CN108919641B - Unmanned aerial vehicle flight path planning method based on improved goblet sea squirt algorithm - Google Patents
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Abstract
The invention provides an unmanned aerial vehicle track planning method based on an improved goblet sea squirt algorithm, and belongs to the field of unmanned aerial vehicle track planning. Firstly, determining a starting point, a target point position and a threat area range; establishing a track planning cost model through the path cost and the threat cost; optimizing the established cost model, updating the position of a population by adopting a sine-changed iterative factor on the basis of a basic goblet and sea squirt algorithm, and embedding an adaptive genetic operator to improve the optimizing capability of the population; after the iteration upper limit is reached, obtaining an optimal individual position, namely an optimal track point of the unmanned aerial vehicle from the starting point to the target point; and smoothing the connecting line of the found optimal track point to obtain an optimal track, thereby realizing track planning. The invention can plan the optimal flight path from the starting point to the target point, avoids the flight path from entering the threat area, has flexible, simple and quick calculation process, and better solves the problems that the existing flight path planning optimization algorithm has low convergence speed and is easy to fall into local optimization.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle track planning, and particularly relates to an unmanned aerial vehicle track planning method based on an improved goblet sea squirt algorithm.
Background
With the maturity of the related technologies of unmanned aerial vehicles and the increasing interest of people, the use of unmanned aerial vehicles in military, work and life is becoming more and more extensive. In the unmanned aerial vehicle flight path planning, the optimal flight path of the unmanned aerial vehicle from a starting point to a target point is searched in a given flight space, meanwhile, the threat in a flight area is avoided, and the flight mission requirement is completed.
In the field of unmanned aerial vehicle flight path planning, a plurality of planning methods exist, and a random search optimization algorithm is an important method. The random search optimization algorithm commonly used for route planning at present comprises: particle swarm algorithm, pigeon swarm algorithm, ant swarm algorithm and the like. The algorithms simulate the behavior characteristics of biological groups in nature, and complete the search of the optimal solution by simulating the social behavior and the life habit of organisms through information sharing and mutual cooperation among individuals in the groups. These algorithms have good flexibility and are simple to implement and are therefore widely used in the field of trajectory planning. However, these algorithms have the problem of slow convergence speed, are easy to fall into a local optimal solution, and are difficult to meet the actual requirements of track planning.
In order to overcome the disadvantages of the above methods, new natural inspiring methods are continuously proposed. A new group intelligence Algorithm, the goblet sea squirt group Algorithm, was proposed by Mirjalli et al in "Salp Swarm Algorithm: A bio-induced optimizer for Engineering designs" [ Advances in Engineering Software,2017,114:163-191 ]. The goblet sea squirt group algorithm is easy to realize, and the most obvious advantage is high convergence speed. However, the basic goblet sea squirt group algorithm has the defect of falling into local optimization, and is not suitable for being applied to the field of unmanned aerial vehicle flight path planning.
Disclosure of Invention
Aiming at the problems that the basic goblet sea squirt algorithm is easy to fall into local optimization and premature convergence, the invention provides an unmanned aerial vehicle track planning method based on the improved goblet sea squirt algorithm.
The invention adopts the following technical scheme:
an unmanned aerial vehicle flight path planning method based on an improved goblet sea squirt algorithm comprises the following steps:
step 1: determining initial conditions of unmanned aerial vehicle track planning;
firstly, setting the positions of a starting point and a target point, and an abscissa matrix and an ordinate matrix of the range of the flight area of the unmanned aerial vehicle; secondly, setting a threat center point and a threat range, and establishing a threat information matrix;
step 2: establishing a track planning cost function model, wherein the track planning cost function model comprises a path cost function and a threat cost function;
and step 3: optimizing the track planning cost function model established in the step 2 by applying an improved ascidian algorithm to obtain an optimal track point;
and 4, step 4: and smoothing the connecting line of the track points obtained by optimizing through cubic spline interpolation to obtain the unmanned aerial vehicle track.
