CN116088576A - Unmanned aerial vehicle three-dimensional path planning method based on improved whale algorithm - Google Patents
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Abstract
The invention belongs to the field of path planning, and particularly relates to an unmanned aerial vehicle three-dimensional path planning method based on an improved whale algorithm, which comprises the following steps: constructing an unmanned aerial vehicle flight environment, and determining a start point and an end point of unmanned aerial vehicle flight; constructing constraint conditions of unmanned aerial vehicle path planning problems according to the determined starting points and the determined ending points of unmanned aerial vehicle flight; establishing a cost function F of the unmanned aerial vehicle flight path quality according to constraint conditions of the unmanned aerial vehicle path planning problem, and converting the path planning problem into a minimization problem for solving the cost function F; updating the whale position by utilizing an improved whale algorithm, and solving the whale position when the cost function F is minimum to obtain a predicted optimal path point of the unmanned aerial vehicle; and smoothly connecting the predicted optimal path points through a gradient descent method to obtain the optimal flight path points of the unmanned aerial vehicle. According to the invention, the flight environment of the unmanned aerial vehicle is simulated, and an improved whale algorithm is adopted, so that an accurate and reasonable flight path of the unmanned aerial vehicle can be realized.
Description
Technical Field
The invention belongs to the field of path planning, and particularly relates to an unmanned aerial vehicle three-dimensional path planning method based on an improved whale algorithm.
Background
With the progress of science and technology, unmanned aerial vehicles have been widely used in various fields such as military, industry, life, etc., for example: military investigation, electric power inspection, cargo transportation, logistics distribution and the like. The solution of the path planning problem is an important guarantee for efficiently, accurately and safely completing the tasks of the unmanned aerial vehicle, and has wide prospect and research significance. Therefore, a reasonable planning of the unmanned aerial vehicle path is required.
Because the unmanned plane path planning is an optimization problem that the objective function is very complex and the optimization difficulty is high, the unmanned plane path planning has strong nonlinearity and non-convexity and is not easy to process. Traditional path planning methods (such as an a-algorithm, a D-algorithm, an RRT algorithm, etc.) are difficult to adapt to unstructured environments when solving the three-dimensional path planning problem of an unmanned aerial vehicle. The intelligent optimization algorithm is developed by simulating or revealing intelligent behaviors of certain phenomena and processes or biological groups in nature, and has the advantages of simplicity, universality, convenience in parallel processing and the like compared with the traditional path planning method.
The whale optimization algorithm is a novel intelligent swarm optimization algorithm obtained by simulating whale hunting behaviors. From the aspects of algorithm structure and calculation efficiency, the algorithm is superior to other meta-heuristic algorithms based on swarm intelligence, such as a particle swarm algorithm, a gray wolf algorithm and the like, and has strong competitiveness. The method has the advantages of high flexibility, strong robustness and few control parameters. However, whale algorithms tend to be prone to local optima and to problems with premature convergence, resulting in a path that is planned that is not the most reasonable and therefore further improvements are needed.
The prior art has the following problems: when the conventional path planning method (such as an a-algorithm, a D-algorithm, an RRT algorithm, etc.) is used for solving the three-dimensional path planning problem of the unmanned aerial vehicle, the conventional path planning method is difficult to adapt to an unstructured environment, and the planned path is not the most reasonable.
