CN115903888A - Rotor unmanned aerial vehicle autonomous path planning method based on longicorn swarm algorithm - Google Patents
Rotor unmanned aerial vehicle autonomous path planning method based on longicorn swarm algorithm Download PDFInfo
- Publication number
- CN115903888A CN115903888A CN202211402120.5A CN202211402120A CN115903888A CN 115903888 A CN115903888 A CN 115903888A CN 202211402120 A CN202211402120 A CN 202211402120A CN 115903888 A CN115903888 A CN 115903888A
- Authority
- CN
- China
- Prior art keywords
- longicorn
- unmanned aerial
- aerial vehicle
- path
- iteration
- 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.)
- Pending
Links
Images
Classifications
-
- 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
Landscapes
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a rotor unmanned aerial vehicle autonomous path planning method based on a longicorn swarm algorithm, which belongs to the technical field of aviation military operations and civil inspection and comprises the following steps: step1, constructing a rotor unmanned aerial vehicle path obstacle model and carrying out environment modeling; step 2, designing unmanned aerial vehicle performance constraint and cost function; and 3, carrying out search iteration based on a space-cow swarm algorithm, and connecting the positions of the unmanned aerial vehicles after each iteration by using a smooth curve after the maximum iteration number is reached to obtain the optimal path of the rotor unmanned aerial vehicle. The advantages of the longicorn whisker algorithm and the particle swarm algorithm are fused and improved, the longicorn swarm algorithm is provided, the unmanned aerial vehicle path planning method based on the longicorn swarm algorithm enhances the capability of the unmanned aerial vehicle in identifying the target direction, plans a good flight path for the unmanned aerial vehicle, and effectively shortens the flight time of the unmanned aerial vehicle.
Description
Technical Field
The invention belongs to the technical field of aviation military operations and civil inspection, and particularly relates to a rotor unmanned aerial vehicle autonomous path planning method based on a longicorn swarm algorithm.
Background
Rotor unmanned aerial vehicle has can VTOL, freely hover, control advantage such as nimble and the various environment of adaptation ability reinforce, obtains extensively promoting in military operation and civilian investigation field. No matter in military use or civilian aspect, unmanned aerial vehicle mainly carries out tasks such as investigation, aerial photography, monitoring at spacious open-air plain and high altitude area, and in the face of the unknown environment in the city, rotor unmanned aerial vehicle route planning lacks autonomy and practicality. The path planning technology is not mature, manual operation and control are still needed, and the accident rate is high. When the unmanned aerial vehicle faces buildings under unknown conditions such as communities, schools and the like, the unmanned aerial vehicle makes autonomous flight decisions, and often collides when an unknown obstacle appears, so that the maneuvering capacity of the unmanned aerial vehicle is greatly reduced. Therefore, the method for planning the autonomous path of the rotor unmanned aerial vehicle is researched, the optimal collision-free path is obtained, the path length, the flight time and the energy consumption are reduced to the maximum extent, and the method has important practical value.
The searching individual of the longicorn algorithm is a longicorn, the searching result has a large amount of uncertainty, the searching result is not suitable for the unmanned aerial vehicle to search the path in the high-dimensional complex environment, and the problem that the path planned by the rotor unmanned aerial vehicle in the high-dimensional complex environment generates contingency cannot be solved.
Disclosure of Invention
In order to solve the problem that the planned path of the rotor unmanned aerial vehicle in a high-dimensional complex environment generates contingency, the invention provides a rotor unmanned aerial vehicle autonomous path planning method based on a celestial cow cluster algorithm, the advantages of celestial cow whiskers and particle swarm algorithms are fused and improved, the celestial cow cluster algorithm is provided and is applied to the rotor unmanned aerial vehicle path planning, the autonomous planning capability of the rotor unmanned aerial vehicle is improved, an optimal collision-free path is obtained, and the path length, the flight time and the energy consumption are reduced to the maximum extent.
The technical scheme of the invention is as follows:
a rotor unmanned aerial vehicle autonomous path planning method based on a longicorn algorithm specifically comprises the following steps:
step1, constructing a rotor unmanned aerial vehicle path obstacle model and carrying out environment modeling;
step 3, searching iteration is carried out based on a space-cow group algorithm, after the maximum iteration times are reached, the positions of the unmanned aerial vehicles after each iteration are connected through smooth curves, and the optimal path of the rotor unmanned aerial vehicle is obtained;
the search process of the Tianniu group algorithm is as follows:
(1) Firstly, initializing longicorn herds; initializing the number and initial step length of the population and the speed and position of each individual in an initialization search space to obtain the coordinates of the left and right tentacles of the longicorn;
(2) Acquiring individual extreme values and population extreme values of a longicorn population; calculating the fitness values of all individuals as cost function values, and comparing the extreme values of all individuals one by one to obtain the population extreme values of all longicorn;
(3) Entering an iteration process, updating the step length after each iteration, and obtaining a new inertia weight and a new step factor value so as to obtain a new increment function value;
(4) Updating the speed and the position of the longicorn through the individual optimal position and the population optimal position of the longicorn;
(5) Cycle termination conditions: and judging whether the iteration times reach the maximum iteration times or not, and when a termination condition is met, terminating the operation.
Further, step1 specifically includes the following substeps:
step 1.1, constructing a rotor unmanned aerial vehicle path obstacle model: the method comprises the following steps of simulating various actual objects in a flight environment by adopting barriers in different shapes, specifically, simulating various buildings encountered by the unmanned aerial vehicle in the flight process by adopting a cylinder barrier, and simulating huge stones and soil dunes encountered by the unmanned aerial vehicle in the flight process by adopting a sphere;
and step 1.2, based on the step 1.1, selecting obstacle models with circular, square and L-shaped top views respectively in the search space to simulate the flight path environment of the rotor unmanned aerial vehicle.
