CN112925317B - AUV path planning method based on improved brainstorming optimization algorithm - Google Patents
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Abstract
The invention relates to an AUV path planning method based on an improved brainstorming optimization algorithm, which specifically comprises the following steps: 1. constructing a three-dimensional optimal navigation curved surface, randomly generating n groups of initial path control parameters, dividing the n groups of initial path control parameters into m classes, taking one group of parameters as an individual, calculating cost function values of the individual, and enabling the optimal individual of each class to become a center of the class; 2. selecting individuals to be mutated according to a brainstorm optimization algorithm, applying a tracking global optimal strategy to the individuals to be mutated, obtaining new individuals through differential mutation, and selecting the updated population; 3. and iterating for multiple times until the iteration times meet the termination condition, and outputting the optimal individual. The method fully utilizes the advantages of the global optimum and differential variation-based brainstorming optimization algorithm in processing low-dimensional problems, well captures cost function characteristics through differential variation, fully utilizes ocean currents to reduce navigation time, and combines a following global optimum strategy to enable the algorithm to have higher potential to find a better solution in a shorter time.
Description
Technical Field
The invention relates to the field of AUV path planning and intelligent optimization algorithms, in particular to an AUV path planning method based on an improved brainstorming optimization algorithm.
Background
The current sea fighting mode is changed to intellectualization and informatization so as to pursue smaller action risk and higher fighting efficiency, and the importance of an underwater unmanned fighting system is increasingly highlighted. The AUV is an important component of an underwater unmanned combat system, the path planning is an important technology for safely and effectively completing combat missions by the AUV, constraint conditions such as ocean currents, obstacle avoidance, enemy investigation, enemy attack, self performance and the like need to be considered, and indexes such as energy consumption, navigation time, safety concealment and the like are pursued to be optimal.
The currently common path planning methods mainly include a search method based on a network graph, a heuristic search algorithm, an artificial potential field method, an intelligent optimization algorithm, and the like. For the AUV path planning problem in a large-scale planning area, it is more meaningful to obtain a fast acceptable path satisfying all the constraints than taking a long time to find an optimal path, and a biological intelligent optimization algorithm based on high efficiency and stronger searching capability still remains a key point and a hot point for the research of the current and future path planning algorithms.
The Brain Storm Optimization (BSO) inspired by the human brain storm process can be considered as a very potential algorithm. After a population is initialized, new individuals are generated through operations such as clustering, replacing, selecting, mutating and the like, but the problems that only part of individuals are updated to follow global optimum in the selecting operation and the step length in the mutating operation cannot be self-adapted exist.
Disclosure of Invention
The invention aims to solve the technical problem of providing an AUV path planning method based on an improved brainstorming optimization algorithm.
In order to solve the technical problems, the invention adopts the following technical scheme:
an AUV path planning method based on an improved brainstorming optimization algorithm comprises the following steps:
step 1, acquiring terrain data, ocean current information and underwater threat information, constructing a three-dimensional optimal navigation curved surface according to the terrain data, and randomly generating n groups of initial path control parameters, wherein each group of path control parameters expresses 1 initial planning path, and each group of parameters has D data;
step 2, regarding n groups of path control parameters as a population, regarding each group of path control parameters as an individual, dividing the individuals in the population into m classes by using a k-means clustering algorithm, calculating cost function values of the individuals by using cost functions, and enabling the optimal individual of each class to become a class center;
step 3, randomly selecting a class, generating random individuals to replace class centers, wherein the probability of the step 3 is preplaceIf the step 3 is not executed, directly switching to the step 4;
step 4, selecting an individual to be mutated according to a selection operation method of a brainstorming optimization algorithm, and applying a tracking global optimal strategy to the individual to be mutated to obtain a new individual;
step 6, judging whether the number of individuals in the new population is equal to n, if so, judging that the population is updated, adding 1 to the iteration number, and turning to step 7, otherwise, turning to step 3;
and 7, judging whether the iteration times meet a preset termination condition, if so, outputting the optimal individual in the new population obtained by the last iteration, otherwise, turning to the step 2, and using the new population after the last updating as the population in the step 2.
