CN115079704A - Path planning method and device, storage medium and electronic equipment - Google Patents
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Abstract
The disclosure relates to the technical field of artificial intelligence, in particular to a path planning method, a device storage medium and an electronic device, wherein the path planning method comprises the following steps: acquiring a grid map of an environment to be inspected, and generating an inspection path according to the grid map, wherein the inspection path comprises inspection nodes; inputting the routing inspection path as an individual of a parent population in a genetic algorithm into the genetic algorithm, and acquiring an optimal solution of the genetic algorithm, wherein the optimal solution is a first routing inspection path determined by the genetic algorithm; determining the first connection times of any two routing inspection nodes from the first routing inspection path; and inputting the first connection times into an ant colony algorithm, and performing iterative operation according to the first connection times by using the ant colony algorithm to obtain an optimal routing inspection path. The method and the device can improve the path planning efficiency and improve the accuracy of the path planning result.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to a path planning method and apparatus, a storage medium, and an electronic device.
Background
In many industries, it is necessary to manually inspect the working environment, such as power inspection, mine inspection, machine room inspection, and the like. The manual inspection has low efficiency and high labor cost, and has the problems of great safety risk and incapability of timely inspection in extreme environments such as high temperature, high humidity, toxicity or other dangerous environments. In order to avoid the above problems in manual inspection, an intelligent robot is generally adopted to inspect the working environment at present. In order to enable the intelligent robot to automatically patrol the patrol environment, the path of the intelligent robot needs to be planned in advance, so that the intelligent robot can automatically patrol the working environment according to the planned path.
In the prior art, an ant colony algorithm or a genetic algorithm is generally adopted to plan a path of an intelligent robot. Ant colony algorithm was first proposed by italian scholars Marco Dorigo in 1992 in his doctor's paper as a probabilistic algorithm for finding optimal paths, the inspiration of which comes from the ant's behavior in finding paths in the process of finding food. The algorithm has the characteristics of distribution calculation, information positive feedback and heuristic search, and is essentially a heuristic global optimization algorithm in an evolutionary algorithm. However, the ant colony algorithm has a slow convergence speed, so that the efficiency is low when the ant colony algorithm is adopted for path planning.
The genetic algorithm is a calculation model of a biological evolution process simulating natural selection and genetic mechanism of Darwinian biological evolution theory, and is a method for searching an optimal solution by simulating the natural evolution process. The algorithm converts the solving process of the problem into the processes of crossover, variation and the like of chromosome genes in the similar biological evolution by a mathematical mode and by utilizing computer simulation operation. When a complex combinatorial optimization problem is solved, a better optimization result can be obtained generally faster. However, the genetic algorithm is easy to converge too early, so that a certain difference exists between a path planning result determined by the genetic algorithm and an actual optimal routing inspection path.
It is noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure and therefore may include information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The present disclosure is directed to a path planning method, apparatus, storage medium, and electronic device, so as to overcome at least some of the problems of low data acquisition efficiency and low computational efficiency due to limitations and defects of the related art.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a path planning method, including:
acquiring a grid map of an environment to be inspected, and generating an inspection path according to the grid map, wherein the inspection path comprises inspection nodes;
inputting the routing inspection path as an individual of a parent population in a genetic algorithm into the genetic algorithm, and acquiring an optimal solution of the genetic algorithm, wherein the optimal solution is a first routing inspection path determined by the genetic algorithm;
determining the first connection times of any two routing inspection nodes from the first routing inspection path;
and inputting the first connection times into an ant colony algorithm, and performing iterative operation according to the first connection times by using the ant colony algorithm to obtain an optimal routing inspection path.
In an exemplary embodiment of the present disclosure, the obtaining the optimal solution of the genetic algorithm includes:
circulating a first execution preset step until a preset condition is met, wherein the first preset step comprises the following steps:
acquiring the fitness of each individual in the parent population, and determining the average fitness of the parent population according to the fitness of each individual;
performing elite retention on the parent population according to the fitness and/or the average fitness of each individual to obtain an elite retention result;
calculating the variation rate and/or the crossing rate of each individual according to the average fitness, and performing variation and/or crossing operation on the elite retention result according to the variation rate and/or the crossing rate to obtain a progeny population;
if the preset condition is determined not to be met, taking the child population as the parent population, and executing the first preset step again;
and if the condition is determined to meet the preset condition, determining the optimal solution from the filial generation population, wherein the optimal solution is M first individuals with the maximum fitness in the filial generation population, and M is an integer greater than 1.
In an exemplary embodiment of the present disclosure, the determining that the preset condition is satisfied includes:
and when the first time of circularly executing the first preset step is greater than or equal to a first preset threshold or the fitness of the individuals in the filial generation population is consistent with the fitness of the individuals in the parent generation population, determining that the preset condition is met.
In an exemplary embodiment of the present disclosure, the performing elite reservation on the population according to the fitness and/or the average fitness of each individual, and obtaining an elite reservation result includes:
sequencing the individuals according to the fitness of the individuals from high fitness to low fitness to generate an individual sequence;
copying individuals with a preset proportion in front of the individual sequence to obtain copied individuals;
and determining the elite retention result from each individual and the copied individual according to the fitness and/or the average fitness of each individual.
