CN117724524A - Unmanned aerial vehicle route planning method based on improved spherical vector particle swarm algorithm - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle route planning method based on an improved spherical vector particle swarm algorithm, which relates to the technical field of unmanned aerial vehicle route planning and comprises the following steps: setting a flight task, importing an operation area map and generating a feasible route point when initializing equipment; establishing a path optimization model; establishing a multi-constraint evaluation model of path planning to obtain a final comprehensive cost function; and selecting and optimizing the planned path by a particle swarm optimization algorithm based on the improved spherical vector to obtain an optimal path planning result. Aiming at the flight aerial photography effect problem of path planning in a complex environment of an unmanned aerial vehicle, the selection of an unmanned aerial vehicle autonomous planning flight route is constrained by establishing a mathematical model among the flight speed, the pitch angle and the steering angle of the unmanned aerial vehicle. In addition, the invention is based on a particle swarm algorithm for improving spherical vectors, and the safety of unmanned plane path planning is connected in a vector multiparameter mode, so that the searching efficiency and optimizing capability of particle swarms are expanded.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle route planning, in particular to an unmanned aerial vehicle route planning method based on an improved spherical vector particle swarm algorithm.
Background
The traditional power facility inspection generally adopts modes of high risk or high price such as manual tower climbing and helicopter exploration, and has the problems of low efficiency, potential safety hazard and the like. In recent years, with the rapid development of unmanned aerial vehicle technology, the application of unmanned aerial vehicle combined with artificial intelligence technology brings many innovations and challenges for the power industry, wherein path planning has a crucial role as one of the core application problems of unmanned aerial vehicle technology. Therefore, the intelligent inspection relying on the unmanned aerial vehicle gradually becomes a novel efficient, safe and economic application scheme, and not only can efficiently cover electric power facilities, but also can timely discover abnormal conditions, such as equipment faults, electric leakage and other problems.
The path planning refers to determining an optimal flight path of the unmanned aerial vehicle in the power facility inspection process through an artificial intelligence algorithm, and the path planning not only needs to consider the position and the shape of an inspection area, but also needs to meet the inspection requirements such as coverage range, time efficiency, flight safety and the like. Unmanned aerial vehicle's development direction not only supports the power of endurance on hardware, still realizes on software that the route is independently planned, strengthens the operating efficiency, avoids the sudden barrier, prevents the crash loss. Along with the diversified development of unmanned aerial vehicle application scene, under the research complex environment, how unmanned aerial vehicle plans real-time route, guarantees flight safety, has very important realistic meaning. Based on artificial intelligence technology, unmanned aerial vehicle path planning can utilize a large amount of electric power facility data and environmental information, comprehensively consider a plurality of factors such as regional scope, flight distance, obstacle avoidance, corner flight and the like, and generate an optimal inspection path.
In the last decades, many studies have been made on this topic, most commonly the rapid exploration of random trees (RRT) and Probabilistic Roadmapping (PRM), both algorithms being based on probability, while having a certain robustness, at the same time sacrificing the smoothness of the flight path; machine-learned network models can provide better planning routes, but require a large amount of training data as input, so the path planning problem remains severe, especially how to perform more challenging tasks in a more efficient manner in field operations.
In view of this, there is a need for an unmanned aerial vehicle route planning method based on an improved spherical vector particle swarm algorithm.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an unmanned aerial vehicle route planning method based on an improved spherical vector particle swarm algorithm, which can be used for solving the problem of the aerial flight effect of path planning in a complex unmanned aerial vehicle environment, and restricting the selection of an unmanned aerial vehicle autonomous planning flight route by establishing a mathematical model among the flight speed, the pitch angle and the steering angle of the unmanned aerial vehicle. The specific technical scheme is as follows:
an unmanned aerial vehicle route planning method based on an improved spherical vector particle swarm algorithm comprises the following steps:
s1: setting a flight task, importing an operation area map and generating a feasible route point when initializing equipment;
s2: establishing a path optimization model, wherein the path optimization model forms path selection from a start point to an end point by marking route points based on three-dimensional space coordinate axes;
s3: establishing a multi-constraint evaluation model of path planning, wherein the multi-constraint evaluation model of path planning comprises a plurality of constraint conditions, and the cost functions of the constraint conditions obtain a final comprehensive cost function through weighting coefficients;
s4: and selecting and optimizing a planning path based on a particle swarm optimization algorithm for improving the spherical vector, setting related parameters of the particle swarm optimization algorithm for improving the spherical vector, and taking the comprehensive cost function as an evaluation function for calculating the fitness value of the particle swarm to perform population iterative optimization to obtain an optimal result of path planning.
