CN108919641A - A kind of unmanned aerial vehicle flight path planing method based on improvement cup ascidian algorithm - Google Patents
A kind of unmanned aerial vehicle flight path planing method based on improvement cup ascidian algorithm Download PDFInfo
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
Abstract
The present invention provides a kind of based on the unmanned aerial vehicle flight path planing method for improving cup ascidian algorithm, belongs to unmanned aerial vehicle flight path planning field.The present invention determines starting point, aiming spot first and threatens area's range；Passage path cost threatens cost to establish trajectory planning Cost Model；Optimizing is carried out to the Cost Model of foundation, on the basis of basic cup ascidian algorithm, using the position of the iteration factor Population Regeneration of sinusoidal variations, is embedded in auto-adaptive service providing to improve its optimizing ability；After reaching the iteration upper limit, optimum individual position, as the unmanned plane optimal trajectory point from starting point to target point are obtained；The line for the optimal trajectory point sought is smoothed, optimal trajectory is obtained, realizes trajectory planning.The present invention can plan the optimal trajectory from starting point to target point, and track is avoided to enter threatening area, and calculating process flexibly, simply, quickly, preferably solves the problems, such as that existing trajectory planning optimization algorithm convergence rate is relatively slow, easily falls into local optimum.
Description
Technical field
The invention belongs to unmanned aerial vehicle flight path planning fields, and in particular to a kind of based on the unmanned plane boat for improving cup ascidian algorithm
Mark planing method.
Background technique
With the increasingly increased interest of the maturation of unmanned plane the relevant technologies and people, unmanned plane is in military, work, life
In use it is increasingly extensive.Unmanned aerial vehicle flight path planning is to find unmanned plane in given flight space from starting point and reach mesh
The Optimal Flight Route of punctuate, while to evade the threat in flight range, complete aerial mission requirement.
In the existing many planing methods of unmanned aerial vehicle flight path planning field, stochastic search optimization algorithm is wherein important one
Class method.The currently used stochastic search optimization algorithm applied to trajectory planning has：Particle swarm algorithm, dove group algorithm, ant colony
Algorithm etc..The behavioural characteristic of biocenose in these algorithm simulations nature, pass through the information between individual in population share, phase
The thought of mutually cooperation, the social action of mimic biology body and life habit completes search optimal solution.These algorithms have preferable
Flexibility, and implement simple, therefore be widely used in trajectory planning field.But these algorithms have convergence rate compared with
Slow problem, and locally optimal solution is easily fallen into, it is difficult to meet the actual needs of trajectory planning.
The shortcomings that in order to overcome the above method, constantly has some new natural heuristics to propose.Mirjalili et al. exists
“Salp Swarm Algorithm:A bio-inspired optimizer for engineering design
problems"【Advances in Engineering Software,2017,114:163-191】In propose it is a kind of new
Swarm Intelligence Algorithm --- cup ascidian group's algorithm.Cup ascidian group's algorithm is easy to accomplish, and most significant advantage is convergence rate
Fastly.But basic cup ascidian group's algorithm is not suitable for being applied to unmanned aerial vehicle flight path planning neck there is also local optimum is fallen into
Domain.
Summary of the invention
Aiming at the problem that basic cup ascidian algorithm easily falls into local optimum and Premature Convergence, the invention proposes a kind of bases
In the unmanned aerial vehicle flight path planing method for improving cup ascidian algorithm.
The following technical solution is employed by the present invention：
A kind of unmanned aerial vehicle flight path planing method based on improvement cup ascidian algorithm, includes the following steps：
Step 1：Determine the primary condition of unmanned aerial vehicle flight path planning；
Firstly, the position of setting starting point and target point, the abscissa matrix of the range in unmanned plane during flying region and vertical seat
Mark matrix；Secondly, setting threatens central point and threat range, threat information matrix is established；
Step 2：Establish trajectory planning cost function model, trajectory planning cost function model include path cost function and
Threaten cost function；
Step 3：Application enhancements cup ascidian algorithm carries out optimizing to the trajectory planning cost function model established in step 2,
Obtain optimal trajectory point；
Step 4：It is smoothed by line of the cubic spline interpolation to the track points that optimizing obtains, obtains unmanned plane
Track.