Further, the expression of the three-dimensional optimal navigation curved surface is as follows:
F(x,y)＝f(x,y)+Hc (1)
wherein f (x, y) is the underwater terrain height; hcAn optimal off-bottom height to avoid bottom impact for the AUV.
Furthermore, the planned path is any curve of the three-dimensional optimal navigation curved surface, and the curve projected to the horizontal plane is obtained by fitting a starting point, a terminal point and a plurality of path control points.
Further, the path control parameter is an included angle between a connecting line from the control point to the starting point and a connecting line from the starting point to the end point in the horizontal plane, and a position expression of the path control point is as follows:
wherein: xi、YiRespectively representing the horizontal and vertical coordinates of the ith path control point; l is a radical of an alcoholstThe distance projected in the horizontal plane as the starting point and the end point; thetaiAre path control parameters.
Further, the cost function setting principle of step 2 is as follows: planning the route navigation time to meet the shortest condition of the requirement of the combat mission; the planning path is the condition that the effective reconnaissance time of the enemy is the least; the condition that the effective killing time of the planned path by the enemy is the minimum meets the navigation safety of the AUV, and one expression is as follows:
wherein: w is a1、w2、w3Respectively representing the weight coefficients of AUV navigation time, effective scouting time of enemies and effective killing time; t isiIndicating the AUV voyage time; t ist iIndicating the detection time of the AUV by the enemy on the ith path; pt iRepresenting the detection probability of the AUV in the ith path by the enemy; t iss iIndicating the time that the AUV is in the range of enemy killing in the ith path; ps iIndicating the probability of the AUV being killed by an enemy destructive weapon on the ith path.
Further, the selection operation method in step 4 is any one of the following four methods:
the first method, a class is selected according to the roulette probability, and the center of the class is selected as an individual to be mutated;
selecting a class according to the roulette probability, and selecting a random individual in the class as an individual to be mutated;
the third method randomly selects two classes, and fuses two class centers to become an individual to be mutated;
the fourth method comprises the steps of randomly selecting two classes, randomly selecting an individual from the two classes, and fusing the individuals to be mutated;
the probability of selecting the first method or the second method, i.e. selecting an individual by a class, is pbConfirming that the probability of selecting the class-centered method after selecting an individual by one class is p1Confirming that the probability of selecting the class-centered fusion method after selecting individuals by two classes is p2。
Further, the fusion operation in the third method and the fourth method specifically adopts the following formula:
y＝r·x1+(1-r)·x2
wherein y is an individual to be mutated generated after fusion of two individuals, and x1And x2Is the two individuals who receive the fusion, r is a random number from 0 to 1, which is used to adjust the weight of the two individuals.
Further, the global optimal tracking policy of step 4 is to update each dimension of the to-be-mutated individual by using the following formula:
pa＝pb×p1+(1-pb)×p2 (5)
wherein, ydIs the d-dimension of the new individual; x is a radical of a fluorine atomdIs the d-dimension of the individual to be mutated; rand () represents a random number of 0 to 1; gdDimension d which is a globally optimal individual; p is a radical ofaDetermining the probability of an individual to be mutated through a class center; c is a global optimum influence coefficient; cmaxAnd CminIs the boundary value of the coefficient; e and emaxThe number of iterations and their maximum values, respectively.
Further, the differential mutation of step 5 is to update each dimension of the new individual by using the following formula:
wherein, y'dIs d-dimension, x 'of the differentially mutated individual'dIs the d-th dimension of the new individual,
and
is any two different individuals in the population; l isdAnd HdIs a boundary value of the search space; p is a radical ofrTo obtain the probability of a random new solution.
