CN117346811A - Robot path planning method adopting quantum clustering based on quantum ant colony algorithm - Google Patents
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- 238000003064 k means clustering Methods 0.000 claims description 14
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
Abstract
The invention relates to robot path planning, in particular to a robot path planning method adopting quantum clustering based on a quantum ant colony algorithm, which decomposes a large-scale path planning problem into a small-scale path planning problem; judging whether the number of the currently available qubits can process the small-scale path planning problem obtained by decomposition, if the small-scale path planning problem obtained by decomposition still exceeds the currently available computing resources, further decomposing the small-scale path planning problem until the currently available computing resources can be processed; if the current available computing resources can process the small-scale path planning problem obtained by decomposition, solving each small-scale path planning problem by adopting a quantum ant colony algorithm; combining the solving results of all the small-scale path planning problems to obtain an optimal solution of the large-scale path planning problem; the technical scheme provided by the invention can effectively overcome the defect that the prior art cannot be applied to large-scale path planning.
Description
Technical Field
The invention relates to robot path planning, in particular to a robot path planning method adopting quantum clustering based on a quantum ant colony algorithm.
Background
In autonomous robot path planning, the ant colony algorithm can be used for optimizing the motion path of the robot and reducing the running time. With the continuous development of technology, a quantum ant colony algorithm is also introduced into autonomous robot path planning. The quantum ant colony algorithm is an algorithm combining the advantages of the traditional ant colony algorithm and the quantum computing method, the quantum ant colony algorithm is widely focused after being proposed, and the ant colony algorithm is improved through quantum computing from multiple angles of classical-quantum mixing, quantum inspiring, full quantum computing and the like.
Due to the introduction of quantum computation, some inherent defects of the traditional ant colony algorithm in autonomous robot path planning are remarkably improved, for example, random numbers generated by a quantum circuit are true random numbers compared with a classical computer, and the defect that the traditional ant colony algorithm is easy to fall into a local optimal solution is effectively improved. However, the existing quantum ant colony algorithm also has a certain limitation, and one of the most obvious defects is that the application range of the quantum ant colony algorithm is greatly limited due to the limitation of the quantum bit number, so that the quantum ant colony algorithm can be only applied to the problem of small-scale path planning.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects existing in the prior art, the invention provides a robot path planning method based on a quantum ant colony algorithm and adopting quantum clustering, which can effectively overcome the defect that the prior art cannot be applied to large-scale path planning.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme:
the method comprises the steps that a quantum clustering robot path planning method is adopted based on a quantum ant colony algorithm, and a large-scale path planning problem is decomposed into a small-scale path planning problem;
judging whether the number of the currently available qubits can process the small-scale path planning problem obtained by decomposition, if the small-scale path planning problem obtained by decomposition still exceeds the currently available computing resources, further decomposing the small-scale path planning problem until the currently available computing resources can be processed;
if the current available computing resources can process the small-scale path planning problem obtained by decomposition, solving each small-scale path planning problem by adopting a quantum ant colony algorithm;
and merging the solving results of all the small-scale path planning problems to obtain the optimal solution of the large-scale path planning problem.
Preferably, the method specifically comprises the following steps:
s1, clustering all path identification points by adopting a quantum K-Means clustering algorithm to obtain a primary group, and decomposing a large-scale path planning problem into a small-scale path planning problem;
s2, judging whether the quantum bit number in the path traversal solving quantum circuit can process the small-scale path planning problem obtained by decomposition, if not, adopting a quantum K-Means clustering algorithm to further cluster the primary sub-group until the primary sub-group can be processed, otherwise, utilizing the path traversal solving quantum circuit to perform path calculation on the final sub-group;
s3, assigning a starting point and a terminal point to the last generation subgroup in each last generation subgroup based on the last generation subgroup sequence obtained in the S2, and converting a path planning problem of the random starting point and the terminal point into a path planning problem determined by the starting point and the terminal point;
s4, carrying out path calculation on the previous generation subgroup in the last generation subgroups by utilizing a path traversal solving quantum circuit;
s5, judging whether all paths cover all path identification points, if not, continuing path calculation on the previous generation subgroup in each current generation subgroup by using a path traversal solving quantum circuit until all paths cover all path identification points, otherwise, merging path calculation results of the path identification points in each generation subgroup and each primary generation subgroup to obtain an optimal path.
Preferably, in S1, a quantum K-Means clustering algorithm is used to cluster all path identification points to obtain a primary group, and the large-scale path planning problem is decomposed into small-scale path planning problems, including:
for all path identification points, the conventional data is embedded into the qubit by using a quantum circuit with amplitude embedded into the conventional data for distance calculation, and the initialization characteristics in the IBM quantum calculation script Qiskit are used.