Preferably, in the step 2, path cost function establishment process is：
Setting starting point and target point in flight range is respectively (xS,yS),(xT,yT), between starting point and target point altogether
There is D path point, is followed successively by (x1,y1),...,(xj,yj),...,(xD,yD), the flight path of entire unmanned plane is D+1 sections shared
Path is followed successively by l1,l2,...,lk,...,lD+1, then unmanned aerial vehicle flight path planning path cost function be：
Threaten cost function establishment process be：
It sets and shares m threat in flight range, the coordinate at center is threatened to be followed successively by (x'1,y'1),...,(x'm,y'm),
It threatens and is followed successively by r with the safe distance of unmanned plane1,r2,...,rm, 3 sampled points are taken in every section of path, calculating includes route segment
Totally 5 points are at a distance from the center of threat with terminating point for starting point, then the threat cost function of unmanned aerial vehicle flight path planning is：
Wherein, k=1,2 ..., D+1, i=1,2 ..., m；Indicate 0.25 sampled point and i-th in kth section path
Distance between a threat；At a distance between indicating the starting point in kth section path and threatening for i-th,Indicate kth section path
Terminating point with i-th threaten between at a distance from；
Then trajectory planning cost function model is：
minWcost=λ JL+(1-λ)·JT(3)；
Wherein, λ is the random number of (0,1).
Preferably, the step 3 specifically includes following sub-step：
Step 3.1：Initialization of population
Individual variable and relevant parameter in initialization population, including population number M, the upper limit ub of search space, search
The lower limit lb in space, the dimension D of search space and maximum number of iterations MaxGen, the population position that wherein random initializtion generates
It is set to：
Xi=rand (M, D) (ub-lb)+lb (4)；
Step 3.2：Population location updating
The individual fitness in population is calculated, individual corresponding to adaptive optimal control value is defined as food F；Before population
Half part is set as leader, is responsible for guidance group to optimal solution movement, location updating mode is：
Wherein, c2And c3For the random number between (0,1), c1For iteration factor, l is current iteration number, c1By formula
(6) it determines：
Wherein, n is the adjustable control factor；
The latter half of population is set as follower, and location updating mode is：
Wherein,It is to update preceding m-th of follower in the position that D is tieed up,It is the m-1 follower in the position that D is tieed up
It sets；
Step 3.3：Adaptive Genetic operation
After cup ascidian population location updating, searching process is changed using adaptive intersection and TSP question operation
Into passing through adaptive crossover mutation PcThe higher some individuals of fitness carry out intersection behaviour as transitional population in selection population
Make, adaptive crossover mutation PcFormula be：
Wherein, fbestIndicate the fitness value of optimum individual in current population, fmeanIndicate that the individual of current population is averagely suitable
Answer angle value, ε1For the adjustable control factor；
Crossover operation randomly chooses two individuals as parent in transitional population, and new filial generation is generated by formula (9),
Crossover operation generate filial generation be：
X '=λ1·Xa+(1-λ1)·Xb (9)
Wherein λ1For the random number of (0,1), XaAnd XbFor parent randomly selected in transitional population；
Pass through self-adaptive mutation PmThe random individual chosen in entire population carries out mutation operation as transitional population,
Self-adaptive mutation PmFormula be：
Wherein, fbestIndicate the fitness value of optimum individual in current population, fmeanIndicate that the individual of current population is averagely suitable
Answer angle value, ε2For the adjustable control factor；
Mutation operation randomly chooses an individual as parent in transitional population, and new son is generated by formula (11)
Generation, the filial generation that mutation operation generates are：
X "=Xc·(1+λ2)h (11)
Wherein λ2For the random number of (0,1), XcFor parent randomly selected in transitional population, h is the adjustable control factor；
Step 3.4：Cup ascidian population recruitment
The progeny population generated to Adaptive Genetic operation carries out Fitness analysis, if the fitness value of progeny population is high
In parent individuality fitness value, then offspring individual replaces parent individuality；Meanwhile fitting each of updated group individual
It answers angle value compared with the fitness value of current foodstuff, is better than the individual of food fitness value if it exists, then more with fitness value
Excellent cup ascidian body position is as new food position；If running to regulation maximum number of iterations MaxGen or fitness reaching
To scheduled threshold value, then algorithm terminates, and obtains optimum individual position, as optimal trajectory point.