In an exemplary embodiment of the present disclosure, the determining the elite retention result from each of the individuals and the duplicate individuals according to the fitness of each of the individuals and/or the average fitness comprises:
according to the fitness of each individual, N individuals with the maximum fitness in each individual and the copied individuals are selected, and the N individuals are used as elite retention results, wherein N is an integer greater than 1; or the like, or, alternatively,
and determining a target individual with the fitness greater than or equal to the average fitness from the individuals and the copied individuals according to the fitness of the individuals and the average fitness, and taking the target individual as the elite retention result.
In an exemplary embodiment of the disclosure, the performing an iterative operation according to the first connection number by using the ant colony algorithm includes:
circularly executing a second preset step until a second number of times of circularly executing the second preset step is greater than or equal to a second preset threshold, wherein the second preset step comprises the following steps:
determining first transition probabilities of any two routing inspection nodes according to the first connection times;
placing ants on the starting node for inspection, and determining a next node to be inspected of the ants according to the first transfer probability so that the ants can inspect the next node to be inspected;
when ants reach the next node to be inspected, taking the next node to be inspected as the current inspection node and acquiring the position information of the ants at the current inspection node;
if the ant does not reach the termination node according to the position information, determining a next node to be inspected of the ant according to the first transfer probability so that the ant can inspect the next node to be inspected conveniently;
and if the ant reaches the termination node according to the position information, acquiring a second routing inspection path of the ant.
In an exemplary embodiment of the present disclosure, after the obtaining the second routing inspection path of the ant, the method further includes:
if the second number of times is smaller than the second preset threshold value, determining a second connection number of times of any two routing inspection nodes from the second routing inspection path;
calculating the sum of the first connection times and the second connection times;
and taking the sum as the first connection times, and executing the second preset step again.
In an exemplary embodiment of the present disclosure, the obtaining the optimal patrol route includes:
if the second secondary number is larger than or equal to the second preset threshold value, determining a target routing inspection path with the shortest length from the second routing inspection paths;
and taking the target routing inspection path as the optimal routing inspection path.
In an exemplary embodiment of the present disclosure, the determining a next node to be inspected of an ant according to the first transition probability includes:
acquiring a taboo table of ants, wherein the taboo table comprises routing inspection nodes which are routed by the ants;
determining a node to be patrolled according to the tabu table;
calculating a second transition probability of the current routing inspection node and the node to be routed;
and taking the node to be patrolled and examined with the maximum second transition probability as the next patrolling and examining node.
In an exemplary embodiment of the disclosure, the determining the transition probabilities of the two arbitrary patrol nodes according to the first connection number includes:
calculating pheromones of any two nodes according to the first connection times and pheromone volatilization factors, wherein the pheromone volatilization factors are in negative correlation with the second times;
and calculating the first transition probability according to the pheromone and the distance between any two nodes.
According to a second aspect of the present disclosure, there is provided a path planning apparatus comprising:
the system comprises a routing inspection path generation module, a routing inspection module and a routing inspection module, wherein the routing inspection path generation module is used for acquiring a grid map of an environment to be inspected and generating a routing inspection path according to the grid map, and the routing inspection path comprises routing inspection nodes;
the optimal solution acquisition module is used for inputting the routing inspection path into the genetic algorithm as an individual of a parent population in the genetic algorithm and acquiring an optimal solution of the genetic algorithm, wherein the optimal solution is a first routing inspection path determined by the genetic algorithm;
the connection frequency determining module is used for determining the first connection frequency of any two routing inspection nodes from the first routing inspection path;
and the optimal routing inspection path determining module is used for inputting the first connection times into an ant colony algorithm and performing iterative operation according to the first connection times by using the ant colony algorithm so as to obtain an optimal routing inspection path.
According to a third aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of any one of the first aspects.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the steps of the method of any one of the first aspect via execution of the executable instructions.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
in summary, according to the method provided by the disclosure, a grid map of an environment to be inspected is obtained, and an inspection path is generated according to the grid map, wherein the inspection path comprises inspection nodes; inputting the routing inspection path as an individual of a parent population in a genetic algorithm into the genetic algorithm, and acquiring an optimal solution of the genetic algorithm, wherein the optimal solution is a first routing inspection path determined by the genetic algorithm; determining the first connection times of any two routing inspection nodes from the first routing inspection path; and inputting the first connection times into an ant colony algorithm, performing iterative operation according to the first connection times by using the ant colony algorithm to obtain an optimal routing inspection path, accelerating the convergence speed by using a genetic algorithm, improving the path planning efficiency, and obtaining the optimal routing inspection path by using the ant colony algorithm to improve the accuracy of a path planning result.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 schematically illustrates a flow chart of a path planning method in an exemplary embodiment of the present disclosure;
FIG. 2 schematically illustrates an architecture diagram of a path planning system in an exemplary embodiment of the present disclosure;
fig. 3 schematically illustrates a schematic diagram of a grid map of an environment to be inspected in an exemplary embodiment of the present disclosure;
FIG. 4 schematically shows a flowchart of a genetic algorithm optimal solution acquisition method in an exemplary embodiment of the present disclosure;
FIG. 5 schematically illustrates a flow chart of a method of iterative operations in an exemplary embodiment of the present disclosure;
fig. 6 schematically illustrates a schematic diagram of an optimal routing inspection path in an exemplary embodiment of the present disclosure;
fig. 7 schematically illustrates a block diagram of a path planning apparatus in an exemplary embodiment of the present disclosure;
FIG. 8 schematically illustrates a schematic diagram of a storage medium in an exemplary embodiment of the disclosure;
fig. 9 schematically shows a block diagram of an electronic device in an exemplary embodiment of the disclosure.