Preferably, in S2, determining whether the number of qubits in the path traversal solution quantum circuit can handle the small-scale path planning problem obtained by decomposition includes:
if the number of the current generation group is larger than the number of decimal numbers which can be represented by 2 adjacent quantum bits, judging that the processing can not be performed, otherwise, judging that the processing can be performed;
wherein the number of decimal numbers that two adjacent qubits can represent is 4.
Preferably, the path traversal solution quantum circuit comprises q1 0 ～q1 7 Representative 8 qubits, auxiliary inversion bit q0 and Ry quantum gate;
the quantum bit codes the path identification point and each generation group through decimal numbers represented by 2 adjacent quantum bit;
the auxiliary inversion bit q0 is used for enabling a problem solution generated by a path traversal solving quantum circuit to be mutated, so that the possibility that a quantum ant colony algorithm falls into a local optimal solution is reduced;
and the Ry quantum gate is used for setting weight for each quantum bit and is equivalent to the pheromone which is generated in the traditional ant colony algorithm and guides ants to search paths.
Preferably, in the process of performing path calculation by using the path traversal solution quantum circuit, if the problem solution generated by the path traversal solution quantum circuit is not changed within a preset time period, the path traversal solution quantum circuit generates random solutions by using an auxiliary inversion bit q0, so that the possibility that a quantum ant colony algorithm falls into a locally optimal solution is reduced.
Preferably, when the path traversal solving quantum circuit is used for path calculation, the quantum Hammingdistance circuit is used for correcting the problem solution which does not accord with the path planning rule, so that a reasonable problem solution is quickly generated, and the updating efficiency of the pheromone in the initial stage of path searching is improved.
Preferably, R in the quantum HammingDistance circuit x For storing binary X variables, R v For storing binary V variables, R D For storing the result of Hammingdistance operation, r ij Represents the ith binary variable v i And the j-th binary variable x j Results of the comparison between Hammingdistance.
Preferably, the mathematical expression of the Ry quantum gate is as follows:
and the theta represents the rotation angle of the Ry quantum gate, and corresponding assignment is carried out on the theta, so that the weight of a path generated by a path traversing solving quantum circuit can be changed, and the function of updating the pheromone in the traditional ant colony algorithm is realized.
(III) beneficial effects
Compared with the prior art, the robot path planning method based on the quantum ant colony algorithm and adopting quantum clustering has the following beneficial effects:
1) The quantum K-Means clustering algorithm is introduced, so that the improvement of the quantum ant colony algorithm in the autonomous robot path planning is realized, the limit of the quantum bit quantity to the quantum ant colony algorithm is effectively relieved, and the quantum ant colony algorithm can be effectively applied to the large-scale path planning problem;
2) The K-means clustering algorithm is improved through quantum calculation, so that the speed of clustering data can be increased;
3) The Hammingdistance algorithm is improved through quantum calculation, so that the process of converting an unreasonable problem solution into a reasonable problem solution conforming to a path planning rule can be quickened.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic flow chart of the present invention;
FIG. 3 is a schematic diagram of a quantum circuit with amplitude embedded in conventional data according to the present invention;
FIG. 4 is a schematic diagram of a path traversal solution quantum circuit according to the present invention;
FIG. 5 is a schematic diagram of a quantum Hammingdistance circuit according to the present invention;
FIG. 6 is the inc of FIG. 5 according to the present invention n A schematic diagram of a gate circuit;
FIG. 7 is a graph of results of testing a quantum K-Means clustering algorithm using a point dataset;
fig. 8 is a graph of the results of testing the technical solution of the present application using the standard dataset 29 of the business problem.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The robot path planning method based on the quantum ant colony algorithm adopts quantum clustering, and as shown in figure 1, the large-scale path planning problem is decomposed into small-scale path planning problems.
In the technical scheme, the corresponding clustering method can be determined according to the characteristics of the input large-scale path planning problem:
1) For the case that the data set to be classified is a convex set, classifying the data set to be classified by using a K-means, K-means++, quantum K-means and bi-kmmeans clustering algorithm;
2) For the case that the data set to be classified is a non-convex set, classifying the data set to be classified by using a DBSCAN algorithm and an OPTICS algorithm;
3) And introducing an Agglimerate algorithm to classify the data set to be classified under the condition that the data set to be classified has a chain effect in the classifying process.