The invention has the advantages that：
The present invention determines starting point, aiming spot first and threatens area's range；Passage path cost threatens cost to establish
Trajectory planning Cost Model；Optimizing is carried out to the Cost Model of foundation, on the basis of basic cup ascidian algorithm, is become using sine
The position of the iteration factor Population Regeneration of change is embedded in auto-adaptive service providing to improve its optimizing ability；After reaching the iteration upper limit,
Obtain optimum individual position, as the unmanned plane optimal trajectory point from starting point to target point；To the optimal trajectory point sought
Line is smoothed, and obtains optimal trajectory, realizes trajectory planning.The present invention can be planned from starting point to target point most
Excellent track, and track is avoided to enter threatening area, calculating process flexibly, simply, quickly, preferably solves existing track rule
Draw the problem of optimization algorithm convergence rate falls into local optimum relatively slowly, easily.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Fig. 2 is the schematic diagram that unmanned plane section path threatens cost to calculate.
Fig. 3 is the optimal trajectory figure of unmanned plane planning.
Fig. 4 is fitness value convergence curve in trajectory planning searching process.
Specific embodiment
A specific embodiment of the invention is described further in the following with reference to the drawings and specific embodiments：
It is a kind of based on the unmanned aerial vehicle flight path planing method for improving cup ascidian algorithm in conjunction with Fig. 1 to Fig. 4, include the following steps：
Step 1：Determine the primary condition of unmanned aerial vehicle flight path planning；
Firstly, the position of setting starting point and target point, the abscissa matrix of the range in unmanned plane during flying region and vertical seat
Mark matrix；Secondly, setting threatens central point and threat range, threat information matrix is established.
Step 2：Establish trajectory planning cost function model, trajectory planning cost function model include path cost function and
Threaten cost function.
Wherein, path cost function establishment process is：
Setting starting point and target point in flight range is respectively (xS,yS),(xT,yT), between starting point and target point altogether
There is D path point, is followed successively by (x1,y1),...,(xj,yj),...,(xD,yD), the flight path of entire unmanned plane is D+1 sections shared
Path is followed successively by l1,l2,...,lk,...,lD+1, then unmanned aerial vehicle flight path planning path cost function be：
Threaten cost function establishment process be：
It sets and shares m threat in flight range, the coordinate at center is threatened to be followed successively by (x'1,y'1),...,(x'm,y'm),
It threatens and is followed successively by r with the safe distance of unmanned plane1,r2,...,rm, 3 sampled points are taken in every section of path, calculating includes route segment
Totally 5 points are at a distance from the center of threat with terminating point for starting point, then the threat cost function of unmanned aerial vehicle flight path planning is：
Wherein, k=1,2 ..., D+1, i=1,2 ..., m；Indicate kth section path 0.25 sampled point and i-th
Distance between threat；At a distance between indicating the starting point in kth section path and threatening for i-th,Indicate the end in kth section path
Stop with i-th threaten between at a distance from；
Then trajectory planning cost function model is：
minWcost=λ JL+(1-λ)·JT(3)；
Wherein, λ is the random number of (0,1).
Step 3：Application enhancements cup ascidian algorithm carries out optimizing to the trajectory planning cost function model established in step 2,
Obtain optimal trajectory point.
Firstly, to improve cup ascidian algorithm various parameters initialize, including Population Size, maximum number of iterations,
The information such as population dimension, and random initial population is generated according to initialization condition.Each individual of cup ascidian population represents one group
Track points, substitute into the fitness value of cost function calculation individual, and arrange in descending order, and optimum individual is set as food position；
Secondly, carrying out location updating to cup ascidian population, and carry out Adaptive Genetic to updated population using sinusoidal iteration factor
Operation；According to the fitness value of each individual of cost function calculation, a body position and food position of Population Regeneration start next
Secondary circulation obtains optimum individual position, as optimal trajectory point after reaching the iteration upper limit.