After the technical scheme is adopted, compared with the prior art, the invention has the following advantages:
the differential variation strategy adopted by the invention only has random functions and four mixed operations, so that the operation amount is greatly reduced. The variation amount of the differential variation is based on other individuals in the contemporary population, and self-adaptive adjustment is carried out according to the discrete degree of the population individuals. When the population is dispersed, the variation is large; the variation is small in the population, the variation is confirmed in real time according to the population feedback condition, and the algorithm can capture the search characteristics well. The invention fully utilizes the advantages of the global optimum and differential variation-based brainstorming storm optimization algorithm in processing the low-dimensional problem, well captures the cost function characteristics through the differential variation, fully utilizes ocean current to reduce the navigation time, and combines with the following global optimum strategy, so that the algorithm has higher potential to find a more optimal solution in a shorter time. The global optimal information is fully utilized, and the convergence is accelerated in the later stage of algorithm search.
The present invention will be described in detail below with reference to the accompanying drawings and examples.
Drawings
Fig. 1 is a comparison histogram of a path planning simulation experiment provided by an embodiment of the present invention;
fig. 2 is a path planning score chart provided in the embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
An AUV path planning method based on an improved brainstorming optimization algorithm comprises the following steps:
step 1: the method comprises the steps of obtaining terrain data, ocean current information and underwater threat information, constructing a three-dimensional optimal navigation curved surface according to the terrain information, randomly generating n groups of initial path control parameters to express n initial planning paths, and enabling each group of parameters to have D data.
And selecting an E116.8-117 degree and N23-23.2 degree area as a path planning area, and adopting data with the resolution of 1 nautical mile published by the American Scripps ocean research institute as the original data of the underwater topography.
And the latitude and longitude coordinates of the task area are converted and then reduced by 100 times to form an example underwater terrain, the ocean current information, the underwater threat information and the like are synchronously reduced by 100 times, and the ocean current and the underwater threat are more intuitively reflected.
And obtaining underwater elevation terrain data with the resolution of 100m by using a mobile Kriging interpolation algorithm, and constructing the optimal navigation curved surface of the AUV by using the Kriging interpolation algorithm.
Step 1, obtaining the three-dimensional optimal navigation curved surface with the expression of
F(x,y)＝f(x,y)+HcWherein f (x, y) is the underwater terrain height; hcThe optimal height of the AUV from the bottom to avoid the collision is 20 m.
Ocean current is simulated by superposing 20 viscous Lamb vortex motion equations, each vortex has the radius of 2m, and vortex positions are generated randomly.
The underwater threat information is as follows
The AUV has the starting point of (132820,25540-80), the end point of (133300,25360-80) and the navigational speed of 9.252 m/s.
The planned path in the step 1 is any curve of a three-dimensional optimal navigation curved surface, the curve projected to a horizontal plane can be obtained by fitting a starting point, a terminal point and a plurality of path control points, and the B-spline curve method is applied to fitting in the embodiment.
Step 1, the path control parameters are included angles between a connecting line from a control point to a starting point in a horizontal plane and a connecting line from the starting point to an end point, and the position expressions of the path control points are as follows:
wherein: xi、YiRespectively representing the horizontal and vertical coordinates of the ith path control point; l isstThe distance projected in the horizontal plane as the starting point and the end point; thetaiAre path control parameters.
Taking the parameter n as 30; d ═ 10.
Step 2: and (3) considering n groups of path control parameters as a population, dividing the population into m classes by using a k-means clustering algorithm, taking one group of parameters as an individual, calculating cost function values of the individual, and enabling the optimal individual of each class to become the center of the class.
And taking the parameter m as 5.
The cost function in step 2 has the setting principle that: the planned path has the advantage that the navigation time is as short as possible, and the requirement of the combat mission is met; the effective reconnaissance time of the planned path by the enemy is as short as possible; the time for the planned path to be effectively killed and killed by the enemy is short, and the navigation safety of the AUV is met. The present example uses the following expression:
wherein: w is a1、w2、w3Weighting coefficients respectively representing AUV navigation time, effective scouting time of enemies and effective killing time, wherein the examples are 1, 10 and 100; t isiIndicating the AUV voyage time; t ist iIndicating the detection time of the AUV by the enemy on the ith path; pt iRepresenting the detection probability of the AUV in the ith path by the enemy; t iss iIndicating the time that the AUV is in the range of enemy killing in the ith path; p iss iIndicating the probability of the AUV being killed by an enemy destructive weapon on the ith path.
