CN112880688A - Unmanned aerial vehicle three-dimensional flight path planning method based on chaotic self-adaptive sparrow search algorithm - Google Patents
Unmanned aerial vehicle three-dimensional flight path planning method based on chaotic self-adaptive sparrow search algorithm Download PDFInfo
- Publication number
- CN112880688A CN112880688A CN202110110630.4A CN202110110630A CN112880688A CN 112880688 A CN112880688 A CN 112880688A CN 202110110630 A CN202110110630 A CN 202110110630A CN 112880688 A CN112880688 A CN 112880688A
- Authority
- CN
- China
- Prior art keywords
- sparrow
- algorithm
- aerial vehicle
- unmanned aerial
- adaptive
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention discloses an unmanned aerial vehicle three-dimensional flight path planning method based on a chaotic self-adaptive sparrow search algorithm, which comprises the following steps of: establishing a flight environment model according to the flight environment; establishing a flight cost function of the unmanned aerial vehicle, and evaluating the track performance of the unmanned aerial vehicle; a chaotic initialization population strategy, a self-adaptive weight strategy and a Cauchy-Gaussian mixed variation strategy are adopted to improve a sparrow search algorithm, and the chaotic self-adaptive sparrow search algorithm is provided; planning the unmanned aerial vehicle track in the three-dimensional environment by adopting a chaotic self-adaptive sparrow search algorithm to obtain an optimal solution of unmanned aerial vehicle track planning and obtain a planning result; the improved sparrow algorithm has obvious advantages in solving quality, the chaos strategy and the self-adaptive strategy enable the algorithm to have high convergence speed and excellent convergence precision, and the variation strategy enables the algorithm to have strong capability of jumping out of local optimum, so that an excellent unmanned aerial vehicle flight path can be obtained quickly.
Description
Technical Field
The invention relates to the research field of unmanned aerial vehicle track optimization, in particular to an unmanned aerial vehicle three-dimensional track planning method based on a chaotic self-adaptive sparrow search algorithm.
Background
As is well known, unmanned aerial vehicle trajectory planning has become one of the important elements in defining unmanned aerial vehicle missions. The three-dimensional path planning required for the unmanned aerial vehicle flight mission can be defined as a non-deterministic polynomial (NP) problem, whose main purpose is to optimize the path between the departure point and the target point. In the process, a plurality of constraints, such as the performance constraint, threat constraint and environment constraint of the unmanned aerial vehicle, need to be processed. In order to make the planned flight path meet the actual requirement of flying, the flight path must be smoothed. In conclusion, the three-dimensional flight path planning problem of the unmanned aerial vehicle can be regarded as a multi-constraint optimization problem.
With the increasing complexity of the planning problem, the difficulty and the calculation amount of the corresponding solution are also rapidly increased. As can be seen from the lack of free lunch theorem (NFL), one optimization algorithm may perform well in one series of questions and poorly in another series of questions. Therefore, an unmanned aerial vehicle track planning method capable of effectively coping with all complex environments is difficult to find in the existing algorithm.
The Sparrow Search Algorithm (SSA) is a new type of group intelligence optimization Algorithm. The algorithm is proposed by elicitation of foraging and anti-predation behaviors of sparrows. The standard SSA algorithm has the advantages of less adjustment parameters, high convergence speed, simple calculation and the like. However, in the aspect of defects, when the SSA algorithm is used for solving a complex engineering optimization problem, an early-maturing phenomenon starts to occur, so that the convergence precision is not high, and the problem is easy to fall into local optimization, so that the flight path flyability obtained when the standard SSA algorithm is used for directly solving a flight path planning problem is not high.
Disclosure of Invention
The invention mainly aims to overcome the defects and shortcomings of the prior art and provides an unmanned aerial vehicle three-dimensional track planning method based on a chaotic self-adaptive sparrow search algorithm.
The purpose of the invention is realized by the following technical scheme:
an unmanned aerial vehicle three-dimensional flight path planning method based on a chaotic self-adaptive sparrow search algorithm is characterized by comprising the following steps:
establishing a flight environment model according to the flight environment;
establishing a flight cost function of the unmanned aerial vehicle, and evaluating the track performance of the unmanned aerial vehicle;
a chaotic initialization population strategy, a self-adaptive weight strategy and a Cauchy-Gaussian mixed variation strategy are adopted to improve a sparrow search algorithm, and the chaotic self-adaptive sparrow search algorithm is provided;
and planning the unmanned aerial vehicle track in the three-dimensional environment by adopting a chaotic self-adaptive sparrow search algorithm to obtain an optimal solution of unmanned aerial vehicle track planning and obtain a planning result.
Further, the establishing of the flight environment model according to the flight environment specifically includes: and acquiring a digital elevation model of the target flight area, and obtaining a three-dimensional map model through MATLAB processing.
Further, the three-dimensional map model comprises a threat area, and a flight starting point, a target coordinate point and threat distribution coordinates are set in the three-dimensional map model.
Further, the establishing of the three-dimensional flight path planning model, that is, the establishing of the flight cost function of the unmanned aerial vehicle, and the evaluation of the flight path performance of the unmanned aerial vehicle specifically include:
setting the performance indexes of the three-dimensional track planning of the unmanned aerial vehicle: track length, flight height, maximum turn angle;
acquiring the track length through a track length cost function; acquiring the flying height through a flying height model; obtaining a maximum rotation angle through a rotation angle cost model;
obtaining a track cost function through the track length cost function, the flight height model and the corner cost model:
Jcost＝w1Lpath+w2Hheight+w3Jturn，
wherein, JturnIs the total track cost function, LpathAs a path length cost function, hheightIs a heightStandard deviation cost function of, JturnIs a corner cost function, parameter wiI is 1,2,3 represents the weight of each cost function, and the following condition is satisfied:
and obtaining a flight path consisting of line segments by solving the cost function, calling a B-spline curve function by using an interpolation mode, and smoothing the obtained flight path to finally obtain the actual flight path of the unmanned aerial vehicle.
Further, the track length is obtained through a track length cost function, as follows: setting n nodes of the complete track, wherein the distance between the ith track point and the (i + 1) th track point is liThe coordinates of the two track points are denoted by g (i) ═ xi,yi,zi),g(i+1)＝(xi+1,yi+1,zi+1) The flight path needs to satisfy the following conditions:
wherein L ispathFor track length cost function, say
The flying height is obtained through the flying height model, and the method comprises the following steps: the flying height model is as follows:
wherein h isheightIs a standard deviation cost function of the altitude, n is the number of track nodes,
is a height average value, ziThe height of the ith track point;
the maximum rotation angle is obtained through the rotation angle cost model, as follows: the corner cost model is as follows:
wherein, JturnIs a corner cost function, phi is the maximum corner, theta is the current corner, aiIs the ith track vector, | aiAnd | represents the length of the vector a.