Specifically include following sub-step：
Step 3.1：Initialization of population
Individual variable and relevant parameter in initialization population, including population number M, the upper limit ub of search space, search
The lower limit lb in space, the dimension D of search space and maximum number of iterations MaxGen, the population position that wherein random initializtion generates
It is set to：
Xi=rand (M, D) (ub-lb)+lb (4)；
Step 3.2：Population location updating
The individual fitness in population is calculated, individual corresponding to adaptive optimal control value is defined as food F；Before population
Half part is set as leader, is responsible for guidance group to optimal solution movement, location updating mode is：
Wherein, c2And c3For the random number between (0,1), c1For iteration factor, l is current iteration number, c1By formula
(6) it determines：
Wherein, n is the adjustable control factor；
The latter half of population is set as follower, and location updating mode is：
Wherein,It is to update preceding m-th of follower in the position that D is tieed up,It is the m-1 follower in the position that D is tieed up
It sets；
Step 3.3：Adaptive Genetic operation
After cup ascidian population location updating, searching process is changed using adaptive intersection and TSP question operation
Into passing through adaptive crossover mutation PcThe higher some individuals of fitness carry out intersection behaviour as transitional population in selection population
Make, adaptive crossover mutation PcFormula be：
Wherein, fbestIndicate the fitness value of optimum individual in current population, fmeanIndicate that the individual of current population is averagely suitable
Answer angle value, ε1For the adjustable control factor；
Crossover operation randomly chooses two individuals as parent in transitional population, and new filial generation is generated by formula (9),
Crossover operation generate filial generation be：
X '=λ1·Xa+(1-λ1)·Xb (9)
Wherein λ1For the random number of (0,1), XaAnd XbFor parent randomly selected in transitional population；
Pass through self-adaptive mutation PmThe random individual chosen in entire population carries out mutation operation as transitional population,
Self-adaptive mutation PmFormula be：
Wherein, fbestIndicate the fitness value of optimum individual in current population, fmeanIndicate that the individual of current population is averagely suitable
Answer angle value, ε2For the adjustable control factor；
Mutation operation randomly chooses an individual as parent in transitional population, and new son is generated by formula (11)
Generation, the filial generation that mutation operation generates are：
X "=Xc·(1+λ2)h (11)
Wherein λ2For the random number of (0,1), XcFor parent randomly selected in transitional population, h is the adjustable control factor；
Step 3.4：Cup ascidian population recruitment
The progeny population generated to Adaptive Genetic operation carries out Fitness analysis, if the fitness value of progeny population is high
In parent individuality fitness value, then offspring individual replaces parent individuality；Meanwhile fitting each of updated group individual
It answers angle value compared with the fitness value of current foodstuff, is better than the individual of food fitness value if it exists, then more with fitness value
Excellent cup ascidian body position is as new food position；If running to regulation maximum number of iterations MaxGen or fitness reaching
To scheduled threshold value, then algorithm terminates, and obtains optimum individual position, as optimal trajectory point.
Step 4：Track smoothing processing.Using cubic spline interpolation, piecewise fitting is carried out to the track points that optimizing obtains,
Obtain one section of smooth track curve, as optimal unmanned aerial vehicle flight path.The track curve of cubic spline interpolation fitting possesses very
Good smoothness, and can also approach the line of obtained optimal trajectory point well on the whole.
Embodiment 1
Emulation experiment is carried out, the simulation space of 1000*1000 is chosen；5 threats are set, coordinate is respectively (200,100),
(300,500), (300,300), (550,700), (850,550), threatening radius is respectively 100,120,100,80,110；Starting
Point coordinate is (0,0), and terminating point coordinate is (1000,1000)；Initializing cup ascidian population quantity is 300, maximum number of iterations
It is 100, adjustable control factor of n is set as 1, h and is set as 1, ε1It is set as 5, ε2It is set as 40.It is obtained by Matlab such as Fig. 3 institute
The track and searching process convergence curve as shown in Figure 4 shown, the time that planning obtains optimal trajectory is 3.7596241s.
Certainly, the above description is not a limitation of the present invention, and the present invention is also not limited to the example above, this technology neck
The variations, modifications, additions or substitutions that the technical staff in domain is made within the essential scope of the present invention also should belong to of the invention
Protection scope.
Claims (3)
1. a kind of based on the unmanned aerial vehicle flight path planing method for improving cup ascidian algorithm, which is characterized in that include the following steps：
Step 1：Determine the primary condition of unmanned aerial vehicle flight path planning；
Firstly, the position of setting starting point and target point, the abscissa matrix and ordinate square of the range in unmanned plane during flying region
Battle array；Secondly, setting threatens central point and threat range, threat information matrix is established；
Step 2：Trajectory planning cost function model is established, trajectory planning cost function model includes path cost function and threat
Cost function；
Step 3：Application enhancements cup ascidian algorithm carries out optimizing to the trajectory planning cost function model established in step 2, obtains
Optimal trajectory point；
Step 4：It is smoothed by line of the cubic spline interpolation to the track points that optimizing obtains, obtains unmanned plane boat
Mark.
