space, the processing of the constraints greatly increases the amount of calculation. 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 by a spot drilling machine. The spot drilling machine can greatly shorten the production cycle and improve the processing efficiency of the production line according to 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 point distance matrix is ​​obtained according to the locations of the diamonds and the locations of the drill holes, and a diamond classification table is obtained according to the properties of the diamonds and the properties of the diamonds matched and pasted to each drill hole; the pick-and-place point distance matrix records the distance between each diamond and each drill hole, and the diamond classification table classifies diamonds with different properties as different categories, and records the diamonds contained in each category and the matched and pasted drill holes;

[0010] Based on the pick-and-place point distance matrix and the diamond classification table, the pairing problem of diamonds and holes of the same type is constructed as an optimal assignment problem, and the optimal diamond-drilling hole pairing solution is obtained by solving it through 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 taboo 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, but are merely further supplements or preferences. Under the premise that there are no technical or logical contradictions, each optional method can be combined with the above-mentioned overall solution separately, and multiple optional methods can also 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 i does not drill into hole j, the value is 0. When diamond i drills into hole 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 comprises:

[0017] Extracting 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 that 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 least number of straight lines to cover all zero elements in the cost matrix. If the number of lines covering zero elements is equal to the required number of paired diamonds, output the preliminary diamond drilling pairing scheme as the optimal diamond drilling pairing scheme; 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 scheme according to all zero elements in the current cost matrix comprises:

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

[0026] Preferably, the adjusting cost matrix generates a new zero element distribution, comprising:

[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 using an ant colony algorithm to generate an initial diamond picking sequence comprises:

[0032] Initialize the ant colony algorithm;

[0033] Determine the starting position for each ant, and randomly select the diamond hole pair in the optimal diamond hole pairing scheme as the next position;

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

[0035] According to the adaptive pheromone volatilization factor and pheromone reward and punishment mechanism, pheromones are updated;

[0036] Determine whether the number of iterations reaches 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 the iteration.

[0037] The present invention provides an optimal path planning method for multi-specification spot drilling tasks. According to the constraint characteristics of the spot drilling machine pick-and-place model, the path planning process is divided into two stages. First, the pairing problem of diamond drilling holes of the same type is processed, and invalid paths with longer intervals are filtered. By screening the path and the shortest pairing combination for each diamond drilling hole, the diamond type constraint is eliminated. Under the condition of ensuring the quality of the solution space, the present invention optimizes the solution space by eliminating the constraints, and saves most of the excellent path solutions on the basis of greatly reducing the path search cost. The present invention uses the ant colony-tabu search combined algorithm to perform subsequent processing on the problem. On the ant path selection method of the ant colony algorithm, the present invention regards the diamond drilling hole pair as a unit for the probability selection of the path according to the diamond drilling hole pairing information and the distance matrix, eliminates the path access order constraint of the pick-and-place model, thereby simplifying the multi-specification spot drilling problem into a generalized traveling salesman problem of a directed graph, and greatly simplifies the calculation amount of path selection. Therefore, the present invention shortens the path of the spot drilling machine pick-and-place process, improves the spot drilling efficiency, reduces the equipment operation stroke, reduces energy consumption, and saves costs. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0039] Figure 2 is a flow chart of the Hungarian algorithm of the present invention;

[0040] Figure 3 It is a flow chart 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are 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 summarized as a traveling salesman problem with priority constraints. The ordinary traveling salesman problem refers to a salesman who needs to visit a set of cities and can only visit each city once and finally return to the starting city. The goal is to find a shortest path to minimize the total travel distance. The traveling salesman problem that conforms to the point drilling rig pick-and-place model has the following two constraints:

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

[0047] 2) During the picking and placing process, the properties of the diamonds must correspond, and the situation where one type of diamond is drilled into the placement position of another type of diamond cannot occur.

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

[0049]

[0050] Where L is the length of the diamond access 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 value of 0 indicates no connection, and a value of 1 indicates connection), and m indicates the number of diamonds or 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 loops from occurring, 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, and a value of 1 indicates connection). Constraint (5) Attribute matching constraint, diamond k can only be placed in a hole h that satisfies 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 the traditional method of the ordinary traveling salesman problem 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, thereby completing the search for the optimal path while preserving a large number of high-quality solutions, 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 location of the diamond, the location of the drill hole, the properties of the diamond, and the properties of the diamond to be pasted to each drill hole. The diamond properties include the size, color, shape, etc. of the diamond, but the teaching process only marks one type of diamond as one property, and the characteristics of this type of diamond are shown as being able to be universally pasted to a certain type of drill hole.

