Drilling robot path planning method based on improved ant colony algorithm

Abstract

The invention discloses a drilling robot path planning method based on an improved ant colony algorithm, and the method comprises the steps: firstly forming a drilling point set based on the drilling point position information of a drilling robot; initializing pheromones to further correspond to the optimal solution of the optimal path; sequentially carrying out damage operation, recombination operation, repair operation and local search operation for providing a larger-range search space on the optimal solution to obtain a final solution; the pheromone concentration is updated at the improved pheromone volatilization rate, iteration of a new final solution is obtained through the updated pheromone concentration, iteration is terminated when an iteration termination condition is met, and the final solution meeting a preset target is selected as a drilling robot path planning scheme. According to the scheme, the damage operation, the recombination operation and the repair operation can provide a larger range of search space, so that better and more path choices can be provided for the drilling robot, and the energy consumption loss, the mechanical wear and the time consumption of the mechanical arm are reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of robot path planning, and in particular relates to a drilling robot path planning method based on an improved ant colony algorithm. Background Art

[0002] At present, the drilling method using drilling robots is gradually replacing the traditional manual drilling method. The development pace and achievements of drilling automation are extremely significant worldwide. Drilling robots are an important part of industrial robots and are widely used in industrial fields such as door control, automobiles, nuclear power equipment maintenance, construction, aerospace, etc. Among them, path planning is the basis for drilling robots to complete the given tasks under various working conditions, and plays an important role in the field of door control.

[0003] However, the traditional path planning method usually outputs multiple paths, and it is impossible to further optimize and screen the planned paths. In addition, it does not have the function of autonomously planning a reasonable path based on the current drilling point to determine the next drilling point, which increases the energy loss, mechanical wear and time consumption of the robot arm, which is particularly unfavorable for large-scale construction. The robot can quickly and accurately perform drilling operations at predetermined locations, and can avoid misoperations that occurred in previous manual drilling processes, greatly improving production efficiency and ensuring product quality. With the development of technology, the design and operation of drilling robots are increasingly focused on improving flexibility and adaptability. For example, intelligent algorithms are used for path planning and trajectory optimization to further improve drilling efficiency and reduce resource waste.

[0004] However, during operation, drilling robots face challenges such as poor drilling path planning and poor trajectory stability during long-term operations. In order to improve drilling efficiency and save energy, the automotive manufacturing industry in technologically advanced countries has used optimization algorithms to determine the best drilling path and optimize the trajectory. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention proposes a drilling robot path planning method based on an improved ant colony algorithm.

[0006] The technical solution of the present invention is as follows:

[0007] A drilling robot path planning method based on an improved ant colony algorithm is used for path planning between N drilling points, comprising:

[0008] S1. forming a drilling point set based on the drilling point position information of the drilling robot;

[0009] S2, initialize pheromone, obtain the pheromone concentration and heuristic information between any two drilling points in the drilling point set, determine the transition probability matrix according to the pheromone concentration and heuristic information, complete the path selection according to the transition probability matrix, thereby forming a path set representing the drilling sequence, calculate the length of each path in the path set, and select the optimal solution C;

[0010] S3, performing a destruction operation, a reorganization operation, a repair operation, and a local search operation on the selected optimal solution in order to provide a larger search space, and obtaining a final solution;

[0011] S4. Update the pheromone concentration with the improved pheromone volatilization rate, and iterate to obtain a new final solution from the updated pheromone concentration. When the iteration termination condition is reached, terminate the iteration, and select the final solution that meets the preset goal as the drilling robot path planning solution.

[0012] Furthermore, the destruction operation includes initial destruction and secondary destruction, and the specific steps are as follows:

[0013] During the initial destruction, a drilling point a and a drilling point b located on the right side of the drilling point a are randomly selected from the optimal solution to be removed, thereby completing the initial destruction, and the removed drilling points a and b are stored in the set Sremoved;

[0014] The second destruction is to perform a second destruction on the solution consisting of L adjacent drilling points on the left and right sides of the drilling points a and b as the center, to obtain a destroyed solution C1 and store it in the set Sdestroy.

