CN110058613B - Multi-unmanned-aerial-vehicle multi-ant-colony collaborative target searching method - Google Patents

Multi-unmanned-aerial-vehicle multi-ant-colony collaborative target searching method Download PDF

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CN110058613B
CN110058613B CN201910395051.1A CN201910395051A CN110058613B CN 110058613 B CN110058613 B CN 110058613B CN 201910395051 A CN201910395051 A CN 201910395051A CN 110058613 B CN110058613 B CN 110058613B
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岳伟
席云
王丽媛
刘中常
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Dalian Maritime University
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    • G05D1/10—Simultaneous control of position or course in three dimensions
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Abstract

The invention discloses a multi-ant colony collaborative target searching method for multiple unmanned aerial vehicles, which comprises the following steps: s1, dividing and labeling the search sea area by adopting a grid method, and establishing a target probability graph model; s2, establishing a target function, and carrying out weighted summation on the unmanned aerial vehicle steering cost, the unmanned aerial vehicle collision threat cost and the search probability; s3, adopting a multi-ant colony algorithm to carry out cooperative path optimization design on the multiple unmanned aerial vehicles, and setting the maximum iteration number NmaxS32 and S33 are executed until the maximum number of iterations is satisfied and the best search path is output. The method fully utilizes the probability map characteristics of the targets in the sea area to design new ant colony pheromones, including local and global initialization and updating rules, so that the ant algorithm can rapidly complete unmanned aerial vehicle trajectory planning, the problem of repeated searching is avoided, unmanned aerial vehicle searching paths are crossed, and the searching efficiency is improved.

Description

Multi-unmanned-aerial-vehicle multi-ant-colony collaborative target searching method
Technical Field
The invention relates to the technical field of unmanned aerial vehicle target searching, in particular to a multi-ant colony collaborative target searching method for multiple unmanned aerial vehicles.
Background
In recent years, due to rapid development of technologies such as sensors, microprocessors, information processing, and the like, functions of the unmanned cluster system are rapidly increased, and the application range thereof is also expanding. Due to the flexibility, expandability and strong cooperative operation capability of the unmanned aerial vehicle group, cooperative theory and application research of the unmanned aerial vehicle group are paid more and more attention in academic circles, industrial circles and defense fields. The multi-unmanned aerial vehicle collaborative search system can effectively improve search efficiency, and particularly has great advantages under complex sea conditions of uncertainty, strong interference and the like in search sea areas, so that multi-unmanned aerial vehicle collaborative sea area search is one of important directions for unmanned cluster system research.
The ant colony algorithm mainly solves the problem that a path with low threat cost from a starting point to an end point is searched for under the condition that a single unmanned aerial vehicle has threats, and is not suitable for sea area searching. Firstly, the single unmanned aerial vehicle has long searching time and low searching efficiency; secondly, the flight safety of the unmanned aerial vehicles needs to be considered when the multiple unmanned aerial vehicles execute the search task, but the current research aims at finding the target with the maximum probability, and the problems of navigation cost, frequent turning of the unmanned aerial vehicles and cross superposition of paths among the unmanned aerial vehicles in the search process are not considered.
Disclosure of Invention
According to the problems in the prior art, the invention discloses a multi-ant colony collaborative target searching method for multiple unmanned aerial vehicles, which specifically comprises the following steps:
s1, dividing and labeling the search sea area by adopting a grid method, and establishing a target probability graph model;
s2, establishing a target function, and carrying out weighted summation on the unmanned aerial vehicle steering cost, the unmanned aerial vehicle collision threat cost and the search probability;
s3, adopting a multi-ant colony algorithm to carry out collaborative path optimization design on the multiple unmanned aerial vehicles:
s31: initializing the pheromone concentration of each ant population according to a target probability graph model, wherein each ant population corresponds to an unmanned aerial vehicle respectively, and constructing a search path for the unmanned aerial vehicles;
s32: designing a state transition rule according to the path heuristic information, the concentration of the pheromone of the current population and the concentrations of the pheromones of other populations, wherein ants of each population select the next grid according to the state transition rule, and saving a search path when the maximum step length is reached;
s33: after the ants of each population complete path planning, storing the search path, selecting the search path corresponding to the maximum objective function according to the objective function value, and updating pheromone concentration information according to the pheromone updating rule;
s34: setting a maximum number of iterations NmaxS32 and S33 are executed until the maximum number of iterations is satisfied and the best search path is output.