Further, in the step 2, the performance constraints comprise a maximum electric quantity constraint, an unmanned aerial vehicle performance constraint and an obstacle constraint;
the step 2 specifically comprises the following substeps:
step 2.1, maximum electric quantity constraint: the electric quantity carried by the rotor unmanned aerial vehicle in the navigation process is limited, the flight distance of the aircraft is limited by the capacity of a battery, and the electric quantity consumption of the unmanned aerial vehicle is related to the flight path of the unmanned aerial vehicle and the curvature cost of the flight path; assuming that the flight path of the airplane is divided into n sections and the maximum flight path is L max And then the ith flight is represented as L i (ii) a Cost function f of ith segment path length d Comprises the following steps:
in the formula x i ,y i ,z i Respectively representing the coordinates of the previous path point in the x, y, z axes, x i+1 ,y i+1 ,z i+1 Respectively representing the coordinates of the latter path point on the x, y and z axes;
representing the distance between the previous path point and the next path point; because the path generated by algorithm search cannot meet the requirement of unmanned aerial vehicle flight, the generated path needs to be smoothed, and a curvature cost function f is introduced c ：
Wherein, y i ' is described as y i Relative to x i In the sitting positionLabel (x) i ,y i ) First derivative of the next, y i "is described as y i Relative to x i At the coordinate (x) i ,y i ) Second derivative of lower, n i Representing the number of path points on the nth path;
energy consumption f of unmanned aerial vehicle battery b Comprises the following steps:
f b ＝f d +f c (3)
wherein f is d Cost function representing the length of the ith segment path, f c Representing a curvature cost function;
step 2.2, self-performance constraint function f of unmanned aerial vehicle o Comprises the following steps:
pitch angle constraint function f from current point to next intermediate point pitch_angle (x i ) The following formula is satisfied:
in the formula, theta is the pitching angle of the unmanned aerial vehicle, theta max The maximum pitching angle of the unmanned aerial vehicle is set, and M is a constraint value;
yaw angle constraint function f from current point to next position yaw_angle (x i ) The following formula is satisfied:
in the formula, Ψ i Yaw angle, psi, for unmanned aerial vehicle max The maximum yaw angle of the unmanned aerial vehicle is M, and M is a constraint value;
constraint function f of flight height opt_height (x i ) Expressed as:
where H is the optimal flying height derived from environmental analysis and mission requirements, H i For the height of the unmanned aerial vehicle from the ground, M h Is a constraint value;
step 2.3, obstacle constraint: the unmanned aerial vehicle can meet various obstacles in the flight process, and the obstacle constraint function f t Expressed as:
wherein Q represents the number of obstacles, d n,q Represents the distance between a waypoint and a q-th obstacle in the nth path segment, r q Represents the radius of the qth obstacle;
step 2.4, analyzing the constraint functions to obtain a cost function of the flight path of the unmanned aerial vehicle, wherein the cost function is as follows:
f＝ω 1 f b +ω 2 f o +ω 3 f t (11)
in the formula, ω is the cost function 1 、ω 2 And ω 3 The constraint functions representing the respective parts account for the cost function, and the sum of the coefficients is 1.
Further, in step 3, the specific steps of obtaining the optimal path of the rotor unmanned aerial vehicle based on the longicorn algorithm are as follows:
step 3.1, initializing a longicorn group, initializing the number of the longicorn group and initializing the speed and the position of each longicorn in a search space; the specific process is as follows:
defining the orientation of the generated longicorn beard and the position of the longicorn in a three-dimensional search space, standardizing the orientation and the position of the longicorn beard
To indicate that the position of the movable member,
wherein, rands (.) is a random function, 3 represents that the random function is three-dimensional, and 1 represents that the coordinates are normalized;
the position vector of the mth longicorn in the three-dimensional space is represented as:
in the formula (I), the compound is shown in the specification,
representing the position parameter of the mth individual of the longicorn population in a j-dimensional space at the kth iteration;
creating the spatial coordinates of the initial left and right antennae of the longicorn:
wherein x is rk Denotes the position coordinate, x, of the right antenna at the k-th iteration lk Representing the coordinates of the left antenna at the kth iteration; d k The search distance between the two antennae of the longicorn; the velocity of the mth longicorn is expressed as:
wherein
Representing the velocity parameter of the mth individual of the population in a j-dimensional space during the kth iteration;
step 3.2, setting an initial step length; the specific process is as follows:
the initial step size of the longicorn in the longicorn group is step, and the value of the initial step size is set as follows:
step＝(ub-lb)*2 (17)
wherein ub represents an upper limit of the boundary value in the search space, lb represents a lower limit of the boundary value in the search space;
step 3.3, calculating the fitness values of all longicorn and comparing to obtain global optimum; the specific process is as follows:
acquiring individual extreme values and population extreme values of a population by adopting a cost function suitable for the rotor unmanned aerial vehicle, wherein the cost function is shown in step 2.4;
by individual optimum extremum f m And the population optimum extremum f g Obtaining individual optimal position p m And the population optimum position p g ；
M individual optimum extreme value f m Corresponding currently searched individual optimal position p m Comprises the following steps:
wherein f is m ＝(f m1 ,f m2 ,f m3 ) T Represents the optimal extreme value parameter, p, of the m-th individual in the population in the j-dimensional space mj (j =1.. 3) represents an optimal position parameter of the mth individual of the population in a j-dimensional space;
the population optimal position of the longicorn population is expressed as:
wherein f is g ＝(f g1 ,f g2 ,f g3 ) T Represents the optimal extreme value, p, of the population in the j-dimensional space gj (j =1.. 3) represents a position parameter of the population-optimal individual in a j-dimensional space;
the update rule of the step size is then set,
eta＝step1*(step0/step1) (1/(1+10*k/K)) (20)
δ k ＝eta*step (21)
in this step, the positions of the left and right antennaes of the longicorn are extended to the high dimension, δ k Represents the current step size; step0 and step1 are set random numbers in the range of [0,1]Eta represents the updating of the step coefficient, which is used for updating the step;
