CN114840020A - Unmanned aerial vehicle flight path planning method based on improved whale algorithm - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle flight path planning method based on an improved whale algorithm. The method includes the steps of simulating the flight environment of the unmanned aerial vehicle, constructing an unmanned aerial vehicle track index function, setting parameters to initialize a whale population by taking the unmanned aerial vehicle track index function as a target function based on an improved whale algorithm, calculating a new convergence factor a and a new calculation coefficient vector A by taking the unmanned aerial vehicle track index function as the fitness of an individual, setting a random probability P1 to judge an individual whale updating mechanism, introducing inertial weight W, and updating the individual position of the whale through the position updating mechanism of the whale algorithm to obtain the optimal flight track. According to the invention, when the improved whale algorithm is adopted to plan the flight path of the unmanned aerial vehicle, the stability and accuracy of the flight path planning are improved.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an unmanned aerial vehicle flight path planning method based on an improved whale algorithm.
Background
The flight trajectory planning of the unmanned aerial vehicle has become one of the most important elements for defining the mission planning of the unmanned aerial vehicle, and great potential is exerted in the military and civil fields, so that the unmanned aerial vehicle can autonomously calculate the optimal trajectory from a starting point to a target point according to mission requirements and constraint conditions. In the use process, the unmanned aerial vehicle is provided with intelligent equipment, can simulate the environment of operation in real time, confirms self position, control self flight state, surveys the barrier and calculates the route of avoiding, makes unmanned aerial vehicle can successfully avoid the barrier and accomplish the settlement task smoothly. The safety path is calculated, and the safety path is an important guarantee that the unmanned aerial vehicle can reach a target point from a flying point and is also an important precondition for completing tasks. The correct selection of the path point is an important link in the unmanned aerial vehicle path planning. Therefore, selecting a suitable algorithm is very important for unmanned aerial vehicle path planning.
In recent years, various algorithms are proposed to solve the problem of planning the flight trajectory of the unmanned aerial vehicle, such as an Artificial Potential Field (APF), a Genetic Algorithm (GA), an artificial intelligence algorithm and the like, and because the unmanned aerial vehicle is interfered by the external environment in the flight process (for example, in the environment with weak GPS signals in remote areas and severe air), the algorithms cannot provide effective and accurate flight trajectory guidance for the unmanned aerial vehicle, and the unmanned aerial vehicle does not have flexible and autonomous capability in the face of uncertain environments.
Currently, the whale algorithm has a good effect in the fields of multi-objective optimization, function optimization, neural network training and the like; by adopting a whale algorithm, when target optimization is carried out, although the population can quickly jump out of a local extreme value, the overall optimization capability cannot be improved; the invention provides an unmanned aerial vehicle flight path planning method based on an improved whale algorithm, which defines the flight cost in the flight path: flight length cost, cargo quality cost, flight energy consumption cost and weather threat cost, so that the stability and accuracy of flight trajectory planning are improved, and the optimal trajectory is obtained.
In summary, the technical problems of the prior art are as follows: how to improve the stability and accuracy of flight path planning and obtain the optimal path when the flight path planning operation is performed on the unmanned aerial vehicle.
Disclosure of Invention
The invention aims to solve the technical problems and provides an unmanned aerial vehicle flight path planning method based on an improved whale algorithm, wherein when the unmanned aerial vehicle is subjected to flight path planning operation, the stability and the accuracy of path planning are improved, and an optimal path is obtained.
An unmanned aerial vehicle flight path planning method based on an improved whale algorithm comprises the following steps:
s1, simulating the flight environment of the unmanned aerial vehicle according to the flight length cost, the flight energy consumption cost, the cargo quality cost and the weather threat cost, and constructing an unmanned aerial vehicle track index function;
s2: taking an unmanned aerial vehicle track index function as an objective function to be optimized based on an improved whale algorithm, and solving the unmanned aerial vehicle track index function to obtain an optimal track of unmanned aerial vehicle flight;
wherein, the step S2 specifically includes:
s21, initializing a whale population, setting the maximum iteration time T, the current iteration time T, the initial probability P, a new convergence factor a and the whale number n, and setting T to 1;
s22: taking the unmanned aerial vehicle track index function as the fitness of an individual, calculating a new convergence factor a through the maximum iteration times T and the iteration times T, and calculating a coefficient vector A;
s23: establishing a random probability P1 of 1-log (1+9T/T), and introducing an inertial weight W to update the individual position of the whale according to an individual updating mechanism of the whale algorithm;
if the initial probability P is less than P1 and | A | >1, updating the position by adopting a random search mode according to the new convergence factor a;
if the initial probability P is less than P1 and | A | is less than or equal to 1, updating the position in a wrapping mode according to the new convergence factor a;
if the initial probability P is more than or equal to P1, updating the position in a spiral mode according to a new convergence factor a;
s24: if T is less than the maximum iteration number T, making T equal to T +1, and returning to the step S22; if T is more than or equal to T, outputting a global optimal solution; the solution is taken as the minimum value of the unmanned aerial vehicle path index evaluation function, namely the optimal solution of the objective function;
s25: forming an n-dimensional discrete point set by the optimal solution obtained in the step S24;
s26: and smoothly connecting the discrete track points according to the sequence to obtain the optimal track of the unmanned aerial vehicle.