Furthermore, the improved chaotic self-adaptive sparrow search algorithm is provided, a chaotic initialization population strategy is adopted in an algorithm initialization stage to enhance the stability of the algorithm, a self-adaptive weight factor is introduced in a finder position updating stage, and a Cauchy-Gaussian mixed variation strategy is adopted for elite sparrow individuals; as follows:
simulating a sparrow group into a finder-joiner model by adopting a standard SSA algorithm, and simultaneously superposing a reconnaissance early warning mechanism; the finder is an individual who can find food easily, other individuals are participants, meanwhile, a certain proportion of individuals are selected as early-warning persons in the population, and if threats are detected, foraging at the position is abandoned; based on a standard SSA algorithm, an improved chaotic self-adaptive sparrow search algorithm is provided, a chaotic initialization population strategy is adopted in an algorithm initialization stage to enhance the stability of the algorithm, a self-adaptive weight factor is introduced in a finder position updating stage, and a Cauchy-Gaussian mixed variation strategy is adopted for elite sparrow individuals;
further, the standard SSA algorithm has the following process:
initialization: setting the sparrow population scale N, the number Pd of discoverers, the number Sd of early-warning users, the dimensionality D of a target function, the upper bound ub and the lower bound lb of an initial value, and the maximum iteration number Tmax；
In the D-dimensional search space, N sparrows exist, and the position of the ith sparrow in the D-dimensional search space is:
Xi＝[x1,…xD],
wherein i is 1, 2., N,
the fitness value of the ith sparrow may be expressed as:
wherein f represents a fitness value;
the finder with good adaptability can preferentially obtain food in the foraging process and provide foraging directions for all the entrants, so that the finder has a larger search range than the entrants; the position update formula is as follows:
in the formula, XiThe position of the ith sparrow in the D-dimensional space is represented, and t represents the current iteration times of the algorithm; alpha epsilon (0,1)]Represents a random number; r2∈(0,1]Representing an early warning value, and ST belonging to [0.5,1) representing a safety value; q represents a random number following a normal distribution; l represents a matrix of 1 row and d columns with all elements being 1; when R is2If ST, meaning the foraging environment is safe, the finder can perform a wide range of search operations, if R2ST is greater than or equal to the threshold value, the situation that part of sparrows find predators and send out alarm signals is indicated, and all sparrows need to be transferred to a safe position;
the rest sparrows in the population are all the participants, and the updating formula of the positions of the participants is as follows:
wherein, XiThe position of the ith sparrow in a D-dimensional space is shown, T represents the number of current iterations of the algorithm, and TmaxRepresents the maximum number of iterations of the algorithm, XbestThe position of the current population is the best position occupied by the current population, namely the position of an elite sparrow individual, Xworst is the worst position, Q represents a random number which obeys normal distribution, A is a d x d matrix, and each element of the matrix is randomly assigned with 1 or-1; l represents a matrix of 1 row and D columns with all elements 1 when
It is foraged near the optimal position,
when the situation is met, the ith participant does not obtain food and needs to fly to the ith participant to find food;
all sparrows in the population have a reconnaissance early warning mechanism, the dangerous sparrows generally account for 10% -20% of the population, and the position updating formula of the reconnaissance early warning sparrows is as follows:
in the formula, XiThe position of the ith sparrow in a D-dimensional space is shown, t represents the number of current iterations of the algorithm, and XbestThe position is the best position occupied by the current population, beta is a step length parameter which is a random number with the mean value of 0 and the variance of 1 and obeying normal distribution; k ∈ [ -1,1]Is a random number, fiIs the fitness of the ith sparrow, fbIs the current best fitness value, fwIs the current worst fitness value; epsilon is a constant and takes values between (1.00E-10 and 1.00E-9), so that the condition that the denominator is equal to zero can be avoided in the formula.
Further, the improved chaotic self-adaptive sparrow search algorithm specifically comprises the following steps:
carrying out population initialization on a sparrow algorithm by adopting a cubic mapping chaotic operator:
pi+1＝4pi 3-3pi
-1＜pi＜1,pi≠0,i＝0,1,...,N
Xi＝Xlb+(Xlb-Xub)×(pi+1)×0.5
wherein p represents a cubic mapping chaotic operator, which is a D-dimensional vector with each dimension value of (-1,1), wherein X is in the formulaiFor values of sparrow individual variables, Xlb，XubThe upper and lower boundaries of sparrow individuals in all dimensions are shown;
initializing sparrow population with population size N by using cubic mapping chaotic operator: randomly generating a d-dimensional vector, wherein each dimension is (-1,1) as a first operator, performing iterative operation on each dimension of the first operator to obtain the rest (N-1) operators, and mapping an operator value generated by cubic mapping to a sparrow individual by adopting a formula (10), wherein X isiFor values of sparrow individual variables, Xlb，XubThe upper and lower boundaries of sparrow individuals in all dimensions are shown;
introducing an adaptive weight factor strategy:
introducing a nonlinear time-varying adaptive weight factor w into a position updating formula of a finder, wherein a small fitness value enables the algorithm to have a large optimizing range in the initial iteration stage by introducing the adaptive weight factor, and a large fitness value is beneficial to improving the convergence precision of the algorithm in the later stage; alpha in the standard SSA finder position updating formula is a random number, and a dynamic nonlinear change weight factor is introduced to control the value range of alpha, which is defined as follows:
in the formula fiIs the fitness value of the current sparrow individual, fgIs the global optimum fitness value, t is the current iteration number;
introducing a Cauchy-Gaussian mixed mutation strategy:
in the later stage of the solution of the sparrow algorithm, sparrow populations gradually gather to the optimal individuals, so that population diversity is insufficient, and the algorithm is stagnated. In order to solve the problem, the invention introduces a Cauchy-Gaussian mixed variation strategy, performs variation operation on the individual with the best current fitness, and selects the optimal individual from the individuals before and after variation to enter the next iteration, which is defined as follows:
in the formula (I), the compound is shown in the specification,
is the position of the elite sparrow with the best population fitness at present,
is the position of an elite individual after variation, cauchy (0,1) is a random variable satisfying Cauchy distribution, Gauss (0,1) is a random variable satisfying Gaussian distribution,
is a dynamic parameter which is self-adaptively adjusted along with the iteration number.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the improved sparrow algorithm provided by the invention has obvious advantages in solving quality, the chaos strategy and the self-adaptive strategy enable the algorithm to have rapid convergence speed and excellent convergence precision, and the variation strategy enables the algorithm to have strong capability of jumping out of local optimum, so that an excellent unmanned aerial vehicle flight path can be obtained quickly.