In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Detailed Description
The principles and spirit of the present invention will be described with reference to a number of exemplary embodiments. It is understood that these embodiments are given solely for the purpose of enabling those skilled in the art to better understand and to practice the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
In an exemplary embodiment of the present disclosure, a path planning method is first provided. Referring to fig. 1, the path planning method may include the following steps:
s11, acquiring a grid map of an environment to be inspected, and generating an inspection path according to the grid map, wherein the inspection path comprises inspection nodes;
s12, inputting the routing inspection path into the genetic algorithm as an individual of a parent population in the genetic algorithm, and acquiring an optimal solution of the genetic algorithm, wherein the optimal solution is a first routing inspection path determined by the genetic algorithm;
s13, determining the first connection times of any two routing inspection nodes from the first routing inspection path;
and S14, inputting the first connection times into an ant colony algorithm, and performing iterative operation according to the first connection times by using the ant colony algorithm to obtain an optimal routing inspection path.
In summary, in the method provided by the present disclosure, a grid map of an environment to be inspected is obtained, and an inspection path is generated according to the grid map, where the inspection path includes inspection nodes; inputting the routing inspection path as an individual of a parent population in a genetic algorithm into the genetic algorithm, and acquiring an optimal solution of the genetic algorithm, wherein the optimal solution is a first routing inspection path determined by the genetic algorithm; determining the first connection times of any two routing inspection nodes from the first routing inspection path; and inputting the first connection times into an ant colony algorithm, performing iterative operation according to the first connection times by using the ant colony algorithm to obtain an optimal routing inspection path, accelerating the convergence speed by using a genetic algorithm, improving the path planning efficiency, and obtaining the optimal routing inspection path by using the ant colony algorithm to improve the accuracy of path planning.
Hereinafter, each step in the path planning method in the present exemplary embodiment will be described in more detail with reference to the drawings and the embodiments.
In S11, a grid map of the environment to be inspected is obtained, and an inspection path is generated according to the grid map, wherein the inspection path comprises inspection nodes.
In an exemplary embodiment of the present disclosure, referring to the system architecture shown in fig. 2, may include: the intelligent terminal device 201, the server 203 and the like, wherein the intelligent terminal device 201 can be arranged on a path inspection device such as an intelligent robot. Data transmission can be performed between the intelligent terminal device 201 and the server 203 through the network 202. The network may include various connection types, such as wired communication links, wireless communication links, and so forth. The path planning method can be executed at a server side, an intelligent terminal device or by the intelligent terminal device and the server side in a cooperation manner. Taking the above method executed at the server side as an example, the server may obtain a grid map of the environment to be inspected, and obtain the optimal inspection path according to the grid map. Further, after the server acquires the optimal routing inspection path, a routing inspection instruction is sent to the intelligent robot according to the optimal routing inspection path, so that the intelligent robot can inspect the environment to be inspected according to the optimal routing inspection path.
In an exemplary embodiment of the present disclosure, the environment to be inspected may be an inspection environment such as power inspection, mine inspection, machine room inspection, and the like, which is not limited herein. A grid map of an environment to be inspected is shown in fig. 2, where the grid map includes a plurality of grids, and each grid represents a node of the environment to be inspected. Wherein the number within each grid represents the location information of the node. In an exemplary embodiment of the disclosure, the position information of each node of the environment to be patrolled can be represented by each value in a continuous array. Further, a blank grid on the grid map represents a node which can pass through the environment to be inspected, a grid with a dotted line on the grid map represents a node which needs to be inspected (hereinafter referred to as an inspection node), and a grid with a solid line on the grid map represents a node which cannot pass through the environment to be inspected, namely an obstacle node.
And further, after the grid map is obtained, generating a routing inspection path according to the grid map. Each path consists of all routing inspection nodes and nodes capable of passing through.
In step S12, the inspection path is input to the genetic algorithm as an individual of a parent population in the genetic algorithm, and an optimal solution of the genetic algorithm is obtained, where the optimal solution is a first inspection path determined by the genetic algorithm.
Based on the above, in an exemplary embodiment of the present disclosure, as shown in fig. 4, the obtaining the optimal solution of the genetic algorithm includes:
circulating a first execution preset step until a preset condition is met, wherein the first preset step comprises the following steps:
s121, acquiring the fitness of each individual in the parent population, and determining the average fitness of the parent population according to the fitness of each individual;
s122, performing elite retention on the parent population according to the fitness and/or the average fitness of each individual to obtain an elite retention result;
s123, calculating the variation rate and/or the crossing rate of each individual according to the average fitness, and performing variation and/or crossing operation on the elite retention result according to the variation rate and/or the crossing rate to obtain an offspring population;
s124, if the condition that the preset condition is not met is determined, the child population is used as the parent population, and the first preset step is executed again;
and S125, if the condition that the preset condition is met is determined, determining the optimal solution from the filial generation population, wherein the optimal solution is M first individuals with the maximum fitness in the filial generation population, and M is an integer greater than 1.