Judging whether the number of the currently available qubits can process the small-scale path planning problem obtained by decomposition, if the small-scale path planning problem obtained by decomposition still exceeds the currently available computing resources, further decomposing the small-scale path planning problem until the currently available computing resources can be processed:
1) If the calculation can be performed by using a quantum computer, whether the number of the currently available quantum bits can process the small-scale path planning problem obtained by decomposition is examined, and if the small-scale path planning problem obtained by decomposition still exceeds the currently available calculation resources, the small-scale path planning problem is further decomposed until the currently available calculation resources can be processed; if the small-scale path planning problem obtained by decomposition can be processed, performing the next processing;
2) If the quantum computing simulation environment can be used, acquiring the quantum bit quantity which can be simulated by the quantum computing simulation environment of the current classical computer, then examining whether the current available quantum bit quantity can process the small-scale path planning problem obtained by decomposition, if the small-scale path planning problem obtained by decomposition still exceeds the current available computing resources, further decomposing the small-scale path planning problem until the current available computing resources can be processed; if the decomposed small-scale path planning problem can be processed, the next processing is performed.
If the small-scale path planning problem obtained by decomposition can be processed by the currently available computing resources, solving each small-scale path planning problem by adopting a quantum ant colony algorithm.
And merging the solving results of all the small-scale path planning problems to obtain the optimal solution of the large-scale path planning problem.
As shown in FIG. 2, S1, clustering all path identification points by adopting a quantum K-Means clustering algorithm to obtain a primary group, and decomposing a large-scale path planning problem into a small-scale path planning problem, wherein the method specifically comprises the following steps:
for all path identification points, the conventional data is embedded in the qubit using a quantum circuit (as shown in fig. 3) with amplitude embedded in the conventional data for distance calculation, and using the initialization characteristics in the IBM quantum calculation script Qiskit.
In order to solve the problem of large-scale path planning into a small-scale path planning problem which can be processed by a small number of quantum bits, and simultaneously improve the calculation speed of a clustering process, in a plurality of clustering algorithms, the technical scheme adopts a quantum K-Means clustering algorithm to perform clustering.
As shown in fig. 7, the quantum K-Means clustering algorithm was tested using a dataset with 16 points, classifying all points into 4 classes. The introduction of quantum computation changes the distance computation from serial to parallel, and effectively improves the speed of data clustering.
S2, judging whether the quantum bit number in the path traversal solving quantum circuit can process the small-scale path planning problem obtained by decomposition, if not, further clustering the primary sub-group by adopting a quantum K-Means clustering algorithm until the primary sub-group can be processed, and if not, carrying out path calculation on the final sub-group by utilizing the path traversal solving quantum circuit.
Specifically, judging whether the number of the quantum bits in the path traversal solving quantum circuit can process the small-scale path planning problem obtained by decomposition or not, including:
if the number of the current generation group is greater than the number of decimal numbers (for example, "00" represents decimal number 0, "01" represents decimal number 1, "10" represents decimal number 2, "11" represents decimal number 3) which can be represented by 2 adjacent qubit bits, judging that the processing can not be performed, otherwise, judging that the processing can be performed;
wherein the number of decimal numbers that two adjacent qubits can represent is 4.
And S3, assigning a starting point and a terminal point to the last generation subgroup in each last generation subgroup based on the last generation subgroup sequence obtained in the S2, and converting the path planning problem of the random starting point and the terminal point into the path planning problem determined by the starting point and the terminal point.
And S4, carrying out path calculation on the previous generation subgroup in the last generation subgroups by utilizing a path traversal solving quantum circuit.
S5, judging whether all paths cover all path identification points, if not, continuing path calculation on the previous generation subgroup in each current generation subgroup by using a path traversal solving quantum circuit until all paths cover all path identification points, otherwise, merging path calculation results of the path identification points in each generation subgroup and each primary generation subgroup to obtain an optimal path.
In the technical scheme, as shown in fig. 4, the path traversal solving quantum circuit includes q1 0 ～q1 7 Representative 8 qubits, auxiliary inversion bit q0 and Ry quantum gate;
the quantum bit codes the path identification point and each generation group through decimal numbers represented by 2 adjacent quantum bit;
the auxiliary inversion bit q0 is used for enabling a problem solution generated by a path traversal solving quantum circuit to be mutated, so that the possibility that a quantum ant colony algorithm falls into a local optimal solution is reduced;
ry quantum gate, which is used to set weight for each quantum bit, and is equivalent to the pheromone which is generated in the traditional ant colony algorithm and guides ants to search paths;
wherein, the mathematical expression of Ry quantum gate is as follows:
and the theta represents the rotation angle of the Ry quantum gate, and corresponding assignment is carried out on the theta, so that the weight of a path generated by a path traversing solving quantum circuit can be changed, and the function of updating the pheromone in the traditional ant colony algorithm is realized.