Preferably, the flying environment of the unmanned aerial vehicle is simulated, in a three-dimensional coordinate system, the height information of the unmanned aerial vehicle is obtained through a logistic-Tent sequence, the three-dimensional coordinate is converted into a two-dimensional coordinate, the coordinate of a starting point is S (0,0), the coordinate of an end point is D (xD, yD), a perpendicular line is drawn from the D point to a Y axis, the Y axis is intersected with D '(xD, 0), a line segment SD' is divided into n equal parts, the end points are marked as Y1, Y2, … and yn, and then Y is obtained i I × SD'/n (i ═ 1,2,3.. n); respectively making the vertical lines of the y axis at the over-end points, respectively marked as L1, L2, … and Ln, converting the path planning problem into the optimization of an X coordinate sequence, namely finding the X axis coordinate [ X ] of the optimal waypoint 1 *(t),X 2 *(t),X 3 *(t)......X n *(t)]And the value of the unmanned aerial vehicle track index function is minimum.
Further, in a three-dimensional coordinate system, acquiring the flight height information of the unmanned aerial vehicle through a logistic-Tent sequence:
wherein rand () is a random number of an interval (0,1), N is the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster, r is a parameter, and is located at [0,4 ]]To (c) to (d); z i The coordinate of the unmanned aerial vehicle mapped to the Z axis is obtained, Z' is the coordinate of the Z axis after the unmanned aerial vehicle is mapped by the logistic-Tent, and the coordinate of the unmanned aerial vehicle obtained after mapping by the logistic-Tent is updated to be (X) i ，Y i ，Z′)。
Preferably, an objective function is constructed according to the flight length cost, the flight energy consumption cost, the cargo quality cost and the weather threat cost:
F(X)＝w 1 *E 1 +w 2 *FL+w 3 *G+w 4 *FM
wherein, F (X) is an unmanned aerial vehicle track index function, namely an objective function; x is any position of whale; e 1 The flight energy consumption cost; FL is flight length penalty; g is the cargo quality cost; FM is the total weather threat cost; w is a 1 +w 2 +w 3 +w 4 ＝1，w 1 、w 2 、w 3 、w 4 The weights represent length cost, flight energy cost, cargo quality cost, weather threat cost, respectively.
Further, the flight length cost, the flight energy consumption cost, the cargo quality cost, and the weather threat cost are:
flight energy consumption cost: the unmanned aerial vehicle flies in two flight states of uniform linear flight and variable speed flight with acceleration in a simulation environment, and an energy consumption cost function is as follows:
E 1 ＝t 1 *E(q(t))+t 2 *E(V,a(t))
wherein E (q (t)) is an unmanned aerial vehicleEnergy consumption during uniform flight; e sLF (V) energy consumption of the unmanned aerial vehicle during accelerated flight; t is t 1 For unmanned aerial vehicle at uniform speed flight time, t 2 Accelerating flight time for the unmanned aerial vehicle; v is the unmanned plane velocity vector; q (t) coordinates of the unmanned aerial vehicle projected into the two-dimensional plane;
flight length penalty:
wherein (X) i ，Y i ) Two-dimensional coordinates of any track point; (X) i-1 ，Yi i-1 ) Two-dimensional coordinates of adjacent track points; i is the number of designed unmanned aerial vehicle track points;
cargo quality cost:
k is an infinite penalty coefficient of the overload of the unmanned aerial vehicle; gi is the demand of the ith distribution point on the goods; i is the number of distribution points with goods demands; fji denotes delivery point i is completed by drone j; v is the maximum bearing capacity of the unmanned aerial vehicle;
weather threat costs:
wherein F is a locus L i Segment weather threats; p (d) is the probability that no one will encounter an obstacle.
Further, the energy consumption of the uniform linear flight is as follows:
wherein E (V, a (T)) is the energy consumption of the drone when changing direction, T is the total time of flight of the drone, and T is T ═ T 1 +t 2 ；c 1 And c 2 The method comprises the following steps that two parameters related to the weight of an airplane, the area of wings and the air density are included, a (t) represents an acceleration vector of the unmanned aerial vehicle, g is the gravity acceleration, and V is the speed of the unmanned aerial vehicle;
further, the variable speed flight energy consumption with acceleration is:
wherein q (t) represents the coordinates of the drone projected into a two-dimensional plane; x (t), y (t) denotes a horizontal coordinate position; v (t) represents the instantaneous velocity vector of the drone; a (t) represents the drone acceleration vector; c. C 1 And c 2 The values of (a) are two parameters relating to aircraft weight, wing area and air density; g represents the acceleration of gravity; m represents the mass of the drone; a is T (t) represents centrifugal acceleration; v (T) represents the magnitude of the unmanned aerial vehicle speed at time T; v (0) represents the unmanned aerial vehicle speed at the time when T is 0; a (t) represents the acceleration magnitude of the unmanned aerial vehicle; v (t) represents the velocity of the drone at time t; t represents the total time of flight of the drone.