[0015] Furthermore, the secondary destruction method includes: respectively calculating the number v of drilling points located to the left of the drilling point a in the optimal solution C LN 、The number of drilling points V to the right of drilling point b RN and the number of drilling points v between drilling point a and drilling point b LN , and remove the n on the left side of drilling point a as follows L Drilling point b and the number of drilling points n to the right of drilling point b R And the number of drilling points n in the middle M ; If V LN +v MN ≤v RN , then n R 0 to v RN The random number, n L is the remaining Ln M -n R drilling points; if v LN +v MN >v RN , then n L 0 to vLN The random number is n R The remaining Ln M -n L drilling points; if v LN +v RN ≤v MN , then n M is 0 to v MN The random number is n R and n L The remaining Ln M Drilling points.

[0016] Furthermore, the recombination operation includes primary recombination and secondary recombination;

[0017] During the initial reorganization, the third drilling point of every three drilling points in the destructive solution C1 is placed between the first two drilling points to obtain a destructive solution C2;

[0018] During the reorganization, any drilling point other than drilling point b is selected in the set Sdestroy, and the drilling point is inserted into the bad solution C2 in different ways, and the destructive solutions C3 corresponding to the different insertion methods are obtained respectively, and the fitness values ​​f(C3) of the destructive solutions C3 corresponding to the different insertion methods are calculated and recorded, and the highest fitness value is selected to complete the reorganization of the selected drilling point; any unselected drilling point is selected in the set Sdestroy to repeat the above process until the reorganization of all drilling points in the set Sdestroy except drilling point b is completed, and the reorganized solution C3 is obtained.

[0019] Furthermore, the repair operation includes: repairing the destroyed solution C3 by placing the drilling point b in the set Sdestroy at the end of the reorganized solution C3 to form a repair solution.

[0020] Furthermore, the local search operation adopts a 2-opt local search strategy, and the specific method is as follows:

[0021] Randomly select two drilling points a1 and b1 from the repair solution, flip the order of the drilling points between drilling point a1 and drilling point b1, and recombine them to obtain the final path.

[0022] Furthermore, the specific steps of initializing pheromones are as follows:

[0023] An improved chaos mapping expression is constructed, and the improved chaos mapping expression is as follows:

[0024]

[0025] In the formula, x z+1 is the chaotic mapping value output by the chaotic sequence of the z+1th ant, corresponding to the pheromone concentration of the z+1th ant; xz is the chaotic mapping value output by the chaotic sequence of the zth ant, corresponding to the pheromone concentration of the zth ant; N is the number of particles in the chaotic sequence; rand(0,1) is a random number between (0,1);

[0026] Pheromones are initialized based on the constructed improved chaotic mapping expression.

[0027] Furthermore, the preset target is a shortest path target; and the iteration termination condition includes the number of iterations reaching a maximum number of iterations.

[0028] Furthermore, in the transition probability matrix, ant k randomly assigns a drilling point as the starting point, k = 1, 2, ..., m, determines the next drilling point it passes through according to the pheromone concentration on the connection path of each drilling point, and records the path vector C of the drilling point visited by ant k. Ant k will transfer according to the transition probability at the tth iteration time Determine the next drilling point it passes through and the transfer probability It is determined by the pheromone concentration and heuristic information on the path, and its expression is as follows:

[0029]

[0030] Where i, j = 1, 2, ..., N, τ ij (t) is the time point c of the tth iteration i To point c j Pheromone concentration on the path; η ij (t) is the time point c of the tth iteration i To point c j Heuristic information on the path; τ is (t) is the time point c of the tth iteration i To point c s Pheromone concentration on the path; η js (t) represents the tth iteration time from point c i To point c s The expected degree; α is the weight of pheromone concentration; β is the weight of heuristic information; allow k Ant k passes through welding point c i The set of drilling points that have not been drilled yet, k = 1, 2, ..., m.