Preferably, the method further comprises the step of smoothing route points by adopting a cubic spline interpolation method on the obtained route after obtaining the optimal result of route planning.
Preferably, the constraint condition comprises an overall length constraint, a shortest route constraint, a flying height constraint, a flying speed constraint, a switching angle constraint and a threat cost constraint;
the route total length constraint is specifically as follows:
wherein L is the total length of the route section; l (L) i The coordinates corresponding to the ith route point; l (L) i-1 The coordinates corresponding to the i-1 th route point; n is the number of route points;
the shortest route constraint is specifically as follows:
wherein l min The shortest straight line distance between two route points; l (L) i The coordinates corresponding to the ith route point; x is X i 、Y i 、Z i The three-dimensional coordinate parameters corresponding to the ith route point of the unmanned aerial vehicle are obtained; x is X i-1 、Y i-1 、Z i-1 Three-dimensional coordinate parameters corresponding to the ith-1 route point of the unmanned aerial vehicle;
the flying height constraint is specifically as follows:
H min ≤h i ≤H max
wherein H is min 、H max The minimum and maximum flying heights of the unmanned aerial vehicle are respectively; h is a i The flight altitude corresponding to the ith route point is determined;
the flight speed constraint is specifically as follows:
V min ≤v i ≤V max
wherein V is min 、V max The minimum and maximum flying heights of the unmanned aerial vehicle are respectively; v i The flight speed from the unmanned aerial vehicle to the ith route point is the flight speed;
the switching angle constraint is specifically as follows:
0≤β i ≤β max
wherein X is i 、Y i 、Z i The three-dimensional coordinate parameters corresponding to the ith route point of the unmanned aerial vehicle are obtained; x is X i-1 、Y i-1 、Z i-1 Three-dimensional coordinate parameters corresponding to the ith-1 route point of the unmanned aerial vehicle; gamma ray i The pitch/elevation angle when the unmanned plane flies to the (i+1) th route point is set; beta i The steering angle is the steering angle when the unmanned aerial vehicle flies to the (i+1) th route point; beta max The maximum steering angle of the unmanned aerial vehicle is set;
the threat cost constraint is specifically as follows:
wherein X is i 、Y i 、Z i The three-dimensional coordinate parameters corresponding to the ith route point of the unmanned aerial vehicle are obtained; x is X i-1 、Y i-1 、Z i-1 Three-dimensional coordinate parameters corresponding to the ith-1 route point of the unmanned aerial vehicle; r is the radius length of the obstacle; r is the safety distance between the unmanned aerial vehicle and the obstacle; d, d min Is the minimum linear distance between the unmanned aerial vehicle and the obstacle in the flight process.
Preferably, the integrated cost function is defined by an airline overall length cost function F 1 (L), shortest route cost function F 2 (l i -l i-1 ) Fly height cost function F 3 (h i ) Cost function of flying speed F 4 (v i ) Cost function of switching angle F 5 (γ i ,β i ) Threat cost function F 6 (d i ) And (5) weighting and summing.