Further, weather threatens the cost, is equivalent to the spheroid that the radius is R with thunderstorm weather, and unmanned aerial vehicle is D apart from the distance of centre of sphere, and the biggest radius in bad weather influence area is D1, and D2 shows that unmanned aerial vehicle takes place the distance of the regional distance centre of 1 of probability of crash because of bad weather influence, and the probability that the barrier was touched to unmanned aerial vehicle is:
when D > R, p (D) is 0;
when D < R, p (D) is 1;
when D < D1 < D2, P (D) is 1/D.
Preferably, a new convergence factor a is calculated:
calculating a coefficient vector A:
A＝2ar-a
wherein r is a random number in the interval (0, 1); the current iteration number is; t is the maximum iteration number; and pi is a constant.
Preferably, inertial weight W is introduced to update whale position, where W is defined as:
wherein T is the current iteration frequency, T is the maximum iteration frequency, and pi is a constant;
the whale population position updating formula is as follows:
when the initial probability P <1-log (1+9T/T) and | a | >1, there is X (T +1) ═ xranded × W-a × Drand;
when initial probability P<1-log (1+9T/T) and | A | ≦ 1, X (T +1) × X (T) × W-A × D 1 ；
When the initial probability P is more than or equal to 1-log (1+9T/T), X (T +1) ═ D 2 ×e bl cos(2πl)+X*(t)×(1-W)；
Wherein, W is inertia weight, A is vector coefficient, T is current iteration times, T is maximum iteration times, X (T) is position of best solution, X (T) is position of unmanned aerial vehicle at T moment, X (T +1) is position of unmanned aerial vehicle at T +1 moment, D 1 R is [0,1 ] c.x (t) -X (t) - |, land | c.xrad-X (t) - |, c ═ 2r]And Xrand is the position of any whale in the whale population.
According to the method, an unmanned aerial vehicle track index function is constructed by simulating the flight environment of an unmanned aerial vehicle, the unmanned aerial vehicle track index function is used as a target function based on an improved whale algorithm, a parameter initialization whale population is set, the unmanned aerial vehicle track index function is used as the fitness of an individual to calculate a new convergence factor a and a new calculation coefficient vector A, a random probability P1 which is 1-log (1+9T/T) is set to judge a whale individual updating mechanism, an inertia weight W is introduced, and the whale individual position is updated through a position updating mechanism of the whale algorithm to obtain an optimal flight track; according to the invention, when the improved whale algorithm is adopted to plan the flight path of the unmanned aerial vehicle, the stability and accuracy of the flight path planning are improved.
Drawings
FIG. 1 is a top view of the trajectory of the unmanned aerial vehicle of the present invention
FIG. 2 is a flow chart of the improved whale algorithm of the present invention
FIG. 3 is a diagram illustrating the influence of weather on flight trajectory of an unmanned aerial vehicle
FIG. 4 is a graph of the convergence factor a of the present invention as a function of iteration number
FIG. 5 is a diagram of the present invention for simulating flight in a restricted scene
FIG. 6 is a flow chart of the present invention for planning the flight path of an unmanned aerial vehicle by using an improved whale algorithm
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An unmanned aerial vehicle flight path planning method based on an improved whale algorithm is shown in figures 1-4 and comprises the following steps:
s1, simulating the flight environment of the unmanned aerial vehicle according to the flight length cost, the flight energy consumption cost, the cargo quality cost and the weather threat cost, and constructing an unmanned aerial vehicle track index function;
s2: taking an unmanned aerial vehicle track index function as an objective function to be optimized based on an improved whale algorithm, and solving the unmanned aerial vehicle track index function to obtain an optimal track of unmanned aerial vehicle flight;
wherein, the step S2 specifically includes:
s21, initializing a whale population, setting the maximum iteration time T, the current iteration time T, the initial probability P, a new convergence factor a and the whale number n, and setting T to 1;
s22: taking the unmanned aerial vehicle track index function as the fitness of an individual, calculating a new convergence factor a through the maximum iteration times T and the iteration times T, and calculating a coefficient vector A;
s23: setting a random probability P1 which is 1-log (1+9T/T) to replace the original probability, introducing an inertia weight W according to a whale individual updating mechanism, and updating individual positions through three searching modes of a whale algorithm;
if the initial probability P is less than P1 and the value of A is greater than 1, updating the position by adopting a random search mode according to the new convergence factor a;
if the initial probability P is less than P1 and | A | is less than or equal to 1, updating the position in a wrapping mode according to the new convergence factor a;
if the initial probability P is more than or equal to P1, updating the position in a spiral mode according to a new convergence factor a;
s24: if T is less than the maximum iteration time T, making T equal to T +1, and returning to step S22; if T is more than or equal to T, outputting a global optimal solution; the solution is taken as the minimum value of the unmanned aerial vehicle path index evaluation function, namely the optimal solution of the objective function;
s25: forming an n-dimensional discrete point set by the optimal solution obtained in the step S24;
s26: and smoothly connecting the discrete track points according to the sequence to obtain the optimal track of the unmanned aerial vehicle.