Preferably, in step 2, the path cost function establishing process is as follows:
setting the starting point and the target point in the flight area as (x)S,yS),(xT,yT) The total number of the path points between the starting point and the target point is D, and the path points are (x) in sequence1,y1),...,(xj,yj),...,(xD,yD) The flight path of the whole unmanned aerial vehicle has D +1 sections of paths which are l in sequence1,l2,...,lk,...,lD+1And then the path cost function of the unmanned aerial vehicle track planning is as follows:
the establishment process of the threat cost function is as follows:
the total m threats in the flight area are set, and the coordinates of the threat centers are (x'1,y'1),...,(x'm,y'm) The safe distance between the threat and the unmanned aerial vehicle is r in sequence1,r2,...,rmTaking 3 sampling points in each path, calculating the starting point and the ending point of the path segmentAnd 5, the distance between the point and the threat center is obtained, and the threat cost function of unmanned aerial vehicle track planning is as follows:
wherein k is 1,2, …, D +1, i is 1,2, …, m;
representing the distance between 0.25 sampling point of the kth path and the ith threat;
representing the distance between the start point of the kth segment of the path and the ith threat,
representing the distance between the end point of the kth path and the ith threat;
the model of the cost function for planning the flight path is as follows:
minWcost＝λ·JL+(1-λ)·JT (3)；
wherein λ is a random number of (0, 1).
Preferably, the step 3 specifically includes the following sub-steps:
step 3.1: population initialization
Initializing individual variables and related parameters in the population, wherein the individual variables and the related parameters comprise the population number M, the upper limit ub of a search space, the lower limit lb of the search space, the dimension D of the search space and the maximum iteration number MaxGen, and the population position generated by random initialization is as follows:
Xi＝rand(M,D)·(ub-lb)+lb (4)；
step 3.2: population location update
Calculating individual adaptation values in the population, and defining an individual corresponding to the optimal adaptation value as food F; setting the first half part of the population as a leader, and guiding the population to move to the optimal solution, wherein the position updating mode is as follows:
wherein, c2And c3Is a random number between (0,1), c1As an iteration factor, l is the current iteration number, c1Determined by equation (6):
wherein n is an adjustable control factor;
the second half of the population is set as a follower, and the position updating mode is as follows:
wherein the content of the first and second substances,
is the position of the mth follower in the D dimension before the update,
is the position of the (m-1) th follower in the D dimension;
step 3.3: adaptive genetic manipulation
After updating the position of the sea squirt population, the optimization process is improved by adopting self-adaptive crossing and self-adaptive variation operation, and the probability P of the self-adaptive crossing is adoptedcSelecting partial individuals with higher fitness in the population as intermediate population to carry out cross operation, and self-adapting to cross probability PcThe formula of (1) is:
wherein f isbestRepresenting the fitness value of the best individual in the current population, fmeanRepresents the average fitness value of the individual of the current population, epsilon1To be regulated and controlledPreparing a factor;
and (3) randomly selecting two individuals in the intermediate population as parents by the crossover operation, and generating new filial generations by the formula (9), wherein the filial generations generated by the crossover operation are as follows:
X′＝λ1·Xa+(1-λ1)·Xb (9)
wherein λ1Is a random number of (0,1), XaAnd XbA randomly selected parent in the intermediate population;
by adapting the probability of variation PmRandom individuals in the whole population are selected as an intermediate population to carry out mutation operation, and the probability P of mutation is self-adaptedmThe formula of (1) is:
wherein f isbestRepresenting the fitness value of the best individual in the current population, fmeanRepresents the average fitness value of the individual of the current population, epsilon2Is an adjustable control factor;
mutation operation randomly selects an individual as a parent in the intermediate population, and generates new filial generation through a formula (11), wherein the filial generation generated by the mutation operation is as follows:
X″＝Xc·(1+λ2)h (11)
wherein λ2Is a random number of (0,1), XcIs a randomly selected parent in the intermediate population, and h is an adjustable control factor;
step 3.4: updating group of goblet sea squirt
Evaluating the fitness of the offspring population generated by the self-adaptive genetic operation, and replacing the parent individuals with the offspring population if the fitness value of the offspring population is higher than that of the parent individuals; meanwhile, the fitness value of each individual in the updated population is compared with the fitness value of the current food, and if the individual superior to the food fitness value exists, the position of the individual of the sea squirt with the better fitness value is taken as a new food position; if the maximum iteration times MaxGen are operated to be specified or the fitness reaches a preset threshold value, the algorithm is terminated, and the optimal individual position is obtained, namely the optimal track point.