Drawings
FIG. 1 is a flow chart of a method for planning a three-dimensional flight path of an unmanned aerial vehicle based on a chaotic self-adaptive sparrow search algorithm, which is disclosed by the invention;
FIG. 2 is a schematic diagram of a three-dimensional map model according to an embodiment of the invention;
FIG. 3 is a schematic representation of a threat distribution model in an embodiment of the invention;
FIG. 4 is a flow chart of a chaotic adaptive sparrow search algorithm in an embodiment of the present invention;
FIG. 5 is a simulation diagram of three-dimensional flight path planning of the unmanned aerial vehicle based on the CASSA algorithm in the embodiment of the present invention;
FIG. 6 is a two-dimensional track planning simulation in accordance with an embodiment of the present invention;
fig. 7 is a graph showing the convergence of the CASSA algorithm in the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Example (b):
an unmanned aerial vehicle three-dimensional flight path planning method based on a chaotic self-adaptive sparrow search algorithm is shown in fig. 1 and comprises the following steps:
establishing a flight environment model according to the flight environment;
establishing a three-dimensional track planning model, namely establishing a flight cost function of the unmanned aerial vehicle, and evaluating the track performance of the unmanned aerial vehicle;
an improved chaotic self-adaptive sparrow search algorithm is provided, and specifically comprises the following steps: a chaos initialization population strategy is adopted in an algorithm initialization stage to enhance the stability of the algorithm, a self-adaptive weight factor is introduced in a finder position updating stage, and a Cauchy-Gaussian mixed variation strategy is adopted for Elite individuals;
and applying the chaotic self-adaptive sparrow search algorithm to a flight path planning model to obtain an optimal solution of the unmanned aerial vehicle flight path planning, namely a planning result.
The method comprises the following specific steps:
the first step is as follows: establishing a flight environment model: firstly, a Digital Elevation Model (DEM) of a target flight area is acquired, and a three-dimensional map model is obtained through MATLAB processing, as shown in FIG. 2. In addition, when the unmanned aerial vehicle performs a flight task, the unmanned aerial vehicle often encounters areas with flight safety threats, namely threat areas, such as enemy radar or missile systems, and the like, and the unmanned aerial vehicle is likely to crash once entering the threat areas. To simplify the model, the present invention represents the threat zone with a cylindrical region of radius r. Setting unmanned aerial vehicle flight starting point (10, 90) and target point coordinates (140, 10), threat distribution coordinates (10, 60), (30, 40), (60, 20), (70, 60), (120, 70), (110, 30), and a threat distribution model as shown in fig. 3.
The second step is that: and establishing a three-dimensional track planning model, establishing a reasonable unmanned aerial vehicle flight cost function, and evaluating the unmanned aerial vehicle track performance. The performance indexes of the three-dimensional flight path planning of the unmanned aerial vehicle mainly comprise flight path length, flight height and maximum rotation angle, and specifically comprise the following steps: .
The length of the flight path is important for planning the mission, and a shorter flight path saves more fuel and time, and has a lower probability of encountering other unknown threats. The definition of the track is the value of the distance traveled by the drone from the starting point to the end point.
A complete track has n nodes, wherein the distance between the ith track point and the (i + 1) th track point is liThe coordinates of the two track points are denoted by g (i) ═ xi,yi,zi),g(i+1)＝(xi+1,yi+1,zi+1) The flight path needs to satisfy the following conditions:
wherein L ispathFor track length cost function, if the unmanned aerial vehicle is trapped in a dangerous area or collides with an obstacle facing crash danger, the collision is recorded as LpathAnd processing in a punitive mode in the simulation.
The flying height needs sufficient stability, and too high height is unfavorable for avoiding unknown threat, and too low height can increase unmanned aerial vehicle and massif obstacle collision probability. The invention uses the following fly height model:
wherein h isheightIs a standard deviation cost function of the altitude, n is the number of track nodes,
is the average value of height, ziIs the height of the ith track point.
The corner cost function affects the operability and flight stability of the unmanned aerial vehicle, and in the flight path planning process, the corner of the flight path is not larger than the preset maximum corner. The invention uses the following corner cost model:
wherein, JturnIs a corner cost function, phi is the maximum corner, theta is the current corner, aiIs the ith track vector, | aiAnd | represents the length of the vector a.
Establishing a cost function for the flight path length, the flight height and the maximum rotation angle, wherein the obtained flight path cost function is as follows:
Jcost＝w1Lpath+w2Hheight+w3Jturn (4)
wherein, JturnIs the total track cost function, parameter wiI is 1,2,3 represents the weight of each cost function, and the following condition is satisfied:
and solving the cost function to obtain a flight path consisting of line segments, calling a B-spline curve function in an interpolation mode, and smoothing the obtained flight path to finally obtain the actual flight path of the unmanned aerial vehicle.
The third step: planning a flight path based on a chaotic self-adaptive sparrow search algorithm: the chaotic self-adaptive sparrow search algorithm is provided, a chaotic initialization population strategy is adopted in an algorithm initialization stage to enhance the stability of the algorithm, a self-adaptive weight factor is introduced in a finder position updating stage, and then a Cauchy-Gaussian mixed variation strategy is adopted for elite sparrow individuals to improve the algorithm jumping-out local optimum capability.
The standard SSA algorithm is to find a solution to the optimization problem by simulating the foraging process of sparrows. The algorithm principle is as follows: the sparrow population is simulated as a finder-joiner model, and meanwhile, a reconnaissance early warning mechanism is superposed. The finder is an individual who can find food easily, other individuals are participants, meanwhile, a certain proportion of individuals are selected as early-warning persons in the population, and if threats are detected, foraging in the position is abandoned.
step 1: initialization: setting the sparrow population scale N, the number Pd of discoverers, the number Sd of early-warning users, the dimensionality D of a target function, the upper bound ub and the lower bound lb of an initial value, and the maximum iteration number Tmax。
step 2: in the D-dimension search space, N sparrows exist, and the position of the ith sparrow in the D-dimension space is Xi＝[x1,...xD]Where i ═ 1, 2., N, the fitness value of the ith sparrow may be expressed as:
where f represents the fitness value.
step 3: a finder with better fitness will preferentially acquire food during foraging and provide directions for foraging to all of the enrollees, so the finder has a larger search range than the enrollees. The position update formula is as follows:
where t represents the number of current iterations of the algorithm. Alpha epsilon (0,1)]Representing a random number. R2∈(0,1]Represents the early warning value, and ST ∈ [0.5,1) represents the safety value. Q represents a random number following a normal distribution. L represents a matrix of 1 row and d columns with all elements 1. When R is2If ST, meaning the foraging environment is safe, the finder can perform a wide range of search operations, if R2ST ≧ ST, this indicates that some sparrows found predators and signaled an alarm, at which time all sparrows needed to be transferred to a safe location.
step 4: the rest sparrows in the population are all the participants, and the updating formula of the positions of the participants is as follows:
wherein, XbestIs that the current population is occupiedIs the location of the elite sparrow, Xworst is the worst location, and a is a d x d matrix, each element of which is randomly assigned a value of 1 or-1. When in use
It is foraged near the optimal position,
when the food is not obtained by the ith participant, the ith participant needs to fly to the ith participant for foraging.