[0031] Furthermore, the specific steps of updating the pheromone concentration with the improved pheromone volatilization rate include:

[0032] τ ij (t+1)=(1-ρ)τ ij (t)+Δτ ij

[0033]

[0034] In the formula, τ ij (t) is the time point c of the tth iteration i To point c j Pheromone concentration on the path; τ ij (t+1) is the time point c of the t+1th iteration i To point c j The pheromone concentration on the path; ρ is the improved pheromone volatilization rate; Δτ ij For point c i The pheromone increment on the path to point c; ρ min is the minimum value of the improved pheromone volatilization rate; ρ max is the maximum value of the improved pheromone volatilization rate; μ is the dynamic factor.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention proposes a drilling robot path planning method based on an improved ant colony algorithm. The method first forms a drilling point set based on the drilling point position information of the drilling robot; then initializes the pheromone, further corresponding to the optimal solution of the optimal path; then the optimal solution sequentially performs a destruction operation, a reorganization operation, a repair operation and a local search operation for providing a larger search space to obtain a final solution; then the pheromone concentration is updated with an improved pheromone volatilization rate, and an iteration is performed to obtain a new final solution from the updated pheromone concentration, and the iteration is terminated when the iteration termination condition is reached, and the final solution that meets the preset goal is selected as the drilling robot path planning scheme. In the scheme, the destruction operation, the reorganization operation, the repair operation and the local search operation can provide a larger search space, so a higher quality solution can be obtained, the robot search path efficiency can be improved, and better and more path selections can be provided for the drilling robot, thereby reducing the energy loss, mechanical wear and time consumption of the robot arm.

[0037] The method of the present invention constructs an improved chaotic mapping expression, and initializes pheromones based on the constructed improved chaotic mapping expression, thereby avoiding the problem that a conventional chaotic sequence falls into a small periodic point and an unstable periodic point during iteration.

[0038] The method of the present invention adopts a 2-opt local search strategy, thereby using fewer computing resources and improving the ability of path search.

[0039] The present invention adopts an improved pheromone volatilization rate to update the pheromone concentration, thereby enhancing the guiding effect of the pheromone on the ant colony and improving the working efficiency of the robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1Flow chart of a drilling robot path planning method based on an improved ant colony algorithm in an embodiment;

[0041] Figure 2 This is an example diagram of the process of destruction operation, reconstruction operation and repair operation in the embodiment;

[0042] Figure 3 Schematic diagram of 2-opt algorithm in the embodiment;

[0043] FIG4( a ) is a path planning diagram before improvement in the embodiment;

[0044] FIG4( b ) is an improved path planning diagram in the embodiment. DETAILED DESCRIPTION

[0045] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0046] Embodiment 1:

[0047] The present invention provides a drilling robot path planning method based on an improved ant colony algorithm, which is used for path planning between N drilling points. Figure 1 As shown, including:

[0048] S1. forming a drilling point set based on the drilling point position information of the drilling robot;

[0049] S2, initialize pheromone, obtain the pheromone concentration and heuristic information between any two drilling points in the drilling point set, determine the transition probability matrix according to the pheromone concentration and heuristic information, complete the path selection according to the transition probability matrix, thereby forming a path set representing the drilling sequence, calculate the length of each path in the path set, and select the optimal solution corresponding to the optimal path;

[0050] S3, performing a destruction operation, a reorganization operation, a repair operation, and a local search operation on the selected optimal solution in order to provide a larger search space, and obtaining a final solution;

[0051] S4. Update the pheromone concentration with the improved pheromone volatilization rate, and iterate to obtain a new final solution from the updated pheromone concentration. When the iteration termination condition is reached, terminate the iteration, and select the final solution that meets the preset goal as the drilling robot path planning solution.