Further, wherein the unmanned aerial vehicle steering cost is expressed as:
Figure BDA0002057894420000021
Figure BDA0002057894420000022
n-th representing mth droneθAbsolute value of steering angle in secondary steering, CθIs a coefficient, NθIs the total number of turns.
Further, wherein the unmanned aerial vehicle collision threat cost is expressed as:
Figure BDA0002057894420000023
wherein
Figure BDA0002057894420000024
lapped is the number of m, v repeated search grids of the unmanned plane, CcIs a coefficient;
wherein the objective function is:
Figure BDA0002057894420000025
k is a coefficient, N represents the number of search path grids, piIn order to search for the probability,
Figure BDA0002057894420000026
for the navigation cost of the unmanned aerial vehicle m,
Figure BDA0002057894420000027
mainly considers the steering cost of the unmanned aerial vehicles and the collision prevention cost among the unmanned aerial vehicles,
Figure BDA0002057894420000028
the calculation is as follows:
Figure BDA0002057894420000029
further, in S3, the following method is specifically adopted for performing cooperative path optimization design on multiple unmanned aerial vehicles by using the multiple ant colony algorithm:
each ant selects the next grid from the starting point according to the state transition rule, and the state transition rule that the ith ant transfers from the grid i to the grid j at the moment t is designed as follows:
Figure BDA00020578944200000210
wherein U isKRepresenting a selected set of grids, UKN-Tabuk, where Tabuk denotes the visited grid set; etaij(t) is heuristic information of the path, and
Figure BDA00020578944200000211
k1and k2Is a constant; phi is ajk(t) represents the values of pheromones of other ant subgroups at grid j, alpha represents the importance degree of the pheromones in grid selection, beta represents the importance degree of heuristic information in ant routing decision, and gamma represents the influence of the pheromones of other groups on route point selection;
in each iteration process, pheromone is updated on the grid through which ants pass, and pheromone on the grid j is updated according to the following formula:
τjk(t+1)＝(1-ρ)τjk(t)+ρΔτjk(t+1)
wherein, taujk(t +1) and τjk(t) is the value of the kth population pheromone in the grid j before and after updating, respectively, rho is the pheromone volatility coefficient, and delta taujk(t +1) is a pheromone update value, and the pheromone is updated according to the following expression:
Figure BDA0002057894420000031
wherein,
Figure BDA0002057894420000032
the pheromone left by the i-th ant of the kth population in the grid j after the t-th search is defined as:
Figure BDA0002057894420000033
wherein
Figure BDA0002057894420000034
uklIs the total pheromone amount of the kth population of the kth ant l after the t-th search in the grid,
Figure BDA0002057894420000035
representing the total amount of other population pheromones; j. the design is a squareklFor the search yield of the first ant in the kth population after completing one search,
Figure BDA0002057894420000036
and ranking the revenue values of all ants in the ant colony
Figure BDA0002057894420000037
k1And k2Respectively are search gain weight coefficients; when in use
Figure BDA0002057894420000038
When the concentration of pheromone of the first u ants is increased; when M is in the range of [ u +1, M]The pheromone concentration of m-u ants is weakened;
after the whole ant colony completes one iteration, selecting the ant with the optimal iteration solution in each colony, and updating pheromone according to the following formula:
Figure BDA0002057894420000039
wherein,
Figure BDA00020578944200000310
for the optimal ant l in the population k in the iterative processbestThe pheromone increment generated at grid j is calculated as follows,
Figure BDA00020578944200000311
wherein k is*As a weight, f (J)klbest) Searching a revenue function for the optimal in the population k;
limiting the pheromone concentration of each population k in the grid to [ tau ]min,τmax]，
Figure BDA0002057894420000041