Preferably, the flying environment of the unmanned aerial vehicle is simulated, in a three-dimensional coordinate system, the flying height of the unmanned aerial vehicle is fixed through a logistic-Tent sequence, the three-dimensional coordinate is converted into a two-dimensional coordinate, the coordinate of a starting point is S (0,0), the coordinate of an end point is D (xD, yD), a perpendicular line is drawn from the D point to a Y axis, the Y axis is intersected with D '(xD, 0), a line segment SD' is divided into n equal parts, the end points are marked as Y1, Y2, … and yn, and then Y is obtained i I × SD'/n (i ═ 1,2,3.. n); respectively making the vertical lines of the y axis at the over-end points, respectively marked as L1, L2, … and Ln, converting the path planning problem into the optimization of an X coordinate sequence, namely finding the X axis coordinate [ X ] of the optimal waypoint 1 *(t),X 2 *(t),X 3 *(t)......X n *(t)](ii) a Acquiring flight height information of the unmanned aerial vehicle through a logistic-Tent sequence:
wherein, rand () is random number of interval (0,1), and N is unmanned aerial vehicle setThe number of unmanned aerial vehicles in the group, r is a parameter, and is located at [0,4 ]]To (c) to (d); z i The coordinate of the unmanned aerial vehicle mapped to the Z axis is obtained, Z' is the coordinate of the Z axis after the unmanned aerial vehicle is mapped by the logistic-Tent, and the coordinate of the unmanned aerial vehicle obtained after mapping by the logistic-Tent is updated to be (X) i ，Y i ，Z′)。
Preferably, the whale population position X-axis coordinate is initialized to [ X ] based on the improved logistic-Tent chaotic sequence 1 ,X 2 ,X 3 ,.....X n ]And the position of the unmanned plane, and according to the X-axis coordinate [ X ] of the initial whale population position 1 beginning of ,X 2 beginning of ,X 3 Is to say.X n is as ]Substituting unmanned aerial vehicle trajectory index function F (X) ═ F (X) 1 First, X 2 First, X 3 Is to say.X n is as ) Calculating an initial fitness value of a whale population, setting a maximum iteration time T, the population number N, the iteration time T being 1, determining a whale population dimension N (namely, the search space is N-dimensional, and the whale searches for the optimal position in the N-dimensional space), and regarding the unmanned aerial vehicle trajectory index function obtained in S1 as an objective function to be optimized.
Preferably, as shown in fig. 1, simulating a flight environment of the unmanned aerial vehicle, and establishing a maneuvering performance function model of the unmanned aerial vehicle, wherein the maneuvering performance function model includes flight length cost, flight energy consumption cost, cargo quality cost and weather threat cost;
flight energy consumption cost: the drone flies in two flight regimes, namely a uniform linear flight and a variable speed flight with acceleration, for which the total energy consumption of the drone consists of two components, the first of which is the energy associated with the communication, due to the radiation, the signal processing and other circuits. Another component is the propulsion energy, which is the energy required to ensure that the drone remains aloft and to support its maneuverability when needed. The drone is in a three-dimensional cartesian coordinate system, the ground terminal has coordinates of (0,0,0) UAV flying at a constant height H, the trajectory of the drone projected on a horizontal plane may be represented as q (t) ═ x (t), y (t) ], and the energy consumption model may be represented as:
wherein q (t) represents the actual position of the drone; x (t), y (t) denotes a horizontal coordinate position; v (t) represents the instantaneous velocity vector of the drone; a (t) represents the drone acceleration vector; c. C 1 And c 2 The values of (a) are two parameters relating to aircraft weight, wing area and air density; g represents the acceleration of gravity; m represents the mass of the drone; for level flight at fixed altitude, the energy consumption of the drone depends only on the velocity vector v (t) and the acceleration vector a (t), and not on its actual position q (t); the integral term is ensured to be positive, and is the work of the aircraft engine for overcoming the air resistance; dependent on UAV speed v (t) and its centrifugal acceleration
The direction is vertical to the velocity vector of the unmanned aerial vehicle, and according to the kinetic energy theorem, the second term of the energy consumption model represents the kinetic energy change of the unmanned aerial vehicle; this is the collective effect of the UAV tangential acceleration component, parallel to the UAV velocity vector; the magnitude of this term therefore depends on the final velocity v (t) and the initial velocity v (0) of the drone, independently of the intermediate state velocity of the drone;
for uniform linear flight, | | V (t) | V, a (t) ═ 0. The energy consumption model can be translated into:
for the same flying speed, compared with the straight line flying, the energy consumption is additionally consumed by changing the direction, and the energy consumption model is converted into the following steps:
the energy required by the unmanned aerial vehicle during acceleration is more than that required by the unmanned aerial vehicle during uniform speed, so that the acceleration flight time and the uniform speed flight time should be balanced, the acceleration flight time is t1, and the uniform speed flight time is t2, then the energy consumption cost function is as follows:
E 1 ＝t 1 *E(q(t))+t 2 2*E sLF (V)
cargo quality cost: calculating the overload penalty cost of the unmanned aerial vehicle:
k is an infinite penalty coefficient of the overload of the unmanned aerial vehicle; gi is the demand of the ith distribution point on the goods; i is the number of distribution points with goods demands; fji denotes delivery point i is completed by drone j; v is the maximum bearing capacity of the unmanned aerial vehicle;
flight length penalty: because the kinetic energy change in the energy consumption cost of the unmanned aerial vehicle already contains the height change, in order to calculate the flight length cost, the flight trajectory of the unmanned aerial vehicle is converted into a two-dimensional plane and is represented by a grid method, as shown in fig. 2, a black area is an obstacle area, a white area is a flyable area, and the unmanned aerial vehicle needs to bypass the obstacle to reach a target point. The green square is taken as a starting point, and the red square is taken as an end point; the horizontal and vertical coordinate is accurate to a unit length, and the cost of each flight path segment in the linear flight process of the unmanned aerial vehicle is calculated:
the flight path of the unmanned aerial vehicle is uniformly divided into 11 points, the division of the points is defined according to the direction change times of the unmanned aerial vehicle, each time the direction is changed, one track point is obtained, the head and tail track points are added, 11 track points are provided in total, the number of the existing 10 track sections is 10, and in a two-dimensional plane, the unmanned aerial vehicle tracks points (X) on the unmanned aerial vehicle tracks i ,Yi i ) Position and adjacent track point (X) i-1 ,Y i-1 ) Is a distance L i Then, then
Then the flight length penalty of the drone is:
wherein (X) i ，Yi i ) Two-dimensional coordinates of any track point; (X i-1 ，Y i-1 ) Two-dimensional coordinates of adjacent track points; i is the number of designed unmanned aerial vehicle track points;
weather threat costs: as shown in fig. 4, because unmanned aerial vehicle can receive the influence of bad weather when flying, should consider the influence of weather to the unmanned aerial vehicle orbit, as follows rain, the hail, sleet weather, to bad weather, unmanned aerial vehicle should carry out the mode of keeping away the barrier and guarantee safety, be the spheroid that the radius is R with thunderstorm weather equivalence based on this, unmanned aerial vehicle is D apart from the distance of centre of sphere, the biggest radius in bad weather influence region is D1, D2 represents that unmanned aerial vehicle takes place the distance of the region distance centre of sphere that the probability of crashing is 1 because of bad weather influence, then the probability that unmanned aerial vehicle was met the barrier is:
when D > R, p (D) is 0;
when D < R, p (D) is 1;
when D is less than D1 and less than D2, P (D) is 1/D;
in order to calculate the threat cost of a section of route, the route is divided into 11 equal parts, and the route is divided into 11 track points, namely 10 route sections, L 1 -L 10 For path L, then i Above, there are C threatening weather points, i.e., C spherical threat regions, then L i The weather threat to a segment is expressed as:
the weather threat cost is calculated as:
wherein F is a locus L i Segment weather threats; p (d) is the probability that no one will encounter an obstacle.
Then the unmanned aerial vehicle trajectory index function:
F＝w 1 *E 1 +w 2 *FL+w 3 *G+w 4 *FM
wherein E is 1 To fly inEnergy consumption cost; FL is flight length penalty; g is the cargo quality cost; FM is the total weather threat cost; w is a 1 +w 2 +w 3 +w 4 ＝1，w 1 、w 2 、w 3 、w 4 Weights representing length cost, flight energy consumption cost, cargo quality cost and weather threat cost respectively;
the above problem thus translates into a constrained and objective optimization problem, i.e. the fitness function is an objective function, and in order to minimize the cost, the value f (x) of the path metric function should be minimized.
Furthermore, the unmanned aerial vehicle path index function is composed of a plurality of different constraint conditions, the problem is converted into a plurality of known constraint conditions, the problem of objective function minimization is solved, the problem is an NP problem and is difficult to solve, and therefore an improved whale optimization algorithm is provided to optimize the objective function, the objective function is the unmanned aerial vehicle path index function, the unmanned aerial vehicle path index function needs to be minimized, namely the objective function is minimized, and the minimum unmanned aerial vehicle path index function is achieved.