And step 3: randomly selecting a class, generating random individuals to replace the class center, and executing the step 3 with the probability that p isreplaceIf the step 3 is not executed, directly switching to the step 4;
taking parameter preplace＝0.2。
And 4, step 4: and selecting the individuals to be mutated according to four selection methods of a brainstorm optimization algorithm, and applying a following global optimal strategy to the individuals to be mutated.
Four selection methods of the brainstorming optimization algorithm are as follows:
the first method, a class is selected according to the roulette probability, and the center of the class is selected as an individual to be mutated;
selecting a class according to the roulette probability, and selecting a random individual in the class as an individual to be mutated;
the third method randomly selects two classes, and fuses two class centers to become an individual to be mutated;
and the fourth method randomly selects two classes, randomly selects an individual from the two classes, and fuses the individuals to be mutated.
The probability of selecting the first method or the second method, i.e. selecting an individual by a class, is pbTaking the value as 0.8; confirming that the probability of selecting the class-centered method after selecting individuals by one class is p1Taking the value as 0.4; confirming that the probability of selecting the class-centered fusion method after selecting individuals by two classes is p2It was taken to be 0.5.
The fusion operation in the third method and the fourth method is as follows:
y＝r·x1+(1-r)·x2
wherein y is an individual to be mutated generated after fusion of two individuals, and x1And x2Are the two individuals who received the fusion. r is a random number from 0 to 1, adjusting the weight of the two individuals.
The following global optimal strategy is formulated as follows:
pa＝pb×p1+(1-pb)×p2
wherein: y isdIs the d-dimension of the new individual; x is the number ofdIs the d-dimension of the individual to be mutated; gdDimension d which is a globally optimal individual; p is a radical ofaDetermining the probability of an individual to be mutated through a class center; c is global maximumA coefficient of preferential influence; cmaxAnd CminIs the boundary value of the coefficient, taken as 0.2 and 0.8; e. e.g. of the typemaxFor the number of iterations and its maximum value, take emax＝100。
The advantage of taking the maximum number of iterations to be 100 is that: the maximum iteration times are increased, so that the optimization result of the algorithm is better, the time spent is greatly increased, the battlefield situation is changed instantly, the path which can be accepted is planned in a short time and is better than the path which is planned in a long time, the method has more important significance, and a better path can be planned by taking 100 as the maximum iteration times.
The invention adopts the following global optimal strategy, and has the advantages that: the global optimal information is fully utilized, and the convergence is accelerated in the later stage of algorithm search.
Step five: carrying out differential variation on the new individuals generated in the step (4) to obtain individuals subjected to differential variation, evaluating the individuals subjected to differential variation and the individuals to be subjected to variation, comparing the individuals subjected to differential variation with one of the individuals which are not compared in the population, selecting more optimal individuals and storing the individuals into a new population;
the differential variation of step 5 is to update each dimension of the new individual by using the following formula:
wherein, y'dIs d-dimension, x 'of the differentially mutated individual'dIs the d-th dimension of the new individual,
and
is any two different individuals in the population; l isdAnd HdIs a boundary value of the search space; p is a radical ofrTo obtain the probability of a random new solution.
The invention adopts the differential mutation strategy to perform mutation operation, and has the advantages that: only random functions and four-rule mixed operation are performed, and the operation amount is greatly reduced. The variation amount of the differential variation is based on other individuals in the contemporary population, and self-adaptive adjustment is carried out according to the discrete degree of the population individuals. When the population is dispersed, the variation is large; the variation is small in the population, the variation is confirmed in real time according to the population feedback condition, and the algorithm can capture the search characteristics well.
prThe advantage of taking 0.005 is that: too small prThe value lets the algorithm accelerate convergence, but is easy to fall into local optimum and is too large prThe values help the algorithm jump out of local optimality, but more like random search, reducing search efficiency. The key for improving the solving precision of the algorithm is to ensure the balance of the two. The example D is 10, which belongs to the problem of medium and low dimensionality, and the search efficiency is more important to choose smaller pr。
Step 6, judging whether the number of individuals in the new population is equal to n, if so, judging that the population is updated, adding 1 to the iteration number, and turning to step 7, otherwise, turning to step 3;
and 7, judging whether the iteration times meet a preset termination condition, if so, outputting the optimal individual in the new population obtained by the last iteration, and otherwise, turning to the step 2.