By adopting the technical scheme, the method designs new ant colony pheromone (including local and global) initialization and updating rules by fully utilizing the probability map characteristics of the targets in the sea area, so that the ant algorithm can quickly complete unmanned aerial vehicle trajectory planning, the problem of repeated search is avoided, and the search efficiency is improved. The method disclosed by the invention can fully utilize the prior information and effectively and quickly update the prior information, thereby improving the efficiency of searching a large number of targets in the sea area by the unmanned aerial vehicle group.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a schematic view of a possible flight direction of the unmanned aerial vehicle according to the present invention;
FIG. 3 is a graph of probability of distribution of objects in the present invention;
FIG. 4 is a diagram illustrating the structure of the multiple ant colony collaborative search according to the present invention
Detailed Description
In order to make the technical solutions and advantages of the present invention clearer, the following makes a clear and complete description of the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention:
as shown in fig. 1, a method for multi-unmanned aerial vehicle multi-ant colony collaborative target search: the method specifically comprises the following steps:
s1: and modeling unknown sea area search environment, adopting a grid method to search area division and numbering grids, and establishing a target probability graph model by the unmanned aerial vehicle based on the feasible flight direction of the current position.
S2: and establishing an objective function, and carrying out weighted summation on the steering cost of the unmanned aerial vehicle, the collision threat cost of the unmanned aerial vehicle and the search probability, wherein in the multi-unmanned aerial vehicle cooperative target search problem, the optimized search path is required to find a target with the minimum cost and the maximum probability as much as possible.
S3: and adopting a multi-ant colony algorithm to carry out collaborative path optimization design on the multiple unmanned aerial vehicles. Because the problem of searching targets by multiple unmanned aerial vehicles is very similar to the foraging behavior of an ant colony, the method carries out cooperative path optimization design on multiple UAVs by a multiple ant colony algorithm.
S31: initializing the pheromone concentration of each ant population according to a target probability graph model, wherein each ant population corresponds to an unmanned aerial vehicle respectively, and constructing a search path for the unmanned aerial vehicle;
s32: designing a state transition rule according to the path heuristic information, the concentration of the pheromone of the current population and the concentrations of the pheromones of other populations, selecting the next grid by ants of each population according to the state transition rule, and saving the search path when the maximum search step length is reached;
s33: after completing path planning for each ant population, storing the search path, selecting the search path corresponding to the maximum objective function according to the objective function value, and updating pheromones according to pheromone updating rules;
s34: setting a maximum number of iterations NmaxS32 and S33 are executed until the maximum number of iterations is satisfied to output the best search path.
Further, the unknown sea area is modeled in S1 in the following manner:
dividing the search sea area into L as shown in FIG. 2x×LyA square grid. The corresponding grid is labeled (x, y), where x ∈ {1,2x}，y∈{1,2,...,LyAnd numbering the grids Z ∈ {1, 2., L ∈x×Ly}，
Z＝x+(y-1)×Lx (1)
Constrained by the steering angle, only three drones can be selected to fly from the current position to the next position. Establishing normal distribution target probability graph model description NTThe existence state of the targets, i.e. the position of each target has the same probability distribution as shown in fig. 3, the initial position of target m is known from a priori information
Figure BDA0002057894420000051
The joint probability density function for the initial position of the target m can be expressed as:
Figure BDA0002057894420000052
further, wherein the unmanned aerial vehicle steering cost is expressed as:
Figure BDA0002057894420000053
Figure BDA0002057894420000054
n-th representing mth droneθAbsolute value of steering angle in secondary steering, CθIs a coefficient, NθIs the total number of turns.
Further, wherein the drone collision threat cost is expressed as:
Figure BDA0002057894420000055
wherein
Figure BDA0002057894420000056
lapped is the number of m, v repeated search grids of the unmanned plane, CcAre coefficients.