Disclosure of Invention
In order to solve the technical problems, the invention provides an unmanned aerial vehicle three-dimensional path planning method based on an improved whale algorithm, which comprises the following steps:
s1: constructing an unmanned aerial vehicle flight environment, and determining a start point and an end point of unmanned aerial vehicle flight;
s2: constructing constraint conditions of unmanned aerial vehicle path planning problems according to the determined starting points and the determined ending points of unmanned aerial vehicle flight;
s3: establishing a cost function F of the unmanned aerial vehicle flight path quality according to constraint conditions of the unmanned aerial vehicle path planning problem, and converting the path planning problem into a minimization problem for solving the cost function F;
s4: updating the whale position by utilizing an improved whale algorithm, solving the whale position when the cost function F is minimum, and obtaining the predicted optimal path point [ x ] of the unmanned aerial vehicle 1 ,x 2 ,x 3 ...x n ]；
S5: the optimal path point x is predicted by gradient descent method 1 ,x 2 ,x 3 ...x n ]Smooth connection to obtain the optimal flight path point [ y ] of the unmanned aerial vehicle 1 ,y 2 ,y 3 ...y n ]。
The invention has the beneficial effects that: according to the invention, the flight environment of the unmanned aerial vehicle is simulated, and an improved whale algorithm is adopted, so that an accurate and reasonable flight path of the unmanned aerial vehicle can be realized.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic view of a basic terrain model of the present invention;
FIG. 3 is a vertical plan view of a threat zone in the threat zone model of the invention;
FIG. 4 is a schematic illustration of a hybrid terrain model of the present invention;
fig. 5 is a schematic representation of a three-dimensional path of the improved whale algorithm program of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
An unmanned aerial vehicle three-dimensional path planning method based on an improved whale algorithm, as shown in fig. 1, comprises the following steps:
s1: constructing an unmanned aerial vehicle flight environment, and determining a start point and an end point of unmanned aerial vehicle flight;
s2: constructing constraint conditions of unmanned aerial vehicle path planning problems according to the determined starting points and the determined ending points of unmanned aerial vehicle flight;
s3: establishing a cost function F of the unmanned aerial vehicle flight path quality according to constraint conditions of the unmanned aerial vehicle path planning problem, and converting the path planning problem into a minimization problem for solving the cost function F;
s4: updating the whale position by utilizing an improved whale algorithm, solving the whale position when the cost function F is minimum, and obtaining the predicted optimal path point [ x ] of the unmanned aerial vehicle 1 ,x 2 ,x 3 ...x n ]；
S5: the optimal path point x is predicted by gradient descent method 1 ,x 2 ,x 3 ...x n ]Smooth connection to obtain the optimal flight path point [ y ] of the unmanned aerial vehicle 1 ,y 2 ,y 3 ...y n ]。
As shown in fig. 2, when the unmanned aerial vehicle flies in the three-dimensional space, due to the complex terrain environment, a plurality of uneven obstacles exist on the ground, the obstacles do not have obvious height differences, namely the height differences among the obstacles are not very large, the height differences among the obstacles are limited in a certain range, but the unmanned aerial vehicle still needs to keep a certain distance with the obstacles, so that collision is avoided;
the basic terrain model comprises:
wherein Z is 1 (x, y) represents a basic topography model, x and y are two-dimensional coordinate values, a, b, c, d, e, f are constants, the constants determine the topography of the basic topography, and different actual topographical features can be simulated and used as known topographical information of the unmanned aerial vehicle flight area.
The existence of a plurality of mountain terrains on the ground can influence the flight of the unmanned aerial vehicle, compared with a basic topography map, the unmanned aerial vehicle has larger height difference and wider range, has larger threat to the safe flight of the unmanned aerial vehicle, and can be characterized by an exponential function;
the mountain terrain model comprises:
wherein Z is 2 (x, y) represents a mountain terrain model, x j And y j The x-axis and y-axis coordinates of the jth mountain are respectively expressed as the transverse slope and the longitudinal slope of the jth mountain, and h is a parameter for determining the shape of the mountain j The height corresponding to the jth mountain, namely the corresponding z-axis coordinate.
The method comprises the steps that 4 peaks are arranged, a threat area model is placed in an environment based on the peak model and a basic terrain model, the threat area is an interception area of a ground air-defense missile, and the shape of the threat area is a sphere area from a near boundary to a far boundary in space; in the horizontal direction, the cross-sectional shape of the missile striking zone approximates a circle, which corresponds to a larger radius when the vertical height of the missile reaches a maximum; when the unmanned aerial vehicle is outside the threat area, the probability of the unmanned aerial vehicle being hit is the inverse of the distance from the missile; when the unmanned aerial vehicle enters the threat area, the probability of hitting the unmanned aerial vehicle is higher than 0.95; as shown in fig. 3, a vertical plan view of the threat zone is shown;
the missile threat model includes:
Z 3 (x,y)＝k m ×D max 2 -(x-x 0 ) 2 -(y-y 0 ) 2
wherein Z is 3 (x, y) represents a missile threat model, D max Corresponding skew, k, for furthest boundary arc BC of missile threat m Is a constant whose magnitude is related to the missile velocity coefficient, and x and y are two-dimensional coordinate values.