[0052] Embodiment 2:

[0053] This embodiment is further designed on the basis of the first embodiment in that: Figure 2As shown, the destruction operation in this example includes the initial destruction and the secondary destruction. The specific steps are as follows:

[0054] During the initial destruction, the drilling point a and the drilling point b located on the right side of the drilling point a are randomly selected from the optimal solution and removed, thereby completing the initial destruction, and the removed drilling points a and b are stored in the set Sremoved;

[0055] Destroy again, and perform secondary destruction on the solution consisting of L adjacent drilling points randomly selected on the left and right sides of the drilling points a and b as the center, and obtain the destroyed solution C1 and store it in the set Sdestroy.

[0056] The destruction operation of the present invention is further described below with an example. Figure 2 As shown, the drilling points in the initial solution are recorded as point 1, point 2, point 3, point 4, point 5, point 6, point 7 and point 8 respectively. The drilling point a and drilling point b selected in this example are point 4 and point 8. The destroyed solution C1 after destruction is point 1, point 2, point 3, point 5, point 6 and point 7.

[0057] Embodiment three:

[0058] This embodiment is further designed on the basis of the second embodiment in that: Figure 2 As shown, the secondary destruction method in this example includes: respectively calculating the number of drilling points v located to the left of drilling point a in the optimal solution C LN 、The number of drilling points v to the right of drilling point b RN and the number of drilling points v between drilling point a and drilling point b LN , and remove the n on the left side of drilling point a as follows L Drilling point b and the number of drilling points n to the right of drilling point b R And the number of drilling points n in the middle M ; if v LN +v MN ≤v RN , then n R 0 to v RN The random number, n L is the remaining Ln M -n R drilling points; if v LN +v MN >v RN , then n L 0 to v LN The random number is n R The remaining Ln M -n L drilling points; if v LN +v RN ≤v MN , then n Mis 0 to v MN The random number is n R and n L The remaining Ln M Drilling points.

[0059] Embodiment 4:

[0060] This embodiment is further designed on the basis of the third embodiment in that: Figure 2 As shown, the reorganization operation in this example includes the initial reorganization and the secondary reorganization;

[0061] During the initial reorganization, the third drilling point of every three drilling points in the destructive solution C1 is placed between the first two drilling points to obtain the destructive solution C2;

[0062] When reorganizing again, select any drilling point other than drilling point b in the set Sdestroy, insert the drilling point into the bad solution C2 in different ways, obtain the destructive solutions C3 corresponding to different insertion methods, calculate the fitness values ​​f(C3) of the destructive solutions C3 corresponding to different insertion methods and record them, and select the highest fitness value to complete the reorganization of the selected drilling point; select any unselected drilling point in the set Sdestroy and repeat the above process until the reorganization of all drilling points in the set Sdestroy except drilling point b is completed, and the reorganized solution C3 is obtained.

[0063] The destruction and reconstruction operations of the present invention are further described below with an example. Figure 2 As shown, the destructive solution C1 is point 1, point 2, point 3, point 5, point 6 and point 7. Every third drilling point 3 and point 7 in the destructive solution C1 is inserted into the third drilling point in the destructive solution C1 between the first two drilling points to form a destructive solution C2. The destructive solution C2 is point 1, point 3, point 2, point 5, point 7 and point 6. Select any drilling point except drilling point b in the set Sdestroy, and randomly insert it into the destructive solution C2 to obtain the reorganized solution C3. Suppose it is randomly inserted between point 3 and point 2, and the reorganized solution C3 is point 1, point 3, point 4, point 2, point 5, point 7 and point 6.

[0064] Embodiment five:

[0065] This embodiment is further designed on the basis of the fourth embodiment in that the repair operation in this embodiment includes: repairing the destroyed solution C3 by randomly inserting the drilling point b in the set Sdestroy at the end of the reorganized solution C3 to form a repair solution.

[0066] The following is an example to further illustrate the recombination operation of the present invention. Figure 2As shown, the drilling point b in the set Sdestroy is randomly inserted at the tail of the reorganized solution C3. Suppose a repair solution is formed by randomly inserting point 5 and point 7. The repair solution is 1, point 3, point 4, point 2, point 5, point 8, point 7 and point 6.