Preferably, the course overall length cost function F 1 (L) is specifically as follows:
wherein L is the total length of the route section; l (L) i The coordinates corresponding to the ith route point; l (L) i-1 The coordinates corresponding to the i-1 th route point; n is the number of route points;
the shortest route cost function F 2 (l i -l i-1 ) The method comprises the following steps:
wherein l min The shortest straight line distance between two route points; l (L) i The coordinates corresponding to the ith route point; l (L) i-1 The coordinates corresponding to the i-1 th route point;
the flying height cost function F 3 (h i ) The method comprises the following steps:
wherein H is min 、H max The minimum and maximum flying heights of the unmanned aerial vehicle are respectively; h is a i The flight altitude corresponding to the ith route point is determined;
the flying speed cost function F 4 (v i ) The method comprises the following steps:
wherein V is min 、V max The minimum and maximum flying heights of the unmanned aerial vehicle are respectively; v i The flight speed from the unmanned aerial vehicle to the ith route point is the flight speed;
the switching angle isThe function F 5 (γ i ,β i ) The method comprises the following steps:
wherein X is i 、Y i 、Z i The three-dimensional coordinate parameters corresponding to the ith route point of the unmanned aerial vehicle are obtained; x is X i-1 、Y i-1 、Z i-1 Three-dimensional coordinate parameters corresponding to the ith-1 route point of the unmanned aerial vehicle; gamma ray i The pitch/elevation angle when the unmanned plane flies to the (i+1) th route point is set; beta i The steering angle is the steering angle when the unmanned aerial vehicle flies to the (i+1) th route point; beta max The maximum steering angle of the unmanned aerial vehicle is set;
the threat cost function F 6 (d i ) The method comprises the following steps:
wherein X is i 、Y i 、Z i The three-dimensional coordinate parameters corresponding to the ith route point of the unmanned aerial vehicle are obtained; x is X i-1 、Y i-1 、Z i-1 Three-dimensional coordinate parameters corresponding to the ith-1 route point of the unmanned aerial vehicle; r is the radius length of the obstacle; r is the safety distance between the unmanned aerial vehicle and the obstacle; d, d min The minimum linear distance between the unmanned aerial vehicle and an obstacle in the flight process;
the comprehensive cost function is specifically as follows:
wherein,is a weight coefficient.
Preferably, the step S4 specifically includes the following steps:
assuming that each path can be encoded as a set of vectors, eachThe group vector represents each waypoint parameter passed by the unmanned aerial vehicle from the starting position to the end point, and each waypoint vector in the spherical coordinate system is represented by 3 components, which are respectively the path length l epsilon (l min ,l i ) The depression/elevation angle gamma epsilon (-pi/2, pi/2) and the steering angle beta epsilon (-pi, pi), then the path omega with M route points i The three-dimensional vector can be expressed as: omega shape i ＝(l i1 ,γ i1 ,β i1 ,l i2 ,γ i2 ,β i2 ...,l iM ,γ iM ,β iM )；
The particle swarm algorithm adopts omega i Expressed as a particle initialization position, then the particle update rate can be expressed by a positional relationship: ΔΩ i ＝(Δl i1 ,Δγ i1 ,Δβ i1 ,Δl i2 ,Δγ i2 ,Δβ i2 ...,Δl iM ,Δγ iM ,Δβ iM )；
Then, spherical vector u im Represented as (l) im ,γ im ,β im ) Speed Deltau im Denoted as (Δl) im ,Δγ im ,Δβ im ) The method comprises the steps of carrying out a first treatment on the surface of the The update formula of the algorithm is expressed as:
wherein,is the locally optimal position of the kth generation particle i; />A vector set which is the global optimal position in the kth generation of particles; omega is the inertial weight; η (eta) 1 、η 2 Is an acceleration factor; r is (r) 1 、r 2 Is [0,1]Random numbers in between;
setting related parameters of an improved spherical vector particle swarm algorithm, and taking the comprehensive cost function as an evaluation function for calculating the fitness value of a particle swarm to carry out iterative optimization of the population to obtain an optimal result of path planning.
A computer readable storage medium comprising a stored program, wherein the program when run controls a device in which the computer readable storage medium resides to perform a method of unmanned aerial vehicle route planning based on an improved spherical vector particle swarm algorithm as described above.