The invention has the beneficial effects that:
firstly, determining a starting point, a target point position and a threat area range; establishing a track planning cost model through the path cost and the threat cost; optimizing the established cost model, updating the position of a population by adopting a sine-changed iterative factor on the basis of a basic goblet and sea squirt algorithm, and embedding an adaptive genetic operator to improve the optimizing capability of the population; after the iteration upper limit is reached, obtaining an optimal individual position, namely an optimal track point of the unmanned aerial vehicle from the starting point to the target point; and smoothing the connecting line of the found optimal track point to obtain an optimal track, thereby realizing track planning. The invention can plan the optimal flight path from the starting point to the target point, avoids the flight path from entering the threat area, has flexible, simple and quick calculation process, and better solves the problems that the existing flight path planning optimization algorithm has low convergence speed and is easy to fall into local optimization.
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FIG. 1 is a flow chart of the present invention.
Fig. 2 is a schematic diagram of threat cost calculation of a certain section of path of the unmanned aerial vehicle.
Fig. 3 is an optimal track diagram for drone planning.
FIG. 4 is a convergence curve of fitness value in the process of optimizing the flight path planning.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings:
with reference to fig. 1 to 4, an unmanned aerial vehicle flight path planning method based on the improved goblet and ascidian algorithm includes the following steps:
step 1: determining initial conditions of unmanned aerial vehicle track planning;
firstly, setting the positions of a starting point and a target point, and an abscissa matrix and an ordinate matrix of the range of the flight area of the unmanned aerial vehicle; secondly, a threat center point and a threat range are set, and a threat information matrix is established.
Step 2: and establishing a track planning cost function model, wherein the track planning cost function model comprises a path cost function and a threat cost function.
The path cost function establishing process comprises the following steps:
setting the starting point and the target point in the flight area as (x)S,yS),(xT,yT) The total number of the path points between the starting point and the target point is D, and the path points are (x) in sequence1,y1),...,(xj,yj),...,(xD,yD) The flight path of the whole unmanned aerial vehicle has D +1 sections of paths which are l in sequence1,l2,...,lk,...,lD+1And then the path cost function of the unmanned aerial vehicle track planning is as follows:
the establishment process of the threat cost function is as follows:
the total m threats in the flight area are set, and the coordinates of the threat centers are (x'1,y'1),...,(x'm,y'm) The safe distance between the threat and the unmanned aerial vehicle is r in sequence1,r2,...,rmAnd 3 sampling points are taken in each section of path, the distance between 5 points including the starting point and the ending point of the path section and the threat center is calculated, and the threat cost function of the unmanned aerial vehicle track planning is as follows:
wherein k is 1,2, …, D +1, i is 1,2, …, m;
representing the distance between 0.25 sampling point of the kth path and the ith threat;
representing the distance between the start point of the kth segment of the path and the ith threat,
representing the distance between the end point of the kth path and the ith threat;
the model of the cost function for planning the flight path is as follows:
minWcost＝λ·JL+(1-λ)·JT (3)；
wherein λ is a random number of (0, 1).
And step 3: and (3) optimizing the track planning cost function model established in the step (2) by applying an improved ascidian algorithm to obtain an optimal track point.
Firstly, initializing various parameters of the improved goblet sea squirt algorithm, including information such as population size, maximum iteration times, population dimension and the like, and generating a random initial population according to initialization conditions. Each individual of the goblet sea squirt population represents a group of track points, the track points are substituted into the cost function to calculate the fitness value of the individual, the fitness value is arranged according to the descending order, and the optimal individual is set as the food position; secondly, adopting a sine iteration factor to update the position of the goblet sea squirt population and carrying out self-adaptive genetic operation on the updated population; and calculating the fitness value of each individual according to the cost function, updating the individual position and the food position of the population, and starting the next cycle until the iteration upper limit is reached to obtain the optimal individual position, namely the optimal track point.