A further objective function is:
Figure BDA0002057894420000061
k is a coefficient, N represents the number of search path grids, piIn order to search for the probability,
Figure BDA0002057894420000062
for the navigation cost of the unmanned aerial vehicle m,
Figure BDA0002057894420000063
mainly considers the steering cost of the unmanned aerial vehicles and the collision prevention cost among the unmanned aerial vehicles,
Figure BDA0002057894420000064
the calculation is as follows:
Figure BDA0002057894420000065
further, fig. 4 is a structural diagram of the multi-ant colony collaborative search. In the S3, the following method is specifically adopted for performing collaborative path optimization design on multiple unmanned aerial vehicles by using the multiple ant colony algorithm: each ant selects the next grid from the starting point according to the state transition rule, and the state transition rule that the ith ant transfers from the grid i to the grid j at the moment t is designed as follows:
Figure BDA0002057894420000066
wherein U isKRepresenting a selected set of grids, UKN-Tabuk, where Tabuk denotes the visited grid set; etaij(t) is heuristic information of the path, and
Figure BDA0002057894420000067
k1and k2Is a constant; phi is a unit ofjk(t) represents the value of the rest of ant subgroup pheromones at grid j, and alpha is represented atThe importance degree of pheromones in grid selection, beta represents the importance degree of heuristic information in ant routing decision, and gamma represents the influence of pheromones of other populations on route point selection;
in each iteration process, pheromone is updated on the grid through which ants pass, and pheromone on the grid j is updated according to the following formula:
τjk(t+1)＝(1-ρ)τjk(t)+ρΔτjk(t+1)
wherein, taujk(t +1) and τjk(t) is the value of the kth population pheromone in the grid j before and after updating, respectively, rho is the pheromone volatility coefficient, and delta taujk(t +1) is a pheromone update value, and the pheromone is updated according to the following expression:
Figure BDA0002057894420000068
wherein,
Figure BDA0002057894420000069
the pheromone left by the i-th ant of the kth population in the grid j after the t-th search is defined as:
Figure BDA0002057894420000071
wherein
Figure BDA0002057894420000072
uklIs the total pheromone amount of the kth population of the kth ant l after the t-th search in the grid,
Figure BDA0002057894420000073
representing the total amount of other population pheromones; j. the design is a squareklFor the search yield of the first ant in the kth population after completing one search,
Figure BDA0002057894420000074
and all the ants in the ant colony are treatedRanking the earnings of ants
Figure BDA0002057894420000075
k1And k2Respectively are search gain weight coefficients; when the temperature is higher than the set temperature
Figure BDA0002057894420000076
Then, the concentration of pheromone of the first u ants is enhanced; when M is in the range of [ u +1, M]The pheromone concentration of m-u ants is weakened;
after the whole ant colony completes one iteration, selecting the ant with the optimal iteration solution in each colony, and updating pheromone according to the following formula:
Figure BDA0002057894420000077
wherein,
Figure BDA0002057894420000078
for the optimal ant l in the population k in the iterative processbestThe pheromone increment generated at grid j is calculated as follows,
Figure BDA0002057894420000079
wherein k is*As a weight, f (J)klbest) Searching a revenue function for the optimal in the population k;
limiting the pheromone concentration of each population k in the grid to [ tau ]min,τmax]，
Figure BDA00020578944200000710
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (1)

1. A multi-unmanned aerial vehicle multi-ant colony collaborative target searching method is characterized by comprising the following steps:
s1, dividing and labeling the search sea area by adopting a grid method, and establishing a target probability graph model;
s2, establishing a target function, and carrying out weighted summation on the unmanned aerial vehicle steering cost, the unmanned aerial vehicle collision threat cost and the search probability;
s3, adopting a multi-ant colony algorithm to carry out collaborative path optimization design on the multiple unmanned aerial vehicles:
s31: initializing the pheromone concentration of each ant population according to a target probability graph model, wherein each ant population corresponds to an unmanned aerial vehicle respectively, and constructing a search path for the unmanned aerial vehicles;