In an exemplary embodiment of the present disclosure, the fitness of each of the individuals in the parent population may be calculated according to equation (1). Equation (1) is as follows:
wherein, Fit represents the fitness degree,
L(P i +P i+1 ) Represents the distance, P, of each two adjacent nodes in each individual (i.e., each routing path) i And P i+1 Representing two adjacent nodes and T representing the number of nodes in the patrol path.
Further, after the fitness of each individual is determined, the average fitness of the parent population is determined according to the fitness of each individual, and elite retention is performed on the parent population according to the fitness of each individual and/or the average fitness to obtain an elite retention result.
In an exemplary embodiment of the present disclosure, performing elite reservation on the population according to the fitness of each individual and/or the average fitness, and obtaining an elite reservation result includes:
sequencing the individuals according to the fitness of the individuals from high fitness to low fitness to generate an individual sequence; copying individuals with a preset proportion in front of the individual sequence to obtain copied individuals; and determining the elite retention result from each individual and the copied individual according to the fitness and/or the average fitness of each individual.
For example, the individual sequence generated by sorting the individuals in the order of increasing fitness is A, B, C, D, E, F, G, H. The individual at the front 1/4 of the individual sequence is duplicated twice, namely the individual A and the individual B are duplicated twice respectively, and the individual at the middle 1/2 of the individual sequence is duplicated once, namely C, D, E, F is duplicated to obtain a duplicated individual. And determining the elite retention result from each individual and the copied individual according to the fitness and/or the average fitness of each individual.
In an exemplary embodiment of the present disclosure, the determining the elite retention result from each of the individuals and the duplicate individuals according to the fitness of each of the individuals and/or the average fitness comprises:
according to the fitness of each individual, N individuals with the maximum fitness in each individual and the copied individuals are selected, and the N individuals are used as elite retention results, wherein N is an integer greater than 1; or determining a target individual with the fitness greater than or equal to the average fitness from each individual and the copied individual according to the fitness of each individual and the average fitness, and taking the target individual as the elite retention result.
For example, the value of N is 8, and each of the individuals is A, B, C, D, E, F, G, H. The individual replicators are A, A, B, B, C, D, E, F. The 8 individuals with the maximum fitness determined from each of the individuals and the duplicated individuals are A, B, C, A, A, B, B, C respectively, and then A, B, C, A, A, B, B, C of the 8 individuals with the maximum fitness are used as elite retention results.
For another example, if the average fitness is between the fitness of the individual D and the fitness of the individual E, the target individual determined from each of the individuals and the replicated individual is A, B, C, D, A, A, B, B, C, D, and then the target individual A, B, C, D, A, A, B, B, C, D is used as the elite retention result.
Further, after the elite retention result is obtained, the variation rate and/or the crossing rate of each individual is calculated according to the average fitness, and variation and/or crossing operation is performed on the elite retention result according to the variation rate and/or the crossing rate to obtain an offspring population.
In an exemplary embodiment of the present disclosure, the variability rate and/or crossover rate of each of the individuals may be according to equation (2). Equation (2) is as follows:
wherein, P c Denotes the crossing rate, P m Indicates the rate of variation, N 1 Denotes the fitness of the individual whose fitness is greater than or equal to the average fitness among the individuals, N 2 Representing the mean fitness value, N ％ Representing the maximum fitness among each of said individuals.
In an exemplary embodiment of the disclosure, if the individual a in the parent population is 123456789 and the individual B in the parent population is 142358679, when the intersection operation is performed at A, B, the node after the node 5 in a may be replaced with the node after the node 5 in B, and the node before the node 5 in B may be replaced with the node before the node 5 in a. A, B are crossed, and the individuals a and b in the obtained offspring population are 123458679 and 142356789 respectively.
From the formula (2), it can be seen that the higher the fitness of an individual is, the higher the crossing rate is, and the lower the variation rate is. The lower the fitness of an individual is, the smaller the crossing rate is, and the larger the variation rate is. Therefore, the genes of the good individuals in the population can be well preserved without being destroyed. And for bad individuals in the population, the method is beneficial to introducing new genes and can improve the performance of a genetic algorithm.
In an exemplary embodiment of the present disclosure, the individual a in the parent population is 123456789, and when performing mutation operation on a, the node 3 and the node 7 in a may be interchanged, and the individual a in the generated child population is 127456389.
Further, after obtaining a child population, if it is determined that the preset condition is not met, taking the child population as the parent population, and executing the first preset step again; and if the condition is determined to meet the preset condition, determining the optimal solution from the filial generation population, wherein the optimal solution is M first individuals with the maximum fitness in the filial generation population, and M is an integer greater than 1.
In an exemplary embodiment of the present disclosure, the determining that the preset condition is satisfied includes:
and when the first time of circularly executing the first preset step is greater than or equal to a first preset threshold or the fitness of the individuals in the filial generation population is consistent with the fitness of the individuals in the parent generation population, determining that the preset condition is met.
In an exemplary embodiment of the disclosure, if the first preset threshold is 100, when the first number is greater than or equal to 100, it is determined that a preset condition is satisfied, and M first individuals with the maximum fitness are obtained from the offspring population as the optimal solution.
