CN112306097A - Novel unmanned aerial vehicle path planning method - Google Patents

Novel unmanned aerial vehicle path planning method Download PDF

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CN112306097A
CN112306097A CN202011179235.3A CN202011179235A CN112306097A CN 112306097 A CN112306097 A CN 112306097A CN 202011179235 A CN202011179235 A CN 202011179235A CN 112306097 A CN112306097 A CN 112306097A
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pheromone
path
algorithm
updating
ant colony
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颜成钢
万斌
王廷宇
孙垚棋
张继勇
张勇东
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Hangzhou Dianzi University
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Abstract

The invention discloses a novel unmanned aerial vehicle path planning method. Firstly, determining a flight mathematical model of the unmanned aerial vehicle, wherein the flight mathematical model comprises an unmanned aerial vehicle body motion model and an unmanned aerial vehicle dynamics model; then, modeling the two-dimensional path by adopting a Maklink graph theory method; and finally, optimizing the parameters of the ant colony algorithm through a particle swarm algorithm, and finding out the optimal path in the Maklink environment model through the optimized ant colony algorithm. The particle swarm optimization is introduced into the ant colony algorithm, so that the parameters needing to be manually adjusted in the original ant colony algorithm can be automatically adjusted on line, and the optimization time of the algorithm is greatly shortened. Compared with the traditional algorithm for path planning, the algorithm provided by the invention can shorten the operation time of the algorithm on the basis of finding out the optimal path, and provides a method for saving the operation cost.

Description

Novel unmanned aerial vehicle path planning method
Technical Field
The invention belongs to the field of unmanned aerial vehicle path planning, and particularly relates to a novel unmanned aerial vehicle path planning system for a flight path of a single unmanned aerial vehicle.
Background
The path planning is an important branch in the field of robots, and aims to calculate a shortest path which can avoid obstacles on the path to reach a target position according to a sensor of the robot under the condition that the robot can sense the surrounding environment. The route planning expert in foreign countries classifies the route planning method into a grid method, a Maklink graph theory, an ant colony algorithm, a Dijkstra algorithm and an A*And (4) an algorithm.
Aiming at the problems of low efficiency and insufficient optimization of the traditional ant colony algorithm, the invention designs a path planning method combining a particle swarm algorithm and an ant colony algorithm, and takes the shortest time and the shortest path as path criteria.
The four-rotor aircraft has the characteristics of high maneuverability, vertical take-off and landing, autonomous flight and the like, and is stable in the face of complex environmental conditions, so that the four-rotor aircraft is increasingly applied to real life. The four-rotor aircraft controls the pitch angle (theta), the yaw angle (psi) and the roll angle of the aircraft body
Figure BDA0002749668700000011
A series of complex flying movements of the body in the air are completed, but in the actual flying process, aiming at complex flying conditions such as obstacles on a flying path, the shortest path needs to be found while avoiding the obstacles as much as possible. Path planning is therefore introduced into a quad-rotor aircraft.
The ant colony algorithm is proposed by Drigo, Maniezzo and Colorni, which is implemented by simulating a special chemical called pheromone released by ants in nature when foraging. However, due to the fact that the solving speed of the algorithm and the quality of the obtained solution are seriously affected by improper setting of the parameters alpha, beta and rho in the ant colony algorithm, the particle swarm algorithm is introduced to optimize the parameters.
The particle swarm algorithm is an adaptive evolutionary computing technique for swarm search proposed by kennedy et al. Each particle in the population represents a feasible solution, while the location of the food represents the globally optimal solution. Each particle gets the optimal solution by continuously updating the global optimal position and the individual optimal position, and finally approaching the food.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a novel unmanned aerial vehicle path planning method.
The invention aims to solve the problems of large error and overlong operation time of the traditional ant colony algorithm in the aspect of path planning, introduces the particle swarm algorithm, and optimizes the parameters of the ant colony algorithm through continuous updating of particles in the particle swarm algorithm, thereby shortening the operation time of the ant colony algorithm. The technical scheme adopted by the invention for solving the technical problem specifically comprises the following steps:
step 1, determining a flight mathematic model of an unmanned aerial vehicle
The unmanned aerial vehicle flight mathematical model comprises an unmanned aerial vehicle body motion model and an unmanned aerial vehicle dynamics model, and specifically comprises the following steps:
unmanned aerial vehicle organism motion model
The four-rotor aircraft controls the rotating speed of the four rotors to adjust the pitch angle (theta), the yaw angle (psi) and the roll angle
Figure BDA0002749668700000021
And adjusting to complete a series of flying movements of the machine body.
First, two coordinate systems are established: a body coordinate system and a conventional coordinate system.
The conventional coordinate system e (oexyz) is stationary with respect to the earth's surface.
The machine body coordinate system A (oxyz) is coincident with the center of mass of the machine body, the horizontal axis ox points to the first motor, the longitudinal axis oy points to the fourth motor, and the oz is perpendicular to the oxyy surface.
Euler angles of four-rotor aircraft:
(1) roll angle
Figure BDA0002749668700000022
The machine body rotates around the ox shaft by an angle.
(2) Pitch angle (θ): the body rotates around the oy axis.
(3) Yaw angle (ψ): and the body rotates the included angle between the projection of the longitudinal axis of the aircraft around the oz axis in the horizontal plane and the axis of the inertial coordinate system OX.
Unmanned aerial vehicle dynamics model
Attitude vector based on four rotors
Figure BDA00027496687000000311
Wherein
Figure BDA00027496687000000312
Roll angle, yaw angle,. phi.phi.theta.pitch angle, and displacement vector [ x, y, z]The kinetic equation is as follows:
Figure BDA0002749668700000031
Figure BDA0002749668700000032
Figure BDA0002749668700000033
Figure BDA0002749668700000034
Figure BDA0002749668700000035
Figure BDA0002749668700000036
wherein:
Figure BDA0002749668700000037
Figure BDA0002749668700000038
Figure BDA0002749668700000039
Figure BDA00027496687000000310
u1representing the total lift of four rotors, u2Representing roll moment, u3Representing the pitching moment, u4Representing yaw moment, w1、w2、w3、w4Respectively representing the rotational speeds of four rotors, Ix、Iy、IzRepresenting the moment of inertia of the body in the xyz axis.
Step 2, Maklink environment model construction
Modeling the two-dimensional path by adopting a Maklink graph theory method:
(1) selecting an obstacle, selecting a vertex on the obstacle, connecting the vertex with fixed points of other obstacles, and making a vertical line segment from the point to the boundary of the environment space;
(2) adding the obtained connecting line segments of all the vertexes and fixed points into a line segment storage table according to the sequence from short to long in length;
(3) selecting a header line segment in a line segment storage table;
(4) and checking whether the header line segment crosses any barrier boundary in the environmental space, and if the header line segment crosses any barrier boundary in the environmental space, the header line segment is not a free connecting line. Thus, the current line segment is discarded and the next line segment in the line segment storage table is selected. Repeating the checking process until a line segment is found to be not intersected with all the barrier boundaries;
(5) connecting the midpoints of the connecting line segments in the line segment storage table, checking whether the link line segment crosses the boundary of the obstacle, and deleting the line segment if the link line segment crosses the obstacle.
Step 3, optimizing the ant colony algorithm parameters by adopting a particle swarm algorithm, and planning a two-dimensional path through the optimized ant colony algorithm;
and optimizing the parameters of the ant colony algorithm through a particle swarm algorithm, and finding out an optimal path in the Maklink environment model through the optimized ant colony algorithm. The path length is calculated by equation (11):
Figure BDA0002749668700000041
wherein xiyiAs current position coordinates, xi+1yi+1And m is the number of nodes from the starting point to the target point. Dist is the total path length.
Determining an ant colony algorithm pheromone updating method:
the pheromone updating comprises real-time pheromone updating and path pheromone updating, wherein the real-time pheromone updating refers to the pheromone tau of a certain node which must be treated by each ant after the node is selectedi，jUpdating is carried out, namely:
τij＝(1-ρ)τij+ρτ0 (12)
wherein tau is0P is the adjustable parameter between intervals (01) for initial pheromone values.
Path pheromone updating shows that when all ants go from an initial point to a terminal point and complete iterative search in sequence, all ants are selected to pass through one path with the shortest length, and pheromones of all points on the path are updated, namely:
τij＝(1-ρ)τij+ρΔτij (13)
where Δ τ isij＝1/L*,L*Bit shortest path length, ρ is an adjustable parameter between intervals (0, 1).
In conducting the path search, the next node location may be selected according to equation (14).
Figure BDA0002749668700000051
Wherein tau isi，jIs a pheromone of ηi，jFor heuristic values, α, β are τ, respectivelyi，j、ηi，jA weight coefficient.
However, in the actual simulation process, the situation of premature convergence occurs when the traditional ant colony algorithm searches for a path, and in order to avoid the situation and prevent a stagnation state, the ant colony algorithm pheromone is updated by adopting a method of combining global asynchronism and a novel pheromone updating strategy.
The novel pheromone updating strategy is as follows:
Figure BDA0002749668700000052
wherein
Figure BDA0002749668700000053
By utilizing the pheromone concentrations of the maximum value and the minimum value on the path, the real-time updating and adjusting amplitude of the pheromone is increased, the pheromone is not updated every evolutionary generation of the ant colony, and the pheromone is updated only when a solution with stronger adaptability occurs.
The global asynchronous updating method comprises the following steps:
τij(t+1)＝τij1(t) (16)
wherein tau isij1And (t) is the pheromone which continues to the current generation according to the global synchronous pheromone updating mode.
Further, the process of the particle swarm optimization for optimizing the ant colony algorithm parameters is as follows:
the particle swarm optimization algorithm finds the maximum solution and the optimal solution in the whole population through particle tracking, and updates according to the formulas (17) and (18):
Vi(t+1)＝w*Vi(t)+c1*r1(pi(t)-xi(t))+c2*r2(pg(t)-xi(t)) (17)
xi(t+1)＝xi(t)+Vi(t+1) (18)
where w is the inertial weight, r1r2Is a random number between (0, 1), c1c2Is a constant，Vi(t) is the velocity vector of the particle, xi(t) is the current position of the particle.
(1) Firstly, parameters alpha, beta and rho are taken as inlet parameters.
(2) Setting learning factor c in particle swarm optimization1c2And inertial weights w, and then randomly selected for the initial position and velocity of each particle.
(3) Judging whether the positions of the particles are good or bad according to the result of the ant colony algorithm, and updating the optimal solution p of the individual extremumbestAnd global extremum optimal solution gbest。
(4) And updating the position and the speed of the particles according to the formulas (17) and (18).
(5) If the conditions are met, the algorithm is ended, and the current global optimal particle position is returned. And if the condition is not met, returning to the step (3). And finally, obtaining the optimal control parameters by continuously updating the particles.
Updating the inertia weight by adopting an inertia weight updating method in the chaotic inertia weight particle swarm optimization algorithm, namely:
Figure BDA0002749668700000061
z1＝4×z2×(1-z2) (20)
wherein z is2Is a random number between (0, 1), wmax、wminRespectively representing the values of the inertial weight w at the beginning and at the end, stepmaxRepresenting the maximum number of iterations, and t being the current number of iterations. Parameter c1,c2A trial and error approach is used to find the appropriate value.
The invention has the following beneficial effects:
the particle swarm optimization is introduced into the ant colony algorithm, so that the parameters needing to be manually adjusted in the original ant colony algorithm can be automatically adjusted on line, and the optimization time of the algorithm is greatly shortened. Compared with the traditional algorithm for path planning, the algorithm provided by the invention can shorten the operation time of the algorithm on the basis of finding out the optimal path, and provides a method for saving the operation cost.
Drawings
Fig. 1 is a schematic diagram of a coordinate system of a quad-rotor drone;
fig. 2 is a schematic view of euler angles of a quad-rotor drone;
FIG. 3 is a schematic view of an environmental model;
FIG. 4 is a schematic diagram of ant colony path search;
FIG. 5 is a schematic flow chart of an ant colony algorithm;
FIG. 6 is a schematic diagram of a path planning for a novel ant colony algorithm;
FIG. 7 is a schematic diagram of a conventional ant colony algorithm path planning;
FIG. 8 is a diagram illustrating iteration times of the novel ant colony algorithm;
fig. 9 is a diagram illustrating the iteration number of the conventional ant colony algorithm.
Detailed description of the invention
The present invention will be described in detail with reference to specific embodiments.
The improved ant colony path planning method provided by the invention is implemented according to the following steps:
step 1, determining a flight mathematic model of an unmanned aerial vehicle
The unmanned aerial vehicle flight mathematical model comprises an unmanned aerial vehicle body motion model and an unmanned aerial vehicle dynamics model, and specifically comprises the following steps:
unmanned aerial vehicle organism motion model
The four-rotor aircraft controls the rotating speed of the four rotors to adjust the pitch angle (theta), the yaw angle (psi) and the roll angle
Figure BDA0002749668700000071
And a series of flying movements of advancing and retreating, ascending and descending, left and right flying and the like of the airframe are further completed through adjustment.
First, two coordinate systems are established: a body coordinate system and a conventional coordinate system. The conventional coordinate system e (oexyz) is stationary with respect to the earth's surface as shown in figure 1.
The machine body coordinate system A (oxyz) is coincident with the center of mass of the machine body, the horizontal axis ox points to the first motor, the longitudinal axis oy points to the fourth motor, and the oz is perpendicular to the oxyy surface.
The Euler angles of the four-rotor aircraft are as shown in the attached figure 2:
(1) b transverse roll angle
Figure BDA00027496687000000813
The machine body rotates around the ox shaft by an angle.
(2) c pitch angle (θ): the body rotates around the oy axis.
(3) a yaw angle (ψ): and the body rotates the included angle between the projection of the longitudinal axis of the aircraft around the oz axis in the horizontal plane and the axis of the inertial coordinate system OX.
Unmanned aerial vehicle dynamics model
Attitude vector based on four rotors
Figure BDA0002749668700000081
Wherein
Figure BDA0002749668700000082
Roll angle, yaw angle,. phi.phi.theta.pitch angle, and displacement vector [ x, y, z]The kinetic equation is as follows:
Figure BDA0002749668700000083
Figure BDA0002749668700000084
Figure BDA0002749668700000085
Figure BDA0002749668700000086
Figure BDA0002749668700000087
Figure BDA0002749668700000088
wherein:
Figure BDA0002749668700000089
Figure BDA00027496687000000810
Figure BDA00027496687000000811
Figure BDA00027496687000000812
u1representing the total lift of four rotors, u2Representing roll moment, u3Representing the pitching moment, u4Representing yaw moment, w1、w2、w3、w4Respectively representing the rotational speeds of four rotors, Ix、Iy、IzRepresenting the moment of inertia of the body in the xyz axis.
Step 2, Maklink environment model construction
There are many methods for environment modeling, such as grid method, artificial potential field method, Maklink graph theory, etc.
As shown in fig. 3, Maklink graph theory is selected herein to model the two-dimensional path:
modeling the two-dimensional path by adopting a Maklink graph theory method:
(1) selecting an obstacle (convex polygon), selecting a vertex on the obstacle, connecting the vertex with fixed points of other obstacles, and making a vertical line segment from the point to the boundary of the environment space;
(2) adding the obtained connecting line segments of all the vertexes and fixed points into a line segment storage table according to the sequence from short to long in length;
(3) selecting a header line segment in a line segment storage table;
(4) and checking whether the header line segment crosses any barrier boundary in the environmental space, and if the header line segment crosses any barrier boundary in the environmental space, the header line segment is not a free connecting line. Thus, the current line segment is discarded and the next line segment in the line segment storage table is selected. Repeating the checking process until a line segment is found to be not intersected with all the barrier boundaries;
(5) connecting the midpoints of the connecting line segments in the line segment storage table, checking whether the link line segment crosses the boundary of the obstacle, and deleting the line segment if the link line segment crosses the obstacle.
Step 3, optimizing the ant colony algorithm parameters by adopting a particle swarm algorithm, and planning a two-dimensional path through the optimized ant colony algorithm;
overview of Ant colony Algorithm
It has been found in practice that ants always secrete a chemical substance called pheromone in the path of food, and the pheromone remains on the path for a while, and when other ants search the path again, the path with higher concentration of pheromone is selected and the pheromone is left in the path, so that the pheromone on the path is increased. Its optimal path finds figure 4.
Fig. 4 shows the process of ant searching for a path. Ants pass through the paths A-D-E-C and A-B-C from the point A, pheromones do not exist on the two paths at the beginning, and because the length ratio between the two paths is 4:3, the number of the ants from the point A-C is 3:4 in the same time, the pheromones on the paths A-B-C are thicker and thicker. The remaining ants will eventually choose the path a-B-C.
FIG. 5 is a schematic flow chart of an ant colony algorithm;
and optimizing the parameters of the ant colony algorithm through a particle swarm algorithm, and finding out an optimal path in the Maklink environment model through the optimized ant colony algorithm. The path length is calculated by equation (11):
Figure BDA0002749668700000101
wherein xiyiAs current position coordinates, xi+1yi+1And m is the number of nodes from the starting point to the target point. Dist is the total path length.
Determining an ant colony algorithm pheromone updating method:
the pheromone updating comprises real-time pheromone updating and path pheromone updating, wherein the real-time pheromone updating refers to the pheromone tau of a certain node which must be treated by each ant after the node is selectedi，jUpdating is carried out, namely:
τij＝(1-ρ)τij+ρτ0 (12)
wherein tau is0P is the adjustable parameter between intervals (01) for initial pheromone values.
Path pheromone updating shows that when all ants go from an initial point to a terminal point and complete iterative search in sequence, all ants are selected to pass through one path with the shortest length, and pheromones of all points on the path are updated, namely:
τij＝(1-ρ)τij+ρΔτij (13)
where Δ τ isij＝1/L*,L*Bit shortest path length, ρ is an adjustable parameter between intervals (0, 1).
In conducting the path search, the next node location may be selected according to equation (14).
Figure BDA0002749668700000111
Wherein tau isi，jIs a pheromone of ηi，jFor heuristic values, α, β are τ, respectivelyi，j、ηi，jA weight coefficient.
However, in the actual simulation process, the situation of premature convergence occurs when the traditional ant colony algorithm searches for a path, and in order to avoid the situation and prevent a stagnation state, the ant colony algorithm pheromone is updated by adopting a method of combining global asynchronism and a novel pheromone updating strategy.
The novel pheromone updating strategy is as follows:
Figure BDA0002749668700000112
wherein
Figure BDA0002749668700000113
By utilizing the pheromone concentrations of the maximum value and the minimum value on the path, the real-time updating and adjusting amplitude of the pheromone is increased, the pheromone is not updated every evolutionary generation of the ant colony, and the pheromone is updated only when a solution with stronger adaptability occurs.
The global asynchronous updating method comprises the following steps:
τij(t+1)＝τij1(t) (16)
wherein tau isij1And (t) is the pheromone which continues to the current generation according to the global synchronous pheromone updating mode.
Further, the particle swarm optimization parameter process is as follows:
the particle swarm optimization algorithm finds the maximum solution and the optimal solution in the whole population through particle tracking, and updates according to the formulas (17) and (18):
Vi(t+1)＝w*Vi(t)+c1*r1(pi(t)-xi(t))+c2*r2(pg(t)-xi(t)) (17)
xi(t+1)＝xi(t)+Vi(t+1) (18)
where w is the inertial weight, r1r2Is a random number between (0, 1), c1c2Is a constant number, Vi(t) is the velocity vector of the particle, xi(t) is the current position of the particle.
(5) Firstly, parameters alpha, beta and rho are taken as inlet parameters.
(6) Setting learning factor c in particle swarm optimization1c2And inertial weights w, and then randomly selected for the initial position and velocity of each particle.
(7) Judging whether the positions of the particles are good or bad according to the result of the ant colony algorithm, and updating the optimal solution p of the individual extremumbestAnd global extremum optimal solution gbest。
(8) And updating the position and the speed of the particles according to the formulas (17) and (18).
(5) If the conditions are met, the algorithm is ended, and the current global optimal particle position is returned. And if the condition is not met, returning to the step (3). And finally, obtaining the optimal control parameters by continuously updating the particles.
The optimization program comprises the following steps:
VStep(j,:)＝w*VStep(j,:)+c1*rand*(pbest(j,:)-Swarm(j,:))+c2*rand*(gbest-Swarm(j,:))；
If VStep(j,:)>Vmax,VStep(j,:)＝Vmax；end
If VStep(j,:)<Vmin,VStep(j,:)＝Vmin；end Swarm(j,:)＝Swarm(j,:)+VStep(j,:)；
for k＝1:Dim
if Swarm(j,k)>Ub(k),Swarm(j,k)＝Ub(k)；end
if Swarm(j,k)<Lb(k),Swarm(j,k)＝Lb(k)；end
end
wherein Ub (k), Lb (k) are upper and lower limits of the particle group. Then, calculating a fitness value fSwarm (j) of the particle according to the updated particle, and then comparing:
if fSwarm(j)<fpbest(j)
pbest(j,:)＝Swarm(j,:)；
fpbest(j)＝fSwarm(j)；
end
if fSwarm(j)<fgbest
gbest＝Swarm(j,:)；
fgbest＝fSwarm(j)；
end
and finally, obtaining the optimal control parameters by continuously updating the particles.
However, when the particle swarm optimization is adopted, the introduction of part of necessary parameters such as (w, c) cannot be avoided1,c2) In order to avoid affecting the final result, chaotic inertial weight particle swarm is adopted in the textThe inertia weight updating method in the optimization algorithm updates the inertia weight, namely:
Figure BDA0002749668700000131
z1＝4×z2×(1-z2) (20)
wherein z is2Is a random number between (0, 1), wmax、wminRespectively representing the values of the inertial weight w at the beginning and at the end, stepmaxRepresenting the maximum number of iterations, and t being the current number of iterations. Parameter c1,c2A trial and error approach is used to find the corresponding appropriate value.
Examples
The algorithm provided by the invention adopts a particle swarm optimization algorithm to optimize parameters alpha, beta and rho, and pheromones are updated once when particles with stronger adaptability appear.
Firstly, initializing parameters and setting a value range, and carrying out limit processing on particles when the parameters exceed the value range.
The operation result is shown in fig. 7, and it can be seen by comparing with fig. 6 that the path length after the traditional ant colony algorithm of fig. 7 is optimized is slightly longer than that of the design method of the present invention. FIG. 6 is a schematic diagram of a path planning for a novel ant colony algorithm; FIG. 7 is a schematic diagram of a conventional ant colony algorithm path planning;
through fig. 8 and fig. 9, it can be seen that in the two methods, when the optimal path is reached, the iteration number of the ant colony algorithm designed herein is small, and the optimal path can be found in a short time.
FIG. 8 is a diagram illustrating iteration times of the novel ant colony algorithm;
fig. 9 is a diagram illustrating the iteration number of the conventional ant colony algorithm.

Claims (3)

1. A novel unmanned aerial vehicle path planning method is characterized by comprising the following steps:
step 1, determining a flight mathematic model of an unmanned aerial vehicle
The unmanned aerial vehicle flight mathematical model comprises an unmanned aerial vehicle body motion model and an unmanned aerial vehicle dynamics model, and specifically comprises the following steps:
unmanned aerial vehicle organism motion model:
the four-rotor aircraft controls the rotating speed of the four rotors to adjust the pitch angle (theta), the yaw angle (psi) and the roll angle
Figure FDA0002749668690000017
Adjusting to complete a series of flying movements of the machine body;
first, two coordinate systems are established: a body coordinate system, a conventional coordinate system;
the conventional coordinate system e (oexyz) is stationary with respect to the earth's surface;
a machine body coordinate system A (oxyz) is superposed with the mass center of the machine body, a transverse axis ox points to a first motor, a longitudinal axis oy points to a fourth motor, and the oz is vertical to an oxy surface;
euler angles of four-rotor aircraft:
(1) roll angle
Figure FDA0002749668690000018
The machine body rotates around the ox shaft by an angle;
(2) pitch angle (θ): the machine body rotates around the oy axis by an angle;
(3) yaw angle (ψ): the aircraft body rotates around the oz axis to form an included angle between the projection of the longitudinal axis of the aircraft in the horizontal plane and the axis of an inertial coordinate system OX;
an unmanned aerial vehicle dynamic model:
attitude vector based on four rotors
Figure FDA0002749668690000019
Wherein
Figure FDA00027496686900000110
Roll angle, yaw angle,. phi.phi.theta.pitch angle, and displacement vector [ x, y, z]The kinetic equation is as follows:
Figure FDA0002749668690000011
Figure FDA0002749668690000012
Figure FDA0002749668690000013
Figure FDA0002749668690000014
Figure FDA0002749668690000015
Figure FDA0002749668690000016
wherein:
Figure FDA0002749668690000021
Figure FDA0002749668690000022
Figure FDA0002749668690000023
Figure FDA0002749668690000024
u1representing the total lift of four rotors, u2Representing roll moment, u3Representing the pitching moment, u4Indicating yaw moment，w1、w2、w3、w4Respectively representing the rotational speeds of four rotors, Ix、Iy、IzRepresenting the moment of inertia of the body in the xyz axis;
step 2, Maklink environment model construction
Modeling the two-dimensional path by adopting a Maklink graph theory method:
(1) selecting an obstacle, selecting a vertex on the obstacle, connecting the vertex with fixed points of other obstacles, and making a vertical line segment from the point to the boundary of the environment space;
(2) adding the obtained connecting line segments of all the vertexes and fixed points into a line segment storage table according to the sequence from short to long in length;
(3) selecting a header line segment in a line segment storage table;
(4) checking whether the header line segment crosses any barrier boundary in the environmental space, if so, the line segment is not a free connecting line; thus, the current segment is discarded and the next segment in the segment storage table is selected; repeating the checking process until a line segment is found to be not intersected with all the barrier boundaries;
(5) connecting the midpoints of all the connected line segments in the line segment storage table, checking whether the link line segment passes through the boundary of the barrier or not, and deleting the line segment if the link line segment passes through the barrier;
step 3, optimizing the ant colony algorithm parameters by adopting a particle swarm algorithm, and planning a two-dimensional path through the optimized ant colony algorithm;
optimizing the parameters of the ant colony algorithm through a particle swarm algorithm, and finding out an optimal path in the Maklink environment model through the optimized ant colony algorithm; the path length is calculated by equation (11):
Figure FDA0002749668690000031
wherein xi yiAs current position coordinates, xi+1 yi+1The position coordinate of the next point is taken as m is the number of nodes from the starting point to the target point;dist is the total path length.
2. The novel unmanned aerial vehicle path planning method according to claim 1, wherein the determination of the ant colony algorithm pheromone updating method specifically comprises the following operations:
the pheromone updating comprises real-time pheromone updating and path pheromone updating, wherein the real-time pheromone updating refers to the pheromone tau of a certain node which must be treated by each ant after the node is selectedi，jUpdating is carried out, namely:
τij＝(1-ρ)τij+ρτ0 (12)
wherein tau is0Is an initial value of pheromone, and rho is an adjustable parameter between intervals (01);
path pheromone updating shows that when all ants go from an initial point to a terminal point and complete iterative search in sequence, all ants are selected to pass through one path with the shortest length, and pheromones of all points on the path are updated, namely:
τij＝(1-ρ)τij+ρΔτij (13)
where Δ τ isij＝1/L*,L*Bit shortest path length, rho is the adjustable parameter between intervals (0, 1);
when performing the path search, a next node position may be selected according to equation (14);
Figure FDA0002749668690000032
wherein tau isi，jIs a pheromone of ηi，jFor heuristic values, α, β are τ, respectivelyi，j、ηi，jA weight coefficient;
however, in the actual simulation process, the situation of premature convergence occurs when the traditional ant colony algorithm searches for a path, and in order to avoid the situation and prevent a stagnation state, the ant colony algorithm pheromone is updated by adopting a method combining global asynchronism and a novel pheromone updating strategy;
the novel pheromone updating strategy is as follows:
Figure FDA0002749668690000041
wherein
Figure FDA0002749668690000042
By utilizing the pheromone concentrations of the maximum value and the minimum value on the path, the real-time updating and adjusting amplitude of the pheromone is increased, the pheromone is not updated every evolutionary generation of the ant colony, and the pheromone is updated only when a solution with stronger adaptability occurs;
the global asynchronous updating method comprises the following steps:
τij(t+1)＝τij1(t) (16)
wherein tau isij1And (t) is the pheromone which continues to the current generation according to the global synchronous pheromone updating mode.
3. The novel unmanned aerial vehicle path planning method according to claim 1, further comprising the following procedure of optimizing ant colony algorithm parameters by using a particle swarm algorithm:
the particle swarm optimization algorithm finds the maximum solution and the optimal solution in the whole population through particle tracking, and updates according to the formulas (17) and (18):
Vi(t+1)＝w*Vi(t)+c1*r1(pi(t)-xi(t))+c2*r2(pg(t)-xi(t)) (17)
xi(t+1)＝xi(t)+Vi(t+1) (18)
where w is the inertial weight, r1 r2Is a random number between (0, 1), c1 c2Is a constant number, Vi(t) is the velocity vector of the particle, xi(t) is the current position of the particle;
(1) firstly, taking parameters alpha, beta and rho as inlet parameters;
(2) setting learning factor c in particle swarm optimization1 c2And inertia weight w, and then randomly selecting the initial position and the velocity of each particle;
(3) judging whether the positions of the particles are good or bad according to the result of the ant colony algorithm, and updating the optimal solution p of the individual extremumbestAnd global extremum optimal solution gbest；
(4) Updating the position and the speed of the particles according to the formulas (17) and (18);
(5) if the conditions are met, the algorithm is ended, and the current global optimal particle position is returned; if the condition is not met, returning to the step (3); the optimal control parameters are finally obtained by continuously updating the particles;
updating the inertia weight by adopting an inertia weight updating method in the chaotic inertia weight particle swarm optimization algorithm, namely:
Figure FDA0002749668690000051
z1＝4×z2×(1-z2) (20)
wherein z is2Is a random number between (0, 1), wmax、wminRespectively representing the values of the inertial weight w at the beginning and at the end, stepmaxRepresenting the maximum iteration times, wherein t is the current iteration times; parameter c1,c2A trial and error approach is used to find the appropriate value.
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