the position coordinate updating rules of the left antenna and the right antenna are respectively expressed as follows:
increment function at iteration k +1
The calculation is as follows:
wherein m =1,2.., s; k is the current number of iterations;
represents the speed of the longicorn at the mth longicorn at k iterations, and/or the ratio of the longicorn to the average longicorn in the number of k iterations>
Represents the (k + 1) th iterationIncreased amplitude of time-of-flight longicorn motion; />
The difference of the left and right antenna fitness function values of the longicorn is represented and used for determining whether the longicorn moves to the left or to the right;
updating the speed and the position of the longicorn population, wherein the speed updating of the mth longicorn and the k +1 iteration is as follows:
wherein
Represents the optimal position parameter of the mth individual in k iterations in three-dimensional space, and->
Representing a position parameter of the mth individual at the kth iteration in the three-dimensional space; />
Represents an optimal position parameter of the population in k iterations in three-dimensional space, and->
Representing a population position parameter of a population in a three-dimensional space during the kth iteration of the population;
the position update of the mth longicorn and k +1 iterations is as follows:
wherein x is a positive number, wherein x is,
is an increment function value obtained by the formula (24)>
Is the speed of the longicorn individual obtained by the formula (25);
step 3.4, setting inertia weight;
setting the value of partial coefficient, adopting the strategy of reducing the inertia weight, and the formula is as follows:
c 1 ＝1-exp(-|f m -f g |) (28)
c 2 ＝1-c 1 (29)
wherein the learning factor c 1 Controlling the self-learning ability of an individual, learning factor c 2 Controlling the social learning ability of the group, which are two positive numbers; ω is the inertial weight, ω max And ω min Respectively represent the maximum value and the minimum value of omega; k and K are the current iteration number and the maximum iteration number; step 3.5, after the speed and the position of the longicorn are updated in each iteration, the step length coefficient, the inertia weight and the step length are required to be updated, then the fitness value of each longicorn is recalculated and compared again to obtain the global optimum, and the iteration times are increased by one until the maximum iteration times are reached, and the algorithm is ended;
and 3.6, continuously updating based on the steps to obtain an optimal value of the longicorn group, replacing the position of the rotor unmanned aerial vehicle with the optimal position of the current longicorn group in each iteration updating, connecting the positions of the rotor unmanned aerial vehicles after each iteration by using a smooth curve after the maximum iteration number is reached, and finally obtaining the optimal path planned by using the longicorn group algorithm.
The invention has the following beneficial technical effects:
the particle swarm algorithm is introduced on the basis of the longicorn stigma algorithm, the advantages of the two algorithms are fused and improved, the powerful global search capability of the longicorn stigma algorithm is guaranteed, the special individual and group extreme values of the particle swarm algorithm are utilized, the rationality and the feasibility of path planning are greatly enhanced, and the algorithm can help the rotor unmanned aerial vehicle to efficiently fly in a three-dimensional environment and accurately reach a target point;
according to the route planning method based on the longicorn swarm algorithm, the number of the longicorn herds is increased on the basis of the longicorn beard algorithm, the problem that the target point of the rotor unmanned aerial vehicle is not accurately judged in the flight direction is solved, simulation experiments prove that compared with a conventional longicorn beard algorithm, the unmanned aerial vehicle route planning method based on the longicorn beard algorithm not only enhances the capability of the unmanned aerial vehicle in identifying the target direction, but also plans a good flight route for the unmanned aerial vehicle, and effectively shortens the flight time of the unmanned aerial vehicle.
Drawings
FIG. 1 is a flow chart of the longicorn whisker algorithm;
FIG. 2 is a flow chart of the longicorn herd algorithm of the present invention;
FIG. 3 is a graph of the comparison results of a simulation experiment longicorn swarm algorithm BSO and a longicorn whisker algorithm BAS in a parameter space and a target space respectively;
fig. 4 is a graph showing a comparison result of the movement distances of the longicorn swarm algorithm BSO and the longicorn whisker algorithm BAS in the directions of the x axis, the y axis and the z axis, respectively, in the simulation experiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following figures and embodiments:
a rotor unmanned aerial vehicle autonomous path planning method based on a longicorn swarm algorithm specifically comprises the following steps:
step1, constructing a rotor unmanned aerial vehicle path obstacle model and carrying out environment modeling; the method specifically comprises the following substeps:
step 1.1, constructing a rotor unmanned aerial vehicle path obstacle model: the method mainly uses obstacles with different shapes to simulate various actual objects in the flying environment, such as various buildings which are met by the unmanned aerial vehicle in the flying process by adopting cylinder obstacles, huge stones and soil dunes which are met by the unmanned aerial vehicle in the flying process by adopting spheres, and the like.
And step 1.2, based on the step 1.1, selecting 13 barrier models with top views respectively in a circular shape, a square shape and an L shape in the search space to simulate the flight path environment of the rotor-wing unmanned aerial vehicle.
step 2.1, maximum electric quantity constraint: the electric quantity that rotor unmanned aerial vehicle carried in navigation process is limited, and the flight distance of aircraft receives the restriction of battery capacity. The power consumption of the drone is mainly related to the flight path of the drone and the cost of the curvature of the flight path. Assuming that the flight path of the airplane is divided into n sections and the maximum flight path is L max Then the ith flight can be represented as L i . Cost function f of ith segment path length d Comprises the following steps:
in the formula x i ,y i ,z i Respectively representing the coordinates of the previous path point in the x, y, z axes, x i+1 ,y i+1 ,z i+1 The coordinates of the latter waypoint on the x, y, z axes are indicated, respectively.