In another exemplary embodiment of the present disclosure, when the fitness of the individual in the child population is consistent with the fitness of the individual in the parent population, it is determined that a preset condition is satisfied, and M first individuals with the highest fitness are obtained from the child population as the optimal solution.
In step S13, a first connection count of any two patrol nodes is determined from the first patrol path.
In an exemplary embodiment of the present disclosure, in the acquired optimal solution of the genetic algorithm, M first entities, that is, M first patrol paths, the first connection times s of any two patrol points (i, j) are calculated ij (0)。
In step S14, the first connection times are input into an ant colony algorithm, and an iteration operation is performed according to the first connection times by using the ant colony algorithm to obtain an optimal routing inspection path.
Based on the above, as shown in fig. 5, in an exemplary embodiment of the present disclosure, the performing an iterative operation according to the first connection number by using the ant colony algorithm includes:
circularly executing a second preset step until a second number of times of circularly executing the second preset step is greater than or equal to a second preset threshold, wherein the second preset step comprises the following steps:
s141, determining a first transfer probability of any two routing inspection nodes according to the first connection times;
s142, placing ants on the starting node for inspection, and determining a next node to be inspected of the ants according to the first transfer probability so that the ants can inspect the next node to be inspected;
s143, when the ants reach the next node to be inspected, taking the next node to be inspected as the current node to be inspected and acquiring the position information of the ants at the current node to be inspected;
s144, if the ant does not reach the termination node according to the position information, determining a next node to be inspected of the ant according to the first transfer probability so that the ant can inspect the next node to be inspected conveniently;
and S145, if the ants reach the termination nodes according to the position information, acquiring a second routing inspection path of the ants.
In an exemplary embodiment of the disclosure, the determining the transition probabilities of the two arbitrary patrol nodes according to the first connection times includes:
calculating pheromones of any two nodes according to the first connection times and pheromone volatilization factors, wherein the pheromone volatilization factors are in negative correlation with the second times; and calculating the first transition probability according to the pheromone and the distance between any two nodes.
Specifically, the pheromone may be calculated according to equation (3). Equation (3) is as follows:
wherein τ represents the pheromone, t represents the second degree, ρ represents the normalized second degree, f (ρ) represents a pheromone volatilization factor, τ ij The pheromone is represented, n represents the number of the routing inspection nodes in the environment to be routed, lambda represents a parameter of scale, and u represents a parameter of position.
It should be noted that, before the second preset step is executed in a loop, that is, when t is equal to 0, the first connection number is s calculated according to the optimal solution M first patrol routes of the genetic algorithm ij (0)，τ ij (0)＝S ij (0) + p, p is the pheromone constant, and p is greater than 0.
According to the formula (3), the pheromone volatilization factors adopt Laplace probability density function distribution, so that the method is suitable for updating the pheromone volatilization factors, and the pheromone volatilization factors are increased at the initial iteration stage of the ant colony algorithm on the basis of certain pheromones, so that the speed of searching the optimal routing inspection path of the ant colony algorithm is increased; and in the later iteration stage of the algorithm, pheromone volatilization factors are reduced, and the convergence speed of the ant colony algorithm is accelerated.
Further, after determining the pheromone, the first transition probability is calculated according to equation (4). Equation (4) is as follows:
wherein A is k ＝{C－tabU k }，tabU k And a taboo table representing the kth ant and used for counting the polling nodes polled by the kth ant, wherein k is less than or equal to m, and m represents the number of ants. C represents the set of routing inspection nodes, alpha represents pheromone heuristic factor, eta is Expressing the expectation degree between the patrol node i and the patrol node s, beta expressing an expectation degree elicitation factor, eta is ＝1/d is ，d is And the distance from the routing inspection node i to the routing inspection node s is represented.
In an exemplary embodiment of the disclosure, the determining a next node to be inspected of an ant according to the first transition probability includes:
acquiring a taboo table of an ant, wherein the taboo table comprises an inspection node inspected by the ant; determining a node to be patrolled according to the tabu table; calculating a second transition probability of the current routing inspection node and the node to be routed; and taking the node to be inspected with the maximum second transition probability as the next inspection node.
Specifically, j in the formula (4) is the inspection node to be inspected except the inspection node in the tabu table. When the current routing inspection node is i, respectively calculating the second transition probability of the current routing inspection node and each node to be routed
And then corresponding to each node to be inspected
And determining the maximum value, and taking the node to be inspected corresponding to the maximum value as the next inspection node.
Based on the above, in an exemplary embodiment of the disclosure, after obtaining the second polling path of the ant, the method further includes:
s146, if the second time is smaller than the second preset threshold value, determining a second connection time of any two routing inspection nodes from the second routing inspection path;
s147, calculating the sum of the first connection times and the second connection times;
and S148, taking the sum as the first connection times, and executing the second preset step again.
Specifically, when the second time t is smaller than the second preset threshold, after the second routing inspection paths of m ants are obtained, the second connection times of any two routing inspection nodes i and j are obtained from the m second routing inspection paths, the sum of the first connection times and the second connection times is calculated, the sum is used as the first connection times, and the second preset step is executed again.