[0067] Embodiment six:

[0068] This embodiment is further designed on the basis of the first embodiment in that the local search operation in this embodiment adopts a 2-opt local search strategy, and the specific method is as follows:

[0069] Randomly select two drilling points a1 and b1 from the repair solution, keep the path before drilling point a1 unchanged, flip the order of drilling points between drilling point a1 and drilling point b1 and add them to the path before drilling point a1, keep the path after drilling point b1 unchanged and add it to the end of the sum of the above two paths, and recombine to obtain the final path

[0070] The 2-opt local search strategy of the present invention is specifically described below with an example. Figure 3 As shown, two drilling points are randomly selected from the repair solution, and the two randomly selected drilling points are assumed to be point 3 and point 7. The path before point 3 remains unchanged, and the order of the drilling points between point 3 and point 7 is reversed and added to the path before point 3. The formed path is point 1, point 3, point 8, point 5, point 2, point 4 and point 7. The path after point 7 remains unchanged and is added to the end of the sum of the above two paths. The final path is recombined, and the final path is point 1, point 3, point 8, point 5, point 2, point 4, point 7 and point 6.

[0071] Embodiment seven:

[0072] This embodiment is further designed on the basis of the first embodiment in that the specific steps of initializing pheromones in this embodiment are as follows:

[0073] Construct an improved chaos mapping expression, the improved chaos mapping expression is as follows:

[0074]

[0075] In the formula, x z+1 is the chaotic mapping value output by the chaotic sequence of the z+1th ant, corresponding to the pheromone concentration of the z+1th ant; x z is the chaotic mapping value output by the chaotic sequence of the zth ant, corresponding to the pheromone concentration of the zth ant; N is the number of particles in the chaotic sequence; rand(0,1) is a random number between (0,1);

[0076] Pheromones are initialized based on the constructed improved chaotic mapping expression.

[0077] Embodiment eight:

[0078] This embodiment is further designed on the basis of the first embodiment in that the preset target in this embodiment is the shortest path target; and the iteration termination condition in this embodiment includes that the number of iterations reaches the maximum number of iterations.

[0079] Embodiment nine:

[0080] This embodiment is further designed on the basis of the first embodiment in that, in the transfer probability matrix in this embodiment, ant k is randomly assigned a drilling point as the starting point, k = 1, 2, ..., m, and the next drilling point to be passed is determined according to the pheromone concentration on the connection path of each drilling point, and the path vector C of the drilling point visited by ant k is recorded. Ant k will transfer according to the transfer probability at time t Determine the next drilling point it passes through and the transfer probability It is determined by the pheromone concentration and heuristic information on the path, and its expression is as follows:

[0081]

[0082] Where i, j = 1, 2, ..., N, τ ij (t) is the time point c of the tth iteration i To point c j Pheromone concentration on the path; η ij (t) is the time point c of the tth iteration i To point c j The heuristic information on the path is the inverse of the corresponding path length, which means that the t-th iteration starts from point c i To point c j The expected degree, that is, the shorter the path length, the greater the expected degree; τ is (t) is the time point c of the tth iteration i To point c s Pheromone concentration on the path; η js (t) represents the tth iteration time from point c i To point c s α is the weight of pheromone concentration. The larger its value, the more the ant will rely on the pheromone concentration on the path when choosing the next path during the transfer process; β is the weight of heuristic information. The larger its value, the more ant k will pay attention to the heuristic information when moving next, that is, it tends to choose a shorter or better path; allow k Ant k passes through welding point c i The set of drilling points that have not been drilled yet, k = 1, 2, ..., m.