Indicating the distance between the previous waypoint and the next waypoint. Because the path generated by algorithm search cannot meet the requirement of unmanned aerial vehicle flight, the generated path needs to be smoothed, and a curvature cost function f is introduced c ：
Wherein, y i ' described as y i Relative to x i At the coordinate (x) i ,y i ) First derivative of the next, y i "is described as y i Relative to x i In the coordinate (x) i ,y i ) Second derivative of lower, n i Indicating the number of waypoints on the nth path.
Energy consumption f of unmanned aerial vehicle battery b Comprises the following steps:
f b ＝f d +f c (32)
wherein f is d Cost function representing the length of the ith segment path, f c Representing a curvature cost function.
Step 2.2, self-performance constraint function f of unmanned aerial vehicle o Comprises the following steps:
rotor unmanned aerial vehicle is carrying out the path planning in-process, and unmanned aerial vehicle's climbing and dive angle are an important restraint, and the maximum pitch angle is that unmanned aerial vehicle flies to next intermediate point position angle restriction on the vertical direction from the present position, and unmanned aerial vehicle's pitch angle can not exceed this maximum pitch angle. Pitch angle constraint function f from the current point to the next intermediate point pitch_angle (x i ) The following formula is satisfied:
in the formula, theta is the pitching angle of the unmanned aerial vehicle, theta max And M is an appropriate constraint value for the maximum pitching angle of the unmanned aerial vehicle.
Rotor unmanned aerial vehicle is at the actual flight in-process, and the restraint of maximum yaw angle is that rotor unmanned aerial vehicle receives the restriction of self mobility when turning to next position by the current position, and the yaw angle can only be less than or equal to maximum yaw angle, and the aircraft can fly to next position. Yaw angle constraint function f from current point to next position yaw_angle (x i ) Satisfy the requirement ofThe following equation:
in the formula, Ψ i Yaw angle, psi, for unmanned aerial vehicle max M is a suitable constraint value for the maximum yaw angle of the drone.
Rotor unmanned aerial vehicle's flight place majority is in city and mountain area, and unmanned aerial vehicle flight altitude crosses lowly can make unmanned aerial vehicle and complicated topography collide, and too high can consume too much electric energy of unmanned aerial vehicle flight altitude, consequently will select suitable altitude range, and constraint function f of flight altitude opt_height (x i ) Can be expressed as:
where H is the optimal flying height derived from environmental analysis and mission requirements, H i For the height of the unmanned aerial vehicle from the ground, M h Is a constraint value.
Step 2.3, obstacle constraint: the unmanned aerial vehicle can meet various obstacles in the flight process, and the obstacle constraint function f t Expressed as:
wherein Q represents the number of obstacles, d n,q Represents the distance between the path point in the nth path segment and the q obstacle, r q The radius of the qth obstacle is indicated.
Step 2.4, analyzing the constraint functions to obtain a cost function of the flight trajectory of the unmanned aerial vehicle, wherein the cost function is as follows:
f＝ω 1 f b +ω 2 f o +ω 3 f t (40)
ω in cost function in equation 1 、ω 2 And ω 3 The proportion of the constraint functions of all parts in the cost function is represented, the sum of the coefficients is 1, and the proportion coefficients are reasonably adjusted according to different environments, so that the unmanned aerial vehicle can obtain a better flight path.
And 3, carrying out search iteration based on a space-cow swarm algorithm, and connecting the positions of the unmanned aerial vehicles after each iteration by using a smooth curve after the maximum iteration number is reached to obtain the optimal path of the rotor unmanned aerial vehicle.
The process of the longicorn whisker algorithm is as follows: firstly, randomly initializing the orientation and position of longicorn, standardizing, and processing
To show that:
where rands () is a random function, j on the left of the comma represents the dimension of the random function, and 1 on the right of the comma represents normalizing the coordinates.
Then the space coordinates of the left and right whiskers of the longicorn are created,
wherein x is rk Denotes the position coordinate, x, of the right antenna at the k-th iteration lk The coordinates of the left antenna at the kth iteration are indicated. d 0 Is the distance between the two antennas of the longicorn. Using fitness function value f (x) lk ) And f (x) rk ) To indicate the odor intensity at the left and right touch corners. Next one isStep, setting an iteration mechanism of the longicorn to make a detection behavior, wherein the model is as follows:
wherein x k+1 、x k Coordinates of the longicorn centroid at the k +1 th iteration and the k th iteration, delta k Representing the step size. sign () represents a sign function, the zenith direction if the fitness of the right antenna is greater than the left
Is moved by delta k And vice versa>
In the opposite direction. Search distance d k And step size delta k The relationship between them is as follows:
δ k ＝eta*δ k-1 (45)
d k ＝δ k /c 2 (46)
wherein c is 1 、c 2 To be constant to be adjusted, eta represents a step factor for updating the step, and the value of eta is a fixed value in the longicorn algorithm. After the position of the longicorn is updated every time in an iteration mode, the step length needs to be updated, then the iteration times are increased by one, and the historical optimal position of the longicorn is output until the iteration times are reached. The specific flow chart is shown in fig. 1.
In the algorithm, each longicorn represents a potential solution of an optimization problem, and each longicorn corresponds to a fitness value determined by a fitness function. Like the particle swarm algorithm, the longicorn also shares information, but the distance and direction of the longicorn depend on their speed and the information intensity to be detected by the long antenna, and the idea of the particle swarm algorithm is borrowed mathematically.
The search process of the skyhook group algorithm is as follows:
(1) Firstly, initializing a longicorn group; initializing the number and initial step length of the population and the speed and position of each individual in the initial search space to obtain the coordinates of the left and right tentacles of the longicorn.
(2) Acquiring individual extreme values and population extreme values of a longicorn population; calculating fitness values of all individuals, namely cost function values, and comparing extreme values of all individuals one by one to obtain population extreme values of all longicorn.
(3) Entering an iterative process and setting a coefficient omega max 、ω min 、c 1 、c 2 Lambda and step0 and step1, updating the step after each iteration, obtaining new values of omega and eta, and further obtaining a new value of an increment function xi.
(4) And updating the speed and the position of the longicorn through the individual optimal position and the population optimal position of the longicorn.
(5) Cycle termination conditions: and judging whether the iteration times reach the maximum iteration times or not, and when a termination condition is met, terminating the operation.
The algorithm flow chart of the skynet herd algorithm is shown in fig. 2. The detailed search process of the longicorn algorithm is as follows:
and 3.1, initializing a longicorn group, namely initializing the number of the longicorn group and the speed and the position of each longicorn in the search space. The specific process is as follows:
defining the orientation of the generated longicorn beard and the position of the longicorn in a three-dimensional search space, standardizing the orientation and the position of the longicorn beard
To indicate.
Here, rands (.) is a random function, and 3 on the left side of the comma indicates that the random function is three-dimensional, and 1 on the right side of the comma indicates that the coordinates are normalized.
The position vector of the mth longicorn in the three-dimensional space can be expressed as:
in the formula (I), the compound is shown in the specification,
and the position parameter of the mth individual of the longicorn population in the j-dimensional space at the kth iteration is shown.
Creating the spatial coordinates of the initial left and right antennae of the longicorn:
wherein x rk Denotes the position coordinate, x, of the right antenna at the k-th iteration lk The coordinates of the left antenna at the kth iteration are indicated. d k Is the search distance between the two antennas of the longicorn. The velocity of the mth longicorn is expressed as:
wherein
Representing the velocity parameter of the mth individual of the population in the j-dimensional space at the kth iteration.
And 3.2, setting an initial step length. The specific process is as follows:
the initial step size of the longicorn in the longicorn group is step, and the value of the initial step size is set as follows:
step＝(ub-lb)*2 (52)
where ub represents the upper bound of the boundary value in the search space and lb represents the lower bound of the boundary value in the search space.
And 3.3, calculating the fitness values of all longicorn and comparing to obtain the global optimum. The specific process is as follows:
the individual extreme value and the population extreme value of the population are obtained by adopting a cost function suitable for the rotor unmanned aerial vehicle, and the cost function is shown as a step 2.4.
The m individual optimal extreme value f can be obtained through the longicorn beard algorithm m And the optimal extreme value f of the population can be obtained by comparing the optimal extreme value of each individual g . By individual optimum extrema f m And the population optimum extremum f g Obtaining the individual optimal position p m And the population optimum position p g 。
M individual optimum extreme value f m Corresponding currently searched individual optimal position p m Comprises the following steps:
p m ＝(p m1 ,p m2 ,p m3 ) T (53)
wherein p is mj (j =1.. 3) represents the optimal location parameter of the mth individual of the population in the j-dimensional space.
The population optimal position of the longicorn population is expressed as:
p g ＝(p g1 ,p g2 ,p g3 ) T (54)
wherein p is gj (j =1.. 3) represents the position parameter of the population-optimized individual in the j-dimensional space.
The update rule of the step size is then set,
eta＝step1*(step0/step1) (1/(1+10*k/K)) (55)
δ k ＝eta*step (56)
in this step, the positions of the left and right antennal of the longicorn are extended to a high dimension, δ k Representing the current step size. step0 and step1 are set random numbers in the range of [0,1]Eta represents the update of the step coefficient for updating the step.
The position coordinate updating rules of the left antenna and the right antenna are respectively expressed as follows:
increment function at iteration k +1
The calculation is as follows:
wherein m =1,2.., s; k is the current number of iterations.
Represents the speed of the m-th longicorn, k iterations, and->
Indicating the magnitude of the increase in longicorn motion at the (k + 1) th iteration, a larger value indicating a more intense motion is not a simple magnitude. />
And the difference of the left and right antenna fitness function values of the longicorn is represented and used for determining whether the longicorn moves to the left or right.
Updating the speed and the position of the longicorn population, wherein the speed updating of the mth longicorn and the k +1 iterations is as follows:
wherein
Represents the m-th individual inAn optimal position parameter in k iterations in three-dimensional space, based on the value of the parameter value->
Representing a position parameter of the mth individual at the kth iteration in the three-dimensional space; />
Represents an optimal position parameter of the population in k iterations in three-dimensional space, and->
And representing the position parameter of the population at the kth iteration of the population in the three-dimensional space.
The position update at the mth longicorn and the k +1 iteration is as follows:
wherein x is a positive number, wherein x is,
is an increment function value obtained by the formula (59)>
Is the speed of the longicorn individual obtained by the formula (60).
And 3.4, setting the inertia weight.
The invention adopts the strategy of reducing the inertia weight by setting the value of partial coefficient, and the formula is as follows:
c 1 ＝1-exp(-|f m -f g |) (63)
c 2 ＝1-c 1 (64)
learning factor c 1 Controlling the self of an individualLearning ability, learning factor c 2 Controls the social learning ability of the group, and is two positive numbers. ω is the inertial weight, ω max And ω min The maximum represents the maximum and minimum of ω, respectively. K and K are the current iteration number and the maximum iteration number. The maximum value of omega is set to be 0.9, the minimum value of omega is set to be 0.4, so that the algorithm can search a larger range when the evolution starts, an optimal solution area can be found as soon as possible, the speed of the longicorn is reduced along with the gradual reduction of omega, and then local search is carried out.
In the early stage of the iteration, f m And f g The value difference is large, thereby ensuring that the c is large 1 And smaller c 2 Therefore, the algorithm can search in a larger value space range; as the iteration progresses, the difference between the two becomes smaller, thus ensuring smaller c 1 And a larger c 2 So that the algorithm can be positioned to a smaller area, thereby converging to the global optimum at a faster speed.
And 3.5, after the speed and the position of the longicorn are updated in each iteration, updating the step length coefficient, the inertia weight and the step length, recalculating the fitness value of each longicorn and comparing again to obtain the global optimum, and adding one to the iteration times. And ending the algorithm until the maximum iteration times are reached.
And 3.6, continuously updating based on the steps to obtain an optimal value of the longicorn group, replacing the position of the rotor unmanned aerial vehicle with the optimal position of the current longicorn group in each iteration updating, and connecting the positions of the rotor unmanned aerial vehicles after each iteration by using a smooth curve after the maximum iteration number is reached, so that an optimal path planned by using the longicorn group algorithm is obtained.
In order to prove the feasibility and the superiority of the invention, a relevant algorithm simulation experiment is carried out for verification. The verification process comprises the following steps:
based on the barrier constraint function f in step 2.4 t On the premise that the sum of the coefficients is 1, the sizes of the coefficients are changed, multiple sets of simulation are performed, and fig. 3 and 4 show one set of simulation results. As can be seen from FIG. 3, the day of useThe path planned by the BSO is smoother than the path planned by the BAS, the convergence rate of the BSO is higher, and the obtained optimal cost value is better. As can be seen from fig. 4, the path lengths planned by the longicorn group algorithm on the x, y and z axes are shorter than the path lengths planned by the conventional longicorn whisker algorithm. According to multiple sets of simulation results, along with the reduction of the threat constraint proportion set in the cost function, the self performance constraint proportion and the maximum electric quantity constraint proportion of the set rotor unmanned aerial vehicle are increased, the optimal cost value obtained by convergence of the two algorithms is also increased, but under the condition that the cost functions are the same, the convergence effect of the longicorn swarm algorithm is obviously better than that of the conventional longicorn algorithm, the obtained optimal cost value is smaller, and the more optimal cost function value is obtained.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (4)
1. A rotor unmanned aerial vehicle autonomous path planning method based on a longicorn algorithm is characterized by specifically comprising the following steps of:
step1, constructing a rotor unmanned aerial vehicle path obstacle model and carrying out environment modeling;
step 2, designing unmanned aerial vehicle performance constraint and cost function;
step 3, searching iteration is carried out based on a space-cow group algorithm, after the maximum iteration times are reached, the positions of the unmanned aerial vehicles after each iteration are connected through smooth curves, and the optimal path of the rotor unmanned aerial vehicle is obtained;
the search process of the Tianniu group algorithm is as follows:
(1) Firstly, initializing a longicorn group; initializing the number and initial step length of the population and the speed and position of each individual in an initialization search space to obtain the coordinates of the left and right tentacles of the longicorn;
(2) Acquiring individual extreme values and population extreme values of a longicorn population; calculating fitness values of all individuals as cost function values, and comparing extreme values of all individuals one by one to obtain population extreme values of all longicorn;
(3) Entering an iteration process, updating the step length after each iteration, and obtaining a new inertia weight and a new step length factor value so as to obtain a new increment function value;
(4) Updating the speed and the position of the longicorn through the individual optimal position and the population optimal position of the longicorn;
(5) Cycle termination conditions: and judging whether the iteration times reach the maximum iteration times or not, and stopping the operation when a termination condition is met.
2. A method for planning the autonomous path of a rotary-wing drone based on the longicorn herd algorithm according to claim 1, characterized in that said step1 comprises in particular the following sub-steps:
step 1.1, constructing a rotor unmanned aerial vehicle path obstacle model: the method comprises the following steps of simulating various actual objects in a flight environment by adopting barriers in different shapes, specifically, simulating various buildings encountered by the unmanned aerial vehicle in the flight process by adopting a cylinder barrier, and simulating huge stones and soil dunes encountered by the unmanned aerial vehicle in the flight process by adopting a sphere;
and 1.2, based on the step 1.1, selecting obstacle models with circular, square and L-shaped top views respectively in the search space to simulate the flying path environment of the rotor wing unmanned aerial vehicle.
3. The method for planning the autonomous path of the rotary-wing unmanned aerial vehicle based on the longicorn algorithm as recited in claim 1, wherein in the step 2, the performance constraints comprise a maximum power constraint, an unmanned aerial vehicle performance constraint and an obstacle constraint;
the step 2 specifically comprises the following substeps:
step 2.1, maximum electric quantity constraint: the electric quantity carried by the rotor unmanned aerial vehicle in the navigation process is limited, the flight distance of the aircraft is limited by the capacity of a battery, and the electric quantity consumption of the unmanned aerial vehicle is related to the flight path of the unmanned aerial vehicle and the curvature cost of the flight path; suppose the flight path of an aircraftThe diameter is divided into n sections, and the maximum voyage is L max And then the ith flight is represented as L i (ii) a Cost function f of ith segment path length d Comprises the following steps:
in the formula x i ,y i ,z i Respectively representing the coordinates of the previous path point in the x, y, z axes, x i+1 ,y i+1 ,z i+1 Respectively representing the coordinates of the latter path point on the x, y and z axes;
representing the distance between the previous path point and the next path point; because the path generated by algorithm search cannot meet the requirement of unmanned aerial vehicle flight, the generated path needs to be smoothed, and a curvature cost function f is introduced c ：
Wherein, y' i Is described as y i Relative to x i In the coordinate (x) i ,y i ) First derivative, y ″, of i Is described as y i Relative to x i At the coordinate (x) i ,y i ) Lower second derivative, n i Representing the number of path points on the nth path;
energy consumption f of unmanned aerial vehicle battery b Comprises the following steps:
f b ＝f d +f c (3)
wherein f is d Cost function representing the length of the ith segment path, f c Representing a curvature cost function;
step 2.2, self-performance constraint function f of unmanned aerial vehicle o Comprises the following steps:
pitch angle constraint function f from current point to next intermediate point pitch_angle (x i ) The following formula is satisfied:
in the formula, theta is the pitching angle of the unmanned aerial vehicle, and theta is max The maximum pitching angle of the unmanned aerial vehicle is set, and M is a constraint value;
yaw angle constraint function f from current point to next position yaw_angle (x i ) The following formula is satisfied:
in the formula, Ψ i Yaw angle, psi, for unmanned aerial vehicles max The maximum yaw angle of the unmanned aerial vehicle is M, and M is a constraint value;
constraint function f of flight height opt_height (x i ) Expressed as:
where H is the optimal flying height derived from environmental analysis and mission requirements, H i For the height of the unmanned aerial vehicle from the ground, M h Is a constraint value;
step 2.3, obstacle constraint: is free ofThe man-machine can meet various obstacles in the flight process, and the obstacle constraint function f t Expressed as:
wherein Q represents the number of obstacles, d n,q Represents the distance between a waypoint and a q-th obstacle in the nth path segment, r q Represents the radius of the qth obstacle;
step 2.4, analyzing the constraint functions to obtain a cost function of the flight path of the unmanned aerial vehicle, wherein the cost function is as follows:
f＝ω 1 f b +ω 2 f o +ω 3 f t (11)
ω in cost function in equation 1 、ω 2 And ω 3 The constraint functions representing the respective parts account for the cost function, and the sum of the coefficients is 1.
4. The method for planning the autonomous path of the rotary-wing unmanned aerial vehicle based on the longicorn swarm algorithm according to claim 3, wherein in the step 3, the specific steps of obtaining the optimal path of the rotary-wing unmanned aerial vehicle based on the longicorn swarm algorithm are as follows:
step 3.1, initializing a longicorn group, initializing the number of the longicorn group and initializing the speed and the position of each longicorn in a search space; the specific process is as follows:
defining the orientation of the generated longicorn beard and the position of the longicorn in a three-dimensional search space, standardizing the orientation and the position of the longicorn beard
To indicate that the position of the movable member,
wherein, rands (.) is a random function, 3 represents that the random function is three-dimensional, and 1 represents that the coordinates are normalized;
the position vector of the mth longicorn in the three-dimensional space is represented as:
in the formula (I), the compound is shown in the specification,
representing the position parameter of the mth individual of the longicorn population in the j-dimensional space at the kth iteration;
creating the spatial coordinates of the initial left and right antennae of the longicorn:
wherein x is rk Denotes the position coordinate, x, of the right antenna at the k-th iteration lk Representing the coordinates of the left antenna at the kth iteration; d is a radical of k The search distance between the two antennae of the longicorn; the velocity of the mth longicorn is expressed as:
wherein
Representing the velocity parameter of the mth individual of the population in the j-dimensional space at the kth iteration;
step 3.2, setting an initial step length; the specific process is as follows:
the initial step size of the longicorn in the longicorn group is step, and the value of the initial step size is set as follows:
step＝(ub-lb)*2 (17)
wherein ub represents an upper limit of the boundary value in the search space and lb represents a lower limit of the boundary value in the search space;
step 3.3, calculating the fitness values of all longicorn and comparing to obtain global optimum; the specific process is as follows:
obtaining an individual extreme value and a population extreme value of a population by adopting a cost function suitable for the rotor unmanned aerial vehicle, wherein the cost function is shown in a step 2.4;
by individual optimum extrema f m And the population optimum extreme value f g Obtaining individual optimal position p m And the population optimum position p g ；
M individual optimum extreme value f m Corresponding currently searched individual optimal position p m Comprises the following steps:
wherein f is m ＝(f m1 ,f m2 ,f m3 ) T An optimal extreme parameter, p, representing the m-th individual in the population in a j-dimensional space mj (j =1.. 3) represents an optimal location parameter of the mth individual of the population in the j-dimensional space;
the population optimal position of the longicorn population is expressed as:
wherein f is g ＝(f g1 ,f g2 ,f g3 ) T Represents the optimal extreme value, p, of the population in the j-dimensional space gj (j =1.. 3) represents a position parameter of the population-optimal individual in a j-dimensional space;
the update rule of the step size is then set,
eta＝step1*(step0/step1) (1/(1+10*k/K)) (20)
δ k ＝eta*step (21)
in this step, the positions of the left and right antennal of the longicorn are extended to a high dimension, δ k Represents the current step size; step0 and step1 are set random numbers in the range of [0,1]Eta represents the updating of the step coefficient, which is used for updating the step;
the position coordinate updating rules of the left antenna and the right antenna are respectively expressed as follows:
increment function at iteration k +1
The calculation is as follows:
wherein m =1,2.., s; k is the current number of iterations;
representing the speed of the longicorn at the m-th longicorn and k iterations,
representing the amplitude of the increase of the longicorn motion at the k +1 iteration; />
The difference of the left and right antenna fitness function values of the longicorn is represented and used for determining whether the longicorn moves to the left or right;
updating the speed and the position of the longicorn population, wherein the speed updating of the mth longicorn and the k +1 iteration is as follows:
wherein
Represents the optimal position parameter of the mth individual in k iterations in three-dimensional space, and->
Representing a position parameter of the mth individual at the kth iteration in the three-dimensional space; />
Represents an optimal location parameter for a population in k iterations in three-dimensional space>
Representing a population position parameter of a population in a three-dimensional space during the kth iteration of the population;
the position update at the mth longicorn and the k +1 iteration is as follows:
wherein x is a positive number, wherein x is,
is an increment function value obtained by the formula (24)>
Is the speed of the longicorn individual obtained by the formula (25);
step 3.4, setting inertia weight;
setting the value of partial coefficient, adopting the strategy of reducing the inertia weight, and the formula is as follows:
c 1 ＝1-exp(-|f m -f g |) (28)
c 2 ＝1-c 1 (29)
wherein the learning factor c 1 Controlling the self-learning ability of an individual, learning factor c 2 Controlling the social learning ability of the group, which are two positive numbers; ω is the inertial weight, ω max And ω min Respectively represent the maximum value and the minimum value of omega; k and K are the current iteration number and the maximum iteration number;
step 3.5, after the speed and the position of the longicorn are updated in each iteration, the step length coefficient, the inertia weight and the step length are required to be updated, then the fitness value of each longicorn is recalculated and compared again to obtain the global optimum, the iteration times are increased by one, and the algorithm is ended until the maximum iteration times are reached;
and 3.6, continuously updating based on the steps to obtain an optimal value of the longicorn group, replacing the position of the rotor unmanned aerial vehicle with the optimal position of the current longicorn group in each iteration updating, connecting the positions of the rotor unmanned aerial vehicles after each iteration by using a smooth curve after the maximum iteration number is reached, and finally obtaining the optimal path planned by using the longicorn group algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211402120.5A CN115903888A (en) | 2022-11-10 | 2022-11-10 | Rotor unmanned aerial vehicle autonomous path planning method based on longicorn swarm algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211402120.5A CN115903888A (en) | 2022-11-10 | 2022-11-10 | Rotor unmanned aerial vehicle autonomous path planning method based on longicorn swarm algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115903888A true CN115903888A (en) | 2023-04-04 |
Family
ID=86493219
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211402120.5A Pending CN115903888A (en) | 2022-11-10 | 2022-11-10 | Rotor unmanned aerial vehicle autonomous path planning method based on longicorn swarm algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115903888A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116090931A (en) * | 2023-03-16 | 2023-05-09 | 南京邮电大学 | Terminal distribution method and device based on customer classification |
| CN117146827A (en) * | 2023-10-30 | 2023-12-01 | 广东电网有限责任公司佛山供电局 | Unmanned aerial vehicle inspection path planning method and device |
| CN117826845A (en) * | 2024-03-04 | 2024-04-05 | 易创智芯(西安)科技有限公司 | Aviation operation safety active obstacle avoidance and planning method |
-
2022
- 2022-11-10 CN CN202211402120.5A patent/CN115903888A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116090931A (en) * | 2023-03-16 | 2023-05-09 | 南京邮电大学 | Terminal distribution method and device based on customer classification |
| CN116090931B (en) * | 2023-03-16 | 2024-02-09 | 南京邮电大学 | Terminal distribution method and device based on customer classification |
| CN117146827A (en) * | 2023-10-30 | 2023-12-01 | 广东电网有限责任公司佛山供电局 | Unmanned aerial vehicle inspection path planning method and device |
| CN117826845A (en) * | 2024-03-04 | 2024-04-05 | 易创智芯(西安)科技有限公司 | Aviation operation safety active obstacle avoidance and planning method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20210020051A1 (en) | Airplane flight path planning method and device based on the pigeon-inspired optimization | |
| CN115903888A (en) | Rotor unmanned aerial vehicle autonomous path planning method based on longicorn swarm algorithm | |
| CN110806756B (en) | Unmanned aerial vehicle autonomous guidance control method based on DDPG | |
| De Filippis et al. | A minimum risk approach for path planning of UAVs | |
| CN112684807A (en) | Unmanned aerial vehicle cluster three-dimensional formation method | |
| CN114840020A (en) | Unmanned aerial vehicle flight path planning method based on improved whale algorithm | |
| Gramajo et al. | An efficient energy constraint based UAV path planning for search and coverage | |
| CN108919818B (en) | Spacecraft attitude orbit collaborative planning method based on chaotic population variation PIO | |
| CN115454115A (en) | Rotor unmanned aerial vehicle path planning method based on hybrid wolf-particle swarm algorithm | |
| CN112327939B (en) | Collaborative path planning method for high-rise fire-fighting multiple unmanned aerial vehicles in city block environment | |
| CN109870906A (en) | A kind of high-speed rotor aircraft paths planning method based on BBO optimization Artificial Potential Field | |
| Lee et al. | RRT-based path planning for fixed-wing UAVs with arrival time and approach direction constraints | |
| CN112414405A (en) | Unmanned aerial vehicle cluster flight path planning method considering emergency task of DSM | |
| CN115060263A (en) | Flight path planning method considering low-altitude wind and energy consumption of unmanned aerial vehicle | |
| Bian et al. | Trajectory following and improved differential evolution solution for rapid forming of UAV formation | |
| CN113220008B (en) | Collaborative dynamic path planning method for multi-Mars aircraft | |
| Wu et al. | Real-time three-dimensional smooth path planning for unmanned aerial vehicles in completely unknown cluttered environments | |
| CN116954239B (en) | Unmanned aerial vehicle track planning method and system based on improved particle swarm optimization | |
| CN112258896A (en) | Unmanned aerial vehicle fusion airspace operation method based on flight path | |
| CN114815875B (en) | Unmanned aerial vehicle cluster formation controller parameter adjustment method based on intelligent optimization of integrated fully-shooting pigeon clusters | |
| CN112161626B (en) | High-flyability route planning method based on route tracking mapping network | |
| Oyana et al. | Three-layer multi-uavs path planning based on ROBL-MFO | |
| Cui | Multi-target points path planning for fixed-wing unmanned aerial vehicle performing reconnaissance missions | |
| Li et al. | Unified path planning for composite UAVs via Fermat point-based grouping particle swarm optimization | |
| Benkhlifa et al. | Improved simplified swarm optimization for UAVs path planning |
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 |