Step 5: all sparrows in the population have a reconnaissance early warning mechanism, the dangerous sparrows generally account for 10% -20% of the population, and the position updating formula of the reconnaissance early warning sparrows is as follows:
where β is a step size parameter, is a random number whose mean is 0, whose variance is 1, and which follows a normal distribution. K ∈ [ -1,1]Is a random number, fiIs the fitness of the ith sparrow, fbIs the current best fitness value, fwIs the current worst fitness value. Epsilon is a constant and takes values between (1.00E-10 and 1.00E-9), so that the condition that the denominator is equal to zero can be avoided in the formula.
The fourth step: the specific improvement and application of the chaotic self-adaptive sparrow search algorithm are as follows:
the flow chart of the chaotic self-adaptive sparrow search algorithm is shown in fig. 4:
introducing a chaos initialization population strategy:
when solving a complex optimization problem, the sparrow algorithm has the defect that the population diversity is reduced in the later iteration stage, is easy to premature and converge and falls into local optimization. Therefore, the invention adopts the cubic mapping chaotic operator to carry out population initialization on the sparrow algorithm, and can improve the population diversity of the algorithm according to the advantages of randomness and regularity of the chaotic operator.
Xi＝Xlb+(Xlb-Xub)×(pi+1)×0.5 (10)
Initializing a sparrow population with the population size of N by adopting a cubic mapping chaotic operator: randomly generating a d-dimensional vector, wherein each dimension is (-1,1) as a first operator, performing iterative operation on each dimension of the first operator by adopting a formula (9) to obtain the rest (N-1) operators, and mapping an operator value generated by cubic mapping to a sparrow individual by adopting a formula (10), wherein X isiFor values of sparrow individual variables, Xlb，XubThe upper and lower bounds of sparrow individuals in each dimension.
Introducing an adaptive weight factor strategy:
the self-adaptive weight factor w of the nonlinear time variation is introduced into the position updating formula of the finder of step3, and by introducing the self-adaptive weight factor, the algorithm has a larger optimizing range due to a smaller adaptability value in the initial stage of iteration, and the convergence precision of the algorithm is favorably improved due to a larger adaptability value in the later stage. Alpha in the standard SSA finder position updating formula is a random number, and the invention introduces a weight factor with dynamic nonlinear change to control the value range of the alpha, which is defined as follows:
in the formula fiIs the fitness value of the current sparrow individual, fgIs the global best fitness value and t is the current iteration number.
Introducing a Cauchy-Gaussian mixed mutation strategy:
in the later stage of the solution of the sparrow algorithm, sparrow populations gradually gather to the optimal individuals, so that population diversity is insufficient, and the algorithm is stagnated. In order to solve the problem, the invention introduces a Cauchy-Gaussian mixed variation strategy, performs variation operation on the individual with the best current fitness, and selects the optimal individual from the individuals before and after variation to enter the next iteration, which is defined as follows:
in the formula (I), the compound is shown in the specification,
is the position of the elite sparrow with the best population fitness at present,
is the position of an elite individual after variation, cauchy (0,1) is a random variable satisfying Cauchy distribution, Gauss (0,1) is a random variable satisfying Gaussian distribution,
is a dynamic parameter which is self-adaptively adjusted along with the iteration number.
Simulation verification:
and solving the three-dimensional track planning problem of the unmanned aerial vehicle by using a CASSA algorithm, namely actually, optimizing and solving an objective function by using the CASSA. In the algorithm, each sparrow represents a track, the dimension of each sparrow represents the number of track points, the value of each sparrow individual in each dimension represents the coordinate value of the track point, and the individual fitness of the sparrow is the objective function value of the track of the unmanned aerial vehicle, and the lower the value is, the better the value is. The sparrow population finder is an individual with a better current objective function value, the follower is a poorer individual, the optimal position of the sparrow population is obtained by updating the positions of the finder and the follower, and the optimal solution returned after the algorithm reaches the maximum iteration number is the found optimal track. The simulation diagram for the three-dimensional flight path planning of the unmanned aerial vehicle based on the CASSA algorithm is shown in FIG. 5, and FIG. 6 is a simulation diagram for the two-dimensional flight path planning; fig. 7 is a graph of the convergence of the CASSA algorithm.
The specific implementation process is as follows:
the first step is as follows: and initializing parameters, namely initializing all parameters of the CASSA algorithm, such as maximum iteration times, population scale, the number of discoverers and early-warning persons and the like, and setting the weights of three cost functions of the unmanned aerial vehicle track planning.
The second step is that: and setting the flight starting point coordinate and the flight finishing point coordinate of the unmanned aerial vehicle, and setting the coordinate of the threat area and the size of the threat range.
The third step: and (4) optimizing and solving the unmanned aerial vehicle track evaluation function by using a CASSA algorithm, and continuously updating and storing the acquired optimal track.
The fourth step: and judging whether the algorithm reaches the maximum iteration times, if not, continuing to execute the loop, and if so, ending the algorithm and outputting the acquired optimal track.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (8)
1. An unmanned aerial vehicle three-dimensional flight path planning method based on a chaotic self-adaptive sparrow search algorithm is characterized by comprising the following steps:
establishing a flight environment model according to the flight environment;
establishing a flight cost function of the unmanned aerial vehicle, and evaluating the track performance of the unmanned aerial vehicle;
a chaotic initialization population strategy, a self-adaptive weight strategy and a Cauchy-Gaussian mixed variation strategy are adopted to improve a sparrow search algorithm, and the chaotic self-adaptive sparrow search algorithm is provided;
and planning the unmanned aerial vehicle track in the three-dimensional environment by adopting a chaotic self-adaptive sparrow search algorithm to obtain an optimal solution of unmanned aerial vehicle track planning and obtain a planning result.
2. The unmanned aerial vehicle three-dimensional flight path planning method based on the chaotic self-adaptive sparrow search algorithm according to claim 1, wherein the flying environment model is established according to the flying environment, and specifically comprises the following steps: and acquiring a digital elevation model of the target flight area, and obtaining a three-dimensional map model through MATLAB processing.
3. The unmanned aerial vehicle three-dimensional flight path planning method based on the chaotic self-adaptive sparrow search algorithm according to claim 2, wherein the three-dimensional map model comprises a threat area, and a flight starting point, a target coordinate point and a threat distribution coordinate are set in the three-dimensional map model.