Preferably, calculating the fitness value of each whale individual, and recording the position of the whale individual with the smallest fitness value; and after the current optimal path planning method is obtained, the optimal path planning method is converted into individual whale position vectors, a controllable selection scheme of each route is regarded as 1 whale, and a whale population set is represented as X ═ X 1 ,X 2 ,X 3 …X n ]Wherein n is the total number of route solutions; when the unmanned aerial vehicle path has N route points and N-1 route sections, selecting the position X (t) with the minimum fitness value after updating the fitness function each time, and generating a position matrix [ X ] after multiple iterations 1 *(t),X 2 *(t),X 3 *(t)......X N *(t)](ii) a And connecting each track point of the position matrix through a straight line to obtain the flight path point of the unmanned aerial vehicle.
Preferably, as shown in fig. 5, a new convergence factor is set, the value of a varies nonlinearly with t, in the early stage of iteration, the proposed a is smaller than the original a, the whale individual focuses on local search, in the late stage of iteration, in order to avoid trapping in local optimality, the whale individual should have the ability to jump out of the local optimality, so that in the late stage of iteration, the proposed a is larger than the original a, and the new convergence factor a is calculated:
calculating a coefficient vector A:
A＝2ar-a
wherein r is a random number in the interval (0, 1); the current iteration number is; t is the maximum iteration number; and pi is a constant.
Preferably, whales walk randomly in an n-dimensional search space to search for optimal whales, namely, the optimal paths are searched in n schemes, and the whales have three hunting mechanisms, namely random search, surrounding search and spiral search; if the initial probability P is less than 1-log (1+9T/T) and | A | >1, enabling whale individuals to conduct random learning, and updating whale positions according to a random search mode; if the initial probability P is less than 1-log (1+9T/T) and | A | is less than or equal to 1, updating the position in a surrounding search mode, and removing the route planning scheme which does not conform to the target function; if the initial probability P is more than or equal to 1-log (1+9T/T), updating the whale position according to a spiral mode;
further, introducing an inertia weight W idea, adding the inertia weight W idea into a whale algorithm position updating formula, and updating the unmanned aerial vehicle waypoint position;
and further, judging whether the maximum iteration number is reached, if the maximum iteration number is reached, outputting an optimal solution which is the minimum value of the unmanned aerial vehicle path index function, and outputting the path after the selection and optimization, so as to complete the selection and optimization of the path.
Preferably, as shown in fig. 3, the objective function minimization is realized by using a modified whale algorithm as follows:
further, the improved whale algorithm is utilized to optimize the position of the unmanned aerial vehicle, whales walk randomly in an n-dimensional search space to search for optimal whales, namely, the optimal tracks are searched in n optional optimal path schemes, and the whales have three hunting mechanisms, namely, random search, surrounding search and spiral search;
further, the present invention replaces the conventional case that the random probability P is 0.5, and sets the current random probability P as:
P1＝1-log(1+9t/T)
the formula shows that P varies with time T and is non-linearly variable, where P is 1 when T is 0 and P is 0 when T is T; the random probability is changed from 1 to 0 in a nonlinear way, and the method enables the algorithm to have better local search capability in the early period of iteration and better global search capability in the later period of iteration.
Preferably, the thought of introducing the inertial weight W updates the whale position, and W is defined as:
and adding the thought of the inertia weight into a position updating formula of a whale algorithm, wherein the new position updating formula is as follows:
when the initial probability P is less than 1-log (1+9T/T) and | A | >1, updating the position by adopting a random search mode: x (t +1) ═ xrad × W-a × land;
when initial probability P<When the position is 1-log (1+9T/T) and | A | ≦ 1, updating the position by adopting a surrounding search mode: x (t +1) ═ X (t) × W-a × D 1 ；
When the initial probability P is more than or equal to 1-log (1+9T/T), updating the position by adopting a spiral searching mode: x (t +1) ═ D 2 ×e bl cos(2πl)+X*(t)×(1-W)；
Based on a position updating formula, the unmanned aerial vehicle selects a value X (t) which enables the fitness value to be minimum each time to update the position of the unmanned aerial vehicle, in an XOY plane, the coordinates of a starting point are S (0,0), the coordinates of an end point are D (xD, yD), a perpendicular line is drawn from the point D to a Y axis, the Y axis is intersected with D '(xD, 0), a line segment SD' is divided into n equal parts, the end points are marked as Y1, Y2, … and yn, and then Y is obtained i I × SD'/n (i ═ 1,2,3.. n); respectively making the vertical lines of the y axis at the over-end points, respectively marked as L1, L2, … and Ln, converting the path planning problem into the optimization of an X coordinate sequence, namely finding the X axis coordinate [ X ] of the optimal waypoint 1 *(t),X 2 *(t),X 3 *(t)......X n *(t)]Form the unmanned plane waypoint position matrix [ (X) 1 *(t),Y 1 ,Z 1 1),(X 2 *(t),Y 2 ,Z 2 2),(X 3 *(t),Y 3 ,Z 3 ′)......(X n *(t),Y n ,Z′ n )]And finally, connecting the discrete track points in sequence to generate a path to obtain the optimal flight track of the unmanned aerial vehicle.