[0083] Embodiment ten:

[0084] This embodiment is further designed on the basis of the first embodiment in that the specific steps of updating the pheromone concentration with the improved pheromone volatilization rate in this embodiment include:

[0085] τ ij (t+1)=(1-ρ)τ ij (t)+Δτ ij

[0086]

[0087] In the formula, τ ij (t) is the time point c of the tth iteration i To point c j Pheromone concentration on the path; τ ij (t+1) is the time point c of the t+1th iteration i To point c j The pheromone concentration on the path; ρ is the improved pheromone volatilization rate; Δτ ij For point c i The pheromone increment on the path to point c; ρ min is the minimum value of the improved pheromone volatilization rate, which is set according to the actual drilling conditions; ρ max is the maximum value of the improved pheromone volatilization rate, which is set according to the actual drilling situation; μ is a dynamic factor, which can be dynamically adjusted according to the actual drilling situation.

[0088] Embodiment eleven:

[0089] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method described in any of the above embodiments.

[0090] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0091] Application examples:

[0092] This example simulates the implementation of the drilling robot path planning of the present invention. This example uses the IRB2600 robot as the research object, and performs optimal path planning for the robot's three-dimensional drilling points. There are 40 three-dimensional coordinate points, and the drilling point coordinates are sorted according to the sequence numbers 1-40. The drilling point information is shown in Table 1:

[0093] Table 1 Drilling point information

[0094]

[0095] As shown in Figure 4(a) and Figure 4(b), the optimal path length in Figure 4(a) is 7209.98 mm, and the optimal path length in Figure 4(b) is 7129.64 mm. The optimal welding path sequence is:

[0096] 11-6-5-10-4-9-17-22-21-27-19-20-18-8-26-1-3-2-29-30-31-32-33-34-35-36-37-38-28-23-24-16-15-7-14-13-12-11.

[0097] It can be seen that the improved robot drilling path is optimized and the efficiency is improved.

[0098] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A drilling robot path planning method based on an improved ant colony algorithm, used for path planning between N drilling points, characterized in that: include: S1. forming a drilling point set based on the drilling point position information of the drilling robot; S2, initialize pheromone, obtain the pheromone concentration and heuristic information between any two drilling points in the drilling point set, determine the transition probability matrix according to the pheromone concentration and heuristic information, complete the path selection according to the transition probability matrix, thereby forming a path set representing the drilling sequence, calculate the length of each path in the path set, and select the optimal solution C; S3, performing a destruction operation, a reorganization operation, a repair operation, and a local search operation on the selected optimal solution in order to provide a larger search space, and obtaining a final solution; S4. Update the pheromone concentration with the improved pheromone volatilization rate, and iterate to obtain a new final solution from the updated pheromone concentration. When the iteration termination condition is reached, terminate the iteration, and select the final solution that meets the preset goal as the drilling robot path planning solution.

2. The drilling robot path planning method based on improved ant colony algorithm according to claim 1 is characterized in that: The destruction operation includes initial destruction and secondary destruction, and the specific steps are as follows: During the initial destruction, a drilling point a and a drilling point b located on the right side of the drilling point a are randomly selected from the optimal solution to be removed, thereby completing the initial destruction, and the removed drilling points a and b are stored in the set Sremoved; The second destruction is to perform a second destruction on the solution consisting of L adjacent drilling points on the left and right sides of the drilling points a and b as the center, to obtain a destroyed solution C1 and store it in the set Sdestroy.

3. The drilling robot path planning method based on improved ant colony algorithm according to claim 2 is characterized in that: The secondary destruction method comprises: respectively calculating the number v of drilling points located to the left of the drilling point a in the optimal solution C LN 、The number of drilling points v to the right of drilling point b RN and the number of drilling points v between drilling point a and drilling point b LN , and remove the n on the left side of drilling point a as follows L Drilling point b and the number of drilling points n to the right of drilling point b R And the number of drilling points n in the middle M ; if v LN +v Mn ≤v RN , then n R 0 to v RN The random number, n L is the remaining Ln M -n R drilling points; if v LN +v MN >v RN , then n L 0 to v LN The random number is n R The remaining Ln M -n L drilling points; if v LN +v RN ≤v MN , then n M is 0 to v MN The random number is n R and n L The remaining Ln M Drilling points.