4. The unmanned aerial vehicle three-dimensional flight path planning method based on the chaotic self-adaptive sparrow search algorithm according to claim 1, wherein the unmanned aerial vehicle flight cost function is established to evaluate the unmanned aerial vehicle flight path performance, and specifically comprises the following steps:
setting the performance indexes of the three-dimensional track planning of the unmanned aerial vehicle: track length, flight height, maximum turn angle;
acquiring the track length through a track length cost function; acquiring the flying height through a flying height model; obtaining a maximum rotation angle through a rotation angle cost model;
obtaining a track cost function through the track length cost function, the flight height model and the corner cost model:
Jcost＝w1Lpath+w2Hheight+w3Jturn，
wherein, JcostIs the total track cost function, LpathAs a path length cost function, hheightAs a standard deviation cost function of height, JturnIs a corner cost function, parameter wiI is 1,2,3 represents the weight of each cost function, and the following condition is satisfied:
and obtaining a flight path consisting of line segments by solving the cost function, calling a B-spline curve function by using an interpolation mode, and smoothing the obtained flight path to finally obtain the actual flight path of the unmanned aerial vehicle.
5. The unmanned aerial vehicle three-dimensional flight path planning method based on the chaotic self-adaptive sparrow search algorithm, according to claim 4, wherein the flight path length is obtained through a flight path length cost function, as follows: setting n nodes of the complete track, wherein the distance between the ith track point and the (i + 1) th track point is liThe coordinates of the two track points are denoted by g (i) ═ xi,yi,zi),g(i+1)＝(xi+1,yi+1,zi+1) The flight path needs to satisfy the following conditions:
wherein L ispathAs a function of the cost of the length of the flight path,
the flying height is obtained through the flying height model, and the method comprises the following steps: the flying height model is as follows:
wherein h isheightIs a standard deviation cost function of the altitude, n is the number of track nodes,
is a height average value, ziThe height of the ith track point;
the maximum rotation angle is obtained through the rotation angle cost model, as follows: the corner cost model is as follows:
wherein, JturnIs a corner cost function, phi is the maximum corner, theta is the current corner, aiIs the ith track vector, | aiAnd | represents the length of the vector a.
6. The unmanned aerial vehicle three-dimensional flight path planning method based on the chaotic self-adaptive sparrow search algorithm, according to claim 1, is characterized in that the chaotic initialization population strategy, the adaptive weight strategy and the cauchy-gaussian mixed variation strategy are adopted to improve the sparrow search algorithm, and the chaotic self-adaptive sparrow search algorithm is provided, as follows:
simulating a sparrow group into a finder-joiner model by adopting a standard SSA algorithm, and simultaneously superposing a reconnaissance early warning mechanism; the finder is an individual who can find food easily, other individuals are participants, meanwhile, a certain proportion of individuals are selected as early-warning persons in the population, and if threats are detected, foraging at the position is abandoned; based on a standard SSA algorithm, an improved chaotic self-adaptive sparrow search algorithm is provided, a chaotic initialization population strategy is adopted in an algorithm initialization stage to enhance the stability of the algorithm, a self-adaptive weight factor is introduced in a finder position updating stage, and a Cauchy-Gaussian mixed variation strategy is adopted for elite sparrow individuals.
7. The unmanned aerial vehicle three-dimensional flight path planning method based on the chaotic self-adaptive sparrow search algorithm according to claim 6, wherein the standard SSA algorithm comprises the following processes:
initialization: setting a sparrow population scale N, the number Pd of discoverers, the number Sd of early-warning users, the dimensionality D of a target function, an upper bound ub and a lower bound lb of an initial value, and the maximum iteration number ss;
in the D-dimensional search space, N sparrows exist, and the position of the ith sparrow in the D-dimensional search space is:
Xi＝[x1,...xD],
wherein i is 1, 2., N,
the fitness value of the ith sparrow may be expressed as:
wherein f represents a fitness value;
the finder with good adaptability can preferentially obtain food in the foraging process and provide foraging directions for all the entrants, so that the finder has a larger search range than the entrants; the position update formula is as follows:
in the formula, XiThe position of the ith sparrow in the D-dimensional space is represented, and t represents the current iteration times of the algorithm; alpha epsilon (0,1)]Represents a random number; r2∈(0,1]Representing an early warning value, and ST belonging to [0.5,1) representing a safety value; q represents a random number following a normal distribution; l represents a matrix of 1 row and d columns with all elements being 1; when R is2If ST, meaning the foraging environment is safe, the finder can perform a wide range of search operations, if R2ST is greater than or equal to the threshold value, the situation that part of sparrows find predators and send out alarm signals is indicated, and all sparrows need to be transferred to a safe position;
the rest sparrows in the population are all the participants, and the updating formula of the positions of the participants is as follows:
wherein, XiThe position of the ith sparrow in a D-dimensional space is shown, T represents the number of current iterations of the algorithm, and TmaxRepresents the maximum number of iterations of the algorithm, XbestThe position of the current population is the best position occupied by the current population, namely the position of an elite sparrow individual, Xworst is the worst position, Q represents a random number which obeys normal distribution, A is a d x d matrix, and each element of the matrix is randomly assigned with 1 or-1; l represents a matrix of 1 row and D columns with all elements 1,when in use
It is foraged near the optimal position,
when the situation is met, the ith participant does not obtain food and needs to fly to the ith participant to find food;
all sparrows in the population have a reconnaissance early warning mechanism, the dangerous sparrows generally account for 10% -20% of the population, and the position updating formula of the reconnaissance early warning sparrows is as follows:
in the formula, XiThe position of the ith sparrow in a D-dimensional space is shown, t represents the number of current iterations of the algorithm, and XbestThe position is the best position occupied by the current population, beta is a step length parameter which is a random number with the mean value of 0 and the variance of 1 and obeying normal distribution; k ∈ [ -1,1]Is a random number, fiIs the fitness of the ith sparrow, fbIs the current best fitness value, fwIs the current worst fitness value; epsilon is a constant and takes a value between 1.00E-10 and 1.00E-9, so that the condition that the denominator is equal to zero is avoided in the formula.