The method specifically comprises the following substeps:
step 3.1: population initialization
Initializing individual variables and related parameters in the population, wherein the individual variables and the related parameters comprise the population number M, the upper limit ub of a search space, the lower limit lb of the search space, the dimension D of the search space and the maximum iteration number MaxGen, and the population position generated by random initialization is as follows:
Xi＝rand(M,D)·(ub-lb)+lb (4)；
step 3.2: population location update
Calculating individual adaptation values in the population, and defining an individual corresponding to the optimal adaptation value as food F; setting the first half part of the population as a leader, and guiding the population to move to the optimal solution, wherein the position updating mode is as follows:
wherein, c2And c3Is a random number between (0,1), c1As an iteration factor, l is the current iteration number, c1Determined by equation (6):
wherein n is an adjustable control factor;
the second half of the population is set as a follower, and the position updating mode is as follows:
wherein the content of the first and second substances,
is the position of the mth follower in the D dimension before the update,
is the position of the (m-1) th follower in the D dimension;
step 3.3: adaptive genetic manipulation
After updating the position of the sea squirt population, the optimization process is improved by adopting self-adaptive crossing and self-adaptive variation operation, and the probability P of the self-adaptive crossing is adoptedcSelecting partial individuals with higher fitness in the population as intermediate population to carry out cross operation, and self-adapting to cross probability PcThe formula of (1) is:
wherein f isbestRepresenting the fitness value of the best individual in the current population,fmeanrepresents the average fitness value of the individual of the current population, epsilon1Is an adjustable control factor;
and (3) randomly selecting two individuals in the intermediate population as parents by the crossover operation, and generating new filial generations by the formula (9), wherein the filial generations generated by the crossover operation are as follows:
X′＝λ1·Xa+(1-λ1)·Xb (9)
wherein λ1Is a random number of (0,1), XaAnd XbA randomly selected parent in the intermediate population;
by adapting the probability of variation PmRandom individuals in the whole population are selected as an intermediate population to carry out mutation operation, and the probability P of mutation is self-adaptedmThe formula of (1) is:
wherein f isbestRepresenting the fitness value of the best individual in the current population, fmeanRepresents the average fitness value of the individual of the current population, epsilon2Is an adjustable control factor;
mutation operation randomly selects an individual as a parent in the intermediate population, and generates new filial generation through a formula (11), wherein the filial generation generated by the mutation operation is as follows:
X″＝Xc·(1+λ2)h (11)
wherein λ2Is a random number of (0,1), XcIs a randomly selected parent in the intermediate population, and h is an adjustable control factor;
step 3.4: updating group of goblet sea squirt
Evaluating the fitness of the offspring population generated by the self-adaptive genetic operation, and replacing the parent individuals with the offspring population if the fitness value of the offspring population is higher than that of the parent individuals; meanwhile, the fitness value of each individual in the updated population is compared with the fitness value of the current food, and if the individual superior to the food fitness value exists, the position of the individual of the sea squirt with the better fitness value is taken as a new food position; if the maximum iteration times MaxGen are operated to be specified or the fitness reaches a preset threshold value, the algorithm is terminated, and the optimal individual position is obtained, namely the optimal track point.
And 4, step 4: and (6) carrying out track smoothing treatment. And (4) performing segmented fitting on the track points obtained by optimization by adopting a cubic spline interpolation method to obtain a smooth track curve, namely the optimal unmanned aerial vehicle track. The track curve fitted by the cubic spline interpolation method has good smoothness, and can well approach the connecting line of the obtained optimal track point on the whole.
Example 1
Carrying out simulation experiments, and selecting 1000 × 1000 simulation spaces; setting 5 threats with coordinates of (200,100), (300,500), (300 ), (550,700), (850,550), and threat radii of 100, 120,100,80, 110; the coordinates of the starting point are (0,0), and the coordinates of the ending point are (1000 ); the number of initialized goblet sea squirt population is 300, the maximum iteration number is 100, the adjustable control factor n is set to be 1, h is set to be 1, epsilon1Is set to 5 epsilon2Set to 40. The flight path shown in fig. 3 and the convergence curve of the optimization process shown in fig. 4 are obtained through Matlab, and the time for planning to obtain the optimal flight path is 3.7596241 s.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (3)
1. An unmanned aerial vehicle flight path planning method based on an improved goblet sea squirt algorithm is characterized by comprising the following steps:
step 1: determining initial conditions of unmanned aerial vehicle track planning;
firstly, setting the positions of a starting point and a target point, and an abscissa matrix and an ordinate matrix of the range of the flight area of the unmanned aerial vehicle; secondly, setting a threat center point and a threat range, and establishing a threat information matrix;
step 2: establishing a track planning cost function model, wherein the track planning cost function model comprises a path cost function and a threat cost function;
and step 3: optimizing the track planning cost function model established in the step 2 by applying an improved ascidian algorithm to obtain an optimal track point;
and 4, step 4: and smoothing the connecting line of the track points obtained by optimizing through cubic spline interpolation to obtain the unmanned aerial vehicle track.