Based on the above, in an exemplary embodiment of the disclosure, the obtaining the optimal patrol route includes:
s149, if the second secondary number is greater than or equal to the second preset threshold value, determining a target inspection path with the shortest length from the second inspection paths;
s150, taking the target routing inspection path as the optimal routing inspection path.
Specifically, when the second number of times t is greater than or equal to the second preset threshold, the target routing inspection path with the shortest length is obtained from the m second routing inspection paths and is used as the optimal routing inspection path.
In an exemplary embodiment of the disclosure, after the method of the disclosure is used to perform path planning on the grid map of the environment to be inspected, which is described in fig. 2, the finally obtained optimal inspection path may be as shown in fig. 6. In fig. 6, the start node and the end node are both 242, the ant starts to patrol the environment to be patrolled at 242, and after the patrol is finished, the ant exits the environment to be patrolled from 242. The finally obtained optimal patrol route is the line in fig. 6.
In summary, according to the method disclosed by the invention, on one hand, superior performances such as parallelism, high efficiency and global search performance of the genetic algorithm are fully utilized as prior information of the ant colony algorithm for solving the optimal routing inspection path, the pheromone can be quickly obtained by changing the inherent calculation mode of the cross rate and the variation rate, and then the robustness and the positive feedback performance of the ant colony algorithm are fully utilized to improve the search efficiency of the optimal routing inspection path. On the other hand, the pheromone volatilization factors in the ant colony algorithm are improved, the pheromone volatilization factors in the early stage are improved, the searching speed of the ant colony algorithm can be improved, the pheromone volatilization factors are reduced in the later stage, the convergence speed of the ant colony algorithm can be increased, and then the optimal routing inspection path can be obtained more quickly and more accurately.
Having introduced the path planning method according to the exemplary embodiment of the present invention, a path planning apparatus according to an exemplary embodiment of the present invention will be described with reference to fig. 7.
Referring to fig. 7, a path planning apparatus 70 according to an exemplary embodiment of the present invention may include: an inspection path generating module 701, an optimal solution obtaining module 702, a connection number determining module 703 and an optimal inspection path determining module 704. Wherein:
the system comprises an inspection path generation module 701, a routing module and a routing module, wherein the inspection path generation module 701 is used for acquiring a grid map of an environment to be inspected and generating an inspection path according to the grid map, and the inspection path comprises inspection nodes;
an optimal solution obtaining module 702, configured to input the inspection path as an individual of a parent population in a genetic algorithm into the genetic algorithm, and obtain an optimal solution of the genetic algorithm, where the optimal solution is a first inspection path determined by the genetic algorithm;
a connection frequency determining module 703, configured to determine a first connection frequency of any two routing inspection nodes from the first routing inspection path;
and an optimal routing inspection path determining module 704, configured to input the first connection times into an ant colony algorithm, and perform iterative operation according to the first connection times by using the ant colony algorithm, so as to obtain an optimal routing inspection path.
In an exemplary embodiment of the present disclosure, the optimal solution obtaining module includes:
a first execution presetting step execution unit, configured to cycle the first execution presetting step until a preset condition is met, where the first presetting step includes:
acquiring the fitness of each individual in the parent population, and determining the average fitness of the parent population according to the fitness of each individual;
performing elite retention on the parent population according to the fitness and/or the average fitness of each individual to obtain an elite retention result;
calculating the variation rate and/or the crossing rate of each individual according to the average fitness, and performing variation and/or crossing operation on the elite retention result according to the variation rate and/or the crossing rate to obtain a progeny population;
if the preset condition is determined not to be met, taking the child population as the parent population, and executing the first preset step again;
and the optimal solution determining unit is used for determining the optimal solution from the filial generation population if the optimal solution meets the preset condition, wherein the optimal solution is M first individuals with the maximum fitness in the filial generation population, and M is an integer greater than 1.
In an exemplary embodiment of the present disclosure, the first execution preset step execution unit includes:
and the preset condition determining unit is used for determining that the preset condition is met when the first time of circularly executing the first preset step is greater than or equal to a first preset threshold or the fitness of the individuals in the filial generation population is consistent with the fitness of the individuals in the parent generation population.
In an exemplary embodiment of the present disclosure, the first execution preset step execution unit further includes:
the individual sorting unit is used for sorting the individuals according to the fitness of the individuals from high to low to generate an individual sequence;
the individual copying unit is used for copying individuals with a preset proportion in front of the individual sequence to obtain copied individuals;
and the elite retention unit is used for determining the elite retention result from each individual and the copy individual according to the fitness of each individual and/or the average fitness.
In an exemplary embodiment of the present disclosure, the elite retention unit includes:
a first elite reservation unit, configured to reserve a result as elite from N individuals with the highest fitness among the individuals and the duplicated individuals according to the fitness of each individual, where N is an integer greater than 1; or the like, or, alternatively,
and the second elite reservation unit is used for determining target individuals with the fitness greater than or equal to the average fitness from the individuals and the copied individuals according to the fitness of the individuals and the average fitness, and taking the target individuals as the elite reservation results.