BSO, GBSO, MBSO and oscillating IWO are used for replacing GDBSO in the scheme, and simulation is carried out 50 times.
The optimal path navigation time planned by BSO, GBSO, GDBSO, MBSO and oscillation type IWO is 62.3730s, 60.0541s, 60.0514s, 60.4880s and 63.0500s respectively, the navigation routes are 628.0711m, 642.0595m, 649.0706m, 647.9892m and 645.4694m respectively, all the paths can bypass enemy detection, fire threat and restricted navigation areas, and the paths can safely reach the task terminal after successfully avoiding underwater obstacles. The navigation distance planned by the GDBSO algorithm is longest, but the navigation time is shortest, which shows that the GDBSO algorithm can fully utilize ocean current to reduce the navigation time.
Fig. 1 compares the optimal value, the worst value, the average value, the standard deviation and the algorithm convergence performance of the planning path costs of different algorithms. The convergence performance is represented by the optimal individual cost function value when the algorithm iterates for 75 times.
The best overall performance of GDBSO can be seen in figure 2.
The foregoing is illustrative of the best mode of the invention and details not described herein are within the common general knowledge of a person of ordinary skill in the art. The scope of the present invention is defined by the appended claims, and any equivalent modifications based on the technical teaching of the present invention are also within the scope of the present invention.
Claims (6)
1. An AUV path planning method based on an improved brainstorming optimization algorithm is characterized by comprising the following steps:
step 1, acquiring terrain data, ocean current information and underwater threat information, constructing a three-dimensional optimal navigation curved surface according to the terrain data, and randomly generating n groups of initial path control parameters, wherein each group of path control parameters expresses 1 initial planning path, and each group of parameters has D data;
step 2, regarding n groups of path control parameters as a population, regarding each group of path control parameters as an individual, dividing the individuals in the population into m classes by using a k-means clustering algorithm, calculating cost function values of the individuals by using cost functions, and enabling the optimal individual of each class to become a class center; the cost function setting principle of the step 2 is as follows: planning the route navigation time to be the shortest condition meeting the requirement of the combat mission; the planning path is the condition that the effective reconnaissance time of the enemy is the least; the condition that the effective killing time of the planned path by the enemy is the minimum meets the safety of the Autonomous Underwater Vehicle (AUV) navigation, wherein one expression is as follows:
wherein: w is a1、w2、w3Respectively representing the weight coefficients of AUV navigation time, effective scouting time of enemies and effective killing time; t isiIndicating the AUV voyage time;
indicating the detection time of the AUV by the enemy on the ith path;
representing the detection probability of the AUV in the ith path by the enemy;
indicating the time that the AUV is in the range of enemy killing in the ith path;
representing the probability of killing of the AUV by an enemy destructive weapon in the ith path;
step 3, randomly selecting a class, generating random individuals to replace class centers, wherein the probability of the step 3 is preplaceIf the step 3 is not executed, directly switching to the step 4;
step 4, selecting an individual to be mutated according to a selection operation method of a brainstorming optimization algorithm, and applying a tracking global optimal strategy to the individual to be mutated to obtain a new individual; the selection operation method in the step 4 is any one of the following four methods:
the first method, a class is selected according to the roulette probability, and the center of the class is selected as an individual to be mutated;
selecting a class according to the roulette probability, and selecting a random individual in the class as an individual to be mutated;
the third method randomly selects two classes, and fuses two class centers to become an individual to be mutated;
the fourth method comprises the steps of randomly selecting two classes, randomly selecting an individual from the two classes, and fusing the individuals to be mutated;