[0058] For ease of description, this embodiment numbers each diamond and drill hole. The numbering sequence of this embodiment has 2q items (e.g., 1-10), where q is the number of diamonds. The first q items (e.g., 1-5) are all diamond picking points, and the last q items (e.g., 6-10) are diamond placement points. Due to the diversity of diamond types, different diamonds have different sizes, shapes, colors and other properties, and each diamond can only be placed in a limited set of placement positions.

[0059] Step 2: Obtain a pick-and-place 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, and this embodiment uses the Euclidean distance to measure. The pick-and-place distance matrix is ​​used as the basis for the distance between cities in the TSP problem (Traveling Salesman Problem). The matrix omits the invalid distance between diamond "cities" and 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, and for example, category 1 includes diamonds numbered 1, 2, and 4 and drill holes numbered 6, 5, and 3, wherein any one of diamonds 1, 2, and 4 can be assigned to any one 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 through the Hungarian algorithm.

[0066] This embodiment abstracts the pairing problem of similar diamonds and holes into a standard optimal assignment problem. After finding the path and the shortest pairing path solution, the paired similar diamond-drilling pairs are treated the same as other independent diamond-drilling pairs and participate in the path sorting as fixed continuous pick-and-place nodes, that is, the fixed continuous pick-and-place nodes are regarded as cities in the generalized traveling salesman problem, and this problem is treated as a generalized traveling salesman problem of a directed graph. The mathematical model for constructing the standard optimal assignment problem is as follows:

[0067]

[0068] In the formula, D represents the path length, and the objective function seeks the pairing scheme 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 i does not drill into hole j, the value is 0. When diamond i drills into hole 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 point 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 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 other zero elements in the column where the zero element is found. If the zero element is not found, perform a column search; then search for a column with only one zero element, mark the zero element found, and delete other zero elements in the row where the zero element is found. If the zero element is not found, perform a preliminary pairing; pair the diamonds and holes corresponding to the same marked zero element to obtain a preliminary diamond-drilling hole pairing plan.

[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 zero elements is equal to the number of required 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 straight line; for the elements in the cost matrix that are not covered by the straight line, subtract δ; for the elements in the cost matrix that are crossed and covered by two straight lines that cover zero elements, add δ; the other elements in the cost matrix remain unchanged and the adjustment is completed.

[0077] (7) Outputting a preliminary diamond drilling pairing scheme as the optimal diamond drilling pairing scheme, the paired diamond drilling pair is recorded as pair(i)=(i, j). Wherein, i represents the diamond drilling pair numbered i, and this number is consistent with the diamond number in this pairing. j represents the drilling number in this pairing. Wherein, 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 taboo search algorithm to obtain the optimal diamond picking sequence, that is, the optimal path.

[0079] like Figure 3 As shown in the figure, 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 amount of pheromone released, reward coefficient, penalty coefficient, maximum number of iterations of the ant colony algorithm and maximum number of iterations of the taboo algorithm. Each parameter is obtained based on experience and multiple experiments.

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

[0082]

[0083] In the formula, τ ii′ represents the pheromone concentration of the path from the drilling position of diamond drilling pair i to the diamond position of diamond drilling pair 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 in the TSP problem is fixed as the robot origin, and the end point is also fixed as the robot origin. Therefore, the ant is placed at the origin, and then the diamond position is visited once according to probability. Then, according to probability, the next diamond drilling pair is randomly selected for visit. The selection probability is as follows:

[0086]

[0087] In the formula, 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 probability selection. ii′ , (t) represents the pheromone concentration from the drilling position of diamond drilling pair No. i to the diamond position of diamond drilling pair No. i′ in the tth iteration, η ii′ (t) is the heuristic function, which indicates the expected degree of the ant's movement from the drilling position of the diamond drilling pair i to the diamond position of the diamond drilling 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, that is, 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, that is, 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. Among them, the heuristic function η 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 at the end of the iteration, the influence of the heuristic function is weakened and the influence of pheromone is increased.