In an exemplary embodiment of the present disclosure, the optimal patrol path determining module includes:
a second preset step execution unit, configured to execute a second preset step in a cyclic manner until a second number of times of executing the second preset step in the cyclic manner is greater than or equal to a second preset threshold, where the second preset step includes:
determining first transition probabilities of any two routing inspection nodes according to the first connection times;
placing ants on the starting node for inspection, and determining a next node to be inspected of the ants according to the first transfer probability so that the ants can inspect the next node to be inspected;
when the ants reach the next node to be inspected, taking the next node to be inspected as the current inspection node and acquiring the position information of the ants at the current inspection node;
if the ant does not reach the termination node according to the position information, determining a next node to be inspected of the ant according to the first transfer probability so that the ant can inspect the next node to be inspected conveniently;
and if the ant reaches the termination node according to the position information, acquiring a second routing inspection path of the ant.
In an exemplary embodiment of the present disclosure, the second preset-step performing unit further includes:
a second connection frequency determining unit, configured to determine, if it is determined that the second frequency is smaller than the second preset threshold, a second connection frequency of any two routing inspection nodes from the second routing inspection path;
a sum value calculation unit for calculating a sum value of the first connection times and the second connection times;
and a first connection number determining unit configured to take the sum as the first connection number, and execute the second preset step again.
In an exemplary embodiment of the present disclosure, the best patrol path determining module further includes:
the target routing inspection path determining unit is used for determining a target routing inspection path with the shortest length from the second routing inspection paths if the second secondary number is greater than or equal to the second preset threshold value;
and the optimal routing inspection path determining unit is used for taking the target routing inspection path as the optimal routing inspection path.
In an exemplary embodiment of the present disclosure, the second preset-step loop execution unit further includes:
the taboo table acquisition unit is used for acquiring a taboo table of an ant, and the taboo table comprises an inspection node inspected by the ant;
the node to be inspected is determined according to the tabu table;
the second transition probability calculation unit is used for calculating second transition probabilities of the current routing inspection node and the node to be routed;
and the next routing inspection node determining unit is used for taking the node to be routed with the largest second transition probability as the next routing inspection node.
In an exemplary embodiment of the present disclosure, the second preset-step performing unit further includes:
the pheromone determining unit is used for calculating pheromones of any two nodes according to the first connection times and pheromone volatilization factors, and the pheromone volatilization factors are in negative correlation with the second times;
and the first transition probability calculation unit is used for calculating the first transition probability according to the pheromone and the distance between any two nodes.
Since each functional module of the path planning apparatus according to the embodiment of the present invention is the same as that of the path planning method according to the embodiment of the present invention, further description is omitted here.
Having described the path planning method and the path planning apparatus according to the exemplary embodiment of the present invention, a storage medium according to an exemplary embodiment of the present invention will be described with reference to fig. 8.
Referring to fig. 8, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a device, such as a personal computer. However, the program product of the present invention is not limited in this respect, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user computing device, partly on a remote computing device, or entirely on the remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to external computing devices (e.g., through the internet using an internet service provider).
Having described the storage medium of an exemplary embodiment of the present invention, next, an electronic device of an exemplary embodiment of the present invention will be described with reference to fig. 9.
The electronic device 90 shown in fig. 9 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 9, the electronic device 90 is in the form of a general purpose computing device. The components of the electronic device 90 may include, but are not limited to: the at least one processing unit 910, the at least one storage unit 920, a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910), and a display unit 940.
Wherein the storage unit stores program code that is executable by the processing unit 910 to cause the processing unit 910 to perform steps according to various exemplary embodiments of the present invention described in the above section "exemplary methods" of the present specification. For example, the processing unit 910 may perform steps S11 through S14 as shown in fig. 1.
The storage unit 920 may include volatile storage units such as a random access storage unit (RAM)9201 and/or a cache storage unit 9202, and may further include a read only storage unit (ROM) 9203. Storage unit 920 may also include a program/utility 9204 having a set (at least one) of program modules 9205, such program modules 9205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The bus 930 may include a data bus, an address bus, and a control bus.
The electronic device 90 may also communicate with one or more external devices 100 (e.g., keyboard, pointing device, bluetooth device, etc.), which may be through an input/output (I/O) interface 950. The electronic device 90 further comprises a display unit 940 connected to an input/output (I/O) interface 950 for displaying. Also, the electronic device 90 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 960. As shown, the network adapter 960 communicates with the other modules of the electronic device 90 via the bus 930. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 90, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
It should be noted that although in the above detailed description several modules or sub-modules of the path planner are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Moreover, while the operations of the method of the invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
While the spirit and principles of the invention have been described with reference to several particular embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, nor is the division of aspects, which is for convenience only as the features in such aspects cannot be combined to advantage. The invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (13)
1. A method of path planning, comprising:
acquiring a raster map of an environment to be inspected, and generating an inspection path according to the raster map, wherein the inspection path comprises inspection nodes;
inputting the routing inspection path as an individual of a parent population in a genetic algorithm into the genetic algorithm, and acquiring an optimal solution of the genetic algorithm, wherein the optimal solution is a first routing inspection path determined by the genetic algorithm;
determining the first connection times of any two routing inspection nodes from the first routing inspection path;
and inputting the first connection times into an ant colony algorithm, and performing iterative operation according to the first connection times by using the ant colony algorithm to obtain an optimal routing inspection path.