2. according to claim 1 a kind of based on the unmanned aerial vehicle flight path planing method for improving cup ascidian algorithm, feature exists
In in the step 2, path cost function establishment process is：
Setting starting point and target point in flight range is respectively (xS,yS),(xT,yT), D are shared between starting point and target point
Path point is followed successively by (x1,y1),...,(xj,yj),...,(xD,yD), the flight path of entire unmanned plane shares D+1 sections of paths,
It is followed successively by l1,l2,...,lk,...,lD+1, then unmanned aerial vehicle flight path planning path cost function be：
Threaten cost function establishment process be：
It sets and shares m threat in flight range, the coordinate at center is threatened to be followed successively by (x'1,y'1),...,(x'm,y'm), it threatens
R is followed successively by with the safe distance of unmanned plane1,r2,...,rm, 3 sampled points are taken in every section of path, calculating includes that route segment originates
Totally 5 points are at a distance from the center of threat with terminating point for point, then the threat cost function of unmanned aerial vehicle flight path planning is：
Wherein, k=1,2 ..., D+1, i=1,2 ..., m；0.25 sampled point in expression kth section path and i-th of threat
Between distance；At a distance between indicating the starting point in kth section path and threatening for i-th,Indicate the terminating point in kth section path
At a distance between i-th of threat；
Then trajectory planning cost function model is：
minWcost=λ JL+(1-λ)·JT(3)；
Wherein, λ is the random number of (0,1).
3. according to claim 1 a kind of based on the unmanned aerial vehicle flight path planing method for improving cup ascidian algorithm, feature exists
In the step 3 specifically includes following sub-step：
Step 3.1：Initialization of population
Individual variable and relevant parameter in initialization population, including population number M, the upper limit ub of search space, search space
Lower limit lb, search space dimension D and maximum number of iterations MaxGen, wherein random initializtion generate population position be：
Xi=rand (M, D) (ub-lb)+lb (4)；
Step 3.2：Population location updating
The individual fitness in population is calculated, individual corresponding to adaptive optimal control value is defined as food F；By the first half of population
Set up separately and be set to leader, is responsible for guidance group to optimal solution movement, location updating mode is：
Wherein, c2And c3For the random number between (0,1), c1For iteration factor, l is current iteration number, c1It is true by formula (6)
It is fixed：
Wherein, n is the adjustable control factor；
The latter half of population is set as follower, and location updating mode is：
Wherein,It is to update preceding m-th of follower in the position that D is tieed up,It is the m-1 follower in the position that D is tieed up；
Step 3.3：Adaptive Genetic operation
After cup ascidian population location updating, searching process is improved using adaptive intersection and TSP question operation, is led to
Cross adaptive crossover mutation PcIt is adaptive as transitional population progress crossover operation to choose the higher some individuals of fitness in population
Answer crossover probability PcFormula be：
Wherein, fbestIndicate the fitness value of optimum individual in current population, fmeanIndicate the individual average fitness of current population
Value, ε1For the adjustable control factor；
Crossover operation randomly chooses two individuals as parent in transitional population, and new filial generation is generated by formula (9), is intersected
Operating the filial generation generated is：
X '=λ1·Xa+(1-λ1)·Xb (9)
Wherein λ1For the random number of (0,1), XaAnd XbFor parent randomly selected in transitional population；
Pass through self-adaptive mutation PmThe random individual chosen in entire population carries out mutation operation as transitional population, adaptive
Should make a variation probability PmFormula be：
Wherein, fbestIndicate the fitness value of optimum individual in current population, fmeanIndicate the individual average fitness of current population
Value, ε2For the adjustable control factor；
Mutation operation randomly chooses an individual as parent in transitional population, and new filial generation is generated by formula (11), is become
ETTHER-OR operation generate filial generation be：
X "=Xc·(1+λ2)h (11)
Wherein λ2For the random number of (0,1), XcFor parent randomly selected in transitional population, h is the adjustable control factor；
Step 3.4：Cup ascidian population recruitment
The progeny population generated to Adaptive Genetic operation carries out Fitness analysis, if the fitness value of progeny population is higher than father
For ideal adaptation angle value, then offspring individual replaces parent individuality；Meanwhile by the fitness of each of updated group individual
Value is better than the individual of food fitness value, then more preferably with fitness value compared with the fitness value of current foodstuff if it exists
Cup ascidian body position is as new food position；If running to regulation maximum number of iterations MaxGen or fitness reaching pre-
Fixed threshold value, then algorithm terminates, and obtains optimum individual position, as optimal trajectory point.
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