[0090] Step 4-3: Each ant completes a tour, calculates the path length obtained by each ant, records the current optimal solution and the average length, compares the current optimal solution with the historical optimal solution, and retains the shorter one as the new historical optimal solution. When executed for the first time, directly record the current optimal solution as the historical optimal solution.

[0091] In one tour, each ant makes choices in units of diamond drill hole pairs, that is, it moves from one diamond drill hole pair to the next diamond drill hole pair, from the diamond to the paired drill hole within the diamond drill hole pair, and 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 volatilization factor and the pheromone reward and punishment mechanism.

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

[0094]

[0095] In the formula, 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 punishment mechanism refers to the fact that after each path search, the ant colony will generate a contemporary global optimal solution and compare it with the previous global optimal solution. If the former is better, the pheromone concentration of the path will be increased, and the prize operator will be introduced to increase the attractiveness of the next generation of ants to choose this path. 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] In the formula, 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 contemporary 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 the diamond drilling pair i to the diamond position of the diamond drilling pair i′.

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

[0102] Δτ ex =-punish / min_Length punish

[0103] In the formula, 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 taboo algorithm are as follows:

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

[0107] Step 4-7: Check whether the current solution satisfies 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 exchange the two positions of the current solution, generate an exchange 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 attribute of the object corresponding to the candidate solution, take the best solution of the non-taboo table object as the current solution, and use this object to replace the first taboo object in the taboo table to enter step 4-7. Finally, an ideal picking path that meets the point drilling rig multi-specification diamond picking-placement constraint model is obtained.

[0112] This embodiment uses the ant colony-tabu search hybrid algorithm to optimize the traveling salesman problem with some constraints eliminated. It not only improves the adaptability of the ant colony algorithm for the remaining constraints, but also combines the taboo search algorithm to enhance the global search capability of the algorithm, and achieves good results in solving this problem. This embodiment uses the ant colony-tabu search combined algorithm, adds pheromones in the initialization phase of the ant colony algorithm; introduces a dynamic factor of the heuristic function; introduces an adaptive volatility factor and a pheromone reward and punishment mechanism; uses the solution of the ant colony algorithm as the initial solution of the taboo algorithm; and uses the 2-opt algorithm for neighborhood search. These processes improve the global search capability of the algorithm to varying degrees and accelerate the convergence speed of the algorithm.

[0113] At present, most enterprises use the sequential placement method, that is, the diamonds are placed in the designated position in the teaching order or the diamond placement order, without using the corresponding optimization algorithm to optimize the path, which wastes time and cost. The present invention optimizes the point drilling path, which can greatly reduce the time cost and mechanical cost.

[0114] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described 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-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached 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 point distance matrix is ​​obtained according to the locations of the diamonds and the locations of the drill holes, and a diamond classification table is obtained according to the properties of the diamonds and the properties of the diamonds matched and pasted to each drill hole; the pick-and-place point distance matrix records the distance between each diamond and each drill hole, and the diamond classification table classifies diamonds with different properties as different categories, and records the diamonds contained in each category and the matched and pasted drill holes; Based on the pick-and-place point distance matrix and the diamond classification table, the pairing problem of diamonds and holes of the same type is constructed as an optimal assignment problem, and the optimal diamond-drilling hole pairing solution is obtained by solving it through 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 taboo 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 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 i does not drill into hole j, the value is 0. When diamond i drills into hole 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 is obtained by solving the Hungarian algorithm, including: Extracting 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 that 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 least number of straight lines to cover all zero elements in the cost matrix. If the number of lines covering zero elements is equal to the required number of paired diamonds, output the preliminary diamond drilling pairing scheme as the optimal diamond drilling pairing scheme; 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 is characterized in that: The method generates a preliminary diamond drilling pairing scheme 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; The diamonds and holes corresponding to the same marked zero element are paired 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 hole pair in the optimal diamond hole pairing scheme as the next position; Each ant completes a tour, 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; According to the adaptive pheromone volatilization factors and pheromone reward and punishment mechanism, pheromones are updated; Determine whether the number of iterations reaches 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 the iteration.

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