2. The method of claim 1, wherein obtaining the optimal solution for the genetic algorithm comprises:
circulating a first execution preset step until a preset condition is met, wherein the first preset step comprises the following steps:
acquiring the fitness of each individual in the parent population, and determining the average fitness of the parent population according to the fitness of each individual;
performing elite retention on the parent population according to the fitness and/or the average fitness of each individual to obtain an elite retention result;
calculating the variation rate and/or the crossing rate of each individual according to the average fitness, and performing variation and/or crossing operation on the elite retention result according to the variation rate and/or the crossing rate to obtain a progeny population;
if the preset condition is determined not to be met, taking the child population as the parent population, and executing the preset step again;
and if the condition is determined to meet the preset condition, determining the optimal solution from the filial generation population, wherein the optimal solution is M first individuals with the maximum fitness in the filial generation population, and M is an integer greater than 1.
3. The method of claim 2, wherein the determining that the preset condition is met comprises:
and when the first time of circularly executing the first preset step is greater than or equal to a first preset threshold or the fitness of the individuals in the filial generation population is consistent with the fitness of the individuals in the parent generation population, determining that the preset condition is met.
4. The method of claim 2, wherein said performing elite preservation on said population according to said fitness of each of said individuals and/or said average fitness, resulting in elite preservation results comprises:
sequencing the individuals according to the fitness of the individuals from high fitness to low fitness to generate an individual sequence;
copying individuals with a preset proportion in front of the individual sequence to obtain copied individuals;
and determining the elite retention result from each individual and the copied individual according to the fitness and/or the average fitness of each individual.
5. The method of claim 4, wherein said determining said elite retention result from each of said individuals and said duplicate individuals based on said fitness of each of said individuals and/or said average fitness comprises:
according to the fitness of each individual, N individuals with the maximum fitness in each individual and the copied individuals are selected, and the N individuals are used as elite retention results, wherein N is an integer greater than 1; or the like, or, alternatively,
and determining a target individual with the fitness greater than or equal to the average fitness from the individuals and the copied individuals according to the fitness of the individuals and the average fitness, and taking the target individual as the elite retention result.
6. The method of claim 1, wherein the performing, with the ant colony algorithm, an iterative operation based on the first number of connections comprises:
circularly executing a second preset step until a second number of times of circularly executing the second preset step is greater than or equal to a second preset threshold, wherein the second preset step comprises the following steps:
determining first transfer probabilities of any two routing inspection nodes according to the first connection times;
placing ants on the starting node for inspection, and determining a next node to be inspected of the ants according to the first transfer probability so that the ants can inspect the next node to be inspected;
when the ants reach the next node to be inspected, taking the next node to be inspected as the current inspection node and acquiring the position information of the ants at the current inspection node;
if the ant does not reach the termination node according to the position information, determining a next node to be inspected of the ant according to the first transfer probability so that the ant can inspect the next node to be inspected conveniently;
and if the ant reaches the termination node according to the position information, acquiring a second routing inspection path of the ant.
7. The method of claim 6, wherein after obtaining the second routing path for the ants, the method further comprises:
if the second number of times is smaller than the second preset threshold value, determining a second connection number of times of any two routing inspection nodes from the second routing inspection path;
calculating the sum of the first connection times and the second connection times;
and taking the sum as the first connection times, and executing the second preset step again.
8. The method of claim 7, wherein the obtaining the optimal routing inspection path comprises:
if the second secondary number is larger than or equal to the second preset threshold value, determining a target routing inspection path with the shortest length from the second routing inspection paths;
and taking the target routing inspection path as the optimal routing inspection path.
9. The method of claim 6, wherein determining a next node to be inspected for ants according to the first transition probability comprises:
acquiring a taboo table of an ant, wherein the taboo table comprises an inspection node inspected by the ant;
determining a node to be patrolled according to the tabu table;
calculating a second transition probability of the current routing inspection node and the node to be routed;
and taking the node to be inspected with the maximum second transition probability as the next inspection node.
10. The method of claim 6, wherein determining the transition probabilities of any two routing inspection nodes according to the first number of connections comprises:
calculating pheromones of any two nodes according to the first connection times and pheromone volatilization factors, wherein the pheromone volatilization factors are in negative correlation with the second times;
and calculating the first transfer probability according to the pheromone and the distance between any two nodes.
11. A path planning apparatus, comprising:
the system comprises a routing inspection path generation module, a routing inspection module and a routing inspection module, wherein the routing inspection path generation module is used for acquiring a grid map of an environment to be inspected and generating a routing inspection path according to the grid map, and the routing inspection path comprises routing inspection nodes;
the optimal solution acquisition module is used for inputting the routing inspection path as an individual of a parent population in a genetic algorithm into the genetic algorithm and acquiring an optimal solution of the genetic algorithm, wherein the optimal solution is a first routing inspection path determined by the genetic algorithm;
the connection frequency determining module is used for determining the first connection frequency of any two routing inspection nodes from the first routing inspection path;
and the optimal routing inspection path determining module is used for inputting the first connection times into an ant colony algorithm and performing iterative operation according to the first connection times by using the ant colony algorithm so as to obtain an optimal routing inspection path.
12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 10.
13. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the steps of the method of any one of claims 1 to 10 via execution of the executable instructions.
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