1) In the process of carrying out path calculation by using the path traversal solving quantum circuit, if the problem solution generated by the path traversal solving quantum circuit is not changed within a preset time period, the path traversal solving quantum circuit generates a random solution by using an auxiliary inversion bit q0, so that the possibility that the quantum ant colony algorithm falls into a local optimal solution is reduced.
2) Because of randomness of the quantum calculation result, when obtaining the problem solution generated by the path traversal solving quantum circuit, especially in the early stage of path searching, because the running times are relatively small, the probability difference of occurrence of each subgroup is relatively small, and the problem solution which does not accord with the path planning rule (for example, a subgroup with a certain number repeatedly occurs or a subgroup in a path is absent, etc.) may occur.
In order to solve the above problems, when a quantum circuit is solved by path traversal to perform path calculation, a quantum Hammingdistance circuit is used to correct a problem solution which does not accord with a path planning rule, so that a reasonable problem solution is quickly generated, and the efficiency of updating pheromone in the initial stage of path searching is improved.
As shown in FIG. 5, R in a quantum Hammingdistance circuit x For storing binary X variables, R v For storing binary V variables, R D For storing the result of Hammingdistance operation, r ij Represents the ith binary variable v i And the j-th binary variable x j Comparing results of HammingDistance, and the inc customized in the quantum HammingDistance circuit n The specific structure of the gate is shown in fig. 6.
Compared with the complexity O (M) of the traditional Hamming Distance algorithm, the complexity of the quantum Hamming Distance algorithm is O (log 2 M), wherein M is the number of V, the computational complexity is obviously improved.
In the technical scheme, specific parameters and calculation modes in the algorithm running process are given by a parameter selection module. Specifically, parameters of the quantum K-Means clustering algorithm are determined by a parameter selection module; the setting mode of the parameters can be manually input by a user through a parameter input interface or a configuration file, and can be automatically generated by a program according to the selected clustering algorithm and the computing environment.
In the whole algorithm operation process, the parameter selection module is responsible for operation by a classical computer according to the calculation modes adopted by each step in the currently available calculation resource control algorithm, namely, the classical calculation part in the algorithm is used for operation by the classical computer, and the quantum calculation part is used for selecting the corresponding calculation modes (for example, superconducting quantum calculation, an ion trap quantum computer or a quantum calculation simulation environment and the like) according to the currently available calculation resources and the problem scale.
As shown in fig. 8, the technical solution of the present application is tested using a standard data set bayg29 (total 29 points) of the business problem, and an optimal path is obtained. The traditional ant colony algorithm is adopted as comparison, the number of ants is set to be 10, the iteration times are 1000, the pheromone volatilization coefficient is 0.6, the pheromone increasing intensity factor is 1, the pheromone weight parameter is 5, the distance parameter is 3, the initial pheromone concentration is 100, the same computer is adopted for calculation, the repeated operation is carried out for 10 times respectively, and the best results of the two methods are adopted for comparison.
The result shows that the optimal path distance by adopting the traditional ant colony algorithm is 25629.3, and the optimal path distance by adopting the improved quantum ant colony algorithm (namely the technical scheme of the application) is 13099.1. From the results, the improved quantum ant colony algorithm is obviously superior to the traditional ant colony algorithm.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. The robot path planning method adopting quantum clustering based on the quantum ant colony algorithm is characterized by comprising the following steps of: decomposing the large-scale path planning problem into a small-scale path planning problem;
judging whether the number of the currently available qubits can process the small-scale path planning problem obtained by decomposition, if the small-scale path planning problem obtained by decomposition still exceeds the currently available computing resources, further decomposing the small-scale path planning problem until the currently available computing resources can be processed;
if the current available computing resources can process the small-scale path planning problem obtained by decomposition, solving each small-scale path planning problem by adopting a quantum ant colony algorithm;
and merging the solving results of all the small-scale path planning problems to obtain the optimal solution of the large-scale path planning problem.