Unmanned aerial vehicle flight environment includes: basic topography, mountain topography, threat areas, which together constitute a hybrid topography of unmanned aerial vehicle flight, as shown in fig. 4.
Constructing constraint conditions of unmanned aerial vehicle path planning problems according to the starting point and the ending point of unmanned aerial vehicle flight, wherein the constraint conditions comprise:
because the unmanned aerial vehicle has limited power, if the number of turns is too large, more oil is consumed, so that the navigation error is increased, and the navigation system of the unmanned aerial vehicle is affected; therefore, the unmanned aerial vehicle cannot frequently turn, and a certain flat flight distance is required before and after turning; assuming that the unmanned plane path has n waypoints, then l 1 、l 2 、l 3 、l 4 、l 5 ...l n For each length of unmanned trajectory, therefore minimum flight path constraints, include:
l i ≥l min (i＝1,2...n)
the speed of the unmanned aerial vehicle is limited in a certain range due to the influence of the performance of the unmanned aerial vehicle and the external environment; let unmanned aerial vehicle's flight speed be V, unmanned aerial vehicle's maximum speed and minimum speed be Vmax, vmin, flight speed restriction constraint includes:
Vmin≤V≤Vmax
the unmanned aerial vehicle needs to reduce the height of the unmanned aerial vehicle when executing certain special tasks, the detection of an enemy local missile system is avoided by utilizing the shielding of irregular obstacles with the height of the ground, but the unmanned aerial vehicle is easy to collide with the ground obstacles during low-altitude flight, the possibility of collision with the ground is reduced while avoiding the detection of the missile system, the unmanned aerial vehicle needs to fly within a certain height range, and the lowest flight height of the unmanned aerial vehicle is assumed to be H min The highest flying height is H max The flying height H of the unmanned plane at the ith path point i A fly-height limit constraint of (1), comprising:
H min ≤H i ≤H max (i＝1,2,...n)
maximum turning angle constraint: because the unmanned aerial vehicle may turn over at too large an angle in the actual leg process, the unmanned aerial vehicle has high requirements on the maneuvering performance of the unmanned aerial vehicle, however, the maneuvering performance of each unmanned aerial vehicle is often limited, and compared with the straight line flight,the cornering flight consumes more fuel; let unmanned aerial vehicle maximum turning angle be theta max The horizontal projection of the ith path is a i ＝(x i+1 -x i ,y i+1 -y i ) T Then the adjacent path segments should satisfy the relationship:
wherein l i Represents the ith flight path length, l, of the unmanned aerial vehicle min Representing a minimum track length, V representing a flight speed of the unmanned aerial vehicle, vmin representing the minimum flight speed of the unmanned aerial vehicle, vmax representing the maximum flight speed of the unmanned aerial vehicle, H i Represents the minimum flight height of the unmanned aerial vehicle, H min Represents the minimum flight height of the unmanned aerial vehicle, H max Represents the highest flying height, x of the unmanned plane i+1 X-axis coordinate, X of the ith+1th section of path point unmanned plane i Representing X-axis coordinate, y of ith section of path point unmanned aerial vehicle i+1 Representing Y-axis coordinate, Y of ith section of path point unmanned aerial vehicle i Representing Y-axis coordinates of the (i+1) -th path point unmanned aerial vehicle, T representing transposition operation, a i+1 Representing a horizontal projection of the i+1st segment path.
Establishing a cost function of an unmanned plane path planning problem, wherein the cost function consists of three parts, namely average flight altitude cost, flight length cost and flight threat cost; the cost function is a weighted sum of the three.
Further, the average fly height cost is calculated as:
wherein n represents the number of path points, (x) i ,y i ,z i ) As the number of the path points, the unmanned plane is positioned at the ith path point (x i ,y i ,z i ) The height at is h i 。
Further, in the flight process of the unmanned aerial vehicle, because the fuel quantity stored by the unmanned aerial vehicle is limited, the fuel quantity needs to support the unmanned aerial vehicle to return to the starting point after executing tasks, so that a shortest path from the starting point to the end point without passing through a no-fly zone needs to be selected, and the consumption of the fuel quantity is reduced; the flight length cost is calculated as:
dividing the whole route into n sections, determining the flight direction of each section through an algorithm, and connecting each section of the route together to form the whole route; wherein l k For the length of the kth path segment, n is the number of path points, (x) k ,y k ,z k ) Coordinates of kth waypoint, (x) k-1 ,y k-1 ,z k-1 ) Is the kth-1 waypoint coordinates adjacent to the kth waypoint.