s32: designing a state transition rule according to the path heuristic information, the concentration of the pheromone of the current population and the concentrations of other pheromones of the population, wherein ants of each population select the next grid according to the state transition rule, and storing a search path when the maximum step length is reached;
s33: after the ants of each population complete path planning, storing the search path, selecting the search path corresponding to the maximum objective function according to the objective function value, and updating pheromone concentration information according to the pheromone updating rule;
s34: setting a maximum number of iterations NmaxExecuting S32 and S33 until the maximum iteration number is met and outputting the optimal search path;
wherein the unmanned aerial vehicle steering cost is expressed as:
Figure FDA0003545821840000011
Figure FDA0003545821840000012
n-th of unmanned planeθAbsolute value of steering angle in secondary steering, CθIs a coefficient, NθTotal number of turns;
wherein the unmanned aerial vehicle collision threat cost is expressed as:
Figure FDA0003545821840000013
wherein
Figure FDA0003545821840000014
lapped is the number of m, v repeated search grids of the unmanned plane, CcIs a coefficient;
wherein the objective function is:
Figure FDA0003545821840000015
k is a coefficient, N represents the number of search path grids, piIn order to search for the probability,
Figure FDA0003545821840000016
for the navigation cost of the unmanned aerial vehicle m,
Figure FDA0003545821840000017
mainly considers the steering cost of the unmanned aerial vehicles and the collision prevention cost among the unmanned aerial vehicles,
Figure FDA0003545821840000018
the calculation is as follows:
Figure FDA0003545821840000021
in the S3, the following method is specifically adopted for performing collaborative path optimization design on multiple unmanned aerial vehicles by using the multiple ant colony algorithm:
each ant selects the next grid from the starting point according to the state transition rule, and the state transition rule that the ith ant transfers from the grid i to the grid j at the moment t is designed as follows:
Figure FDA0003545821840000022
wherein U isKRepresenting a selected set of grids, UKN-Tabuk, where Tabuk denotes the visited grid set; etaij(t) is heuristic information of the path, and
Figure FDA0003545821840000023
T1and T2Is a constant; phi is ajk(t) represents the values of pheromones of other ant subgroups at grid j, alpha represents the importance degree of the pheromones in grid selection, beta represents the importance degree of heuristic information in ant routing decision, and gamma represents the influence of the pheromones of other groups on route point selection;
in each iteration process, pheromone is updated on the grid through which ants pass, and pheromone on the grid j is updated according to the following formula:
τjk(t+1)＝(1-ρ)τjk(t)+ρΔτjk(t+1)
wherein, taujk(t +1) and τjk(t) is the value of the kth population pheromone in the grid j before and after updating, respectively, rho is the pheromone volatility coefficient, and delta taujk(t +1) is a pheromone update value, and the pheromone is updated according to the following expression:
Figure FDA0003545821840000024
wherein,
Figure FDA0003545821840000025
the pheromone left by the i-th ant of the kth population in the grid j after the t-th search is defined as:
Figure FDA0003545821840000026
wherein
Figure FDA0003545821840000027
uklIs the total pheromone amount of the kth population of the kth ant l after the t-th search in the grid,
Figure FDA0003545821840000031
representing the total amount of other population pheromones; j. the design is a squareklFor the search yield of the first ant in the kth population after completing one search,
Figure FDA0003545821840000032
and ranking the revenue values of all ants in the ant colony
Figure FDA0003545821840000033
s1And s2Respectively are search gain weight coefficients; when in use
Figure FDA0003545821840000034
When the concentration of pheromone of the first u ants is increased; when M is in the range of [ u +1, M]The pheromone concentration of m-u ants is weakened;
after the whole ant colony completes one iteration, selecting the ant with the optimal iteration solution in each colony, and updating pheromone according to the following formula:
Figure FDA0003545821840000035
wherein,
Figure FDA0003545821840000036
for the optimal ant l in the population k in the iterative processbestThe pheromone increment generated at grid j is calculated as follows,
Figure FDA0003545821840000037
wherein k is*As a weight, f (J)klbest) Searching a revenue function for the optimal in the population k;
limiting the pheromone concentration of each population k in the grid to [ tau ]min,τmax]，
Figure FDA0003545821840000038
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