2. The unmanned aerial vehicle flight path planning method based on the improved cask and ascidian algorithm as claimed in claim 1, wherein in step 2, the path cost function establishing process is as follows:
setting the starting point and the target point in the flight area as (x)S,yS),(xT,yT) The total number of the path points between the starting point and the target point is D, and the path points are (x) in sequence1,y1),...,(xj,yj),...,(xD,yD) The flight path of the whole unmanned aerial vehicle has D +1 sections of paths which are l in sequence1,l2,...,lk,...,lD+1And then the path cost function of the unmanned aerial vehicle track planning is as follows:
the establishment process of the threat cost function is as follows:
the total m threats in the flight area are set, and the coordinates of the threat centers are (x'1,y'1),...,(x'm,y'm) The safe distance between the threat and the unmanned aerial vehicle is r in sequence1,r2,...,rmAnd 3 sampling points are taken in each section of path, the distance between 5 points including the starting point and the ending point of the path section and the threat center is calculated, and the threat cost function of the unmanned aerial vehicle track planning is as follows:
wherein k is 1,2, …, D +1, i is 1,2, …, m;
representing the distance between 0.25 sampling point of the kth path and the ith threat;
representing the distance between the start point of the kth segment of the path and the ith threat,
representing the distance between the end point of the kth path and the ith threat;
the model of the cost function for planning the flight path is as follows:
minWcost＝λ·JL+(1-λ)·JT (3)；
wherein λ is a random number of (0, 1).
3. The unmanned aerial vehicle flight path planning method based on the improved goblet sea squirt algorithm as claimed in claim 1, wherein the step 3 specifically comprises the following substeps:
step 3.1: population initialization
Initializing individual variables and related parameters in the population, wherein the individual variables and the related parameters comprise the population number M, the upper limit ub of a search space, the lower limit lb of the search space, the dimension D of the search space and the maximum iteration number MaxGen, and the population position generated by random initialization is as follows:
Xi＝rand(M,D)·(ub-lb)+lb (4)；
step 3.2: population location update
Calculating individual adaptation values in the population, and defining an individual corresponding to the optimal adaptation value as food F; setting the first half part of the population as a leader, and guiding the population to move to the optimal solution, wherein the position updating mode is as follows:
wherein, c2And c3Is a random number between (0,1), c1As an iteration factor, l is the current iteration number, c1Determined by equation (6):
wherein n is an adjustable control factor;
the second half of the population is set as a follower, and the position updating mode is as follows:
wherein the content of the first and second substances,
is the position of the mth follower in the D dimension before the update,
is the position of the (m-1) th follower in the D dimension;
step 3.3: adaptive genetic manipulation
After updating the position of the sea squirt population, the optimization process is improved by adopting self-adaptive crossing and self-adaptive variation operation, and the probability P of the self-adaptive crossing is adoptedcSelecting partial individuals with higher fitness in the population as intermediate population to carry out cross operation, and self-adapting to cross probability PcThe formula of (1) is:
wherein f isbestRepresenting the fitness value of the best individual in the current population, fmeanRepresents the average fitness value of the individual of the current population, epsilon1Is an adjustable control factor;
and (3) randomly selecting two individuals in the intermediate population as parents by the crossover operation, and generating new filial generations by the formula (9), wherein the filial generations generated by the crossover operation are as follows:
X′＝λ1·Xa+(1-λ1)·Xb (9)
wherein λ1Is a random number of (0,1), XaAnd XbA randomly selected parent in the intermediate population;
by adapting the probability of variation PmRandom individuals in the whole population are selected as an intermediate population to carry out mutation operation, and the probability P of mutation is self-adaptedmThe formula of (1) is:
wherein f isbestRepresenting the fitness value of the best individual in the current population, fmeanRepresents the average fitness value of the individual of the current population, epsilon2Is an adjustable control factor;
mutation operation randomly selects an individual as a parent in the intermediate population, and generates new filial generation through a formula (11), wherein the filial generation generated by the mutation operation is as follows:
X″＝Xc·(1+λ2)h (11)
wherein λ2Is a random number of (0,1), XcIs a randomly selected parent in the intermediate population, and h is an adjustable control factor;
step 3.4: updating group of goblet sea squirt
Evaluating the fitness of the offspring population generated by the self-adaptive genetic operation, and replacing the parent individuals with the offspring population if the fitness value of the offspring population is higher than that of the parent individuals; meanwhile, the fitness value of each individual in the updated population is compared with the fitness value of the current food, and if the individual superior to the food fitness value exists, the position of the individual of the sea squirt with the better fitness value is taken as a new food position; if the maximum iteration times MaxGen are operated to be specified or the fitness reaches a preset threshold value, the algorithm is terminated, and the optimal individual position is obtained, namely the optimal track point.
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