The unmanned aerial vehicle faces various threats in the flight process, and the threat degree is inversely related to the actual distance from a threat source; the closer the unmanned aerial vehicle is to the dangerous source, the higher the corresponding threat value is, the flight threat cost of the unmanned aerial vehicle is the weighted summation of the threat costs received by all path control points, and the calculation formula of the flight threat cost is as follows:
where m represents the number of threat zones, f d (i, j) represents the threat cost of the jth threat source received by the ith path control point, n is the number of path points, R j And d (i, j) is the distance from the jth threat source to the ith path point.
Establishing a cost function F of the unmanned aerial vehicle flight path according to constraint conditions, wherein the cost function F comprises the following steps:
F＝W 1 ×F 1 +W 2 ×F 2 +W 3 ×F 3
wherein F represents the total cost function of the unmanned aerial vehicle, F 1 Representing the flying height cost of the unmanned aerial vehicle, F 2 Representing the flight length cost of the unmanned aerial vehicle, F 3 Representing the flight threat cost of the unmanned aerial vehicle, m represents the number of threat zones, f d (i, j) represents the threat cost of the jth threat source to which the ith path point is subjected, R j Represents the radius of the ith threat zone, d (i, j) represents the distance of the jth threat source from the ith path point, W 1 、W 2 、W 3 And respectively representing the weight of the flight altitude cost, the flight length cost and the flight threat cost of the unmanned aerial vehicle.
Updating whale positions with an improved whale algorithm, comprising:
s41: setting the maximum iteration number Max_iter, the population scale as N, the current iteration number t, the initial search probability P, a new convergence factor alpha and the number N of whales, and enabling t=1;
s42: initializing a whale population by adopting improved Logistic chaotic mapping to obtain an initial whale population uniformly distributed in a solution space;
s43: calculating a reverse population according to the initialized whale population through a reverse search strategy, calculating individual fitness of 2N mixed populations formed by the initialized population and the reverse population, arranging the individuals, and selecting the first N individuals with the smallest fitness as a new population;
s44: updating the current iteration number as follows: t=t+1;
s45: calculating the fitness value of each individual in the new population, comparing, selecting the individual with the smallest fitness value and recording the positions of the individuals; judging whether the current iteration number exceeds the maximum iteration number Max_iter, if so, stopping iteration, and recording the position of the current optimal solution, otherwise, continuing step S46;
s46: adding a search limit through a Lewy flight strategy, and introducing a variation selection strategy to select a better whale position;
s47: calculating position update mode coefficients a=2ar-a and c=2r according to the new convergence factor, calculating adaptive probability, and updating individual whale positions according to the adaptive probability, initial search probability P and position update mode coefficients, and returning to steps S44 and S45 until the iteration is terminated.
The new convergence factor comprises:
where α represents a new convergence factor, t represents a current iteration number, and max_iter represents a maximum iteration number.
Initializing a whale population with an improved Logistic chaotic map to obtain an initial whale population uniformly distributed in a solution space, comprising:
wherein x is n+1 X-axis coordinate, X of whale population in solution space distribution after improved Logistic chaotic mapping n The X-axis coordinates of the initial whale population at the time of solution space distribution are shown.
Calculating a reverse population by a reverse search strategy, comprising:
reverse population of individuals:
OX jd ＝X mind +X maxd -X jd
reverse population:
OX＝{OX jd ,j＝1,2,3...N,d＝1,2,...,D}
wherein OX jd Representing individuals in the reverse population, X mind Represents the search lower bound, X maxd Represents the search upper bound, X jd The d-th dimension value representing the j-th population of individuals, and OX represents the reverse population.