4. The drilling robot path planning method based on improved ant colony algorithm according to claim 3 is characterized in that: The reorganization operation includes primary reorganization and secondary reorganization; During the initial reorganization, the third drilling point of every three drilling points in the destructive solution C1 is placed between the first two drilling points to obtain a destructive solution C2; During the reorganization, any drilling point other than drilling point b is selected in the set Sdestroy, and the drilling point is inserted into the bad solution C2 in different ways, and the destructive solutions C3 corresponding to the different insertion methods are obtained respectively, and the fitness values ​​f(C3) of the destructive solutions C3 corresponding to the different insertion methods are calculated and recorded, and the highest fitness value is selected to complete the reorganization of the selected drilling point; any unselected drilling point is selected in the set Sdestroy to repeat the above process until the reorganization of all drilling points in the set Sdestroy except drilling point b is completed, and the reorganized solution C3 is obtained.

5. The drilling robot path planning method based on improved ant colony algorithm according to claim 4 is characterized in that: The repair operation includes: repairing the destroyed solution C3 by placing the drilling point b in the set Sdestroy at the end of the reorganized solution C3 to form a repair solution.

6. The drilling robot path planning method based on improved ant colony algorithm according to claim 5 is characterized in that: The local search operation adopts a 2-opt local search strategy, and the specific method is as follows: Randomly select two drilling points a1 and b1 from the repair solution, flip the order of the drilling points between drilling point a1 and drilling point b1, and recombine them to obtain the final path.

7. The drilling robot path planning method based on improved ant colony algorithm according to claim 1, characterized in that: The specific steps of initializing pheromones are as follows: An improved chaos mapping expression is constructed, and the improved chaos mapping expression is as follows: In the formula, x z+1 is the chaotic mapping value output by the chaotic sequence of the z+1th ant, corresponding to the pheromone concentration of the z+1th ant; x z is the chaotic mapping value output by the chaotic sequence of the zth ant, corresponding to the pheromone concentration of the zth ant; N is the number of particles in the chaotic sequence; rand(0,1) is a random number between (0,1); Pheromones are initialized based on the constructed improved chaotic mapping expression.

8. The drilling robot path planning method based on improved ant colony algorithm according to claim 1, characterized in that: The preset target is the shortest path target; the iteration termination condition includes the number of iterations reaching the maximum number of iterations.

9. The drilling robot path planning method based on improved ant colony algorithm according to claim 1, characterized in that: In the transition probability matrix, ant k randomly assigns a drilling point as the starting point, k = 1, 2, ..., m, determines the next drilling point it passes through according to the pheromone concentration on the connection path of each drilling point, and records the path vector C of the drilling point visited by ant k. Ant k will transfer according to the transition probability at the tth iteration time Determine the next drilling point it passes through and the transfer probability It is determined by the pheromone concentration and heuristic information on the path, and its expression is as follows: Where i, j = 1, 2, ..., N, τ ij (t) is the time point c of the tth iteration i To point c j pheromone concentration along the path; η ij (t) is the time point c of the tth iteration i To point c j Heuristic information on the path; τ is (t) is the time point c of the tth iteration i To point c s pheromone concentration along the path; η js (t) represents the tth iteration time from point c i To point c s The expected degree; α is the weight of pheromone concentration; β is the weight of heuristic information; allow k Ant k passes through welding point c i The set of drilling points that have not been drilled yet, k = 1, 2, ..., m.

10. The drilling robot path planning method based on improved ant colony algorithm according to claim 1, characterized in that: The specific steps of updating the pheromone concentration with an improved pheromone volatilization rate include: t ij (t+1)=(1-ρ)τ ij (t)+Δτ ij In the formula, τ ij (t) is the time point c of the tth iteration i To point c j Pheromone concentration on the path; τ ij (t+1) is the time point c of the t+1th iteration i To point c j The pheromone concentration on the path; ρ is the improved pheromone volatilization rate; Δτ ij For point c i To point c j Pheromone increment on the path; ρ min is the minimum value of the improved pheromone volatilization rate; ρ max is the maximum value of the improved pheromone volatilization rate; μ is the dynamic factor.

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