Claims (10)
1. An unmanned aerial vehicle flight path planning method based on an improved whale algorithm is characterized by comprising the following steps:
s1: simulating the flight environment of the unmanned aerial vehicle according to the flight length cost, the flight energy consumption cost, the cargo quality cost and the weather threat cost to construct an unmanned aerial vehicle trajectory index function;
s2: taking an unmanned aerial vehicle track index function as an objective function to be optimized based on an improved whale algorithm, and solving the unmanned aerial vehicle track index function to obtain an optimal track of unmanned aerial vehicle flight;
wherein, the step S2 specifically includes:
s21: initializing a whale population, setting a maximum iteration time T, a current iteration time T, an initial probability P, a new convergence factor a and a whale number n, and setting T to 1;
s22: taking the unmanned aerial vehicle track index function as the fitness of an individual, calculating a new convergence factor a through the maximum iteration times T and the iteration times T, and calculating a coefficient vector A;
s23: establishing a random probability P1 which is 1-log (1+9T/T), and introducing an inertial weight W to update the individual position of the whale according to an individual updating mechanism of the whale algorithm;
if the initial probability P is less than P1 and | A | >1, updating the position by adopting a random search mode according to the new convergence factor a;
if the initial probability P is less than P1 and | A | is less than or equal to 1, updating the position in a wrapping mode according to the new convergence factor a;
if the initial probability P is more than or equal to P1, updating the position in a spiral mode according to a new convergence factor a;
s24: if T is less than the maximum iteration time T, making T equal to T +1, and returning to step S22; if T is more than or equal to T, outputting a global optimal solution; the solution is taken as the minimum value of the unmanned aerial vehicle path index evaluation function, namely the optimal solution of the objective function;
s25: forming an n-dimensional discrete point set by the optimal solution obtained in the step S24;
s26: and smoothly connecting the discrete track points according to the sequence to obtain the optimal track of the unmanned aerial vehicle.
2. The unmanned aerial vehicle flight trajectory planning method based on the improved whale algorithm as claimed in claim 1, wherein the unmanned aerial vehicle flight environment is simulated, the unmanned aerial vehicle height information is obtained through a logistic-Tent sequence in a three-dimensional coordinate system, the three-dimensional coordinate is converted into a two-dimensional coordinate, the starting point coordinate is S (0,0), the end point coordinate is D (xD, yD), a perpendicular line is drawn from the D point to the Y axis, the Y axis is intersected with D '(xD, 0), the line segment SD' is divided into n equal parts, the end points are marked as Y1, Y2, …, yn, and then Y is obtained i I × SD'/n (i ═ 1,2,3.. n); respectively making the vertical lines of the y axis at the over-end points, respectively marked as L1, L2, … and Ln, converting the path planning problem into the optimization of an X coordinate sequence, namely finding the X axis coordinate [ X ] of the optimal waypoint 1 *(t),X 2 *(t),X 3 *(t)......X n *(t)]And the value of the unmanned aerial vehicle track index function is minimum.
3. The unmanned aerial vehicle flight trajectory planning method based on the improved whale algorithm is characterized in that unmanned aerial vehicle flight height information is obtained through a logistic-Tent sequence in a three-dimensional coordinate system:
wherein rand () is a random number of an interval (0,1), N is the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster, r is a parameter, and is located at [0,4 ]]To (c) to (d); z i The coordinate of the unmanned aerial vehicle mapped to the Z axis is obtained, Z' is the coordinate of the Z axis after the unmanned aerial vehicle is mapped by the logistic-Tent, and the coordinate of the unmanned aerial vehicle obtained after mapping by the logistic-Tent is updated to be (X) i ，Y i ，Z′)。
4. The unmanned aerial vehicle flight trajectory planning method based on the improved whale algorithm is characterized in that an objective function is constructed according to flight length cost, flight energy consumption cost, cargo quality cost and weather threat cost:
F(X)＝w 1 *E 1 +w 2 *FL+w 3 *G+w 4 *FM
wherein, F (X) is an unmanned aerial vehicle track index function, namely an objective function; x is any position of whale; e 1 The flight energy consumption cost; FL is flight length penalty; g is the cargo quality cost; FM is the total weather threat cost; w is a 1 +w 2 +w 3 +w 4 ＝1，w 1 、w 2 、w 3 、w 4 And weights representing length cost, flight energy consumption cost, cargo quality cost and weather threat cost respectively.