8. The unmanned aerial vehicle three-dimensional flight path planning method based on the chaotic self-adaptive sparrow search algorithm according to claim 7, wherein the improved chaotic self-adaptive sparrow search algorithm is specifically as follows:
carrying out population initialization on a sparrow algorithm by adopting a cubic mapping chaotic operator:
pi+1＝4pi 3-3pi
-1＜pi＜1,pi≠0,i＝0,1,...,N
Xi＝Xlb+(Xlb-Xub)×(pi+1)×0.5
wherein p representsThe chaos operator of square mapping is a D-dimensional vector with each dimension value of (-1,1), wherein X isiFor values of sparrow individual variables, Xlb，XubThe upper and lower boundaries of sparrow individuals in all dimensions are shown;
initializing a sparrow population with the population size of N by adopting a cubic mapping chaotic operator: randomly generating a d-dimensional vector, taking each dimension as (-1,1) as a first operator, performing iterative operation on each dimension of the first operator to obtain the rest (N-1) operators, and mapping operator values generated by cubic mapping to sparrow individuals, wherein X isiFor values of sparrow individual variables, Xlb，XubThe upper and lower boundaries of sparrow individuals in all dimensions are shown;
introducing an adaptive weight factor strategy:
introducing a nonlinear time-varying adaptive weight factor w into a position updating formula of a finder, wherein a small fitness value enables the algorithm to have a large optimizing range in the initial iteration stage by introducing the adaptive weight factor, and a large fitness value is beneficial to improving the convergence precision of the algorithm in the later stage; alpha in the standard SSA finder position updating formula is a random number, and a dynamic nonlinear change weight factor is introduced to control the value range of alpha, which is defined as follows:
in the formula fiIs the fitness value of the current sparrow individual, fgIs the global optimum fitness value, t is the current iteration number;
introducing a Cauchy-Gaussian mixed mutation strategy:
in the later stage of solving the sparrow algorithm, sparrow populations gradually gather to the optimal individuals, so that population diversity is insufficient, and the algorithm is stagnated; in order to solve the problem, a cauchy-gaussian mixed mutation strategy is introduced, mutation operation is carried out on the individual with the best current fitness, and the optimal individual is selected from the individuals before and after mutation to enter the next iteration, which is defined as follows:
in the formula (I), the compound is shown in the specification,
is the position of the elite sparrow with the best population fitness at present,
is the position of an elite individual after variation, cauchy (0,1) is a random variable satisfying Cauchy distribution, Gauss (0,1) is a random variable satisfying Gaussian distribution,
is a dynamic parameter which is self-adaptively adjusted along with the iteration number.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110110630.4A CN112880688B (en) | 2021-01-27 | 2021-01-27 | Unmanned aerial vehicle three-dimensional track planning method based on chaotic self-adaptive sparrow search algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110110630.4A CN112880688B (en) | 2021-01-27 | 2021-01-27 | Unmanned aerial vehicle three-dimensional track planning method based on chaotic self-adaptive sparrow search algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112880688A true CN112880688A (en) | 2021-06-01 |
| CN112880688B CN112880688B (en) | 2023-05-23 |
Family
ID=76053375
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110110630.4A Active CN112880688B (en) | 2021-01-27 | 2021-01-27 | Unmanned aerial vehicle three-dimensional track planning method based on chaotic self-adaptive sparrow search algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112880688B (en) |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113344828A (en) * | 2021-06-30 | 2021-09-03 | 太原理工大学 | Chaotic sparrow search algorithm-based coal rock boundary image enhancement method |
| CN113762603A (en) * | 2021-08-13 | 2021-12-07 | 广西大学 | Photovoltaic base station short-term photovoltaic power prediction method based on improved sparrow algorithm optimization |
| CN113759313A (en) * | 2021-07-23 | 2021-12-07 | 哈尔滨工程大学 | Time difference/frequency difference positioning method based on chaotic sparrow algorithm |
| CN113805568A (en) * | 2021-08-17 | 2021-12-17 | 北京理工大学 | Man-machine cooperative perception method based on multi-agent space-time modeling and decision making |
| CN113867369A (en) * | 2021-12-03 | 2021-12-31 | 中国人民解放军陆军装甲兵学院 | Robot path planning method based on alternating current learning seagull algorithm |
| CN113947009A (en) * | 2021-08-31 | 2022-01-18 | 西北大学 | Sparse LSTM landslide dynamic prediction method based on Cauchy disturbance sparrow optimization |
| CN114020031A (en) * | 2021-11-16 | 2022-02-08 | 南京航空航天大学 | Improved pigeon swarm optimization-based unmanned aerial vehicle cluster collaborative dynamic target searching method |
| CN114115329A (en) * | 2021-10-28 | 2022-03-01 | 中国人民解放军陆军工程大学 | Relay cooperative unmanned aerial vehicle task planning method and device |
| CN114237282A (en) * | 2021-11-29 | 2022-03-25 | 南京邮电大学 | Intelligent unmanned aerial vehicle flight path planning method for intelligent industrial park monitoring |
| CN114417712A (en) * | 2022-01-01 | 2022-04-29 | 西北工业大学 | Airship propeller reliability estimation method based on chaos initialization SSA-BP neural network |
| CN114460941A (en) * | 2022-02-09 | 2022-05-10 | 云南大学 | Robot path planning method and system based on improved sparrow search algorithm |
| CN114485665A (en) * | 2021-12-31 | 2022-05-13 | 东华大学 | Unmanned aerial vehicle flight path planning method based on sparrow search algorithm |
| CN114995149A (en) * | 2022-06-13 | 2022-09-02 | 南通大学 | Improved chaotic weight-variable sparrow search parameter identification method for hydraulic position servo system |
| CN115018149A (en) * | 2022-06-01 | 2022-09-06 | 南京林业大学 | Helicopter aviation pesticide application airplane take-off and landing point position planning method |
| CN115277770A (en) * | 2022-07-20 | 2022-11-01 | 华北电力大学（保定） | Unmanned aerial vehicle information collection method based on joint optimization of node access and flight strategy |
| CN115542936A (en) * | 2022-10-28 | 2022-12-30 | 中国地质大学（武汉） | Intelligent optimization method and device for layout of aeromagnetic unmanned aerial vehicle steering engine and electronic equipment |
| CN115657693A (en) * | 2022-12-28 | 2023-01-31 | 安徽省交通航务工程有限公司 | Ship path optimization method, electronic device and storage medium |