the probability of selecting the first method or the second method, i.e. selecting an individual by a class, is pbConfirming that the probability of selecting the class-centered method after selecting an individual by one class is p1Confirming that the probability of selecting the class-centered fusion method after selecting individuals by two classes is p2(ii) a The global optimal tracking strategy in step 4 is to update each dimension of the to-be-mutated individual by using the following formula:
pa＝pb×p1+(1-pb)×p2 (5)
wherein, ydIs the d-dimension of the new individual; x is the number ofdIs the d-dimension of the individual to be mutated; rand () represents a random number of 0 to 1; gdDimension d which is a globally optimal individual; p is a radical ofaDetermining the probability of an individual to be mutated through a class center; c is a global optimum influence coefficient; cmaxAnd CminIs the boundary value of the coefficient; e and emaxThe number of iterations and the maximum value thereof are respectively;
step 5, carrying out differential variation on the new individuals generated in the step 4 to obtain individuals after differential variation, comparing the individuals after differential variation with one individual which is not compared in the population, selecting more optimal individuals and storing the individuals into a new population;
step 6, judging whether the number of individuals in the new population is equal to n, if so, judging that the population is updated, adding 1 to the iteration number, and turning to step 7, otherwise, turning to step 3;
and 7, judging whether the iteration times meet a preset termination condition, if so, outputting the optimal individual in the new population obtained by the last iteration, otherwise, turning to the step 2, and using the new population after the last updating as the population in the step 2.
2. The AUV path planning method based on the improved brainstorming optimization algorithm according to claim 1, wherein the three-dimensional optimal navigation curved surface has the expression:
F(x,y)＝f(x,y)+Hc (1)
wherein f (x, y) is the underwater terrain height; hcAn optimal off-bottom height to avoid bottom impact for the AUV.
3. The AUV path planning method based on the improved brainstorming optimization algorithm according to claim 1, wherein the planned path is any curve of a three-dimensional optimal sailing curved surface, and a curve projected to a horizontal plane is obtained by fitting a starting point, an end point and a plurality of path control points.
4. The AUV path planning method based on the improved brainstorming optimization algorithm according to claim 1, wherein the path control parameter is an included angle between a connecting line from a control point to a starting point in a horizontal plane and a connecting line from the starting point to an end point, and the position expression of the path control point is as follows:
wherein: xi、YiRespectively representing the horizontal and vertical coordinates of the ith path control point; l isstThe distance projected in the horizontal plane as the starting point and the end point; thetaiAre path control parameters.
5. The AUV path planning method based on the improved brainstorming optimization algorithm according to claim 1, wherein the fusion operation in the third method and the fourth method specifically adopts the following formula:
y＝r·x1+(1-r)·x2
wherein y is an individual to be mutated generated after fusion of two individuals, and x1And x2Is the two individuals who receive the fusion, r is a random number from 0 to 1, which is used to adjust the weight of the two individuals.
6. The AUV path planning method based on the improved brainstorming optimization algorithm of claim 1, wherein the differential mutation of step 5 is to update each dimension of the new individual by using the following formula:
wherein, y'dIs d-dimension, x 'of differentially mutated individuals'dIs the d-th dimension of the new individual,
and
is any two different individuals in the population; l isdAnd HdIs a boundary value of the search space; p is a radical ofrTo obtain the probability of a random new solution.
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| CN108399105A (en) * | 2018-02-27 | 2018-08-14 | 天津大学 | A kind of Method for HW/SW partitioning based on improvement brainstorming algorithm |
| CN108919641A (en) * | 2018-06-21 | 2018-11-30 | 山东科技大学 | A kind of unmanned aerial vehicle flight path planing method based on improvement cup ascidian algorithm |
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| CN111008788A (en) * | 2019-12-17 | 2020-04-14 | 河海大学常州校区 | Pilot assignment method based on improved discrete brain storm optimization algorithm |
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