5. The unmanned aerial vehicle flight trajectory planning method based on the improved whale algorithm is characterized in that the flight length cost, the flight energy consumption cost, the cargo quality cost and the weather threat cost are as follows:
flight energy consumption cost: the unmanned aerial vehicle flies in two flight states of uniform linear flight and variable speed flight with acceleration in a simulation environment, and an energy consumption cost function is as follows:
E 1 ＝t 1 *E(q(t))+t 2 *E(V,a(t))
wherein E (q (t)) is energy consumption of the unmanned aerial vehicle during uniform-speed flight; e sLF (V) energy consumption of the unmanned aerial vehicle during accelerated flight; t is t 1 For unmanned aerial vehicle at uniform speed flight time, t 2 Accelerating flight time for the unmanned aerial vehicle; v is the unmanned plane velocity vector; q (t) coordinates of the unmanned aerial vehicle projected into the two-dimensional plane;
flight length penalty:
wherein (X) i ，Y i ) Two-dimensional coordinates of any track point; (X) i-1 ，Y i-1 ) Two-dimensional coordinates of adjacent track points are obtained; i is the number of designed unmanned aerial vehicle track points;
cargo quality cost:
k is an infinite penalty coefficient of the overload of the unmanned aerial vehicle; gi is the demand of the ith distribution point on the goods; i is the number of distribution points with goods demands; fji shows that delivery point i is done by drone j; v is the maximum bearing capacity of the unmanned aerial vehicle;
weather threat costs:
wherein F is a locus L i Segment weather threats; p (d) is the probability that no one will encounter an obstacle.
6. The unmanned aerial vehicle flight trajectory planning method based on the improved whale algorithm is characterized in that the uniform linear flight energy consumption is as follows:
wherein E (V, a (T)) is the energy consumption of the drone when changing direction, T is the total time of flight of the drone, and T is T ═ T 1 +t 2 ；c 1 And c 2 The method is characterized in that the method comprises two parameters related to the weight of an airplane, the area of wings and the air density, a (t) represents an acceleration vector of the unmanned aerial vehicle, g is the acceleration of gravity, and V is the speed of the unmanned aerial vehicle.
7. The unmanned aerial vehicle flight trajectory planning method based on the improved whale algorithm is characterized in that the variable speed flight energy consumption with acceleration is as follows:
wherein q (t) represents the coordinates of the drone projected into a two-dimensional plane; x (t), y (t) denotes a horizontal coordinate position; v (t) represents the instantaneous velocity vector of the drone; a (t) represents the drone acceleration vector; c. C 1 And c 2 The values of (a) are two parameters relating to aircraft weight, wing area and air density; g represents the acceleration of gravity; m represents the mass of the drone; a is T (t) represents centrifugal acceleration; v (T) represents the magnitude of the unmanned aerial vehicle speed at time T; v (0) represents the unmanned aerial vehicle speed at the time when T is 0; a (t) represents the acceleration magnitude of the unmanned aerial vehicle; v (t) represents the velocity of the drone at time t; t represents the total time of flight of the drone.
8. The unmanned aerial vehicle flight trajectory planning method based on the improved whale algorithm is characterized in that the weather threat cost is that thunderstorm weather is equivalent to a sphere with a radius R, the distance between the unmanned aerial vehicle and the sphere center is D, the maximum radius of a severe weather influence area is D1, D2 represents the distance between an area where the probability of crash of the unmanned aerial vehicle due to severe weather influence is 1 and the probability of the unmanned aerial vehicle encountering an obstacle is as follows:
when D > R, p (D) is 0;
when D < R, p (D) is 1;
when D < D1 < D2, P (D) is 1/D.
9. The unmanned aerial vehicle flight trajectory planning method based on the improved whale algorithm as claimed in claim 1, wherein a new convergence factor a is calculated:
calculating a coefficient vector A:
A＝2ar-a
wherein r is a random number in the interval (0, 1); the current iteration number is; t is the maximum iteration number; and pi is a constant.
10. The unmanned aerial vehicle flight trajectory planning method based on the improved whale algorithm is characterized in that the whale position is updated by introducing an inertial weight W, wherein W is defined as:
wherein T is the current iteration frequency, T is the maximum iteration frequency, and pi is a constant;
the whale population position updating formula is as follows:
when the initial probability P is less than 1-log (1+9T/T) and | A | >1, updating the position by adopting a random search mode: x (t +1) ═ xrad × W-a × land;
when initial probability P<When the position is 1-log (1+9T/T) and | A | ≦ 1, updating the position by adopting a surrounding search mode: x (t +1) ═ X (t) × W-axd 1 ；
When the initial probability P is more than or equal to 1-log (1+9T/T), updating the position by adopting a spiral searching mode: x (t +1) ═ D 2 ×e bl cos(2πl)+X*(t)×(1-W)；
Wherein, W is inertia weight, A is vector coefficient, T is current iteration times, T is maximum iteration times, X (T) is position of optimal solution, X (T) is position of unmanned aerial vehicle at T moment, X (T +1) is position of unmanned aerial vehicle at T +1 moment, D 1 R is [0,1 ] c.x (t) -X (t) - |, land | c.xrad-X (t) - |, c ═ 2r]And Xrand is the position of any whale in the whale population.
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