| CN116562331A (en) * | 2023-05-19 | 2023-08-08 | 石家庄铁道大学 | Method for optimizing SVM by improving reptile search algorithm and application thereof |
| CN117056847A (en) * | 2023-10-10 | 2023-11-14 | 中南大学 | Method, system, equipment and storage medium for detecting abnormality of stream data |
| CN117170413A (en) * | 2023-11-03 | 2023-12-05 | 北京卓翼智能科技有限公司 | Unmanned aerial vehicle path planning method and device based on modified sine and cosine algorithm |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1835370A2 (en) * | 2006-03-17 | 2007-09-19 | Thales | Automatic trajectory planner |
| CN108413959A (en) * | 2017-12-13 | 2018-08-17 | 南京航空航天大学 | Based on the Path Planning for UAV for improving Chaos Ant Colony Optimization |
| US10140875B1 (en) * | 2017-05-27 | 2018-11-27 | Hefei University Of Technology | Method and apparatus for joint optimization of multi-UAV task assignment and path planning |
| CN109144102A (en) * | 2018-09-19 | 2019-01-04 | 沈阳航空航天大学 | A kind of Path Planning for UAV based on improvement bat algorithm |
| CN109631900A (en) * | 2018-12-24 | 2019-04-16 | 中国矿业大学 | A kind of no-manned plane three-dimensional track multi-objective particle swarm Global Planning |
| CN110608743A (en) * | 2019-10-18 | 2019-12-24 | 南京航空航天大学 | Multi-unmanned aerial vehicle collaborative route planning method based on multi-population chaotic grayling algorithm |
| JP2020019336A (en) * | 2018-07-31 | 2020-02-06 | 旻新科技股▲分▼有限公司 | Planning method for flight path of unmanned aerial vehicle using flight route of bird |
| CN111815055A (en) * | 2020-07-11 | 2020-10-23 | 哈尔滨理工大学 | Unmanned aerial vehicle flight path planning method based on improved Levy flight ant lion optimization algorithm |
| CN112033416A (en) * | 2020-09-18 | 2020-12-04 | 中原工学院 | Static track planning method and device for unmanned aerial vehicle |
| CN112130581A (en) * | 2020-08-19 | 2020-12-25 | 昆明理工大学 | Unmanned aerial vehicle cluster cooperative task planning method for aerial maneuver battle |
| CN112230678A (en) * | 2020-10-29 | 2021-01-15 | 皖江工学院 | Three-dimensional unmanned aerial vehicle path planning method and planning system based on particle swarm optimization |
-
2021
- 2021-01-27 CN CN202110110630.4A patent/CN112880688B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1835370A2 (en) * | 2006-03-17 | 2007-09-19 | Thales | Automatic trajectory planner |
| US10140875B1 (en) * | 2017-05-27 | 2018-11-27 | Hefei University Of Technology | Method and apparatus for joint optimization of multi-UAV task assignment and path planning |
| CN108413959A (en) * | 2017-12-13 | 2018-08-17 | 南京航空航天大学 | Based on the Path Planning for UAV for improving Chaos Ant Colony Optimization |
| JP2020019336A (en) * | 2018-07-31 | 2020-02-06 | 旻新科技股▲分▼有限公司 | Planning method for flight path of unmanned aerial vehicle using flight route of bird |
| CN109144102A (en) * | 2018-09-19 | 2019-01-04 | 沈阳航空航天大学 | A kind of Path Planning for UAV based on improvement bat algorithm |
| CN109631900A (en) * | 2018-12-24 | 2019-04-16 | 中国矿业大学 | A kind of no-manned plane three-dimensional track multi-objective particle swarm Global Planning |
| CN110608743A (en) * | 2019-10-18 | 2019-12-24 | 南京航空航天大学 | Multi-unmanned aerial vehicle collaborative route planning method based on multi-population chaotic grayling algorithm |
| CN111815055A (en) * | 2020-07-11 | 2020-10-23 | 哈尔滨理工大学 | Unmanned aerial vehicle flight path planning method based on improved Levy flight ant lion optimization algorithm |
| CN112130581A (en) * | 2020-08-19 | 2020-12-25 | 昆明理工大学 | Unmanned aerial vehicle cluster cooperative task planning method for aerial maneuver battle |
| CN112033416A (en) * | 2020-09-18 | 2020-12-04 | 中原工学院 | Static track planning method and device for unmanned aerial vehicle |
| CN112230678A (en) * | 2020-10-29 | 2021-01-15 | 皖江工学院 | Three-dimensional unmanned aerial vehicle path planning method and planning system based on particle swarm optimization |
Non-Patent Citations (5)
| Title |
|---|
| WANG Y等: "Obstacle Avoidance of UAV Based on Neural Networks and Interfered Fluid Dynamical System", 《2020 3RD INTERNATIONAL CONFERENCE ON UNMANNED SYSTEMS》 * |
| XUE J等: "A novel swarm intelligence optimization approach：sparrow search algorithm", 《SYSTEMS SCIENCE AND CONTROL ENGINEERING》 * |
| 汤安迪等: "基于混沌麻雀搜索算法的无人机航迹规划方法", 《计算机应用》 * |
| 薛建凯: "一种新型的群智能优化技术的研究与应用 ——麻雀搜索算法", 《中国优秀硕士学位论文全文数据库信息科技辑(月刊)》 * |
| 赵普凡等: "基于动态路径规划的车载自组织网络贪婪路由协议", 《国外电子测量技术》 * |
Cited By (35)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113344828A (en) * | 2021-06-30 | 2021-09-03 | 太原理工大学 | Chaotic sparrow search algorithm-based coal rock boundary image enhancement method |
| CN113344828B (en) * | 2021-06-30 | 2023-05-30 | 太原理工大学 | Coal-rock demarcation image enhancement method based on chaotic sparrow search algorithm |
| CN113759313A (en) * | 2021-07-23 | 2021-12-07 | 哈尔滨工程大学 | Time difference/frequency difference positioning method based on chaotic sparrow algorithm |
| CN113759313B (en) * | 2021-07-23 | 2023-09-29 | 哈尔滨工程大学 | Time difference/frequency difference positioning method based on chaotic sparrow algorithm |
| CN113762603B (en) * | 2021-08-13 | 2023-08-22 | 广西大学 | Photovoltaic base station short-term photovoltaic power prediction method based on improved sparrow algorithm optimization |
| CN113762603A (en) * | 2021-08-13 | 2021-12-07 | 广西大学 | Photovoltaic base station short-term photovoltaic power prediction method based on improved sparrow algorithm optimization |
| CN113805568A (en) * | 2021-08-17 | 2021-12-17 | 北京理工大学 | Man-machine cooperative perception method based on multi-agent space-time modeling and decision making |
| CN113805568B (en) * | 2021-08-17 | 2024-04-09 | 北京理工大学 | Man-machine collaborative perception method based on multi-agent space-time modeling and decision |
| CN113947009B (en) * | 2021-08-31 | 2024-03-08 | 西北大学 | Sparse LSTM landslide dynamic prediction method based on Cauchy disturbance sparrow optimization |
| CN113947009A (en) * | 2021-08-31 | 2022-01-18 | 西北大学 | Sparse LSTM landslide dynamic prediction method based on Cauchy disturbance sparrow optimization |
| CN114115329A (en) * | 2021-10-28 | 2022-03-01 | 中国人民解放军陆军工程大学 | Relay cooperative unmanned aerial vehicle task planning method and device |
| CN114115329B (en) * | 2021-10-28 | 2023-06-16 | 中国人民解放军陆军工程大学 | Relay collaborative unmanned aerial vehicle task planning method and device |
| CN114020031A (en) * | 2021-11-16 | 2022-02-08 | 南京航空航天大学 | Improved pigeon swarm optimization-based unmanned aerial vehicle cluster collaborative dynamic target searching method |