A processor for running a program, wherein the program when run performs the unmanned aerial vehicle route planning method based on the improved spherical vector particle swarm algorithm described above.
Compared with the prior art, the invention has the beneficial effects that:
according to the unmanned aerial vehicle route planning method based on the improved spherical vector particle swarm algorithm, aiming at the flight aerial photography effect problem of path planning in the complex environment of the unmanned aerial vehicle, the selection of the unmanned aerial vehicle self-planned flight route is constrained by establishing a mathematical model among the flight speed, the pitch angle and the steering angle of the unmanned aerial vehicle. In addition, the invention is based on a particle swarm algorithm for improving spherical vectors, and the safety of unmanned plane path planning is connected in a vector multiparameter mode, so that the searching efficiency and optimizing capability of particle swarms are expanded.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a schematic illustration of a total route of unmanned aerial vehicle waypoints according to the present invention;
FIG. 2 is a schematic illustration of the unmanned aerial vehicle flight altitude constraint of the present invention;
FIG. 3 is a schematic view of pitch angle and steering angle produced by adjacent waypoints of the unmanned aerial vehicle;
FIG. 4 is a flow chart of the improved spherical vector particle swarm algorithm.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Embodiments of the present invention are further described below in conjunction with fig. 1-4.
The invention provides an unmanned aerial vehicle route planning method based on an improved spherical vector particle swarm algorithm, which mainly solves the problem of selecting an optimal route point and an operation point when an unmanned aerial vehicle operates practically and autonomously, and builds an unmanned aerial vehicle route model for comprehensive measurement based on the aspects of operation environment, threat factors, self mobility and the like.
In order to solve the above problems, the embodiment of the present invention provides the following technical solutions:
the embodiment provides an unmanned aerial vehicle route planning method based on an improved spherical vector particle swarm algorithm.
The unmanned aerial vehicle path planning problem is that a research object is unmanned aerial vehicle equipment carrying a camera, a battery, a sensor and a CPU. Setting a flight task, importing a map of an operation area and automatically generating feasible route points when initializing equipment, detecting the flight road conditions in real time according to a sensor, and deciding the next route point within an adjustable time range so as to autonomously generate a coherent path;
the unmanned aerial vehicle path planning problem is essentially optimization of route point position selection, and the optimal route point and the position of an operation point are found by establishing a multi-constraint model of an unmanned aerial vehicle autonomous flight path under a complex environment, so that the aim of highest overall economy is fulfilled on the premise of safe and stable flight;
establishing a path optimization model, and forming various path selection models from a start point to an end point by marking route points based on three-dimensional space coordinate axes;
taking the safety of path planning and the maneuvering characteristics of the unmanned aerial vehicle into consideration, establishing a multi-constraint evaluation model of the path planning, wherein the multi-constraint evaluation model comprises 6 constraint conditions: the total length of the route, the shortest route, the flying height, the flying speed, the switching angle and the threat cost are obtained, and a final comprehensive evaluation function is obtained through a weighting coefficient;
because of the fact that unmanned aerial vehicle carries the difference of power duration, shorter airlines generally consume shorter time and less electric energy, and therefore the unmanned aerial vehicle is supported to complete the task of inspection of longer airlines:
wherein L is the total length of the route section; l (L) i The coordinates corresponding to the ith route point; n is the number of route points.
When the unmanned aerial vehicle selects the next optimal route point according to the real-time condition, the unmanned aerial vehicle cannot safely travel to the next route point due to the flight inertia or speed switching, so that the shortest safety distance between the adjacent route points is required to be considered when the next optimal route point is selected, and the stability of the unmanned aerial vehicle flight route adjustment is ensured:
wherein l min The shortest straight line distance between two route points;
the flight height constraint, considering the communication cost of the unmanned aerial vehicle and the ground, is limited by the transmission distance and the application cost of the antenna/base station equipment, the economic cost can be increased by too high flight, and the collision probability of the obstacles such as trees, buildings, mountains and the like can be increased by too low, so that the flight height is controlled within a certain height range based on different real-time altitudes in the unmanned aerial vehicle inspection process:
H min ≤h i ≤H max
wherein H is min 、H max The minimum and maximum flying heights of the unmanned aerial vehicle are respectively; h is a i The flight altitude corresponding to the ith route point is determined;
the flight speed constraint is used for determining a speed interval which can be maintained in normal flight according to the reaction time required by the stable flight of the unmanned aerial vehicle in the air so as to achieve the balance of working efficiency and stable flight:
V min ≤v i ≤V max
wherein V is min 、V max The minimum and maximum flying heights of the unmanned aerial vehicle are respectively; v i The flight speed from the unmanned aerial vehicle to the ith route point is the flight speed;
the switching angle constraint mainly comprises a depression angle/elevation angle or a plane steering angle generated by a flight route in the process of flying the next route point of the unmanned aerial vehicle, and the adjustment of the angle of the unmanned aerial vehicle is within a certain range due to the constraint of the flight speed and the flight distance:
0≤β i ≤β max
x, Y, Z is a three-dimensional coordinate parameter corresponding to the ith route point of the unmanned aerial vehicle; gamma ray i The pitch/elevation angle when the unmanned plane flies to the (i+1) th route point is set; beta i The steering angle is the steering angle when the unmanned aerial vehicle flies to the (i+1) th route point; beta max The maximum steering angle of the unmanned aerial vehicle is set;
threat cost constraint, unmanned aerial vehicle can detect the barrier that appears in the route often when actual environment operation, and select the route point to both satisfy economic cost minimum, also need consider the safe distance between the barrier, establish the mathematical model of barrier as the cylinder:
wherein R is the radius length of the obstacle; r is the safety distance between the unmanned aerial vehicle and the obstacle; d, d min The minimum linear distance between the unmanned aerial vehicle and an obstacle in the flight process;
the total length cost function F of the route 1 (L) is specifically as follows:
wherein L is the total length of the route section; l (L) i The coordinates corresponding to the ith route point; l (L) i-1 The coordinates corresponding to the i-1 th route point; n is the number of route points;
the shortest route cost function F 2 (l i -l i-1 ) The method comprises the following steps:
wherein l min The shortest straight line distance between two route points; l (L) i The coordinates corresponding to the ith route point; l (L) i-1 The coordinates corresponding to the i-1 th route point;
the flying height cost function F 3 (h i ) The method is as follows：
Wherein H is min 、H max The minimum and maximum flying heights of the unmanned aerial vehicle are respectively; h is a i The flight altitude corresponding to the ith route point is determined;
the flying speed cost function F 4 (v i ) The method comprises the following steps:
wherein V is min 、V max The minimum and maximum flying heights of the unmanned aerial vehicle are respectively; v i The flight speed from the unmanned aerial vehicle to the ith route point is the flight speed;
the switching angle cost function F 5 (γ i ,β i ) The method comprises the following steps:
wherein X is i 、Y i 、Z i The three-dimensional coordinate parameters corresponding to the ith route point of the unmanned aerial vehicle are obtained; x is X i-1 、Y i-1 、Z i-1 Three-dimensional coordinate parameters corresponding to the ith-1 route point of the unmanned aerial vehicle; gamma ray i The pitch/elevation angle when the unmanned plane flies to the (i+1) th route point is set; beta i The steering angle is the steering angle when the unmanned aerial vehicle flies to the (i+1) th route point; beta max The maximum steering angle of the unmanned aerial vehicle is set;
the threat cost function F 6 (d i ) The method comprises the following steps:
wherein X is i 、Y i 、Z i For unmanned aerial vehicle at ith routeThree-dimensional coordinate parameters corresponding to the points; x is X i-1 、Y i-1 、Z i-1 Three-dimensional coordinate parameters corresponding to the ith-1 route point of the unmanned aerial vehicle; r is the radius length of the obstacle; r is the safety distance between the unmanned aerial vehicle and the obstacle; d, d min The minimum linear distance between the unmanned aerial vehicle and an obstacle in the flight process;
to sum up, a route total length cost function F is set 1 (l i ) Shortest route cost function F 2 (l i -l i-1 ) Fly height cost function F 3 (h i ) Cost function of flying speed F 4 (v i ) Cost function of switching angle F 5 (γ i ,β i ) Threat cost function F 6 (d i ) The weight coefficients respectively occupying the comprehensive evaluation function areThe cost function is expressed as:
wherein F is cost Is a comprehensive cost function;
s5: a particle swarm optimization (SPSO) algorithm for improving the spherical vector is improved, and a planning path is selected and optimized based on the algorithm;
assuming that each path can be encoded as a set of vectors, each set of vectors representing each waypoint parameter passed by the unmanned aerial vehicle from the start position to the end point, each waypoint vector in the spherical coordinate system is represented using 3 components, each path length l e (l min ,l i ) The depression/elevation angle gamma epsilon (-pi/2, pi/2) and the steering angle beta epsilon (-pi, pi), then the path omega with M route points i The three-dimensional vector can be expressed as: omega shape i ＝(l i1 ,γ i1 ,β i1 ,l i2 ,γ i2 ,β i2 ...,l iM ,γ iM ,β iM )；
The particle swarm algorithm adopts omega i Expressed as particle initialization position, thenThe particle update rate can be expressed by the positional relationship: ΔΩ i ＝(Δl i1 ,Δγ i1 ,Δβ i1 ,Δl i2 ,Δγ i2 ,Δβ i2 ...,Δl iM ,Δγ iM ,Δβ iM )；
Then, spherical vector u im Represented as (l) im ,γ im ,β im ) Speed Deltau im Denoted as (Δl) im ,Δγ im ,Δβ im ) The method comprises the steps of carrying out a first treatment on the surface of the The update formula of the algorithm is expressed as:
wherein,is the locally optimal position of the kth generation particle i; />A vector set which is the global optimal position in the kth generation of particles; omega is the inertial weight; η (eta) 1 、η 2 Is an acceleration factor; r is (r) 1 、r 2 Is [0,1]Random numbers in between;
setting related parameters of an improved spherical vector particle swarm algorithm, and taking the comprehensive cost function as an evaluation function for calculating the fitness value of a particle swarm to carry out iterative optimization of the population to obtain an optimal result of path planning;
taking the smoothness of the unmanned aerial vehicle flight into consideration, smoothing among route points is carried out on the obtained route by adopting a cubic spline interpolation method.
In summary, according to the unmanned aerial vehicle route planning method based on the improved spherical vector particle swarm algorithm, aiming at the flight aerial photography effect problem of path planning in the complex environment of the unmanned aerial vehicle, the selection of the unmanned aerial vehicle autonomous planning flight route is constrained by establishing a mathematical model among the flight speed, the pitch angle and the steering angle of the unmanned aerial vehicle; based on a particle swarm algorithm for improving spherical vectors, the safety of unmanned plane path planning is connected in a vector multi-parameter mode, and the searching efficiency and optimizing capability of particle swarms are expanded. Therefore, the technical scheme of the invention is sufficient to solve the problems in the background technology.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
Claims (8)
1. An unmanned aerial vehicle route planning method based on an improved spherical vector particle swarm algorithm is characterized by comprising the following steps:
s1: setting a flight task, importing an operation area map and generating a feasible route point when initializing equipment;
s2: establishing a path optimization model, wherein the path optimization model forms path selection from a start point to an end point by marking route points based on three-dimensional space coordinate axes;
s3: establishing a multi-constraint evaluation model of path planning, wherein the multi-constraint evaluation model of path planning comprises a plurality of constraint conditions, and the cost functions of the constraint conditions obtain a final comprehensive cost function through weighting coefficients;
s4: and selecting and optimizing a planning path based on a particle swarm optimization algorithm for improving the spherical vector, setting related parameters of the particle swarm optimization algorithm for improving the spherical vector, and taking the comprehensive cost function as an evaluation function for calculating the fitness value of the particle swarm to perform population iterative optimization to obtain an optimal result of path planning.
2. The unmanned aerial vehicle route planning method based on the improved spherical vector particle swarm optimization algorithm according to claim 1, further comprising smoothing route points of the obtained route by adopting a cubic spline interpolation method after obtaining an optimal result of route planning.
3. The unmanned aerial vehicle route planning method based on the improved spherical vector particle swarm algorithm according to claim 1, wherein the constraint conditions comprise a route total length constraint, a shortest route constraint, a flight altitude constraint, a flight speed constraint, a switching angle constraint and a threat cost constraint;
the route total length constraint is specifically as follows:
wherein L is the total length of the route section; l (L) i The coordinates corresponding to the ith route point; l (L) i-1 The coordinates corresponding to the i-1 th route point; n is the number of route points;
the shortest route constraint is specifically as follows:
wherein l min The shortest straight line distance between two route points; l (L) i The coordinates corresponding to the ith route point; x is X i 、Y i 、Z i The three-dimensional coordinate parameters corresponding to the ith route point of the unmanned aerial vehicle are obtained; x is X i-1 、Y i-1 、Z i-1 Three-dimensional coordinate parameters corresponding to the ith-1 route point of the unmanned aerial vehicle;
the flying height constraint is specifically as follows:
H min ≤h i ≤H max
wherein H is min 、H max The minimum and maximum flying heights of the unmanned aerial vehicle are respectively; h is a i The flight altitude corresponding to the ith route point is determined;
the flight speed constraint is specifically as follows:
V min ≤v i ≤V max
wherein V is min 、V max The minimum and maximum flying heights of the unmanned aerial vehicle are respectively; v i The flight speed from the unmanned aerial vehicle to the ith route point is the flight speed;
the switching angle constraint is specifically as follows:
0≤β i ≤β max
wherein X is i 、Y i 、Z i The three-dimensional coordinate parameters corresponding to the ith route point of the unmanned aerial vehicle are obtained; x is X i-1 、Y i-1 、Z i-1 Three-dimensional coordinate parameters corresponding to the ith-1 route point of the unmanned aerial vehicle; gamma ray i The pitch/elevation angle when the unmanned plane flies to the (i+1) th route point is set; beta i The steering angle is the steering angle when the unmanned aerial vehicle flies to the (i+1) th route point; beta max The maximum steering angle of the unmanned aerial vehicle is set;
the threat cost constraint is specifically as follows:
wherein X is i 、Y i 、Z i The three-dimensional coordinate parameters corresponding to the ith route point of the unmanned aerial vehicle are obtained; x is X i-1 、Y i-1 、Z i-1 Three-dimensional coordinate parameters corresponding to the ith-1 route point of the unmanned aerial vehicle; r is the radius length of the obstacle; r is the safety distance between the unmanned aerial vehicle and the obstacle; d, d min Is the minimum linear distance between the unmanned aerial vehicle and the obstacle in the flight process.
4. A method of unmanned aerial vehicle route planning based on an improved spherical vector particle swarm algorithm according to claim 3, wherein the integrated cost function is defined by a route overall length cost function F 1 (L), shortest route cost function F 2 (l i -l i-1 ) Fly height cost function F 3 (h i ) Cost function of flying speed F 4 (v i ) Cost function of switching angle F 5 (γ i ,β i ) Threat cost function F 6 (d i ) And (5) weighting and summing.
5. The unmanned aerial vehicle route planning method based on the improved spherical vector particle swarm algorithm according to claim 4, wherein the route overall length cost function F 1 (L) is specifically as follows:
wherein L is the total length of the route section; l (L) i The coordinates corresponding to the ith route point; l (L) i-1 The coordinates corresponding to the i-1 th route point; n is the number of route points;
the shortest route cost function F 2 (l i -l i-1 ) The method comprises the following steps:
wherein l min The shortest straight line distance between two route points; l (L) i The coordinates corresponding to the ith route point; l (L) i-1 The coordinates corresponding to the i-1 th route point;
the flying height cost function F 3 (h i ) The method comprises the following steps:
wherein H is min 、H max The minimum and maximum flying heights of the unmanned aerial vehicle are respectively; h is a i The flight altitude corresponding to the ith route point is determined;
the flying speedCost function F of degree 4 (v i ) The method comprises the following steps:
wherein V is min 、V max The minimum and maximum flying heights of the unmanned aerial vehicle are respectively; v i The flight speed from the unmanned aerial vehicle to the ith route point is the flight speed;
the switching angle cost function F 5 (γ i ,β i ) The method comprises the following steps:
wherein X is i 、Y i 、Z i The three-dimensional coordinate parameters corresponding to the ith route point of the unmanned aerial vehicle are obtained; x is X i-1 、Y i-1 、Z i-1 Three-dimensional coordinate parameters corresponding to the ith-1 route point of the unmanned aerial vehicle; gamma ray i The pitch/elevation angle when the unmanned plane flies to the (i+1) th route point is set; beta i The steering angle is the steering angle when the unmanned aerial vehicle flies to the (i+1) th route point; beta max The maximum steering angle of the unmanned aerial vehicle is set;
the threat cost function F 6 (d i ) The method comprises the following steps:
wherein X is i 、Y i 、Z i The three-dimensional coordinate parameters corresponding to the ith route point of the unmanned aerial vehicle are obtained; x is X i-1 、Y i-1 、Z i-1 Three-dimensional coordinate parameters corresponding to the ith-1 route point of the unmanned aerial vehicle; r is the radius length of the obstacle; r is the safety distance between the unmanned aerial vehicle and the obstacle; d, d min The minimum linear distance between the unmanned aerial vehicle and an obstacle in the flight process;
the comprehensive cost function is specifically as follows:
wherein,is a weight coefficient.
6. The unmanned aerial vehicle route planning method based on the improved spherical vector particle swarm optimization according to claim 5, wherein the step S4 is specifically as follows:
assuming that each path can be encoded as a set of vectors, each set of vectors representing each waypoint parameter passed by the unmanned aerial vehicle from the start position to the end point, each waypoint vector in the spherical coordinate system is represented using 3 components, each path length l e (l min ,l i ) The depression/elevation angle gamma epsilon (-pi/2, pi/2) and the steering angle beta epsilon (-pi, pi), then the path omega with M route points i The three-dimensional vector can be expressed as: omega shape i ＝(l i1 ,γ i1 ,β i1 ,l i2 ,γ i2 ,β i2 ...,l iM ,γ iM ,β iM )；
The particle swarm algorithm adopts omega i Expressed as a particle initialization position, then the particle update rate can be expressed by a positional relationship: ΔΩ i ＝(Δl i1 ,Δγ i1 ,Δβ i1 ,Δl i2 ,Δγ i2 ,Δβ i2 ...,Δl iM ,Δγ iM ,Δβ iM )；
Then, spherical vector u im Represented as (l) im ,γ im ,β im ) Speed Deltau im Denoted as (Δl) im ,Δγ im ,Δβ im ) The method comprises the steps of carrying out a first treatment on the surface of the The update formula of the algorithm is expressed as:
wherein,is the locally optimal position of the kth generation particle i; />A vector set which is the global optimal position in the kth generation of particles; omega is the inertial weight; η (eta) 1 、η 2 Is an acceleration factor; r is (r) 1 、r 2 Is [0,1]Random numbers in between;
setting related parameters of an improved spherical vector particle swarm algorithm, and taking the comprehensive cost function as an evaluation function for calculating the fitness value of a particle swarm to carry out iterative optimization of the population to obtain an optimal result of path planning.
7. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored program, wherein the program when run controls a device in which the computer readable storage medium is located to perform the unmanned aerial vehicle route planning method based on the improved spherical vector particle swarm algorithm according to any of claims 1 to 6.
8. A processor for running a program, wherein the program when run performs the unmanned aerial vehicle route planning method based on the modified spherical vector particle swarm algorithm of any of claims 1 to 6.
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