Adding search limits through the Lewy flight strategy, introducing a variation selection strategy to select a more optimal whale position, comprising:
updating whale positions through a Lewy flight strategy to obtain optimal individual positions of a new population
Three whale individuals X selected randomly from the current population r1 (g)、X r2 (g)、X r3 (g) According to three randomly selected whale individuals X r1 (g)、X r2 (g)、X r3 (g) Updating variant individual position V using variant selection strategy i (g)＝X r1 (g)+Q×(X r2 (g)-X r3 (g) Judging whether the variant individuals meet the population boundary, if yes, regenerating the variant individuals, and determining the positions V of the variant individuals i (g) Position X to the optimal individual * (t+1) performing fitness comparison, and selecting the individual with the smallest fitness value as the final whale population individual; wherein X is * (t+1) represents the optimal individual position, X, in the new population * (t) represents the individual position in the new population, r represents the random number of the optimal individual position, levy (β) represents the random search path, +.>
Representing a point-to-point multiplication operation, V i (g) Represents the updated variant position, Q represents the variant factor, q=0.3×2 Q1 ，
t is the current iteration number, and Max_iter is the maximum iteration number.
Updating the whale individual position according to the self-adaptive probability, the initial search probability P and the position updating mode coefficient, comprising:
wherein X (t+1) represents the final updated individual whalesPosition X rand Representing randomly selected individuals in the initial whale population, W represents adaptive inertial weights,
t represents the current iteration number, max_iter represents the maximum iteration number, A represents the first location update mode coefficient, c represents the second location update mode coefficient, X * (t) represents the optimal position of whale after iteration, X (t) represents the current position of whale, b represents the logarithmic spiral constant, and l represents the closed interval [ -1,1 []Random number f of (f) p Representing adaptive probability->
IR indicates population evolution rate, < >>
N b Represents the number of whale populations in the whale population for which the current iteration was better than the previous iteration, and N represents the total number of whale individuals.
As shown in FIG. 5, the updated individual whale position is used as the predicted optimal waypoint [ x ] 1 ,x 2 ,x 3 ...x n ]And performing path smoothing, wherein the specific strategy is as follows:
assume that the smoothed path-planned point sequence is [ y ] 1 ,y 2 ,y 3 ...y n ]The path smoothing minimization cost is:
0.6×||x(i)-y(i)|| 2 +0.6×||y(i)-y(i+1)||
the method comprises the following specific steps:
step 1: solving y (i) so that x (i) -y (i) is 2 Minimum; wherein | I x (i) -y (i) | 2 The degree of deviation of the smoothed point y (i) from the original point x (i);
step 2: so that y (i) meets the minimum value of y (i) -y (i+1); wherein y (i) -y (i+1) is the distance between the smoothed points y (i) and y (i+1);
step 3: initializing y (i) =x (i), iterating the loop:
y(i)＝y(i)+A 1 ×[x(i)-y(i)] 2 +B 1 ×[y(i-1)-2×y(i)+y(i+1)]adjusting y (i) so that the cost objective function takes a minimum value to obtain a predicted optimal waypoint [ y ] 1 ,y 2 ,y 3 ...y n ]Smoothly connecting the optimal route points according to the order to obtain an optimal flight path of the unmanned aerial vehicle, wherein A is as follows 1 、B 1 The constant is a physical quantity that determines the degree of path smoothness.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. An unmanned aerial vehicle three-dimensional path planning method based on an improved whale algorithm is characterized by comprising the following steps of:
s1: constructing an unmanned aerial vehicle flight environment, and determining a start point and an end point of unmanned aerial vehicle flight;
s2: constructing constraint conditions of unmanned aerial vehicle path planning problems according to the determined starting points and the determined ending points of unmanned aerial vehicle flight;
s3: establishing a cost function F of the unmanned aerial vehicle flight path quality according to constraint conditions of the unmanned aerial vehicle path planning problem, and converting the path planning problem into a minimization problem for solving the cost function F;
s4: updating the whale position by utilizing an improved whale algorithm, solving the whale position when the cost function F is minimum, and obtaining the predicted optimal path point [ x ] of the unmanned aerial vehicle 1 ,x 2 ,x 3 …x n ]；
S5: the optimal path point x is predicted by gradient descent method 1 ,x 2 ,x 3 …x n ]Smooth connection to obtain the optimal flight path point [ y ] of the unmanned aerial vehicle 1 ,y 2 ,y 3 …y n ]。
2. The unmanned aerial vehicle three-dimensional path planning method based on the improved whale algorithm according to claim 1, wherein the unmanned aerial vehicle flight environment comprises: the unmanned aerial vehicle comprises basic topography, mountain topography and threat areas, wherein the basic topography, the mountain topography and the threat areas jointly form a hybrid topography map of unmanned aerial vehicle flight.