| CN114020031B (en) * | 2021-11-16 | 2023-09-22 | 南京航空航天大学 | Unmanned aerial vehicle cluster collaborative dynamic target searching method based on improved pigeon colony optimization |
| CN114237282A (en) * | 2021-11-29 | 2022-03-25 | 南京邮电大学 | Intelligent unmanned aerial vehicle flight path planning method for intelligent industrial park monitoring |
| CN114237282B (en) * | 2021-11-29 | 2023-10-27 | 南京邮电大学 | Unmanned aerial vehicle flight path intelligent planning method for intelligent industrial park monitoring |
| CN113867369A (en) * | 2021-12-03 | 2021-12-31 | 中国人民解放军陆军装甲兵学院 | Robot path planning method based on alternating current learning seagull algorithm |
| CN114485665A (en) * | 2021-12-31 | 2022-05-13 | 东华大学 | Unmanned aerial vehicle flight path planning method based on sparrow search algorithm |
| CN114417712A (en) * | 2022-01-01 | 2022-04-29 | 西北工业大学 | Airship propeller reliability estimation method based on chaos initialization SSA-BP neural network |
| CN114460941B (en) * | 2022-02-09 | 2023-10-24 | 云南大学 | Robot path planning method and system based on improved sparrow search algorithm |
| CN114460941A (en) * | 2022-02-09 | 2022-05-10 | 云南大学 | Robot path planning method and system based on improved sparrow search algorithm |
| CN115018149A (en) * | 2022-06-01 | 2022-09-06 | 南京林业大学 | Helicopter aviation pesticide application airplane take-off and landing point position planning method |
| CN114995149A (en) * | 2022-06-13 | 2022-09-02 | 南通大学 | Improved chaotic weight-variable sparrow search parameter identification method for hydraulic position servo system |
| CN115277770B (en) * | 2022-07-20 | 2023-04-25 | 华北电力大学（保定） | Unmanned aerial vehicle information collection method based on joint optimization of node access and flight strategy |
| CN115277770A (en) * | 2022-07-20 | 2022-11-01 | 华北电力大学（保定） | Unmanned aerial vehicle information collection method based on joint optimization of node access and flight strategy |
| CN115542936A (en) * | 2022-10-28 | 2022-12-30 | 中国地质大学（武汉） | Intelligent optimization method and device for layout of aeromagnetic unmanned aerial vehicle steering engine and electronic equipment |
| CN115542936B (en) * | 2022-10-28 | 2024-04-16 | 中国地质大学（武汉） | Intelligent optimization method and device for steering engine layout of aeromagnetic unmanned aerial vehicle and electronic equipment |
| CN115657693A (en) * | 2022-12-28 | 2023-01-31 | 安徽省交通航务工程有限公司 | Ship path optimization method, electronic device and storage medium |
| US11941553B1 (en) | 2022-12-28 | 2024-03-26 | Hefei University Of Technology | Methods, electronic devices and storage media for ship route optimization |
| CN116562331A (en) * | 2023-05-19 | 2023-08-08 | 石家庄铁道大学 | Method for optimizing SVM by improving reptile search algorithm and application thereof |
| CN116562331B (en) * | 2023-05-19 | 2023-11-21 | 石家庄铁道大学 | Method for optimizing SVM by improving reptile search algorithm and application thereof |
| CN117056847A (en) * | 2023-10-10 | 2023-11-14 | 中南大学 | Method, system, equipment and storage medium for detecting abnormality of stream data |
| CN117056847B (en) * | 2023-10-10 | 2024-01-30 | 中南大学 | Method, system, equipment and storage medium for detecting abnormality of stream data |
| CN117170413B (en) * | 2023-11-03 | 2024-01-30 | 北京卓翼智能科技有限公司 | Unmanned aerial vehicle path planning method and device based on modified sine and cosine algorithm |
| CN117170413A (en) * | 2023-11-03 | 2023-12-05 | 北京卓翼智能科技有限公司 | Unmanned aerial vehicle path planning method and device based on modified sine and cosine algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112880688B (en) | 2023-05-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112880688A (en) | Unmanned aerial vehicle three-dimensional flight path planning method based on chaotic self-adaptive sparrow search algorithm | |
| CN110608743B (en) | Multi-unmanned aerial vehicle collaborative route planning method based on multi-population chaotic grayling algorithm | |
| CN112947581B (en) | Multi-unmanned aerial vehicle collaborative air combat maneuver decision method based on multi-agent reinforcement learning | |
| Yang et al. | UAV air combat autonomous maneuver decision based on DDPG algorithm | |
| CN111240353B (en) | Unmanned aerial vehicle collaborative air combat decision method based on genetic fuzzy tree | |
| CN108549233B (en) | Unmanned aerial vehicle air combat maneuver game method with intuitive fuzzy information | |
| CN108319132B (en) | Decision-making system and method for unmanned aerial vehicle air countermeasure | |
| CN113791634A (en) | Multi-aircraft air combat decision method based on multi-agent reinforcement learning | |
| CN111859541B (en) | PMADDPG multi-unmanned aerial vehicle task decision method based on transfer learning improvement | |
| CN114489144B (en) | Unmanned aerial vehicle autonomous maneuver decision method and device and unmanned aerial vehicle | |
| CN107063255A (en) | A kind of three-dimensional Route planner based on improvement drosophila optimized algorithm | |
| CN110928329A (en) | Multi-aircraft track planning method based on deep Q learning algorithm | |
| CN114460959A (en) | Unmanned aerial vehicle group cooperative autonomous decision-making method and device based on multi-body game | |
| CN110986958A (en) | Multi-unmanned aerial vehicle collaborative path planning method based on multi-population collaborative drosophila optimization | |
| CN114063644A (en) | Unmanned combat aircraft air combat autonomous decision method based on pigeon flock reverse confrontation learning | |
| CN113962012A (en) | Unmanned aerial vehicle countermeasure strategy optimization method and device | |
| CN113741186B (en) | Double-aircraft air combat decision-making method based on near-end strategy optimization | |
| CN115033022A (en) | DDPG unmanned aerial vehicle landing method based on expert experience and oriented to mobile platform | |
| CN113625569B (en) | Small unmanned aerial vehicle prevention and control decision method and system based on hybrid decision model | |
| CN116661503B (en) | Cluster track automatic planning method based on multi-agent safety reinforcement learning | |
| Kaneshige et al. | Artificial immune system approach for air combat maneuvering | |
| CN113671825A (en) | Maneuvering intelligent decision missile avoidance method based on reinforcement learning | |
| CN112651486A (en) | Method for improving convergence rate of MADDPG algorithm and application thereof | |
| CN115422776A (en) | Multi-base heterogeneous unmanned cluster multi-wave-time unified cooperative task allocation method and system | |
| CN113255893B (en) | Self-evolution generation method of multi-agent action strategy |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |