CN114777789A - Three-dimensional obstacle avoidance airway planning method based on spherical vector pigeon group algorithm - Google Patents

Three-dimensional obstacle avoidance airway planning method based on spherical vector pigeon group algorithm Download PDF

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CN114777789A
CN114777789A CN202210425955.6A CN202210425955A CN114777789A CN 114777789 A CN114777789 A CN 114777789A CN 202210425955 A CN202210425955 A CN 202210425955A CN 114777789 A CN114777789 A CN 114777789A
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刘燕斌
徐文钰
都延丽
步雨浓
魏云聪
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention discloses a three-dimensional obstacle avoidance route planning method based on a spherical vector pigeon group algorithm, which comprises the following steps: step 1, establishing an unmanned aerial vehicle route planning three-dimensional environment; step 2, determining an objective function of an unmanned aerial vehicle route planning task according to various requirements and constraint conditions of the unmanned aerial vehicle and a flight route thereof; and 3, optimizing the route cost function of the unmanned aerial vehicle in the route planning environment by using an improved pigeon swarm algorithm based on the spherical vector and the Tent chaos strategy, and finally obtaining the optimal route of the unmanned aerial vehicle. The invention can improve the quality of the initial solution, enhance the global searching capability of the pigeon swarm algorithm and realize the optimal route searching of the unmanned aerial vehicle.

Description

Three-dimensional obstacle avoidance airway planning method based on spherical vector pigeon group algorithm
Technical Field
The invention relates to the technical field of aircraft mission planning, in particular to a three-dimensional obstacle avoidance airway planning method based on a spherical vector pigeon swarm algorithm.
Background
The unmanned aerial vehicle has the advantages of high cost performance, strong flexibility, good concealment and the like, is widely applied to the military and civil fields in recent years, and is recognized by all countries in the world. With the continuous development of aviation technology, electronic information technology and related manufacturing processes, all countries in the world have increased attention and investment on the field of unmanned aerial vehicles. A perfect task planning system is an important guarantee that the unmanned aerial vehicle can smoothly complete tasks, and the route planning is a core part of the system and is an important means for improving the safety performance of the unmanned aerial vehicle. The unmanned aerial vehicle route planning is to find an optimal or feasible flight route from an initial position to a target position for the unmanned aerial vehicle under a specific task background, meet the physical constraint conditions of the unmanned aerial vehicle and avoid obstacles and threats. The algorithm adopted when the unmanned aerial vehicle carries out the air route planning will directly influence the effect of the air route planning.
The traditional route planning methods such as a mathematical planning method and an artificial potential field method have the defects of low convergence speed when facing a three-dimensional space, too large calculation amount and sometimes incapability of finding an optimal route. At present, along with the increasingly wide application of unmanned aerial vehicles, the task environment of the unmanned aerial vehicles is increasingly complex, and the challenges are brought to the calculation amount and timeliness of the algorithm. And by simulating the behavior law of organisms in the nature, a group intelligent optimization algorithm for exploring the optimal solution distributed in a solution space in a certain range can be used for solving the multidimensional search space, and the method has stronger search capability and robustness, has remarkable advantages in the field of navigation planning, and is popular with numerous researchers.
The pigeon swarm optimization algorithm (PIO) is a novel swarm intelligent optimization algorithm, the search of the optimal solution is realized by simulating the homing behavior of pigeons, the algorithm is simple in structure and few in parameters, and the PiO is already used in various fields. However, the original pigeon swarm optimization algorithm is the same as many other swarm intelligent optimization algorithms, and when the algorithm is used for expanding search near the global optimal solution, the problems of insufficient swarm diversity and easy falling into local optimal are generated.
Disclosure of Invention
The invention aims to solve the technical problem of providing a three-dimensional obstacle avoidance airway planning method based on a spherical vector pigeon swarm algorithm, which can improve the quality of an initial solution, enhance the global search capability of the pigeon swarm algorithm and realize the optimal airway search of an unmanned aerial vehicle.
In order to solve the technical problem, the invention provides a three-dimensional obstacle avoidance route planning method based on a spherical vector pigeon group algorithm, which comprises the following steps:
step 1, establishing an unmanned aerial vehicle route planning three-dimensional environment;
step 2, determining an objective function of an unmanned aerial vehicle route planning task according to various requirements and constraint conditions of the unmanned aerial vehicle and a flight route thereof;
and 3, optimizing the route cost function of the unmanned aerial vehicle in the route planning environment by using an improved pigeon swarm algorithm based on the spherical vector and Tent chaos strategy, and finally obtaining the optimal route of the unmanned aerial vehicle.
Preferably, in step 1, establishing the three-dimensional environment for the route planning of the unmanned aerial vehicle specifically comprises: determining task map boundaries, threat zones, and drone flight task information, including start point coordinates S (x)s,ys,zs) And target point coordinates E (x)e,ye,ze) N, waypoint number;
in the planning space, the flight path of the drone is represented by a number of waypoints, and the spherical vector-based pigeon group algorithm SPIO encodes each path as a set of vectors, each vector describing the movement of the drone from one waypoint to another waypoint, the vectors being represented in a spherical coordinate system comprising three components: amplitude rho epsilon (0, path _ length), elevation angle phi epsilon (-pi/2, pi/2) and azimuth angle phi epsilon (-pi, pi), and a flight path omega with N nodesiIt is represented by a 3N-dimensional hypersphere vector;
Ωi＝(ρi1,ψi1,φi1,ρi2,ψi2,φi2,…,ρiN,ψiN,φiN),N＝n-2
by describing the position of the pigeon as omegaiThe velocity associated with the pigeon may be described by an incremental vector:
ΔΩi＝(Δρi1,Δψi1,Δφi1,Δρi2,Δψi2,Δφi2,…,ΔρiN,ΔψiN,ΔφiN)
will sphere vector (p)ij,ψij,φij) Is denoted by uijVelocity (Δ ρ)ij,Δψij,Δφij) Is expressed as Deltauij。
Preferably, in step 2, according to various requirements and constraint conditions of the unmanned aerial vehicle and its flight path, determining an objective function of the unmanned aerial vehicle path planning task specifically includes: considering and flight path XiRelevant optimality, safety and feasibility constraints, total cost function F (X) of unmanned aerial vehicle in flighti) Defined in the form:
Figure BDA0003609592380000021
wherein b iskIs a weight coefficient, F1(Xi) Is the cost of the length of the flight path, F2(Xi) Refers to the cost of the threat, F3(Xi) Is referred to as the flight altitude penalty, F4(Xi) Referring to the smoothness cost, the decision variable is flight path XiRepresented by n waypoints that the drone needs to fly through, each waypoint corresponding to a path node in the search map with coordinates Pij＝(xij,yij,zij)；
Cost F for the length of the flight path1(Xi) The definition is as follows:
Figure BDA0003609592380000031
let K be the set of all threats, each threat being defined in a cylinder with a projection having a center coordinate CkRadius is RkFor a given route segment
Figure BDA0003609592380000032
Associated threat costs and itTo CkDistance d ofkProportional, by considering the diameter D of the drone and the danger distance S, the cost F of the threat cost2(Xi) The definition is as follows:
Figure BDA0003609592380000033
during flight, the minimum and maximum heights are respectively hminAnd hmaxHigh cost F3(Xi) Calculated by the following formula:
Figure BDA0003609592380000034
Figure BDA0003609592380000035
wherein h isijIs the flying height relative to the ground, HijIs and waypoint PijCalculation of the associated altitude cost, H for all waypointsijThe sum is taken to obtain a high cost F3(Xi)；
Smoothness cost F4(Xi) Evaluating the turning angle and the climbing angle;
corner piece
Figure BDA0003609592380000036
Is two continuous route sections
Figure BDA0003609592380000037
And
Figure BDA0003609592380000038
the angle between the projections on the horizontal plane Oxy, the turning angle is calculated by:
Figure BDA0003609592380000039
climbing angle psiijIs a flight path section
Figure BDA0003609592380000041
Projection thereof on a horizontal plane
Figure BDA0003609592380000042
The climbing angle is calculated by the following formula:
Figure BDA0003609592380000043
smoothness cost F4(Xi) Can be calculated by the following formula:
Figure BDA0003609592380000044
wherein, a1And a2The punishment coefficients of the turning angle and the climbing angle are respectively.
Preferably, in step 3, the improved pigeon swarm algorithm based on the spherical vector and Tent chaos strategy is used for optimizing the route cost function of the unmanned aerial vehicle in the route planning environment, and finally obtaining the optimal route of the unmanned aerial vehicle specifically comprises the following steps:
step 3.1: initializing the population quantity, iteration times of a map and compass operator operation stage and iteration times of a landmark operator operation stage in an algorithm, a map compass factor and each cost weight, determining upper and lower limit values of search according to a planning space, and performing population initialization operation;
step 3.2: starting a map and compass operator iteration stage, and calculating the fitness value of each pigeon in the current pigeon group according to a total cost function formula;
step 3.3: updating the local optimal solution and the global optimal solution, judging whether the iteration times of the map and the compass operator are reached, if so, carrying out the next step, otherwise, jumping to the next step to carry out the next iteration;
step 3.4: starting a landmark operator iteration stage, sequencing all pigeons according to the fitness value of the current pigeon group, wherein a part of pigeons with low fitness values can follow pigeons with high fitness values, then finding the centers of all pigeons, adjusting the flight directions of all pigeons along with the centers, and finally storing the optimal solution parameters and the optimal cost values;
step 3.5: and judging whether the iteration times of the landmark operator is reached, if so, stopping the operation of the landmark operator, and outputting a result, otherwise, executing the previous step.
Preferably, in step 3.1, when initializing the pigeon population, the population is initialized by Tent chaotic mapping, and the formula of Tent chaotic mapping is as follows:
Figure BDA0003609592380000045
namely that
Figure BDA0003609592380000051
Wherein N isTThe number of particles in the chaotic sequence; rand (0,1) is [0,1 ]]A random number within.
Preferably, in step 3.2, in the map and compass operator stage, the velocity and position of the pigeon are updated according to the following equation for each iteration:
Figure BDA0003609592380000052
Figure BDA0003609592380000053
wherein R is a map compass operator, rand is a random number, qgIs the global optimal position of the current population.
Preferably, in step 3.3, updating the local optimal solution and the global optimal solution, and judging whether the iteration times of the map and compass operators are reached, if so, performing the next step, otherwise, jumping to the previous step to perform the next iteration;
Qi＝(qi1,qi2,…,qi,N) And Qg＝(qg1,qg2,…,qg,N) Set of vectors representing local and global optimal solutions, respectively, for determining QiAnd QgThe vector-based flight path Ω is requirediMapping to direct path XiTo evaluate the associated cost, vector uij＝(ρij,ψij,φij)∈ΩiTo waypoint Pij＝(xij,yij,zij)∈XiThe mapping of (c) may be performed as follows:
xij＝xi,j-1+ρijsinψijcosφij
yij＝yi,j-1+ρijsinψijsinφij
zij＝zi,j-1+ρijcosψij
express the map as ξ: Ω → X, the local and global optimal solutions can be calculated by:
Figure BDA0003609592380000054
preferably, in step 3.4, in the landmark operator stage, the position of the pigeon is updated according to the following formula:
Figure BDA0003609592380000055
Figure BDA0003609592380000056
wherein, fitness is a fitness function.
The beneficial effects of the invention are as follows: aiming at the problem of unmanned aerial vehicle route planning in a three-dimensional space, a new pigeon swarm optimization algorithm based on spherical vectors is provided, the safety enhancement of the unmanned aerial vehicle flight can be realized through the interrelation between the amplitude, the elevation angle and the azimuth angle components of the vectors and the speed, the rotation angle and the climbing angle of the unmanned aerial vehicle, the algorithm searches a solution in a configuration space, and the higher probability of searching a high-quality solution can be improved; more importantly, in the course of planning the route, the constraint related to the turning angle and the climbing angle can be directly realized by the elevation angle and the azimuth angle of the spherical vector, thus greatly reducing the search space; in some cases, such as when the drone is flying at a constant speed, the magnitude of the spherical vector may be fixed to further reduce the search space and expand the search capability of the algorithm in the configuration space; a Tent chaotic strategy is introduced in a pigeon swarm algorithm population initialization stage to obtain a chaotic sequence, so that the quality of an initial solution is improved, the global search capability of a pigeon swarm algorithm is enhanced, and the optimal airway search of an unmanned aerial vehicle is realized.
Drawings
FIG. 1 is a schematic representation of the threat costs of the present invention.
FIG. 2 is a high-cost schematic of the present invention.
FIG. 3 is a schematic flow chart of the method of the present invention.
Fig. 4(a) is a three-dimensional view of the unmanned aerial vehicle route obtained based on the spherical vector pigeon swarm algorithm in the embodiment of the present invention.
Fig. 4(b) is a three-dimensional view of the unmanned aerial vehicle route obtained based on the original pigeon flock algorithm in the embodiment of the present invention.
Fig. 5(a) is a top view of the unmanned aerial vehicle airway obtained based on the spherical vector pigeon swarm algorithm in the embodiment of the present invention.
Fig. 5(b) is a top view of the unmanned aerial vehicle route obtained based on the original pigeon flock algorithm in the embodiment of the present invention.
Fig. 6(a) is a convergence curve diagram of the spherical vector-based pigeon swarm algorithm in the embodiment of the present invention.
Fig. 6(b) is a convergence graph based on the original pigeon flock algorithm in the embodiment of the present invention.
Detailed Description
As shown in fig. 3, a three-dimensional obstacle avoidance route planning method based on a spherical vector pigeon group algorithm includes the following steps:
step 1: establishing an unmanned aerial vehicle route planning three-dimensional environment;
determining task map boundaries, threat zones, and drone flight task information, including start point coordinates S (x)s,ys,zs) And target point coordinates E (x)e,ye,ze) N, waypoint number.
In the planning space, the flight path of a drone may be represented by a number of waypoints. Spherical vector based pigeon swarm algorithm (SPIO) encodes each path as a set of vectors, each vector describing the motion of a drone from one waypoint to another waypoint. These vectors are represented in a spherical coordinate system, which includes three components: the magnitude rho ∈ (0, path _ length), the elevation angle ψ ∈ (- π/2, π/2), and the azimuth angle φ ∈ (- π, π). A flight path omega with N nodesiIt is represented by a 3N dimensional hypersphere vector.
Ωi＝(ρi1,ψi1,φi1,ρi2,ψi2,φi2,…,ρiN,ψiN,φiN),N＝n-2
By describing the position of the pigeon as omegaiThe velocity associated with the pigeon may be described by an incremental vector:
ΔΩi＝(Δρi1,Δψi1,Δφi1,Δρi2,Δψi2,Δφi2,…,ΔρiN,ΔψiN,ΔφiN)
will sphere vector (ρ)ij,ψij,φij) Is denoted by uijVelocity (Δ ρ)ij,Δψij,Δφij) Is expressed as Deltau uij。
And 2, step: determining an objective function of an unmanned aerial vehicle route planning task according to various requirements and constraint conditions of the unmanned aerial vehicle and a flight route thereof;
considering and flight path XiRelative optimality, safety and feasibility constraints, total generation of unmanned aerial vehicle in flightPrice function F (X)i) Is defined as follows:
Figure BDA0003609592380000071
wherein b iskIs a weight coefficient, F1(Xi) Is the cost of the length of the flight path, F2(Xi) Is the cost of the threat, F3(Xi) Is referred to as the flight altitude penalty, F4(Xi) Refers to the smoothness penalty. The decision variable being flight path XiRepresented by n waypoints that the drone needs to fly through, each waypoint corresponding to a path node in the search map with coordinates Pij＝(xij,yij,zij)。
Cost F for the length of the flight path1(Xi) The definition is as follows:
Figure BDA0003609592380000072
assuming K is the set of all threats, each threat is defined in a cylinder with a projection having center coordinates CkRadius is RkAs shown in fig. 1. For a given route segment
Figure BDA0003609592380000073
Associated threat costs and its to CkDistance d ofkIs in direct proportion. By considering the diameter D and the hazard distance S of the drone, the cost F of the threat cost2(Xi) The definition is as follows:
Figure BDA0003609592380000081
during flight, the minimum and maximum heights are respectively hminAnd hmaxHigh cost F3(Xi) Can be calculated by the following formula:
Figure BDA0003609592380000082
Figure BDA0003609592380000083
wherein h isijIs the flying height relative to the ground, as shown in FIG. 2, HijIs and waypoint PijCalculation of the associated altitude cost, H for all waypointsijThe sum is performed to obtain a high cost F3(Xi)。
Smoothness cost F4(Xi) The turning angle and the climbing angle were evaluated.
Turning angle
Figure BDA0003609592380000084
Is two consecutive route sections
Figure BDA0003609592380000085
And
Figure BDA0003609592380000086
the angle between the projections on the horizontal plane Oxy. The turning angle can be calculated by the following formula:
Figure BDA0003609592380000087
climb angle psiijIs a section of an airway
Figure BDA0003609592380000088
Projection thereof on a horizontal plane
Figure BDA0003609592380000089
The climbing angle can be calculated by the following formula:
Figure BDA00036095923800000810
smoothness cost F4(Xi) Can be calculated by the following formula:
Figure BDA00036095923800000811
wherein, a1And a2The punishment coefficients of the turning angle and the climbing angle are respectively.
And step 3: optimizing an unmanned aerial vehicle airway cost function in an airway planning environment by using an improved pigeon swarm algorithm based on a spherical vector and Tent chaos strategy to finally obtain an optimal airway of the unmanned aerial vehicle, wherein the method specifically comprises the following steps:
step 3.1: the method comprises the steps of initializing population quantity, iteration times of map and compass operator operation stages, iteration times of landmark operator operation stages, map compass factors and cost weights in an algorithm, determining upper and lower limit values of search according to a planning space, and performing population initialization operation;
the chaos mapping adopts Tent chaos mapping, and the analytic expression is as follows:
Figure BDA0003609592380000091
namely, it is
Figure BDA0003609592380000092
Wherein N isTThe number of particles in the chaotic sequence; rand (0,1) is [0,1 ]]A random number within.
Step 3.2: starting a map and compass operator iteration stage, and calculating the fitness value of each pigeon in the current pigeon group according to a total cost function formula;
in the map and compass operator stage, the speed and position of the pigeon are updated according to the following formula in each iteration:
Figure BDA0003609592380000093
Figure BDA0003609592380000094
wherein R is a map compass operator, rand is a random number, qgThe global optimal position of the current population is obtained.
Step 3.3: updating the local optimal solution and the global optimal solution, judging whether the iteration times of the map and the compass operator are reached, if so, carrying out the next step, otherwise, jumping to the next step to carry out the next iteration;
Qi＝(qi1,qi2,…,qi,N) And Qg＝(qg1,qg2,…,qg,N) Set of vectors representing the local best solution and the global best solution for pigeon i, respectively. To determine QiAnd QgThe vector-based flight path Ω is requirediMapping to direct path XiIn order to evaluate the associated costs. Vector uij＝(ρij,ψij,φij)∈ΩiTo a waypoint Pij＝(xij,yij,zij)∈XiThe mapping of (c) may be performed as follows:
xij＝xi,j-1+ρijsinψijcosφij
yij＝yi,j-1+ρijsinψijsinφij
zij＝zi,j-1+ρijcosψij
express the map as ξ: Ω → X, the local and global optimal solutions can be calculated by:
Figure BDA0003609592380000101
Figure BDA0003609592380000102
step 3.4: starting a landmark operator iteration stage, sequencing all pigeons according to the fitness value of the current pigeon group, finding the centers of all pigeons which follow the pigeons with high fitness values by a part of pigeons with low fitness values, adjusting the flight directions of all pigeons along the centers by all the pigeons, and finally storing the optimal solution parameters and the optimal cost values;
in the landmark operator stage, the position of the pigeon is updated according to the following formula:
Figure BDA0003609592380000103
Figure BDA0003609592380000104
wherein, the fitness is a fitness function.
Step 3.5: and judging whether the iteration times of the landmark operator is reached, if so, stopping the operation of the landmark operator and outputting a result, otherwise, executing the previous step.
In order to verify the feasibility and effectiveness of the method, the following is further described with the simulation experiment.
The planned space is shown in fig. 4(a) and 4(b), and includes 6 threat zones, and the data of the threat zones is shown in table 1. The drone start point coordinates are (200, 100, 150) and the drone end point coordinates are (800, 800, 150).
TABLE 1 threat zone data sheet
Figure BDA0003609592380000105
Figure BDA0003609592380000111
And setting relevant parameters of a pigeon swarm algorithm, wherein the number of the swarm is 50, the iteration times are 200, the map compass operator is 150, and the landmark operator is 50. Under the same threat environment, planning simulation is respectively carried out by using a spherical vector-based pigeon swarm algorithm and an original pigeon swarm algorithm, a simulation result calculated according to set parameters, namely a route planning route map of the spherical vector-based pigeon swarm algorithm is shown in fig. 5(a) and 5(b), and convergence curves of the route planning route map are shown in fig. 6(a) and 6 (b).

Claims (8)

1. A three-dimensional obstacle avoidance route planning method based on a spherical vector pigeon group algorithm is characterized by comprising the following steps:
step 1, establishing an unmanned aerial vehicle route planning three-dimensional environment;
step 2, determining a target function of an unmanned aerial vehicle route planning task according to various requirements and constraint conditions of the unmanned aerial vehicle and a flight route thereof;
and 3, optimizing the route cost function of the unmanned aerial vehicle in the route planning environment by using an improved pigeon swarm algorithm based on the spherical vector and Tent chaos strategy, and finally obtaining the optimal route of the unmanned aerial vehicle.
2. The three-dimensional obstacle avoidance route planning method based on the spherical vector pigeon swarm algorithm as claimed in claim 1, wherein in step 1, establishing the unmanned aerial vehicle route planning three-dimensional environment specifically comprises: determining task map boundaries, threat zones, and drone flight task information, including start point coordinates S (x)s,ys,zs) And target point coordinates E (x)e,ye,ze) N, the number of waypoints;
in the planning space, the flight path of the drone is represented by a number of waypoints, and the spherical vector-based pigeon swarm algorithm SPIO encodes each path as a set of vectors, each vector describing the movement of the drone from one waypoint to another waypoint, the vectors being represented in a spherical coordinate system comprising three components: amplitude rho epsilon (0, path _ length), elevation angle phi epsilon (-pi/2, pi/2) and azimuth angle phi epsilon (-pi, pi), and a flight path omega with N nodesiIt is represented by a 3N-dimensional hypersphere vector;
Ωi＝(ρi1,ψi1,φi1,ρi2,ψi2,φi2,…,ρiN,ψiN,φiN),N＝n-2
by describing the position of the pigeon as omegaiThe velocity associated with the pigeon is described by an incremental vector:
ΔΩi＝(Δρi1,Δψi1,Δφi1,Δρi2,Δψi2,Δφi2,…,ΔρiN,ΔψiN,ΔφiN)
will sphere vector (ρ)ij,ψij,φij) Is denoted by uijVelocity (Δ ρ)ij,Δψij,Δφij) Is expressed as Deltauij。
3. The three-dimensional obstacle avoidance flight path planning method based on the spherical vector pigeon swarm algorithm as claimed in claim 1, wherein in step 2, according to various requirements and constraint conditions of the unmanned aerial vehicle and the flight path thereof, the objective function for determining the unmanned aerial vehicle flight path planning task is specifically as follows: considering and flight path XiRelevant optimality, safety and feasibility constraints, total cost function F (X) of unmanned aerial vehicle in flighti) Is defined as follows:
Figure FDA0003609592370000011
wherein b iskIs a weight coefficient, F1(Xi) Is the cost of the length of the flight path, F2(Xi) Is the cost of the threat, F3(Xi) Is referred to as the flight altitude penalty, F4(Xi) Referring to the smoothness cost, the decision variable is flight path XiRepresented by n waypoints that the drone needs to fly through, each waypoint corresponding to a path node in the search map with coordinates Pij＝(xij,yij,zij)；
Cost F for the length of the flight path1(Xi) The definition is as follows:
Figure FDA0003609592370000021
let K be the set of all threats, each threat being defined in a cylinder with a projection having a center coordinate CkRadius RkFor a given route segment
Figure FDA0003609592370000022
Associated threat costs and its to CkDistance d ofkProportional, by considering the diameter D of the drone and the danger distance S, the cost F of the threat cost2(Xi) The definition is as follows:
Figure FDA0003609592370000023
during flight, the minimum and maximum heights are respectively hminAnd hmaxHigh cost F3(Xi) Calculated by the following formula:
Figure FDA0003609592370000024
Figure FDA0003609592370000025
wherein h isijIs the flying height relative to the ground, HijIs and waypoint PijCalculation of the associated altitude cost, H for all waypointsijThe sum is taken to obtain the height cost F3(Xi)；
Smoothness cost F4(Xi) Evaluating the turning angle and the climbing angle;
turning angle
Figure FDA0003609592370000031
Is two consecutive route sections
Figure FDA0003609592370000032
And
Figure FDA0003609592370000033
the angle between the projections on the horizontal plane Oxy, the turning angle is calculated by:
Figure FDA0003609592370000034
climb angle psiijIs a section of an airway
Figure FDA0003609592370000035
Projection thereof on a horizontal plane
Figure FDA0003609592370000036
The climbing angle is calculated by the following formula:
Figure FDA0003609592370000037
smoothness cost F4(Xi) Calculated by the following formula:
Figure FDA0003609592370000038
wherein, a1And a2The punishment coefficients of the turning angle and the climbing angle are respectively.
4. The three-dimensional obstacle avoidance airway planning method based on the spherical vector pigeon swarm algorithm as claimed in claim 1, wherein in step 3, the improved pigeon swarm algorithm based on the spherical vector and Tent chaos strategy is used to optimize the airway cost function of the unmanned aerial vehicle in the airway planning environment, and the step of obtaining the optimal airway of the unmanned aerial vehicle comprises the following steps:
step 3.1: the method comprises the steps of initializing population quantity, iteration times of map and compass operator operation stages, iteration times of landmark operator operation stages, map compass factors and cost weights in an algorithm, determining upper and lower limit values of search according to a planning space, and performing population initialization operation;
step 3.2: starting a map and compass operator iteration stage, and calculating the fitness value of each pigeon in the current pigeon group according to a total cost function formula;
step 3.3: updating the local optimal solution and the global optimal solution, judging whether the iteration times of the map and the compass operator are reached, if so, carrying out the next step, otherwise, jumping to the previous step to carry out the next iteration;
step 3.4: starting a landmark operator iteration stage, sequencing all pigeons according to the fitness value of the current pigeon group, wherein a part of pigeons with low fitness values can follow pigeons with high fitness values, then finding the centers of all pigeons, adjusting the flight directions of all pigeons along with the centers, and finally storing the optimal solution parameters and the optimal cost values;
step 3.5: and judging whether the iteration times of the landmark operator is reached, if so, stopping the operation of the landmark operator, and outputting a result, otherwise, executing the previous step.
5. The three-dimensional obstacle avoidance navigation planning method based on the spherical vector pigeon swarm algorithm according to claim 4, wherein in step 3.1, when initializing pigeon swarm, the swarm is initialized by Tent chaotic mapping, and the adopted Tent chaotic mapping formula is as follows:
Figure FDA0003609592370000041
namely, it is
Figure FDA0003609592370000042
Wherein N isTThe number of particles in the chaotic sequence; rand (0,1) is [0,1 ]]A random number within.
6. The three-dimensional obstacle avoidance navigation planning method based on the spherical vector pigeon swarm algorithm according to claim 4, characterized in that in step 3.2, in the map and compass operator stage, the velocity and position of the pigeon are updated according to the following formula for each iteration:
Figure FDA0003609592370000043
Figure FDA0003609592370000044
wherein R is a map compass operator, rand is a random number, qgThe global optimal position of the current population is obtained.
7. The three-dimensional obstacle avoidance route planning method based on the spherical vector pigeon swarm algorithm as claimed in claim 4, wherein in step 3.3, the local optimal solution and the global optimal solution are updated, whether the iteration times of the map and the compass operator are reached is judged, if the iteration times are reached, the next step is carried out, otherwise, the next iteration is carried out by jumping to the previous step;
Qi＝(qi1,qi2,…,qi,N) And Qg＝(qg1,qg2,…,qg,N) Set of vectors representing local and global optimal solutions, respectively, for determining QiAnd QgThe vector-based flight path Ω is requirediMapping to direct path XiTo evaluate the associated cost, vector uij＝(ρij,ψij,φij)∈ΩiTo a waypoint Pij＝(xij,yij,zij)∈XiThe mapping of (c) is performed as follows:
xij＝xi,j-1+ρijsinψijcosφij
yij＝yi,j-1+ρijsinψijsinφij
zij＝zi,j-1+ρijcosψij
express the map as ξ: Ω → X, the local and global optimal solutions can be calculated as:
Figure FDA0003609592370000051
8. the three-dimensional obstacle avoidance navigation planning method based on the spherical vector pigeon swarm algorithm according to claim 4, wherein in the step 3.4, in the landmark operator stage, the position of the pigeon is updated according to the following formula:
Figure FDA0003609592370000052
Figure FDA0003609592370000053
wherein, the fitness is a fitness function.
CN202210425955.6A 2022-04-22 2022-04-22 Three-dimensional obstacle avoidance airway planning method based on spherical vector pigeon group algorithm Pending CN114777789A (en)

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CN115435776A (en) * 2022-11-03 2022-12-06 成都沃飞天驭科技有限公司 Method and device for displaying three-dimensional airway route, aircraft and storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115435776A (en) * 2022-11-03 2022-12-06 成都沃飞天驭科技有限公司 Method and device for displaying three-dimensional airway route, aircraft and storage medium
CN115435776B (en) * 2022-11-03 2023-03-14 成都沃飞天驭科技有限公司 Method and device for displaying three-dimensional airway route, aircraft and storage medium

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