3. The method for three-dimensional path planning of an unmanned aerial vehicle based on the improved whale algorithm according to claim 1, wherein the constraint conditions of the unmanned aerial vehicle path planning problem are constructed according to the starting point and the ending point of the unmanned aerial vehicle flight, comprising:
minimum flight path constraints:
l i ≥l min (i＝1,2…n)
flight speed limit constraints:
Vmin≤V≤Vmax
fly height limit constraints:
H min ≤H i ≤H max (i＝1,2,…n)
maximum turning angle constraint: let unmanned aerial vehicle maximum turning angle be theta max The horizontal projection of the ith path is a i ＝(x i+1 -x i ,y i+1 -y i ) T Then the adjacent path segments should satisfy the relationship:
wherein l i Represents the ith flight path length, l, of the unmanned aerial vehicle min Representing a minimum track length, V representing a flight speed of the unmanned aerial vehicle, vmin representing the minimum flight speed of the unmanned aerial vehicle, vmax representing the maximum flight speed of the unmanned aerial vehicle, H i Represents the minimum flight height of the unmanned aerial vehicle, H min Represents the minimum flight height of the unmanned aerial vehicle, H max Represents the highest flying height, x of the unmanned plane i+1 X-axis coordinate, X of the ith+1th section of path point unmanned plane i Representing X-axis coordinate, y of ith section of path point unmanned aerial vehicle i+1 Representing Y-axis coordinate, Y of ith section of path point unmanned aerial vehicle i Y-axis coordinate and T table for representing ith+1th section of path point unmanned planeShowing the transpose operation, a i+1 Representing a horizontal projection of the i+1st segment path.
4. The unmanned aerial vehicle three-dimensional path planning method based on the improved whale algorithm according to claim 1, wherein the cost function F for establishing the advantages and disadvantages of the unmanned aerial vehicle flight path according to the constraint conditions comprises the following steps:
F＝W 1 ×F 1 +W 2 ×F 2 +W 3 ×F 3
wherein F represents the total cost function of the unmanned aerial vehicle, F 1 Representing the cost of the flying height of the unmanned aerial vehicle,
n represents the number of path points, h i Indicating that the drone is at the ith path point (x i ,y i ,z i ) Height, F 2 Representing unmanned aerial vehicle flight length cost->
l k Represents the length of the kth path segment, +.>
(x k ,y k ,z k ) Represents the coordinates of the kth waypoint, (x) k-1 ,y k-1 ,z k-1 ) Represents the kth-1 th waypoint coordinates adjacent to the kth waypoint, F 3 Representing unmanned aerial vehicle flight threat cost->
m represents the number of threat zones, f d (i, j) represents the threat cost,/-for the jth threat source to which the ith waypoint is subjected>
R j Represents the radius of the ith threat zone, d (i, j) represents the distance of the jth threat source from the ith path point, W 1 、W 2 、W 3 And respectively representing the weight of the flight altitude cost, the flight length cost and the flight threat cost of the unmanned aerial vehicle.
5. The unmanned aerial vehicle three-dimensional path planning method of claim 1, wherein updating the whale position using the modified whale algorithm comprises:
s41: setting the maximum iteration number Max_iter, the population scale as N, the current iteration number t, the initial search probability P, a new convergence factor alpha and the number N of whales, and enabling t=1;
s42: initializing a whale population by adopting improved Logistic chaotic mapping to obtain an initial whale population uniformly distributed in a solution space;
s43: calculating a reverse population according to the initialized whale population through a reverse search strategy, calculating individual fitness of 2N mixed populations formed by the initialized population and the reverse population, arranging the individuals, and selecting the first N individuals with the smallest fitness as a new population;
s44: updating the current iteration number as follows: t=t+1;
s45: calculating the fitness value of each individual in the new population, comparing, selecting the individual with the smallest fitness value and recording the positions of the individuals; judging whether the current iteration number exceeds the maximum iteration number Max_iter, if so, stopping iteration, and recording the position of the current optimal solution, otherwise, continuing step S46;
s46: adding a search limit through a Lewy flight strategy, and introducing a variation selection strategy to select a better whale position;
s47: calculating position update mode coefficients a=2ar-a and c=2r according to the new convergence factor, calculating adaptive probability, and updating individual whale positions according to the adaptive probability, initial search probability P and position update mode coefficients, and returning to steps S44 and S45 until the iteration is terminated.