2. The robot path planning method based on quantum ant colony algorithm and adopting quantum clustering according to claim 1, wherein the method is characterized in that: the method specifically comprises the following steps:
s1, clustering all path identification points by adopting a quantum K-Means clustering algorithm to obtain a primary group, and decomposing a large-scale path planning problem into a small-scale path planning problem;
s2, judging whether the quantum bit number in the path traversal solving quantum circuit can process the small-scale path planning problem obtained by decomposition, if not, adopting a quantum K-Means clustering algorithm to further cluster the primary sub-group until the primary sub-group can be processed, otherwise, utilizing the path traversal solving quantum circuit to perform path calculation on the final sub-group;
s3, assigning a starting point and a terminal point to the last generation subgroup in each last generation subgroup based on the last generation subgroup sequence obtained in the S2, and converting a path planning problem of the random starting point and the terminal point into a path planning problem determined by the starting point and the terminal point;
s4, carrying out path calculation on the previous generation subgroup in the last generation subgroups by utilizing a path traversal solving quantum circuit;
s5, judging whether all paths cover all path identification points, if not, continuing path calculation on the previous generation subgroup in each current generation subgroup by using a path traversal solving quantum circuit until all paths cover all path identification points, otherwise, merging path calculation results of the path identification points in each generation subgroup and each primary generation subgroup to obtain an optimal path.
3. The robot path planning method based on quantum ant colony algorithm and adopting quantum clustering according to claim 1, wherein the method is characterized in that: in S1, clustering all path identification points by adopting a quantum K-Means clustering algorithm to obtain a primary group, and decomposing a large-scale path planning problem into a small-scale path planning problem, wherein the method comprises the following steps:
for all path identification points, the conventional data is embedded into the qubit by using a quantum circuit with amplitude embedded into the conventional data for distance calculation, and the initialization characteristics in the IBM quantum calculation script Qiskit are used.
4. The robot path planning method based on quantum ant colony algorithm and adopting quantum clustering according to claim 3, wherein the method is characterized in that: s2, judging whether the quantum bit number in the path traversal solving quantum circuit can process the small-scale path planning problem obtained by decomposition, wherein the method comprises the following steps:
if the number of the current generation group is larger than the number of decimal numbers which can be represented by 2 adjacent quantum bits, judging that the processing can not be performed, otherwise, judging that the processing can be performed;
wherein the number of decimal numbers that two adjacent qubits can represent is 4.
5. The robot path planning method based on quantum ant colony algorithm and adopting quantum clustering according to claim 1, wherein the method is characterized in that: the path traversal solving quantum circuit comprises q1 0 ～q1 7 Representative 8 qubits, auxiliary inversion bit q0 and Ry quantum gate;
the quantum bit codes the path identification point and each generation group through decimal numbers represented by 2 adjacent quantum bit;
the auxiliary inversion bit q0 is used for enabling a problem solution generated by a path traversal solving quantum circuit to be mutated, so that the possibility that a quantum ant colony algorithm falls into a local optimal solution is reduced;
and the Ry quantum gate is used for setting weight for each quantum bit and is equivalent to the pheromone which is generated in the traditional ant colony algorithm and guides ants to search paths.
6. The robot path planning method based on quantum ant colony algorithm and adopting quantum clustering according to claim 5, wherein the method is characterized in that: in the process of carrying out path calculation by using the path traversal solving quantum circuit, if the problem solution generated by the path traversal solving quantum circuit is not changed within a preset time period, the path traversal solving quantum circuit generates random solutions by using an auxiliary inversion bit q0, so that the possibility that a quantum ant colony algorithm falls into a local optimal solution is reduced.
7. The robot path planning method based on quantum ant colony algorithm and adopting quantum clustering according to claim 5, wherein the method is characterized in that: when the path traversal solving quantum circuit is utilized to perform path calculation, the quantum Hamming Distance circuit is utilized to correct the problem solution which does not accord with the path planning rule, so that a reasonable problem solution is quickly generated, and the efficiency of updating the pheromone in the initial stage of path searching is improved.
8. The robot path planning method based on quantum ant colony algorithm and adopting quantum clustering according to claim 7, wherein the method is characterized in that: r in the quantum Hamming Distance circuit x For storing binary X variables, R v For storing binary V variables, R D For storing the result of Hamming Distance operation, r ij Represents the ith binary variable v i And the j-th binary variable x j And (3) comparing the results of the Hamming Distance.
9. The robot path planning method based on quantum ant colony algorithm and adopting quantum clustering according to claim 5, wherein the method is characterized in that: the mathematical expression of the Ry quantum gate is as follows:
and the theta represents the rotation angle of the Ry quantum gate, and corresponding assignment is carried out on the theta, so that the weight of a path generated by a path traversing solving quantum circuit can be changed, and the function of updating the pheromone in the traditional ant colony algorithm is realized.
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