6. The unmanned aerial vehicle three-dimensional path planning method based on the modified whale algorithm of claim 5, wherein the new convergence factor comprises:
where α represents a new convergence factor, t represents a current iteration number, and max_iter represents a maximum iteration number.
7. The unmanned aerial vehicle three-dimensional path planning method based on the improved whale algorithm according to claim 5, wherein initializing the whale population with the improved Logistic chaotic map to obtain an initial whale population uniformly distributed in the solution space comprises:
wherein x is n+1 X-axis coordinate, X of whale population in solution space distribution after improved Logistic chaotic mapping n The X-axis coordinates of the initial whale population at the time of solution space distribution are shown.
8. The unmanned aerial vehicle three-dimensional path planning method based on the modified whale algorithm of claim 5, wherein calculating the reverse population by the reverse search strategy comprises:
reverse population of individuals:
OX jd ＝X min d +X max d -X jd
reverse population:
OX＝{OX jd ,j＝1,2,3…N,d＝1,2,…,D}
wherein OX jd Representing individuals in the reverse population, X min d Represents the search lower bound, X max d Represents the search upper bound, X jd The d-th dimension value representing the j-th population of individuals, and OX represents the reverse population.
9. The unmanned aerial vehicle three-dimensional path planning method based on the improved whale algorithm according to claim 5, wherein the adding of the search limit by the lewy flight strategy, the introduction of the variant selection strategy to select the more optimal whale position, comprises:
updating whale positions through a Lewy flight strategy to obtain optimal individual positions of a new population
Three whale individuals X selected randomly from the current population r1 (g)、X r2 (g)、X r3 (g) According to three randomly selected whale individuals X r1 (g)、X r2 (g)、X r3 (g) Updating variant individual position V using variant selection strategy i (g)＝X r1 (g)+Q×(X r2 (g)-X r3 (g) Judging whether the variant individuals meet the population boundary, if yes, regenerating the variant individuals, and determining the positions V of the variant individuals i (g) Position X to the optimal individual * (t+1) performing fitness comparison, and selecting the individual with the smallest fitness value as the final whale population individual.
Wherein X is * (t+1) represents the optimal individual position, X, in the new population * (t) represents the individual position in the new population, r represents the random number of the optimal individual position, levy (beta) represents the random search path,
representing a point-to-point multiplication operation, V i (g) Represents the updated variant position, Q represents the variant factor, q=0.3×2 Q1 ，
t is the current iteration number, and Max_iter is the maximum iteration number.
10. The unmanned aerial vehicle three-dimensional path planning method based on the improved whale algorithm according to claim 5, wherein updating whale individual positions according to the adaptive probability, the initial search probability P and the position updating mode coefficient comprises:
wherein X (t+1) represents the final updated individual position of whale, X rand Representing randomly selected individuals in the initial whale population, W represents adaptive inertial weights,
t represents the current iteration number, max_iter represents the maximum iteration number, A represents the first location update mode coefficient, c represents the second location update mode coefficient, X * (t) represents the optimal position of whale after iteration, X (t) represents the current position of whale, b represents the logarithmic spiral constant, and l represents the closed interval [ -1,1 []Random number f of (f) p Representing adaptive probability->
IR indicates population evolution rate, < >>
N b Represents the number of whale populations in the whale population for which the current iteration was better than the previous iteration, and N represents the total number of whale individuals. />
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| CN116225074A (en) * | 2023-05-10 | 2023-06-06 | 广东电网有限责任公司佛山供电局 | Planning method and system for nest inspection route of unmanned aerial vehicle of power transmission line |
| 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 |
| CN117609768A (en) * | 2024-01-23 | 2024-02-27 | 昆明理工大学 | Vertical water pump unit fault diagnosis method based on improved whale algorithm |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116225074A (en) * | 2023-05-10 | 2023-06-06 | 广东电网有限责任公司佛山供电局 | Planning method and system for nest inspection route of unmanned aerial vehicle of power transmission line |
| 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 |
| 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 |
| CN117609768A (en) * | 2024-01-23 | 2024-02-27 | 昆明理工大学 | Vertical water pump unit fault diagnosis method based on improved whale algorithm |
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